Add scoped trend controls to feedback center

CLAUDE.md

@@ -135,8 +135,18 @@ backend/
 - 新增：`backend/tests/test_draft_generator_service.py`（草稿生成服务回归：PM 评审草稿包含相似项目、MVP、前景与需求清单）
 - 新增：`backend/tests/test_signal_planner_contract.py`（Signal Planner 契约回归：最新宪章加载、prompt 注入、无宪章降级）
 - 新增：`backend/tests/test_ai_prompt_registry_contract.py`（Prompt registry 契约回归：覆盖率与元数据完整性）
+- 新增：`backend/tests/test_prompt_profile_bundle_contract.py`（默认角色 prompt bundle 契约回归：PMFlow PMO 语气、职责边界与协作口径）
 - 新增：`backend/alembic/versions/20260324_add_project_create_idempotency_indexes.py`（活动项目标题大小写无关唯一索引，收口项目创建重复写入）
 - 新增：`backend/alembic/versions/20260326_add_task_breakdown_payload_fields.py`（tasks 拆解业务对象 JSONB 字段：`ai_breakdown_payload/manual_overrides`）
+- 新增：`backend/alembic/versions/20260326_create_feedback_center_tables.py`（统一反馈学习中心双表建模与 QA/Signal/Task 初始回填）
+- 新增：`backend/app/api/ai_steward_api/routes_feedback_center.py`（AI Steward 反馈中心接口：summary/list/detail/review/reason-catalog；summary 支持 `window_days` / `source_group` 趋势作用域）
+- 新增：`backend/app/models/feedback_center.py`（`FeedbackCenterRecord/FeedbackCenterRecordRevision` 统一反馈中心模型）
+- 新增：`backend/app/schemas/feedback_center.py`（反馈中心 summary/list/detail/review/reason-catalog 对应 Pydantic 模型）
+- 新增：`backend/app/services/feedback_center/`（反馈中心服务层：`service.py/mappers.py/reason_catalog.py`）
+- 新增：`backend/tests/test_feedback_center_service.py`（反馈中心服务回归：tenant/project 过滤、review 修订、映射与原因候选）
+- 新增：`backend/tests/test_feedback_center_api.py`（反馈中心 API 回归：路由、权限与三类来源回填）
+- 更新：`backend/app/services/feedback_center/service.py`（summary 输出新增 metrics 学习指标，并支持 `window_days` / `source_group` 的趋势分析作用域）
+- 更新：`backend/app/schemas/feedback_center.py`（summary 增加 metrics / trend 结构，并回传 `source_group`）
 - 新增：`backend/app/services/signal_planner_service.py`（Signal Planner 异步分析服务）
 - 新增：`backend/app/services/model/model_routing/service.py`（函数级模型路由配置服务）
 - 新增：`backend/app/services/task_brief_service.py`（增量任务包中心服务）
@@ -346,7 +356,9 @@ frontend/
 - 更新：`frontend/src/pages/AIStewardPage.tsx`（降级为轻量入口，转发到 `pages/ai-steward/AIStewardPage`）
 - 新增：`frontend/src/pages/ai-steward/types.ts`、`frontend/src/pages/ai-steward/constants.ts`（AI Steward 类型与常量集中管理）
 - 新增：`frontend/src/pages/ai-steward/components/FeatureOverviewCards.tsx`（AI Steward 首页功能卡片组件）
-- 新增：`frontend/src/pages/ai-steward/sections/OverviewTab.tsx`、`CreateProjectTab.tsx`、`SignalsTab.tsx`、`ConflictsTab.tsx`、`BlockersTab.tsx`、`BriefingTab.tsx`、`EngineTab.tsx`（AI Steward 分阶段 UI 拆分）
+- 新增：`frontend/src/pages/ai-steward/sections/OverviewTab.tsx`、`CreateProjectTab.tsx`、`SignalsTab.tsx`、`ConflictsTab.tsx`、`BlockersTab.tsx`、`BriefingTab.tsx`、`EngineTab.tsx`、`FeedbackCenterTab.tsx`（AI Steward 分阶段 UI 拆分）
+- 新增：`frontend/src/pages/ai-steward/components/feedback-center/FeedbackList.tsx`、`FeedbackDetail.tsx`、`ReasonPicker.tsx`、`MetricsPanel.tsx`、`TrendPanel.tsx`（反馈中心三栏工作台、指标页与趋势页组件；趋势页支持 `7/30 天` 与 `all/task/qa/signal` 局部筛选）
+- 新增：`frontend/src/api/feedbackCenter.ts`（统一反馈中心 API 封装）
 - 新增：`frontend/src/pages/ai-steward/AIStewardPage.tsx`（AI Steward 页面编排层，主文件聚焦状态与事件）
 - 更新：`frontend/src/pages/ProjectBoardPage.tsx`（降级为轻量入口，转发到 `pages/project-board/ProjectBoardPage`）
 - 新增：`frontend/src/pages/project-board/helpers.ts`（Project Board 信号筛选状态与 PMO 工作项文本归一 helper）
@@ -377,6 +389,7 @@ frontend/
 - `SSTCReleaseCreateDialog.tsx`：发布创建弹窗
 - `SSTCReviewCreateDialog.tsx`：复盘创建弹窗
 - `AIVisibilityPanel.tsx`：AI 可视化信息面板
+- `FeedbackSummaryCard.tsx`：反馈中心摘要卡片（任务阶段/执行看板复用）
 - `SSTCGlobalHeader.tsx`：SSTC 全局头部
 - `SSTCStageNav.tsx`：阶段导航
 - `SSTCChainView.tsx`：链路视图

backend/alembic/versions/20260326_create_feedback_center_tables.py

@@ -0,0 +1,542 @@
+"""create feedback center tables
+
+Revision ID: 20260326_create_feedback_center_tables
+Revises: 20260326_add_task_breakdown_payload_fields
+Create Date: 2026-03-26
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from alembic import op
+import sqlalchemy as sa
+
+try:
+    from sqlalchemy.dialects.postgresql import JSONB
+except Exception:  # pragma: no cover
+    JSONB = sa.JSON
+
+
+revision = "20260326_create_feedback_center_tables"
+down_revision = "20260326_add_task_breakdown_payload_fields"
+branch_labels = None
+depends_on = None
+
+
+def _json_type(bind):
+    return JSONB() if bind.dialect.name == "postgresql" else sa.JSON()
+
+
+def _table_exists(bind, table_name: str) -> bool:
+    return sa.inspect(bind).has_table(table_name)
+
+
+def _index_exists(bind, table_name: str, index_name: str) -> bool:
+    inspector = sa.inspect(bind)
+    if not inspector.has_table(table_name):
+        return False
+    return any(str(item.get("name") or "") == index_name for item in inspector.get_indexes(table_name))
+
+
+def _safe_int(value: Any) -> int | None:
+    try:
+        if value is None:
+            return None
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _parse_json_if_needed(value: Any) -> Any:
+    if value is None:
+        return None
+    if isinstance(value, (dict, list)):
+        return value
+    if isinstance(value, str):
+        try:
+            return json.loads(value)
+        except (TypeError, ValueError):
+            return value
+    return value
+
+
+def _build_reason_candidates(*, source_type: str, field_name: str, before_value: Any, after_value: Any) -> list[dict[str, str]]:
+    candidates: list[dict[str, str]] = []
+    field = str(field_name or "").strip().lower()
+    source = str(source_type or "").strip().lower()
+
+    def _add(code: str, label: str, description: str) -> None:
+        if any(str(item.get("code") or "") == code for item in candidates):
+            return
+        candidates.append({"code": code, "label": label, "description": description})
+
+    if before_value not in (None, "", [], {}) and after_value in (None, "", [], {}):
+        _add("incomplete_output", "输出信息不完整", "模型输出缺失关键信息字段")
+    if "estimated" in field or "hours" in field:
+        _add("effort_estimation_bias", "工时估算偏差", "任务工时估算与实际认知偏差较大")
+    if "priority" in field:
+        _add("priority_misalignment", "优先级错配", "优先级建议与业务紧急度不匹配")
+    if "assignee" in field or "skill" in field:
+        _add("capability_mismatch", "能力匹配偏差", "人员能力与任务推荐存在偏差")
+    if "strategic" in field or source == "signal_feedback":
+        _add("strategic_judgement_mismatch", "战略判断偏差", "战略属性判定与人工判断不一致")
+    if source == "qa_feedback":
+        _add("answer_incorrect", "答案不准确", "AI 输出与业务事实不一致")
+    if not candidates:
+        _add("manual_override", "人工覆盖", "由人工介入修改后形成的反馈记录")
+    return candidates
+
+
+def _record_exists(
+    bind,
+    *,
+    tenant_id: int,
+    source_type: str,
+    source_id: int,
+    object_type: str,
+    object_id: int,
+    field_name: str,
+) -> bool:
+    row = bind.execute(
+        sa.text(
+            """
+            SELECT id
+            FROM feedback_center_records
+            WHERE tenant_id = :tenant_id
+              AND source_type = :source_type
+              AND source_id = :source_id
+              AND object_type = :object_type
+              AND object_id = :object_id
+              AND field_name = :field_name
+            LIMIT 1
+            """
+        ),
+        {
+            "tenant_id": int(tenant_id),
+            "source_type": str(source_type),
+            "source_id": int(source_id),
+            "object_type": str(object_type),
+            "object_id": int(object_id),
+            "field_name": str(field_name),
+        },
+    ).first()
+    return row is not None
+
+
+def _insert_record(bind, payload: dict[str, Any]) -> None:
+    bind.execute(
+        sa.text(
+            """
+            INSERT INTO feedback_center_records (
+                tenant_id,
+                project_id,
+                source_type,
+                source_id,
+                object_type,
+                object_id,
+                field_name,
+                before_value,
+                after_value,
+                ai_snapshot,
+                ai_confidence,
+                reason_candidates,
+                reason_code,
+                reason_text,
+                review_status,
+                reviewed_by,
+                reviewed_at,
+                source_title,
+                object_title,
+                auto_assigned
+            ) VALUES (
+                :tenant_id,
+                :project_id,
+                :source_type,
+                :source_id,
+                :object_type,
+                :object_id,
+                :field_name,
+                :before_value,
+                :after_value,
+                :ai_snapshot,
+                :ai_confidence,
+                :reason_candidates,
+                :reason_code,
+                :reason_text,
+                :review_status,
+                :reviewed_by,
+                :reviewed_at,
+                :source_title,
+                :object_title,
+                :auto_assigned
+            )
+            """
+        ),
+        payload,
+    )
+
+
+def _backfill_from_qa_feedback(bind) -> None:
+    if not _table_exists(bind, "qa_feedback") or not _table_exists(bind, "qa_records"):
+        return
+    rows = bind.execute(
+        sa.text(
+            """
+            SELECT
+                f.id AS feedback_id,
+                f.tenant_id AS tenant_id,
+                f.qa_id AS qa_id,
+                f.rating AS rating,
+                f.helpfulness AS helpfulness,
+                f.error_types AS error_types,
+                f.correct_answer AS correct_answer,
+                f.submitted_by AS submitted_by,
+                f.created_at AS created_at,
+                r.project_id AS project_id,
+                r.question AS question,
+                r.answer AS answer
+            FROM qa_feedback f
+            JOIN qa_records r ON r.id = f.qa_id
+            """
+        )
+    ).mappings().all()
+
+    for row in rows:
+        tenant_id = _safe_int(row.get("tenant_id"))
+        source_id = _safe_int(row.get("feedback_id"))
+        object_id = _safe_int(row.get("qa_id"))
+        if tenant_id is None or source_id is None or object_id is None:
+            continue
+        if _record_exists(
+            bind,
+            tenant_id=tenant_id,
+            source_type="qa_feedback",
+            source_id=source_id,
+            object_type="qa_record",
+            object_id=object_id,
+            field_name="qa_feedback",
+        ):
+            continue
+        before_value = {"answer": row.get("answer")}
+        after_value = {
+            "rating": row.get("rating"),
+            "helpfulness": row.get("helpfulness"),
+            "error_types": _parse_json_if_needed(row.get("error_types")),
+            "correct_answer": row.get("correct_answer"),
+        }
+        candidates = _build_reason_candidates(
+            source_type="qa_feedback",
+            field_name="qa_feedback",
+            before_value=before_value,
+            after_value=after_value,
+        )
+        _insert_record(
+            bind,
+            {
+                "tenant_id": tenant_id,
+                "project_id": _safe_int(row.get("project_id")),
+                "source_type": "qa_feedback",
+                "source_id": source_id,
+                "object_type": "qa_record",
+                "object_id": object_id,
+                "field_name": "qa_feedback",
+                "before_value": before_value,
+                "after_value": after_value,
+                "ai_snapshot": {
+                    "question": row.get("question"),
+                    "answer": row.get("answer"),
+                },
+                "ai_confidence": None,
+                "reason_candidates": candidates,
+                "reason_code": candidates[0]["code"] if candidates else None,
+                "reason_text": row.get("correct_answer"),
+                "review_status": "reviewed",
+                "reviewed_by": _safe_int(row.get("submitted_by")),
+                "reviewed_at": row.get("created_at"),
+                "source_title": str(row.get("question") or "")[:500] or None,
+                "object_title": str(row.get("question") or "")[:500] or None,
+                "auto_assigned": False,
+            },
+        )
+
+
+def _backfill_from_signal_feedback(bind) -> None:
+    if not _table_exists(bind, "signal_feedbacks") or not _table_exists(bind, "signals"):
+        return
+    rows = bind.execute(
+        sa.text(
+            """
+            SELECT
+                f.id AS feedback_id,
+                f.tenant_id AS tenant_id,
+                f.signal_id AS signal_id,
+                f.was_strategic AS was_strategic,
+                f.reason AS reason,
+                f.ai_predicted_strategic AS ai_predicted_strategic,
+                f.ai_confidence AS ai_confidence,
+                f.user_id AS user_id,
+                f.created_at AS created_at,
+                s.matched_project_id AS project_id,
+                s.title AS signal_title
+            FROM signal_feedbacks f
+            JOIN signals s ON s.id = f.signal_id
+            """
+        )
+    ).mappings().all()
+
+    for row in rows:
+        tenant_id = _safe_int(row.get("tenant_id"))
+        source_id = _safe_int(row.get("feedback_id"))
+        object_id = _safe_int(row.get("signal_id"))
+        if tenant_id is None or source_id is None or object_id is None:
+            continue
+        if _record_exists(
+            bind,
+            tenant_id=tenant_id,
+            source_type="signal_feedback",
+            source_id=source_id,
+            object_type="signal",
+            object_id=object_id,
+            field_name="strategic_classification",
+        ):
+            continue
+        before_value = {
+            "ai_predicted_strategic": row.get("ai_predicted_strategic"),
+        }
+        after_value = {
+            "was_strategic": row.get("was_strategic"),
+            "reason": row.get("reason"),
+        }
+        candidates = _build_reason_candidates(
+            source_type="signal_feedback",
+            field_name="strategic_classification",
+            before_value=before_value,
+            after_value=after_value,
+        )
+        _insert_record(
+            bind,
+            {
+                "tenant_id": tenant_id,
+                "project_id": _safe_int(row.get("project_id")),
+                "source_type": "signal_feedback",
+                "source_id": source_id,
+                "object_type": "signal",
+                "object_id": object_id,
+                "field_name": "strategic_classification",
+                "before_value": before_value,
+                "after_value": after_value,
+                "ai_snapshot": {
+                    "ai_predicted_strategic": row.get("ai_predicted_strategic"),
+                    "ai_confidence": row.get("ai_confidence"),
+                },
+                "ai_confidence": row.get("ai_confidence"),
+                "reason_candidates": candidates,
+                "reason_code": candidates[0]["code"] if candidates else None,
+                "reason_text": row.get("reason"),
+                "review_status": "reviewed",
+                "reviewed_by": _safe_int(row.get("user_id")),
+                "reviewed_at": row.get("created_at"),
+                "source_title": str(row.get("signal_title") or "")[:500] or None,
+                "object_title": str(row.get("signal_title") or "")[:500] or None,
+                "auto_assigned": False,
+            },
+        )
+
+
+def _backfill_from_task_overrides(bind) -> None:
+    if not _table_exists(bind, "tasks"):
+        return
+    rows = bind.execute(
+        sa.text(
+            """
+            SELECT
+                t.id AS task_id,
+                t.tenant_id AS tenant_id,
+                t.project_id AS project_id,
+                t.title AS task_title,
+                t.ai_breakdown_payload AS ai_breakdown_payload,
+                t.manual_overrides AS manual_overrides,
+                t.updated_at AS updated_at
+            FROM tasks t
+            WHERE t.manual_overrides IS NOT NULL
+            """
+        )
+    ).mappings().all()
+
+    for row in rows:
+        tenant_id = _safe_int(row.get("tenant_id"))
+        task_id = _safe_int(row.get("task_id"))
+        if tenant_id is None or task_id is None:
+            continue
+        overrides = _parse_json_if_needed(row.get("manual_overrides"))
+        ai_payload = _parse_json_if_needed(row.get("ai_breakdown_payload"))
+        if not isinstance(overrides, dict) or not overrides:
+            continue
+        if not isinstance(ai_payload, dict):
+            ai_payload = {}
+
+        for field_name in sorted(overrides.keys()):
+            if _record_exists(
+                bind,
+                tenant_id=tenant_id,
+                source_type="task_override",
+                source_id=task_id,
+                object_type="task",
+                object_id=task_id,
+                field_name=str(field_name),
+            ):
+                continue
+            before_value = ai_payload.get(field_name)
+            after_value = overrides.get(field_name)
+            candidates = _build_reason_candidates(
+                source_type="task_override",
+                field_name=str(field_name),
+                before_value=before_value,
+                after_value=after_value,
+            )
+            _insert_record(
+                bind,
+                {
+                    "tenant_id": tenant_id,
+                    "project_id": _safe_int(row.get("project_id")),
+                    "source_type": "task_override",
+                    "source_id": task_id,
+                    "object_type": "task",
+                    "object_id": task_id,
+                    "field_name": str(field_name)[:128],
+                    "before_value": before_value,
+                    "after_value": after_value,
+                    "ai_snapshot": ai_payload,
+                    "ai_confidence": None,
+                    "reason_candidates": candidates,
+                    "reason_code": candidates[0]["code"] if candidates else None,
+                    "reason_text": None,
+                    "review_status": "reviewed",
+                    "reviewed_by": None,
+                    "reviewed_at": row.get("updated_at"),
+                    "source_title": str(row.get("task_title") or "")[:500] or None,
+                    "object_title": str(row.get("task_title") or "")[:500] or None,
+                    "auto_assigned": False,
+                },
+            )
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+    json_type = _json_type(bind)
+
+    if not _table_exists(bind, "feedback_center_records"):
+        op.create_table(
+            "feedback_center_records",
+            sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
+            sa.Column("tenant_id", sa.BigInteger(), nullable=False),
+            sa.Column("project_id", sa.BigInteger(), nullable=True),
+            sa.Column("source_type", sa.VARCHAR(length=32), nullable=False),
+            sa.Column("source_id", sa.BigInteger(), nullable=False),
+            sa.Column("object_type", sa.VARCHAR(length=32), nullable=False),
+            sa.Column("object_id", sa.BigInteger(), nullable=False),
+            sa.Column("field_name", sa.VARCHAR(length=128), nullable=False),
+            sa.Column("before_value", json_type, nullable=True),
+            sa.Column("after_value", json_type, nullable=True),
+            sa.Column("ai_snapshot", json_type, nullable=True),
+            sa.Column("ai_confidence", sa.Float(), nullable=True),
+            sa.Column("reason_candidates", json_type, nullable=True),
+            sa.Column("reason_code", sa.VARCHAR(length=64), nullable=True),
+            sa.Column("reason_text", sa.Text(), nullable=True),
+            sa.Column("review_status", sa.VARCHAR(length=16), nullable=False, server_default="pending"),
+            sa.Column("reviewed_by", sa.BigInteger(), nullable=True),
+            sa.Column("reviewed_at", sa.TIMESTAMP(), nullable=True),
+            sa.Column("source_title", sa.VARCHAR(length=500), nullable=True),
+            sa.Column("object_title", sa.VARCHAR(length=500), nullable=True),
+            sa.Column("auto_assigned", sa.Boolean(), nullable=False, server_default=sa.text("false")),
+            sa.Column("provider", sa.VARCHAR(length=32), nullable=True),
+            sa.Column("model_name", sa.VARCHAR(length=128), nullable=True),
+            sa.Column("prompt_version", sa.VARCHAR(length=64), nullable=True),
+            sa.Column("schema_version", sa.VARCHAR(length=64), nullable=True),
+            sa.Column("created_at", sa.TIMESTAMP(), nullable=False, server_default=sa.text("now()")),
+            sa.Column("updated_at", sa.TIMESTAMP(), nullable=False, server_default=sa.text("now()")),
+            sa.UniqueConstraint(
+                "tenant_id",
+                "source_type",
+                "source_id",
+                "object_type",
+                "object_id",
+                "field_name",
+                name="uq_feedback_center_records_source_object_field",
+            ),
+        )
+
+    if not _table_exists(bind, "feedback_center_record_revisions"):
+        op.create_table(
+            "feedback_center_record_revisions",
+            sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
+            sa.Column("tenant_id", sa.BigInteger(), nullable=False),
+            sa.Column("record_id", sa.Integer(), sa.ForeignKey("feedback_center_records.id", ondelete="CASCADE"), nullable=False),
+            sa.Column("revision_no", sa.Integer(), nullable=False, server_default="1"),
+            sa.Column("before_snapshot", json_type, nullable=False),
+            sa.Column("after_snapshot", json_type, nullable=False),
+            sa.Column("reason_code", sa.VARCHAR(length=64), nullable=True),
+            sa.Column("reason_text", sa.Text(), nullable=True),
+            sa.Column("review_status", sa.VARCHAR(length=16), nullable=False, server_default="reviewed"),
+            sa.Column("reviewed_by", sa.BigInteger(), nullable=True),
+            sa.Column("reviewed_at", sa.TIMESTAMP(), nullable=True),
+            sa.Column("change_summary", sa.Text(), nullable=True),
+            sa.Column("created_at", sa.TIMESTAMP(), nullable=False, server_default=sa.text("now()")),
+            sa.Column("updated_at", sa.TIMESTAMP(), nullable=False, server_default=sa.text("now()")),
+        )
+
+    if not _index_exists(bind, "feedback_center_records", "ix_feedback_center_records_tenant_project_status"):
+        op.create_index(
+            "ix_feedback_center_records_tenant_project_status",
+            "feedback_center_records",
+            ["tenant_id", "project_id", "review_status"],
+            unique=False,
+        )
+    if not _index_exists(bind, "feedback_center_records", "ix_feedback_center_records_tenant_source"):
+        op.create_index(
+            "ix_feedback_center_records_tenant_source",
+            "feedback_center_records",
+            ["tenant_id", "source_type", "source_id"],
+            unique=False,
+        )
+    if not _index_exists(bind, "feedback_center_records", "ix_feedback_center_records_reason_code"):
+        op.create_index(
+            "ix_feedback_center_records_reason_code",
+            "feedback_center_records",
+            ["reason_code"],
+            unique=False,
+        )
+    if not _index_exists(bind, "feedback_center_record_revisions", "ix_feedback_center_record_revisions_tenant_record"):
+        op.create_index(
+            "ix_feedback_center_record_revisions_tenant_record",
+            "feedback_center_record_revisions",
+            ["tenant_id", "record_id"],
+            unique=False,
+        )
+
+    _backfill_from_qa_feedback(bind)
+    _backfill_from_signal_feedback(bind)
+    _backfill_from_task_overrides(bind)
+
+
+def downgrade() -> None:
+    bind = op.get_bind()
+    if _table_exists(bind, "feedback_center_record_revisions"):
+        if _index_exists(bind, "feedback_center_record_revisions", "ix_feedback_center_record_revisions_tenant_record"):
+            op.drop_index(
+                "ix_feedback_center_record_revisions_tenant_record",
+                table_name="feedback_center_record_revisions",
+            )
+        op.drop_table("feedback_center_record_revisions")
+
+    if _table_exists(bind, "feedback_center_records"):
+        for index_name in (
+            "ix_feedback_center_records_reason_code",
+            "ix_feedback_center_records_tenant_source",
+            "ix_feedback_center_records_tenant_project_status",
+        ):
+            if _index_exists(bind, "feedback_center_records", index_name):
+                op.drop_index(index_name, table_name="feedback_center_records")
+        op.drop_table("feedback_center_records")

backend/app/api/ai_steward_api/__init__.py

@@ -6,6 +6,7 @@ from . import (
     routes_brief_blockers,
     routes_capture,
     routes_conflicts,
+    routes_feedback_center,
     routes_rag_sources,
     routes_stats_config,
     routes_workflow,
@@ -16,6 +17,7 @@ for _module in [
     routes_rag_sources,
     routes_capture,
     routes_conflicts,
+    routes_feedback_center,
     routes_brief_blockers,
     routes_stats_config,
     routes_workflow,

backend/app/api/ai_steward_api/routes_feedback_center.py

@@ -0,0 +1,157 @@
+from __future__ import annotations
+
+from enum import Enum
+from typing import Literal
+
+from fastapi import Depends, HTTPException, Query
+from sqlalchemy import and_, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ...core.response import success_response
+from ...dependencies.permissions import require_manager_user
+from ...models import User, UserProject
+from ...schemas.feedback_center import FeedbackCenterReviewRequest
+from ...services.auth import parse_role_values
+from ...services.feedback_center import FeedbackCenterService, list_reason_catalog
+from .shared import *
+
+MANAGER_ROLES = {"ADMIN", "BOSS"}
+
+
+def _is_manager_user(current_user: User) -> bool:
+    role_value = getattr(current_user, "role", "")
+    if isinstance(role_value, Enum):
+        role_value = role_value.value
+    roles = set(parse_role_values(str(role_value or "")))
+    return bool(roles.intersection(MANAGER_ROLES))
+
+
+async def _ensure_summary_scope(
+    *,
+    current_user: User,
+    db: AsyncSession,
+    project_id: int | None,
+) -> None:
+    if _is_manager_user(current_user):
+        return
+    if project_id is None:
+        raise HTTPException(status_code=403, detail="Manager permission required for global feedback center")
+    membership = await db.execute(
+        select(UserProject.id).where(
+            and_(
+                UserProject.tenant_id == int(current_user.tenant_id),
+                UserProject.project_id == int(project_id),
+                UserProject.user_id == int(current_user.id),
+            )
+        )
+    )
+    if membership.scalar_one_or_none() is None:
+        raise HTTPException(status_code=403, detail="Project membership required")
+
+
+@router.get("/feedback-center/summary")
+async def get_feedback_center_summary(
+    project_id: int | None = Query(default=None),
+    window_days: int | None = Query(default=None),
+    source_group: str | None = Query(default=None),
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
+):
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
+    await _ensure_summary_scope(current_user=current_user, db=db, project_id=project_id)
+    try:
+        data = await FeedbackCenterService(db).summary(
+            tenant_id=effective_tenant_id,
+            project_id=project_id,
+            window_days=window_days,
+            source_group=source_group,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    return success_response(data)
+
+
+@router.get("/feedback-center/items")
+async def list_feedback_center_items(
+    project_id: int | None = Query(default=None),
+    source_type: str | None = Query(default=None),
+    review_status: str | None = Query(default=None),
+    field_name: str | None = Query(default=None),
+    reason_code: str | None = Query(default=None),
+    search: str | None = Query(default=None),
+    limit: int = Query(default=20, ge=1, le=100),
+    offset: int = Query(default=0, ge=0),
+    current_user: User = Depends(require_manager_user),
+    db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
+):
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
+    data = await FeedbackCenterService(db).list_records(
+        tenant_id=effective_tenant_id,
+        project_id=project_id,
+        source_type=source_type,
+        review_status=review_status,
+        field_name=field_name,
+        reason_code=reason_code,
+        search=search,
+        limit=limit,
+        offset=offset,
+    )
+    return success_response(data)
+
+
+@router.get("/feedback-center/items/{record_id}")
+async def get_feedback_center_item_detail(
+    record_id: int,
+    project_id: int | None = Query(default=None),
+    current_user: User = Depends(require_manager_user),
+    db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
+):
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
+    data = await FeedbackCenterService(db).get_detail(
+        tenant_id=effective_tenant_id,
+        record_id=record_id,
+        project_id=project_id,
+    )
+    if data is None:
+        raise HTTPException(status_code=404, detail="feedback record not found")
+    return success_response(data)
+
+
+@router.post("/feedback-center/items/{record_id}/review")
+async def review_feedback_center_item(
+    record_id: int,
+    request: FeedbackCenterReviewRequest,
+    current_user: User = Depends(require_manager_user),
+    db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
+):
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
+    service = FeedbackCenterService(db)
+    reviewed = await service.review(
+        tenant_id=effective_tenant_id,
+        record_id=record_id,
+        reviewer_id=int(current_user.id),
+        review_status=request.review_status,
+        reason_code=request.reason_code,
+        reason_text=request.reason_text,
+    )
+    return success_response(reviewed)
+
+
+@router.get("/feedback-center/reason-catalog")
+async def get_feedback_center_reason_catalog(
+    source_type: str | None = Query(default=None),
+    field_name: str | None = Query(default=None),
+    current_user: User = Depends(require_manager_user),
+    db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
+):
+    _ = db, tenant_id, current_user
+    return success_response(
+        {
+            "items": list_reason_catalog(source_type=source_type, field_name=field_name),
+        }
+    )

backend/app/api/feedbacks.py

@@ -1,57 +1,27 @@
 """反馈统计 API"""
 
+from __future__ import annotations
+
 from fastapi import APIRouter, Depends
 from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy.future import select
 
 from ..core.database import get_db
-from ..models import SignalFeedback
+from ..core.response import success_response
+from ..dependencies.auth import get_current_user
+from ..dependencies.tenant import get_tenant_id
+from ..models import User
+from ..services.feedback_center import FeedbackCenterService
 
 router = APIRouter(prefix="/feedbacks", tags=["feedbacks"])
 
 
 @router.get("/stats")
-async def get_feedback_stats(db: AsyncSession = Depends(get_db)):
+async def get_feedback_stats(
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
+):
     """获取反馈统计"""
-    result = await db.execute(select(SignalFeedback))
-    all_feedbacks = result.scalars().all()
-
-    total = len(all_feedbacks)
-    if total == 0:
-        return {
-            "total_feedbacks": 0,
-            "accuracy": 0.0,
-            "true_positives": 0,
-            "false_positives": 0,
-            "true_negatives": 0,
-            "false_negatives": 0,
-        }
-
-    correct = sum(
-        1 for f in all_feedbacks if f.ai_predicted_strategic == f.was_strategic
-    )
-    true_positives = sum(
-        1 for f in all_feedbacks if f.ai_predicted_strategic and f.was_strategic
-    )
-    false_positives = sum(
-        1 for f in all_feedbacks if f.ai_predicted_strategic and not f.was_strategic
-    )
-    true_negatives = sum(
-        1
-        for f in all_feedbacks
-        if not f.ai_predicted_strategic and not f.was_strategic
-    )
-    false_negatives = sum(
-        1
-        for f in all_feedbacks
-        if not f.ai_predicted_strategic and f.was_strategic
-    )
-
-    return {
-        "total_feedbacks": total,
-        "accuracy": correct / total,
-        "true_positives": true_positives,
-        "false_positives": false_positives,
-        "true_negatives": true_negatives,
-        "false_negatives": false_negatives,
-    }
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
+    data = await FeedbackCenterService(db).get_signal_feedback_stats(tenant_id=effective_tenant_id)
+    return success_response(data)

backend/app/api/rag_api/routes_qa.py

@@ -1,7 +1,11 @@
 from __future__ import annotations
 
+from sqlalchemy import and_, select
+
 from .shared import *
 from .helpers import _resolve_tenant_id
+from ...models import QAFeedback, QARecord
+from ...services.feedback_center import FeedbackCenterService
 
 @router.post("/qa-records/{qa_id}/feedback")
 async def submit_qa_feedback(
@@ -23,6 +27,38 @@ async def submit_qa_feedback(
         )
     except ValueError as exc:
         raise HTTPException(status_code=404, detail=str(exc)) from exc
+    try:
+        feedback_row = (
+            await db.execute(
+                select(QAFeedback)
+                .where(
+                    and_(
+                        QAFeedback.tenant_id == _resolve_tenant_id(current_user, tenant_id),
+                        QAFeedback.qa_id == qa_id,
+                        QAFeedback.submitted_by == int(current_user.id),
+                    )
+                )
+                .order_by(QAFeedback.created_at.desc(), QAFeedback.id.desc())
+            )
+        ).scalars().first()
+        qa_row = (
+            await db.execute(
+                select(QARecord).where(
+                    and_(
+                        QARecord.tenant_id == _resolve_tenant_id(current_user, tenant_id),
+                        QARecord.id == qa_id,
+                    )
+                )
+            )
+        ).scalar_one_or_none()
+        if feedback_row and qa_row:
+            await FeedbackCenterService(db).ingest_qa_feedback(
+                qa_feedback=feedback_row,
+                qa_record=qa_row,
+                commit=True,
+            )
+    except Exception:
+        await db.rollback()
     return success_response(result)
 
 @router.get("/qa-records")
@@ -195,4 +231,3 @@ async def export_qa_records_jsonl(
         date_to=query.date_to,
     )
     return Response(content=payload, media_type="application/x-ndjson")
-

backend/app/api/signals_api/feedback.py

@@ -9,21 +9,33 @@ from fastapi import APIRouter, Depends, HTTPException
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.future import select
 
+from ...core.response import success_response
 from ...core.database import get_db
-from ...models import Signal, SignalFeedback
+from ...dependencies.auth import get_current_user
+from ...dependencies.tenant import get_tenant_id
+from ...models import Signal, SignalFeedback, User
 from ...schemas import SignalFeedbackCreate, SignalFeedbackResponse
+from ...services.feedback_center import FeedbackCenterService
 
 router = APIRouter()
 
 
-@router.post("/{signal_id}/feedback", response_model=SignalFeedbackResponse)
+@router.post("/{signal_id}/feedback")
 async def submit_signal_feedback(
     signal_id: int,
     feedback: SignalFeedbackCreate,
+    current_user: User = Depends(get_current_user),
     db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
 ):
     """提交信号反馈"""
-    result = await db.execute(select(Signal).where(Signal.id == signal_id))
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
+    result = await db.execute(
+        select(Signal).where(
+            Signal.id == signal_id,
+            Signal.tenant_id == effective_tenant_id,
+        )
+    )
     signal = result.scalar_one_or_none()
     if not signal:
         raise HTTPException(status_code=404, detail="Signal not found")
@@ -39,10 +51,11 @@ async def submit_signal_feedback(
             ai_predicted_strategic = signal.impact_score >= 0.9
 
     new_feedback = SignalFeedback(
+        tenant_id=effective_tenant_id,
         signal_id=signal_id,
         was_strategic=feedback.was_strategic,
         reason=feedback.reason,
-        user_id=feedback.user_id,
+        user_id=str(getattr(current_user, "id", feedback.user_id) or feedback.user_id or ""),
         ai_predicted_strategic=ai_predicted_strategic,
         ai_confidence=ai_confidence,
     )
@@ -50,32 +63,48 @@ async def submit_signal_feedback(
     await db.commit()
     await db.refresh(new_feedback)
 
+    try:
+        await FeedbackCenterService(db).ingest_signal_feedback(
+            signal_feedback=new_feedback,
+            signal=signal,
+            commit=True,
+        )
+    except Exception:
+        await db.rollback()
+
     is_correct = ai_predicted_strategic == feedback.was_strategic
 
-    return SignalFeedbackResponse(
+    return success_response(
+        SignalFeedbackResponse(
             id=new_feedback.id,
             signal_id=new_feedback.signal_id,
             was_strategic=new_feedback.was_strategic,
             ai_predicted_strategic=new_feedback.ai_predicted_strategic,
-        ai_confidence=new_feedback.ai_confidence,
+            ai_confidence=new_feedback.ai_confidence or 0.0,
             is_correct=is_correct,
+        ).model_dump()
     )
 
-
-@router.get("/{signal_id}/feedbacks", response_model=List[SignalFeedbackResponse])
+@router.get("/{signal_id}/feedbacks")
 async def get_signal_feedbacks(
     signal_id: int,
+    current_user: User = Depends(get_current_user),
     db: AsyncSession = Depends(get_db),
+    tenant_id: int = Depends(get_tenant_id),
 ):
     """获取某信号的全部反馈"""
+    effective_tenant_id = int(getattr(current_user, "tenant_id", tenant_id) or tenant_id)
     result = await db.execute(
         select(SignalFeedback)
-        .where(SignalFeedback.signal_id == signal_id)
+        .where(
+            SignalFeedback.signal_id == signal_id,
+            SignalFeedback.tenant_id == effective_tenant_id,
+        )
         .order_by(SignalFeedback.created_at.desc())
     )
     feedbacks = result.scalars().all()
 
-    return [
+    items = [
         SignalFeedbackResponse(
             id=feedback.id,
             signal_id=feedback.signal_id,
@@ -83,6 +112,7 @@ async def get_signal_feedbacks(
             ai_predicted_strategic=feedback.ai_predicted_strategic,
             ai_confidence=feedback.ai_confidence or 0.0,
             is_correct=feedback.ai_predicted_strategic == feedback.was_strategic,
-        )
+        ).model_dump()
         for feedback in feedbacks
     ]
+    return success_response({"items": items})

backend/app/api/sstc_api/tasks_ai_breakdown.py

@@ -14,6 +14,7 @@ from ...services.sstc.entity_lookup import _ensure_charter, _ensure_project
 from ...services.sstc.governance import _assert_charter_aligned, _require_manager
 from ...services.sstc.signal_core import _parse_node_id, _parse_project_id, _sync_pmflow_rag_async
 from ...services.context_cache_service import context_cache_service
+from ...services.feedback_center import FeedbackCenterService, map_task_breakdown_to_record_payload
 from ...services.task_breakdown import (
     build_task_breakdown_ai_payload,
     normalize_task_breakdown_payload,
@@ -263,6 +264,23 @@ async def ai_breakdown_tasks(
             user_id=user_id,
         )
     await context_cache_service.invalidate_system_context(tenant_id=tenant_id)
+    try:
+        payloads = [
+            map_task_breakdown_to_record_payload(
+                task=t,
+                project_title=project.title,
+                source_context=ai_breakdown_payload["source_context"],
+            )
+            for t in created
+        ]
+        if payloads:
+            await FeedbackCenterService(db).ingest_payloads(payloads=payloads, commit=True)
+    except Exception:
+        if hasattr(db, "rollback"):
+            try:
+                await db.rollback()
+            except Exception:
+                pass
     return success_response(
         {
             "created_count": len(created),

backend/app/api/sstc_api/tasks_update_delete.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from .shared import *
 
 from ...services.context_cache_service import context_cache_service
+from ...services.feedback_center import FeedbackCenterService
 from ...services.sstc.entity_lookup import _ensure_project, _ensure_task, _parse_due_date
 from ...services.sstc.governance import _require_manager
 from ...services.sstc.signal_core import _parse_node_id, _parse_project_id, _sync_pmflow_rag_async
@@ -118,6 +119,14 @@ async def patch_task(
         title=task.title,
         content=task.description or "",
     )
+    try:
+        await FeedbackCenterService(db).ingest_task_overrides(task=task, commit=True)
+    except Exception:
+        if hasattr(db, "rollback"):
+            try:
+                await db.rollback()
+            except Exception:
+                pass
 
     return success_response({"id": str(task.id), "status": task.status})
 

backend/app/config/ai_prompt_registry.py

@@ -4,6 +4,109 @@ from typing import Any
 
 from .ai_prompts_catalog import __all__ as PROMPT_CATALOG_EXPORTS
 
+PROMPT_FAMILY_CLASSIFICATION = "classification"
+PROMPT_FAMILY_EXTRACTION = "extraction"
+PROMPT_FAMILY_PLANNING = "planning"
+PROMPT_FAMILY_RESPONSE = "response"
+PROMPT_FAMILY_GOVERNANCE = "governance"
+
+PROMPT_FAMILY_ALLOWED = {
+    PROMPT_FAMILY_CLASSIFICATION,
+    PROMPT_FAMILY_EXTRACTION,
+    PROMPT_FAMILY_PLANNING,
+    PROMPT_FAMILY_RESPONSE,
+    PROMPT_FAMILY_GOVERNANCE,
+}
+
+PROMPT_FAMILY_KEYWORD_RULES: tuple[tuple[str, str], ...] = (
+    ("CLASSIFICATION", PROMPT_FAMILY_CLASSIFICATION),
+    ("EXTRACTION", PROMPT_FAMILY_EXTRACTION),
+    ("PREFILL", PROMPT_FAMILY_EXTRACTION),
+    ("INTENT", PROMPT_FAMILY_CLASSIFICATION),
+    ("TASK_ORCHESTRATION", PROMPT_FAMILY_PLANNING),
+    ("PLANNER", PROMPT_FAMILY_PLANNING),
+    ("GENERATE", PROMPT_FAMILY_PLANNING),
+    ("DECOMPOSE", PROMPT_FAMILY_PLANNING),
+    ("RECOMMEND", PROMPT_FAMILY_PLANNING),
+    ("DRAFT", PROMPT_FAMILY_PLANNING),
+    ("TASK_BREAKDOWN", PROMPT_FAMILY_PLANNING),
+    ("QA", PROMPT_FAMILY_RESPONSE),
+    ("REPLY", PROMPT_FAMILY_RESPONSE),
+)
+
+PROMPT_FAMILY_SCENARIO_RULES: tuple[tuple[str, str], ...] = (
+    ("分类", PROMPT_FAMILY_CLASSIFICATION),
+    ("抽取", PROMPT_FAMILY_EXTRACTION),
+    ("预填", PROMPT_FAMILY_EXTRACTION),
+    ("规划", PROMPT_FAMILY_PLANNING),
+    ("编排", PROMPT_FAMILY_PLANNING),
+    ("草案", PROMPT_FAMILY_PLANNING),
+    ("拆解", PROMPT_FAMILY_PLANNING),
+    ("问答", PROMPT_FAMILY_RESPONSE),
+    ("回复", PROMPT_FAMILY_RESPONSE),
+)
+
+RESPONSIBILITY_SCOPE_BY_FAMILY: dict[str, str] = {
+    PROMPT_FAMILY_CLASSIFICATION: "分类层：只负责识别请求类型、归属和优先级，不生成执行方案。",
+    PROMPT_FAMILY_EXTRACTION: "抽取层：只负责结构化提取实体、槽位和证据，不做规划决策。",
+    PROMPT_FAMILY_PLANNING: "规划层：只负责生成可执行规划、任务和负责人建议，不承担最终对话呈现。",
+    PROMPT_FAMILY_RESPONSE: "呈现层：只负责面向用户输出结论与建议，不直接做写操作决策。",
+    PROMPT_FAMILY_GOVERNANCE: "治理层：只负责约束、评估、同步与风控，不承担主对话生成。",
+}
+
+PROMPT_PRIORITY_BY_FAMILY: dict[str, str] = {
+    PROMPT_FAMILY_CLASSIFICATION: "high",
+    PROMPT_FAMILY_EXTRACTION: "high",
+    PROMPT_FAMILY_PLANNING: "high",
+    PROMPT_FAMILY_RESPONSE: "high",
+    PROMPT_FAMILY_GOVERNANCE: "medium",
+}
+
+PROMPT_AUDIENCE_BY_FAMILY: dict[str, str] = {
+    PROMPT_FAMILY_CLASSIFICATION: "signal_router",
+    PROMPT_FAMILY_EXTRACTION: "signal_ingestion",
+    PROMPT_FAMILY_PLANNING: "planning_orchestrator",
+    PROMPT_FAMILY_RESPONSE: "assistant_response",
+    PROMPT_FAMILY_GOVERNANCE: "risk_budget_guardrail",
+}
+
+
+def _normalize_family(prompt_family: str) -> str:
+    value = str(prompt_family or "").strip().lower()
+    if value in PROMPT_FAMILY_ALLOWED:
+        return value
+    return PROMPT_FAMILY_GOVERNANCE
+
+
+def _derive_prompt_family(prompt_key: str, business_scenario: str) -> str:
+    key_text = str(prompt_key or "").strip().upper()
+    scenario_text = str(business_scenario or "").strip()
+
+    for keyword, family in PROMPT_FAMILY_KEYWORD_RULES:
+        if keyword in key_text:
+            return family
+
+    for keyword, family in PROMPT_FAMILY_SCENARIO_RULES:
+        if keyword in scenario_text:
+            return family
+
+    return PROMPT_FAMILY_GOVERNANCE
+
+
+def _derive_responsibility_scope(prompt_family: str) -> str:
+    family = _normalize_family(prompt_family)
+    return RESPONSIBILITY_SCOPE_BY_FAMILY[family]
+
+
+def _derive_priority(prompt_family: str) -> str:
+    family = _normalize_family(prompt_family)
+    return PROMPT_PRIORITY_BY_FAMILY[family]
+
+
+def _derive_audience(prompt_family: str) -> str:
+    family = _normalize_family(prompt_family)
+    return PROMPT_AUDIENCE_BY_FAMILY[family]
+
 
 def _entry(
     prompt_key: str,
@@ -13,7 +116,16 @@ def _entry(
     input_contract: str,
     output_contract: str,
     call_sites: tuple[str, ...] = (),
+    prompt_family: str | None = None,
+    responsibility_scope: str | None = None,
+    priority: str | None = None,
+    audience: str | None = None,
 ) -> dict[str, Any]:
+    resolved_family = _normalize_family(prompt_family or _derive_prompt_family(prompt_key, business_scenario))
+    resolved_scope = str(responsibility_scope or "").strip() or _derive_responsibility_scope(resolved_family)
+    resolved_priority = str(priority or "").strip().lower() or _derive_priority(resolved_family)
+    resolved_audience = str(audience or "").strip() or _derive_audience(resolved_family)
+
     return {
         "prompt_key": prompt_key,
         "module": module,
@@ -21,6 +133,10 @@ def _entry(
         "call_sites": tuple(str(site) for site in call_sites if str(site or "").strip()),
         "input_contract": input_contract,
         "output_contract": output_contract,
+        "prompt_family": resolved_family,
+        "responsibility_scope": resolved_scope,
+        "priority": resolved_priority,
+        "audience": resolved_audience,
     }
 
 
@@ -64,13 +180,6 @@ def _part_01_entries() -> tuple[dict[str, Any], ...]:
             input_contract=JSON_INPUT,
             output_contract="输出 JSON 对象，包含影响结论、风险、建议。",
         ),
-        _entry(
-            "PROMPT_TASK_BREAKDOWN",
-            module="part_01",
-            business_scenario="任务拆解基础片段",
-            input_contract=FRAGMENT_INPUT,
-            output_contract=FRAGMENT_OUTPUT,
-        ),
         _entry(
             "PROMPT_STRATEGIC_ANALYSIS_HEADER",
             module="part_01",
@@ -106,14 +215,6 @@ def _part_01_entries() -> tuple[dict[str, Any], ...]:
             input_contract=JSON_INPUT,
             output_contract="输出 JSON 对象，包含关键洞察与下一步建议。",
         ),
-        _entry(
-            "PROMPT_FEISHU_REQUIREMENT_ANALYSIS",
-            module="part_01",
-            business_scenario="飞书需求解析",
-            input_contract=JSON_INPUT,
-            output_contract="输出 JSON 对象，包含需求抽取、风险与建议。",
-            call_sites=FEISHU_EXTRACT_CALL_SITES,
-        ),
         _entry(
             "PROMPT_FEISHU_HUMANIZED_REPLY",
             module="part_01",

backend/app/config/ai_prompts.py

@@ -9,13 +9,11 @@ AI Prompt 集中管理
 
 from .ai_prompts_catalog import (
     PROMPT_IMPACT_ANALYSIS,
-    PROMPT_TASK_BREAKDOWN,
     PROMPT_STRATEGIC_ANALYSIS_HEADER,
     PROMPT_RAG_CONTEXT_HEADER,
     PROMPT_RAG_CONTEXT_ITEM,
     PROMPT_CODE_AUDIT,
     PROMPT_CONTEXT_INSIGHTS,
-    PROMPT_FEISHU_REQUIREMENT_ANALYSIS,
     PROMPT_FEISHU_HUMANIZED_REPLY,
     PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION,
     PROMPT_FEISHU_LEVEL_CLASSIFICATION,
@@ -62,6 +60,12 @@ from .ai_prompt_registry import (
     iter_prompt_registry_keys,
 )
 
+
+# Deprecated compatibility aliases kept outside the registry surface.
+PROMPT_FEISHU_REQUIREMENT_ANALYSIS = PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION
+PROMPT_TASK_BREAKDOWN = PROMPT_DECOMPOSE_TASKS
+
+
 def build_default_role_prompt_bundle() -> dict:
     """返回默认角色 Prompt 配置（每次调用返回新对象）。"""
     return {
@@ -74,26 +78,25 @@ def build_default_role_prompt_bundle() -> dict:
                 "role_description": "负责查重、评审、起草立项建议并给出确认前的产品方案",
                 "enabled": True,
                 "system_prompt": """
-你是 PMFlow 的产品经理助手。
+你是 PMFlow 的 PMO 协作中枢中的立项评审角色（pm_dispatcher）。
 
-职责定位：
-1. 先判断是否已有同名或相似项目，再给出是否复用、合并或新建的建议。
-2. 先输出产品评审结论，再展开项目定位、MVP、前景、需求清单、风险与负责人建议。
-3. 回复风格要专业、清晰、像真实产品经理评审，允许给出取舍与备选方案。
-4. 只有在用户明确确认后，才进入写入或继续执行流程。
+职责边界（只做评审与起草，不直接执行写操作）：
+1. 聚焦查重、复用/合并、立项评审，优先判断是否已有同名或相似项目。
+2. 输出面向业务决策的结论：是否新建、复用、合并或暂缓。
+3. 基于项目上下文产出可讨论草案，覆盖项目定位、MVP、需求清单、风险与负责人建议。
+4. 若信息不足，提出关键澄清问题（最多 3 个），不凭空补齐。
 
-输出要求：
-1. 先用 1-2 句话复述需求并判断立项方向。
-2. 再给出是否有同名/相似项目的判断。
-3. 再展开项目定位、MVP、前景、需求清单、风险与待补充信息。
-4. 如果信息不足，只提关键澄清问题（最多 3 个），不要臆测。
-5. 如果涉及风险、依赖、排期冲突或重复建设，要明确提醒。
+输出框架：
+1. 先结论：1-2 句复述需求并给出立项方向。
+2. 再查重：明确同名或相似项目判断，给出差异点与复用价值。
+3. 再方案：给出 MVP、关键需求、主要风险、负责人建议、后续动作。
+4. 全程专业、克制、结论先行，避免空泛口号。
 
 禁止项：
-- 不要输出与项目无关的泛化建议。
-- 不要只给机械化模板，不要把评审写成简单入库文案。
-- 不要使用命令式生硬语气。
-- 不要编造不存在的数据或结论。
+- 不要编造不存在的数据、进展或执行结果。
+- 不要把评审写成机械化模板。
+- 不要使用命令式、生硬语气。
+- 不要输出与 PMFlow 业务无关的泛化建议。
 """.strip(),
             },
             {
@@ -102,16 +105,21 @@ def build_default_role_prompt_bundle() -> dict:
                 "role_description": "负责技术方案约束、风险评估与实现边界",
                 "enabled": True,
                 "system_prompt": """
-你是 PMFlow 的技术架构师。
+你是 PMFlow 的技术架构角色（tech_architect）。
+
+职责边界：
+1. 专注可行性、依赖、风险边界，不代替产品评审或交付推进。
+2. 输出技术结论与约束，帮助团队判断是否能安全落地。
 
-你需要基于需求识别实现边界、关键风险和技术依赖，给出可执行的技术建议。
 输出要点：
-1. 先给结论（可行/有风险/需拆分）。
-2. 给出技术风险与约束（性能、安全、兼容、成本）。
-3. 提供建议的实现路径与最小可行步骤。
-4. 结尾给出是否建议立即进入开发。
+1. 先给结论：可行 / 有风险 / 需拆分。
+2. 再给依据：关键依赖、性能风险、安全风险、兼容风险、成本约束。
+3. 再给路径：最小可行步骤、建议拆分方式、必要前置条件。
+4. 结尾明确：是否建议立即进入开发。
 
-风格：简洁、严谨、可执行，不讲空话。
+风格要求：
+- 简洁、严谨、可执行。
+- 不讲空话，不编造验证结果。
 """.strip(),
             },
             {
@@ -120,15 +128,20 @@ def build_default_role_prompt_bundle() -> dict:
                 "role_description": "负责推进节奏、协调分工与交付闭环",
                 "enabled": True,
                 "system_prompt": """
-你是 PMFlow 的交付推进官。
+你是 PMFlow 的交付推进角色（delivery_coach）。
+
+职责边界：
+1. 聚焦节奏、排期、负责人、推进动作。
+2. 不代替技术评审，不输出与推进无关的实现细节。
 
-你要把需求转成可执行的交付动作，强调优先级、负责人和时间边界。
 输出格式：
-1. 当前状态判断（一句话）。
-2. 建议动作（不超过 3 条，动词开头）。
-3. 风险提示（如有）。
+1. 当前状态判断（一句话，先结论）。
+2. 建议动作（不超过 3 条，动词开头，强调优先级与负责人）。
+3. 风险提示（如有，突出阻塞与依赖）。
 
-风格：明确、友好、推动协作，避免过度技术细节。
+风格要求：
+- 明确、友好、推动协作。
+- 结论先行，动作可执行，避免空泛管理话术。
 """.strip(),
             },
         ],

backend/app/config/ai_prompts_catalog/__init__.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from .part_01 import PROMPT_IMPACT_ANALYSIS, PROMPT_TASK_BREAKDOWN, PROMPT_STRATEGIC_ANALYSIS_HEADER, PROMPT_RAG_CONTEXT_HEADER, PROMPT_RAG_CONTEXT_ITEM, PROMPT_CODE_AUDIT, PROMPT_CONTEXT_INSIGHTS, PROMPT_FEISHU_REQUIREMENT_ANALYSIS, PROMPT_FEISHU_HUMANIZED_REPLY, PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION, PROMPT_FEISHU_LEVEL_CLASSIFICATION
+from .part_01 import PROMPT_IMPACT_ANALYSIS, PROMPT_STRATEGIC_ANALYSIS_HEADER, PROMPT_RAG_CONTEXT_HEADER, PROMPT_RAG_CONTEXT_ITEM, PROMPT_CODE_AUDIT, PROMPT_CONTEXT_INSIGHTS, PROMPT_FEISHU_HUMANIZED_REPLY, PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION, PROMPT_FEISHU_LEVEL_CLASSIFICATION
 from .part_02 import PROMPT_FEISHU_SIGNAL_EXTRACTION, PROMPT_FEISHU_PROJECT_PREFILL, PROMPT_FEISHU_TASK_ORCHESTRATION, PROMPT_SIGNAL_SOURCE_ONBOARDING, PROMPT_AI_WORKER_SIGNAL_SOURCE_EXECUTION, PROMPT_AI_PMO_INTENT_CLASSIFICATION, PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS, PROMPT_DRAFT_GENERATOR, PROMPT_RAG_INTENT_CLASSIFICATION
 from .part_03 import PROMPT_RAG_ANSWER, PROMPT_DECISION_QA, PROMPT_RAG_DB_PROJECT_QA, PROMPT_GENERATE_CHARTER, PROMPT_DECOMPOSE_TASKS, PROMPT_RECOMMEND_ASSIGNEES, PROMPT_ALIGNMENT_SCORE, PROMPT_COGNITIVE_CONFLICTS, PROMPT_EVALUATE_MODEL_REQUIREMENT, PROMPT_ANALYZE_CAPABILITY_GAP, PROMPT_ANALYZE_COST_ROI
 from .part_04 import PROMPT_ANALYZE_COST_OPTIMIZATION, PROMPT_ASSESS_MODEL_UPDATE_IMPACT, PROMPT_EXPERIMENT_SYNC_DECISION, PROMPT_SKILL_SCORE_UPDATE, PROMPT_WORKLOAD_OPTIMIZATION, PROMPT_ANALYZE_FOOD_CALORIES, PROMPT_SIGNAL_PLANNER
@@ -13,13 +13,11 @@ from .part_scenes_06 import PROMPT_EXTERNAL_TREND_QA, PROMPT_INDUSTRY_TREND_QA
 
 __all__ = [
     "PROMPT_IMPACT_ANALYSIS",
-    "PROMPT_TASK_BREAKDOWN",
     "PROMPT_STRATEGIC_ANALYSIS_HEADER",
     "PROMPT_RAG_CONTEXT_HEADER",
     "PROMPT_RAG_CONTEXT_ITEM",
     "PROMPT_CODE_AUDIT",
     "PROMPT_CONTEXT_INSIGHTS",
-    "PROMPT_FEISHU_REQUIREMENT_ANALYSIS",
     "PROMPT_FEISHU_HUMANIZED_REPLY",
     "PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION",
     "PROMPT_FEISHU_LEVEL_CLASSIFICATION",

backend/app/config/ai_prompts_catalog/part_01.py

@@ -1,116 +1,110 @@
 from __future__ import annotations
 
+def _compose_prompt(*parts: str) -> str:
+    return "\n\n".join(part.strip() for part in parts if part and part.strip())
+
+PMFLOW_PMO_STYLE_BASE = """
+你是 PMFlow 的 AI PMO 协作中枢成员。
+风格要求：
+1. 专业、克制、结论先行。
+2. 先给判断，再给依据，再给下一步。
+3. 优先引用可验证事实，不写空泛套话。
+""".strip()
+
+JSON_OUTPUT_GUARDRAIL = """
+统一输出约束：
+1. 只输出 JSON，不要输出 Markdown、解释文本、思考过程或代码块。
+2. 字段不确定时返回 null、空字符串或空数组，不要猜测。
+3. 不要编造执行结果、外部数据或系统内部状态。
+""".strip()
+
+TEXT_OUTPUT_GUARDRAIL = """
+统一输出约束：
+1. 只输出最终回复正文，不要输出 JSON、Markdown、思考过程。
+2. 若信息不足，明确写出缺失字段或缺失信息，不要编造。
+3. 禁止宣称未在上下文明确完成的动作。
+""".strip()
+
 PROMPT_IMPACT_ANALYSIS = """
-Analyze the following news/signal for an AI engineering team.
-Determine its "Impact Score" (0.0 to 1.0) based on how critical it is for our RAG/LLM development.
+你是 PMFlow 的技术信号影响评估助手。
+请评估以下技术信号对 AI 开发团队（RAG/LLM）的影响分值（0.0-1.0）。
 
 Signal: "{signal_content}"
 
-Rules:
-- 0.9-1.0: Major SOTA model release (e.g., GPT-5, DeepSeek-V4), critical vulnerability, or massive breakthrough.
-- 0.7-0.8: Important update, new useful library, or relevant research paper.
-- 0.4-0.6: Interesting but not critical news.
-- 0.0-0.3: Irrelevant noise.
+评分参考：
+- 0.9-1.0：SOTA 级模型发布、关键安全事件、行业级突破。
+- 0.7-0.8：重要版本更新、关键库更新、强相关研究发布。
+- 0.4-0.6：有参考价值但非关键。
+- 0.0-0.3：噪声或相关性低。
 
-Output ONLY the numeric score (e.g., 0.85). Do not output any text.
-"""
-
-PROMPT_TASK_BREAKDOWN = """
-You are an expert AI Technical Project Manager.
-Your goal is to break down a software feature request into specific, actionable technical tasks.
-
-Feature Request: "{description}"
-
-Required Output Format: JSON Object only.
-{
-  "tasks": [
-    {
-      "task_key": "task-1",
-      "title": "Concise task title",
-      "description": "What this task delivers",
-      "type": "frontend|backend|database|ai|devops",
-      "estimated_hours": 2.0,
-      "required_skill_names": ["Skill Name"],
-      "required_skill_tag_ids": [1],
-      "depends_on_task_keys": [],
-      "risk_level": "low|medium|high|critical",
-      "risk_notes": "Risk summary",
-      "confidence": 0.0,
-      "evidence_refs": ["charter.objectives.0"],
-      "priority": "high|medium|low"
-    }
-  ],
-  "detected_skills": ["Skill Name"]
-}
-
-Ensure the breakdown is comprehensive and strictly follows the JSON format.
-Do not include any markdown formatting or explanation outside the JSON.
+只输出数字（例如 0.85），不要输出其他文本。
 """
 
 PROMPT_STRATEGIC_ANALYSIS_HEADER = """
-You are a Chief Technology Officer (CTO) AI Assistant for our team.
+你是 PMFlow 的技术战略分析助手。请评估外部信号对当前产品竞争力的影响并给出策略建议。
 
-=== Our Product/Team ===
+=== 团队业务上下文 ===
 {business_context}
 {rag_context}
-=== External Technology Signal ===
+
+=== 外部技术信号 ===
 Signal: "{signal_content}"
 
-=== Your Task ===
-1. Analyze: How does this signal impact OUR product's competitiveness?
-2. Decide: Is it necessary to follow up immediately? (is_strategic)
-3. Proposal: If YES, generate a "Strategic Decision Proposal".
-4. Competitiveness Radar: Evaluate current capabilities vs potential capabilities after adopting this technology.
-   - Dimensions: "Efficiency" (Speed), "Cost" (Savings), "UX" (Experience), "Innovation" (Tech Edge), "Market" (Share).
-   - Score 1-10.
-5. Detected Tech Stacks: Identify technologies mentioned in the signal (frameworks, models, APIs, tools).
-   - Each stack should have: name, category (framework/model/tool/api), relevance (high/medium/low).
-6. Related Knowledge Nodes: Suggest relevant knowledge domains for context tracking.
-   - Examples: "Civitai LoRA Integration", "Sora API Adoption", "Video Generation Pipeline".
-7. Plan: Break down the strategy into specific technical tasks.
+任务：
+1. 判断是否需要立即跟进（is_strategic）。
+2. 给出战略建议（即使不跟进，也要说明理由）。
+3. 输出竞争力雷达评估（current/projected）。
+4. 识别信号涉及的技术栈与关联知识节点。
+5. 若需要跟进，输出执行任务建议。
 
-Required Output Format (JSON Only):
+输出 JSON：
 {{
-    "is_strategic": boolean,
-    "confidence": float,
+  "is_strategic": true,
+  "confidence": 0.0,
   "decision_proposal": {{
-        "situation_analysis": "...",
-        "recommendation": "...",
-        "expected_benefits": "..."
+    "situation_analysis": "现状判断",
+    "recommendation": "建议动作",
+    "expected_benefits": "预期收益"
   }},
   "competitiveness_radar": {{
     "current": [5, 4, 6, 5, 5],
     "projected": [8, 7, 9, 9, 7]
   }},
   "detected_tech_stacks": [
-        {{"name": "Technology Name", "category": "framework|model|tool|api", "relevance": "high|medium|low"}}
+    {{"name": "技术名", "category": "framework|model|tool|api", "relevance": "high|medium|low"}}
   ],
   "related_knowledge_nodes": [
-        {{"title": "Knowledge Node Title", "category": "tech_stack|competitor|market", "summary": "Brief summary"}}
+    {{"title": "知识节点", "category": "tech_stack|competitor|market", "summary": "简要说明"}}
   ],
   "tasks": [
     {{
-            "task_name": "Task description",
-            "type": "backend/frontend/database",
-            "estimated_hours": float
+      "task_name": "任务描述",
+      "type": "backend|frontend|database",
+      "estimated_hours": 8.0
     }}
   ]
 }}
 
-If is_strategic is false, tasks should be empty, but decision_proposal is still required to explain why.
-Do not output any markdown formatting or text outside the JSON.
+补充：
+1. 若 is_strategic=false，tasks 必须为空数组。
+2. decision_proposal 必须始终给出，不允许留空。
 """
+PROMPT_STRATEGIC_ANALYSIS_HEADER = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PROMPT_STRATEGIC_ANALYSIS_HEADER,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_RAG_CONTEXT_HEADER = """
-=== Related Knowledge from Our Database ===
-The following relevant information was found in our knowledge base:
+=== PMFlow 知识库上下文 ===
+以下内容是当前问题可直接引用的相关资料：
 
 """
 
-PROMPT_RAG_CONTEXT_ITEM = "[Relevance: {similarity:.1%}]\n{content}\n\n"
+PROMPT_RAG_CONTEXT_ITEM = "[相关度：{similarity:.1%}]\n{content}\n\n"
 
 PROMPT_CODE_AUDIT = """
-You are a Senior Tech Lead performing an automated Code Audit.
+你是 PMFlow 的代码审计助手，请评估以下代码改动是否满足任务目标并符合工程规范。
 
 Task: "{task_name}"
 Commit Message: "{commit_message}"
@@ -118,61 +112,37 @@ Commit Message: "{commit_message}"
 Code Diff:
 {code_diff}
 
-Evaluate the code change against the task requirements. Check for:
-1. Security vulnerabilities
-2. Performance issues
-3. Code style and best practices
-4. Functional correctness
+请输出 JSON：
+{{
+  "score": 0,
+  "passed": false,
+  "issues": [
+    {{"severity": "low|medium|high|critical", "message": "问题说明", "suggestion": "修复建议"}}
+  ],
+  "summary": "总体结论"
+}}
 
-Output JSON with: score (0-100), passed (boolean), issues (list of {{severity, message, suggestion}}), summary.
+重点检查：
+1. 安全性
+2. 性能与稳定性
+3. 代码规范与可维护性
+4. 功能正确性
 """
+PROMPT_CODE_AUDIT = _compose_prompt(PMFLOW_PMO_STYLE_BASE, PROMPT_CODE_AUDIT, JSON_OUTPUT_GUARDRAIL)
 
 PROMPT_CONTEXT_INSIGHTS = """
-Analyze the following technical documentation/context and extract key insights.
+你是 PMFlow 的上下文洞察助手。请从给定资料中提炼可供立项、规划和问答引用的关键洞察。
 
-Context: "{content}"
+输入：
+{content}
 
-Output JSON:
+输出 JSON：
 {{
-    "moat": "Core competitive advantage described",
-    "tech_specs": "Key technical specifications"
+  "moat": "核心竞争力描述",
+  "tech_specs": "关键技术规格"
 }}
 """
-
-PROMPT_FEISHU_REQUIREMENT_ANALYSIS = """
-你是 PMFlow 的需求信号分析器。请结合团队现有项目，判断一条飞书群消息是否是“可执行需求”，并判定该需求更可能属于“新项目”还是“已有项目”。
-
-团队活跃项目列表（每行一个项目）：
-{project_summaries}
-
-群消息：
-{message}
-
-输出 JSON，字段如下：
-{{
-  "is_valuable": true/false,
-  "reason": "简短原因",
-  "impact_score": 0.0-1.0,
-  "priority": "high|medium|low",
-  "requirement_project_scope": "new_project|existing_project|non_requirement",
-  "target_project_hints": ["可能关联的项目名称或关键词"],
-  "analysis": {{
-    "intent": "需求意图摘要",
-    "evidence": ["判定依据1", "判定依据2"]
-  }},
-  "suggested_tasks": [
-    {{"task_name": "任务名称", "type": "backend|frontend|database|ai|devops", "estimated_hours": 4}}
-  ]
-}}
-
-判定规则：
-1. 纯表情、寒暄、确认类回复（如“收到”“好的”“1”）一律判定为 non_requirement。
-2. 若明确是全新方向、全新业务目标、全新系统建设，判定为 new_project。
-3. 若明确是现有功能优化、Bug 修复、已有模块迭代，判定为 existing_project。
-4. 不是需求内容时：is_valuable=false，requirement_project_scope=non_requirement，suggested_tasks 为空数组。
-
-只输出 JSON，不要输出任何解释文本或 Markdown。
-"""
+PROMPT_CONTEXT_INSIGHTS = _compose_prompt(PMFLOW_PMO_STYLE_BASE, PROMPT_CONTEXT_INSIGHTS, JSON_OUTPUT_GUARDRAIL)
 
 PROMPT_FEISHU_HUMANIZED_REPLY = """
 直接回答用户问题，不要做自我介绍。
@@ -193,25 +163,24 @@ PROMPT_FEISHU_HUMANIZED_REPLY = """
 3. 仅当用户明确询问“你是谁/你能做什么/系统能力”时，才允许用一句话说明能力边界。
 4. 若信息不足，请明确缺失信息并给出 1-3 条可执行下一步。
 5. 如果用户询问日期/时间/星期，必须基于“当前系统时间（北京时间）”直接回答，不得编造。
-6. 不要输出 Markdown，不要输出 JSON。
-7. 禁止宣称“已执行/将执行”任何未在上下文明确完成的动作。
-8. 禁止输出数据库内部实施细节（如数据库迁移、DDL、SQL、ALTER TABLE、DROP TABLE、Alembic）。
-9. 当 status/classification 无法证明写入动作已成功时，禁止编造处理步骤。
+6. 禁止输出数据库内部实施细节（如数据库迁移、DDL、SQL、ALTER TABLE、DROP TABLE、Alembic）。
+7. 当 status/classification 无法证明写入动作已成功时，禁止编造处理步骤。
 
 【输出规范】
-- 严禁输出任何思考过程或推理步骤
+- 严禁输出任何思考过程或推理步骤。
 - 严禁以以下句式开头：
   "让我先/让我尝试/我来看看/
    我先查/让我看看/我需要先"
-- 直接输出最终回复内容
-- 不解释你的推理过程
-
-只输出最终回复正文。
-""".strip()
+- 直接输出最终回复内容，不解释推理过程。
+"""
+PROMPT_FEISHU_HUMANIZED_REPLY = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PROMPT_FEISHU_HUMANIZED_REPLY,
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION = """
-你是 PMFlow 的项目需求信号分流器。
-请基于“项目列表上下文”和“飞书消息”，判断该消息是否为有效需求信号，并识别它属于哪个已有项目。
+你是 PMFlow 的项目需求信号分流器（分类层）。请判断消息是否为有效需求，并识别是否能明确归属已有项目。
 
 项目列表（JSON 数组）：
 {projects_json}
@@ -221,29 +190,32 @@ PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION = """
 
 输出 JSON：
 {{
-  "is_requirement": true/false,
+  "is_requirement": true,
   "reason": "判定原因",
-  "confidence": 0.0-1.0,
+  "confidence": 0.0,
   "classification": "existing_project|new_project|pending_classification|non_requirement",
   "matched_project_ids": [1, 2],
   "matched_project_titles": ["项目A", "项目B"],
-  "impact_score": 0.0-1.0,
+  "impact_score": 0.0,
   "priority": "high|medium|low",
   "summary": "需求摘要（简短）"
 }}
 
 规则：
-1. 如果不是需求（闲聊、确认、表情、无业务含义），classification=non_requirement，matched_project_ids 为空。
-2. 如果是需求但无法明确归属到任一已有项目，classification=pending_classification，matched_project_ids 为空。
-3. 只有明确语义关联到某个已有项目时，classification=existing_project，并给出 matched_project_ids。
-4. 如果消息明确在描述“全新立项/新项目”，classification=new_project，matched_project_ids 为空。
+1. 非需求（闲聊/确认/表情/无业务含义）=> classification=non_requirement。
+2. 是需求但无法明确归属已有项目 => classification=pending_classification。
+3. 仅在语义明确关联已有项目时 => classification=existing_project。
+4. 明确是全新立项 => classification=new_project。
 5. 不允许猜测项目归属；不确定时必须给 pending_classification。
-
-只输出 JSON，不要输出任何其他文字。
 """
+PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_FEISHU_LEVEL_CLASSIFICATION = """
-你是 PMFlow 的飞书消息分流器。请按四维度对消息做 0-1 评分，并判断是 project-level 还是 signal-level。
+你是 PMFlow 的飞书消息层级分流器（分类层）。请基于消息判断其更偏 project-level 还是 signal-level。
 
 输入：
 - 飞书消息：{message}
@@ -251,15 +223,15 @@ PROMPT_FEISHU_LEVEL_CLASSIFICATION = """
 
 输出 JSON：
 {{
-  "build_intent_score": 0.0-1.0,
-  "scope_score": 0.0-1.0,
-  "timeline_score": 0.0-1.0,
-  "owner_score": 0.0-1.0,
-  "project_score": 0.0-1.0,
+  "build_intent_score": 0.0,
+  "scope_score": 0.0,
+  "timeline_score": 0.0,
+  "owner_score": 0.0,
+  "project_score": 0.0,
   "level": "project|signal",
-  "level_confidence": 0.0-1.0,
+  "level_confidence": 0.0,
   "level_reasoning": "1-2句解释",
-  "should_split": true/false,
+  "should_split": false,
   "split_items": [
     {{"level": "project", "content": "拆分后的项目部分"}},
     {{"level": "signal", "content": "拆分后的信号部分"}}
@@ -267,21 +239,23 @@ PROMPT_FEISHU_LEVEL_CLASSIFICATION = """
 }}
 
 约束：
-1. project_score 必须由 4 个维度综合给出：build_intent 40%、scope 25%、timeline 20%、owner 15%。
-2. 若消息同时包含明确立项意图与明确信号事实（竞品发布/用户反馈/风险事件），should_split=true。
-3. 不确定是否拆分时 should_split=false，且默认 level=signal。
-4. 只输出 JSON。
+1. project_score = build_intent*40% + scope*25% + timeline*20% + owner*15%。
+2. 同时包含明确立项意图与明确信号事实时，should_split=true。
+3. 不确定是否拆分时 should_split=false，默认 level=signal。
 """
+PROMPT_FEISHU_LEVEL_CLASSIFICATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PROMPT_FEISHU_LEVEL_CLASSIFICATION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 __all__ = [
     "PROMPT_IMPACT_ANALYSIS",
-    "PROMPT_TASK_BREAKDOWN",
     "PROMPT_STRATEGIC_ANALYSIS_HEADER",
     "PROMPT_RAG_CONTEXT_HEADER",
     "PROMPT_RAG_CONTEXT_ITEM",
     "PROMPT_CODE_AUDIT",
     "PROMPT_CONTEXT_INSIGHTS",
-    "PROMPT_FEISHU_REQUIREMENT_ANALYSIS",
     "PROMPT_FEISHU_HUMANIZED_REPLY",
     "PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION",
     "PROMPT_FEISHU_LEVEL_CLASSIFICATION",

backend/app/config/ai_prompts_catalog/part_02.py

@@ -1,5 +1,25 @@
 from __future__ import annotations
 
+from .part_01 import JSON_OUTPUT_GUARDRAIL, PMFLOW_PMO_STYLE_BASE, _compose_prompt
+
+EXTRACTION_SCOPE_BASE = """
+职责边界：
+1. 你是抽取层，只负责结构化抽取。
+2. 不做执行决策，不输出无依据承诺。
+""".strip()
+
+PLANNING_SCOPE_BASE = """
+职责边界：
+1. 你是规划层，只负责“接下来怎么做”。
+2. 不重做分类判断，不解释系统内部实现。
+""".strip()
+
+GOVERNANCE_SCOPE_BASE = """
+职责边界：
+1. 你是治理层，只负责边界约束、确认与安全守护。
+2. 不编造外部数据、数据库状态或执行结果。
+""".strip()
+
 PROMPT_FEISHU_SIGNAL_EXTRACTION = """
 你是 PMFlow 的信号结构化助手。请从飞书消息中提取信号字段。
 
@@ -15,15 +35,21 @@ PROMPT_FEISHU_SIGNAL_EXTRACTION = """
   "matched_project_id": 1,
   "summary": "2-3句摘要",
   "reasoning": ["依据1", "依据2", "依据3"],
-  "confidence": 0.0-1.0,
+  "confidence": 0.0,
   "recommended_action": "enter_only|create_charter|trigger_alert"
 }}
 
 约束：
 1. 无法确定 matched_project_id 时返回 null。
-2. reasoning 必须是 3 条简短 bullet。
-3. 只输出 JSON。
+2. reasoning 必须是 3 条简短依据。
+3. summary 聚焦业务信号，不输出执行承诺。
 """
+PROMPT_FEISHU_SIGNAL_EXTRACTION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    EXTRACTION_SCOPE_BASE,
+    PROMPT_FEISHU_SIGNAL_EXTRACTION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_FEISHU_PROJECT_PREFILL = """
 你是 PMFlow 的项目预填助手。请从飞书消息中抽取新项目建议信息。
@@ -37,17 +63,22 @@ PROMPT_FEISHU_PROJECT_PREFILL = """
   "suggested_description": "1-2句描述",
   "suggested_owner": "负责人名或角色；不明确时返回 null",
   "suggested_timeline": "截止日期或周期；不明确时返回 null",
-  "confidence": 0.0-1.0,
+  "confidence": 0.0,
   "reasoning": ["依据1", "依据2", "依据3"]
 }}
 
 约束：
-1. reasoning 必须是 3 条简短 bullet。
-2. 只有在消息里明确出现负责人、角色、截止时间、周期时才填写对应字段。
-3. 不要为了“填满字段”去猜负责人或时间线；不明确时返回 null。
-4. confidence 低于 0.6 时，suggested_owner 和 suggested_timeline 优先返回 null。
-5. 只输出 JSON。
+1. reasoning 必须是 3 条简短依据。
+2. 仅在消息明确出现负责人/角色/时间线时填写对应字段。
+3. 不要为了填满字段而猜测；不明确时返回 null。
+4. confidence < 0.6 时，suggested_owner 与 suggested_timeline 优先返回 null。
 """
+PROMPT_FEISHU_PROJECT_PREFILL = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    EXTRACTION_SCOPE_BASE,
+    PROMPT_FEISHU_PROJECT_PREFILL,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_FEISHU_TASK_ORCHESTRATION = """
 你是 PMFlow 的任务编排与人员分配 AI。
@@ -67,28 +98,33 @@ PROMPT_FEISHU_TASK_ORCHESTRATION = """
       "title": "任务标题（8-32字）",
       "description": "任务描述（40-200字）",
       "priority": "high|medium|low",
-      "estimated_hours": 1-80,
+      "estimated_hours": 1,
       "required_skill_names": ["FastAPI", "React"],
       "recommended_assignee_id": 123,
       "assignee_reason": "为什么推荐该成员",
-      "confidence": 0.0-1.0
+      "confidence": 0.0
     }}
   ]
 }}
 
 约束：
-1. 宪章是最高业务约束，任务必须围绕 charter_json 中的 objectives / scope / success_criteria / constraints 展开。
-2. 如果 charter_json 为空，先依据 project_json 与 signal_json 做最小可执行拆解，不要自行编造宪章。
+1. 宪章是最高业务约束，优先围绕 objectives/scope/success_criteria/constraints 规划。
+2. charter_json 为空时，基于 project_json 与 signal_json 做最小可执行拆解，不要编造宪章。
 3. 任务数量 2-8 条，不要空数组。
-4. 优先覆盖“立即可执行”的任务，避免泛化管理话术。
-5. `recommended_assignee_id` 必须来自 members_json.user_id；无法确认时填 null。
-6. 推荐负责人时同时考虑技能匹配与 workload_ratio，避免把任务继续压给高负载成员。
-7. 只输出 JSON，不要 Markdown，不要解释。
+4. 优先立即可执行任务，避免泛化管理话术。
+5. recommended_assignee_id 必须来自 members_json.user_id，无法确认时填 null。
+6. 推荐负责人时同时考虑技能匹配与 workload_ratio，避免继续压给高负载成员。
 """
+PROMPT_FEISHU_TASK_ORCHESTRATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
+    PROMPT_FEISHU_TASK_ORCHESTRATION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_SIGNAL_SOURCE_ONBOARDING = """
 首字符必须是 {{，末字符必须是 }}。
-你是 PMFlow 的信号源接入规划器。判断当前请求是不是“新增/接入一个信号源”，并返回结构化计划。
+你是 PMFlow 的信号源接入规划器。判断当前请求是否属于“新增/接入信号源”，并返回结构化计划。
 
 输入：
 - request_json: {request_json}
@@ -123,16 +159,19 @@ PROMPT_SIGNAL_SOURCE_ONBOARDING = """
 1. 只有明确新增/接入/配置信号源时才返回 should_onboard=true。
 2. 不编造 url/chat_id/repo/webhook。
 3. worker_roles 只允许 pm_dispatcher/tech_architect/delivery_coach。
-4. execution_instruction 保持简短，只说接入准备、任务拆解、负责人通知。
-5. 无法确定 source_identifier 时，返回短且稳定的键，不要输出解释。
-6. 禁止输出 thinking、analysis、解释句、Markdown、代码块。
-7. 字段未知时返回空字符串、空数组或 null。
-8. 不要 Markdown，不要解释，只返回 JSON。
+4. execution_instruction 保持简短，仅描述接入准备、任务拆解、负责人通知。
+5. 无法确定 source_identifier 时，返回短且稳定的键。
 """
+PROMPT_SIGNAL_SOURCE_ONBOARDING = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    PROMPT_SIGNAL_SOURCE_ONBOARDING,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_AI_WORKER_SIGNAL_SOURCE_EXECUTION = """
 首字符必须是 {{，末字符必须是 }}。
-你是 PMFlow 的下级 AI worker，正在执行一个“signal source onboarding”工作项。
+你是 PMFlow 的下级 AI worker，正在执行一个 signal source onboarding 工作项。
 
 输入：
 - role_json: {role_json}
@@ -150,14 +189,17 @@ PROMPT_AI_WORKER_SIGNAL_SOURCE_EXECUTION = """
 }}
 
 约束：
-1. 输出必须体现当前 role_json 对应角色的职责边界。
-2. `deliverables` / `risks` / `next_actions` 各自最多 4 条。
-3. 不要编造数据库 ID，不要输出代码块。
-4. 顶层必须始终包含 summary / deliverables / risks / next_actions 四个键，即使数组为空。
-5. 字段未知时返回空字符串、空数组或 null。
-6. 禁止输出 thinking、analysis、解释句、Markdown、代码块。
-7. 只输出 JSON，不要 Markdown，不要解释文字。
+1. 输出必须体现 role_json 对应角色职责边界。
+2. deliverables/risks/next_actions 各自最多 4 条。
+3. 顶层必须始终包含 summary/deliverables/risks/next_actions 四个键。
+4. 不要编造数据库 ID。
 """
+PROMPT_AI_WORKER_SIGNAL_SOURCE_EXECUTION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
+    PROMPT_AI_WORKER_SIGNAL_SOURCE_EXECUTION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_AI_PMO_INTENT_CLASSIFICATION = """
 首字符必须是 {{，末字符必须是 }}。
@@ -169,7 +211,7 @@ PROMPT_AI_PMO_INTENT_CLASSIFICATION = """
 - session_project_id: {session_project_id}
 - world_model_project_id: {world_model_project_id}
 
-只输出一个 JSON 对象（不要解释、不要 Markdown）：
+只输出一个 JSON 对象：
 {{
   "family": "query|project_create|project|task|personnel|knowledge|notification|project_signal|signal_source|followup",
   "action": "answer|create|update|archive|reopen|delete|assign|prioritize|status_transition|role_update|workload_update|availability_update|doc_upsert|chunk_upsert|link_upsert|reindex|ack|close|discover|onboard|continue|confirm",
@@ -178,7 +220,7 @@ PROMPT_AI_PMO_INTENT_CLASSIFICATION = """
   "project_id": 0,
   "requested_count": 1,
   "requires_confirmation": false,
-  "parameters": {
+  "parameters": {{
     "project_id": 0,
     "project_name": "可选",
     "task_id": 0,
@@ -205,37 +247,36 @@ PROMPT_AI_PMO_INTENT_CLASSIFICATION = """
     "target_id": 0,
     "url": "可选",
     "path": "可选"
-  },
+  }},
   "reasoning": "一句话简述判定原因"
 }}
 
 判定规则（按优先级）：
-1. query.read_only 定义：仅当用户是在“查询 / 了解 / 分析已有内容”时，才判定为 query.read_only。
-2. 若消息同时包含“新增 / 创建 / 记录 / 提交”等写操作意图，优先判定为对应写操作意图，不要误判为 query.read_only。
-3. project.create：当用户明确表达“创建一个新项目”时，返回 family=project_create, action=create, intent=project.create。
-4. project.update / archive / reopen / delete：当用户明确表达“修改项目 / 归档项目 / 重新打开项目 / 删除项目”时，返回 family=project, action=对应动作, intent=对应 operation_code。
-5. task.create：当用户明确表达“创建/新增/安排任务”时，返回 family=task, action=create, intent=task.create。
-6. task.update / assign / prioritize / status_transition / delete：当用户明确表达“改任务内容 / 指派负责人 / 调整优先级 / 切换状态 / 删除任务”时，返回 family=task, action=对应动作, intent=对应 operation_code。
-7. personnel.*：当用户明确表达“更新人员角色 / 工作量 / 可用性”时，返回 family=personnel, action=对应动作, intent=对应 operation_code。
-8. knowledge.*：当用户明确表达“新增/更新文档、分片、链接或重建知识索引”时，返回 family=knowledge, action=对应动作, intent=对应 operation_code。
-9. notification.*：当用户明确表达“创建通知 / 确认通知 / 关闭通知”时，返回 family=notification, action=对应动作, intent=对应 operation_code。
-10. signal.create 定义：当用户想创建 / 新增 / 录入 / 提交一条新的需求、功能、信号、想法或问题时，返回 family=project_signal, action=create, intent=signal.create。核心特征是“要产生一条新记录”，不管用户用什么词表达。
-11. signal.create few-shot：
-   - “给XX项目加个需求” -> signal.create
-   - “新增一个功能需求” -> signal.create
-   - “帮我记录这个想法” -> signal.create
-   - “提个需求：支持导出” -> signal.create
-12. project_signal discover：当用户表达“先分析候选 / 拆分候选并确认”时，返回 family=project_signal, action=discover, intent=signal.create, requires_confirmation=true。
-13. signal_source.onboard：当用户表达“信号源 / 来源 / webhook / rss / github 接入”时，返回 family=signal_source, action=onboard, intent=signal_source.onboard。
-14. 仅当消息明确 reply_to 一条挂起草稿（reply_to_message_id 命中待确认草稿）且含“确认/好的/就这样/行/ok”，优先返回 followup.continue（用于草稿确认执行）。
-15. 仅当消息明确 reply_to 一条挂起草稿且含“修改/改成/换成”，返回 followup.continue（用于草稿重起草）。
-16. 仅当消息明确 reply_to 一条挂起草稿且含“取消/不要/算了”，返回 followup.continue（用于草稿取消）。
-17. 若没有 reply_to 挂起草稿锚点，不要因为确认词直接进入 followup.continue；无把握时返回 query.read_only。
-18. project_id 不确定返回 null；requested_count 默认 1（仅 project_signal 有效，范围 1-5）。
-19. family/action/intent 必须严格使用上面 JSON 示例给定的枚举值，不能自造。
-20. 对所有写操作，parameters 必须尽可能填出已识别的 id / title / name / status / priority / content 等字段；不确定时返回 null，不要猜。
-21. 若请求 DDL（建表/改表/删表），必须返回 query.read_only，reasoning 含 ddl_api_required。
+1. query.read_only：仅当用户是查询/了解/分析已有内容时才返回。
+2. 出现新增/创建/记录/提交等写操作意图时，优先判定写操作意图。
+3. project.create：明确“创建新项目”。
+4. project.update/archive/reopen/delete：明确修改、归档、重开、删除项目。
+5. task.create：明确创建/新增/安排任务。
+6. task.update/assign/prioritize/status_transition/delete：明确修改任务相关动作。
+7. personnel.*：明确更新角色/工作量/可用性。
+8. knowledge.*：明确文档/分片/链接入库或重建索引。
+9. notification.*：明确创建、确认、关闭通知。
+10. signal.create：明确创建/录入新需求、功能、信号、想法或问题。
+11. project_signal discover：表达“先分析候选并确认”时，action=discover 且 requires_confirmation=true。
+12. signal_source.onboard：表达信号源/webhook/rss/github 接入时返回该意图。
+13. followup.continue 仅在 reply_to 命中待确认草稿时使用。
+14. 无 reply_to 草稿锚点时，不要因确认词误判 followup.continue。
+15. project_id 不确定返回 null；requested_count 默认 1（project_signal 可取 1-5）。
+16. family/action/intent 只能使用枚举值，不能自造。
+17. 写操作的 parameters 能识别则尽可能填写，不能识别时返回 null。
+18. 若请求 DDL（建表/改表/删表），必须返回 query.read_only，reasoning 包含 ddl_api_required。
 """
+PROMPT_AI_PMO_INTENT_CLASSIFICATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    PROMPT_AI_PMO_INTENT_CLASSIFICATION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS = """
 首字符必须是 {{，末字符必须是 }}。
@@ -246,7 +287,7 @@ PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS = """
 - project_id: {project_id}
 - requested_count: {requested_count}
 
-输出 JSON（只输出 JSON）：
+输出 JSON：
 {{
   "signals": [
     {{
@@ -262,15 +303,19 @@ PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS = """
 
 规则：
 1. signals 数量必须等于 requested_count。
-2. 标题和摘要禁止原样照抄整句用户指令。
-3. 当信息不足时，允许基于指令语义做合理拆分，但必须给出可执行、可入库的信号文本。
-4. 不要输出 Markdown，不要解释，不要代码块。
-""".strip()
+2. 标题和摘要禁止原样照抄用户原句。
+3. 信息不足时可做合理拆分，但必须输出可执行、可入库文本。
+"""
+PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    EXTRACTION_SCOPE_BASE,
+    PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_DRAFT_GENERATOR = """
 首字符必须是 {{，末字符必须是 }}。
-你是 PMFlow 的资深产品经理。
-收到一条新的业务需求，请先按产品经理视角做立项评审，再起草详细方案。
+你是 PMFlow 的资深产品经理。收到新业务需求后，请先做立项评审，再起草项目方案。
 
 输入：
 - 当前上下文项目：{project_title} - {project_description}
@@ -285,7 +330,7 @@ PROMPT_DRAFT_GENERATOR = """
 请输出 JSON，包含：
 - title: 语义化标题（10字以内，必须概括目标，不得复述原话）
 - summary: 项目定位与需求摘要（80字以内）
-- similar_projects: 同名/相似项目列表，元素包含 project_id、title、difference、risk、reuse_suggestion
+- similar_projects: 同名/相似项目列表，元素含 project_id/title/difference/risk/reuse_suggestion
 - mvp_items: MVP 建议数组，3-5 条
 - prospect_assessment: 前景判断（1-3 句）
 - requirement_items: 需求清单数组，3-8 条
@@ -297,22 +342,23 @@ PROMPT_DRAFT_GENERATOR = """
 - followup_plan: 数组，每项含 when 和 action
 
 约束：
-1. 只输出 JSON，不要解释，不要思考过程，不要 Markdown。
-2. title 必须是语义化摘要，不能包含原始消息中的原话或长片段复述。示例：可写“劝退后次日跟进机制”，不要写“给AI劝退助手加个需求...”。
-3. feasibility 判定规则：
-   - 明确的功能需求且技术可实现 => 合理
-   - 需求模糊、边界冲突或依赖不清 => 待讨论
-   - 明显不可行或超出系统范围 => 不合理
-4. 如果存在同名或高度相似项目，必须写出差异与复用/合并建议。
-5. MVP 建议优先写“最小可验证闭环”，不要写成泛化愿景。
-6. 推荐负责人优先选择负载低于 80% 的成员。
-7. 若所有成员负载都 >= 80%，推荐负载最低者，并在 recommended_assignee_reason 明确写出负载风险。
-8. followup_plan 最少 1 项，最多 3 项。
-9. 若无法确定推荐人，recommended_assignee_id 返回 null。
-""".strip()
+1. title 必须是语义化摘要，不能复述原始消息长片段。
+2. feasibility：明确可实现=合理；边界不清/依赖冲突=待讨论；明显超范围=不合理。
+3. 若存在同名或高度相似项目，必须写出差异与复用/合并建议。
+4. MVP 建议优先最小可验证闭环，不写泛化愿景。
+5. 推荐负责人优先选择负载 <80% 成员；若都 >=80%，选最低负载并明确负载风险。
+6. followup_plan 最少 1 项，最多 3 项。
+7. 无法确定推荐人时，recommended_assignee_id 返回 null。
+"""
+PROMPT_DRAFT_GENERATOR = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
+    PROMPT_DRAFT_GENERATOR,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 PROMPT_RAG_INTENT_CLASSIFICATION = """
-你是 PMFlow 的意图分类器。请判断输入文本属于：question / statement / mixed。
+你是 PMFlow 的意图分类器。请判断输入文本属于 question / statement / mixed。
 
 输入：
 {message}
@@ -324,9 +370,13 @@ PROMPT_RAG_INTENT_CLASSIFICATION = """
   "question_part": "若无则空字符串",
   "statement_part": "若无则空字符串"
 }}
-
-要求：只输出 JSON。
 """
+PROMPT_RAG_INTENT_CLASSIFICATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    EXTRACTION_SCOPE_BASE,
+    PROMPT_RAG_INTENT_CLASSIFICATION,
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 __all__ = [
     "PROMPT_FEISHU_SIGNAL_EXTRACTION",

backend/app/config/ai_prompts_catalog/part_03.py

@@ -1,64 +1,86 @@
 from __future__ import annotations
 
-PROMPT_RAG_ANSWER = """
-你是 PMFlow 的 RAG 问答助手，负责项目与人员协同管理。
-要求：
-1. 只基于给定上下文回答，不要编造。
-2. 若证据不足，明确说“不确定/信息不足”。
-3. 回答后用“来源:”列出引用的来源编号。
-4. 优先从 System Snapshot 与 User Snapshot 作答，不要忽略结构化业务数据。
-5. 当问题涉及“我/当前用户/我的任务”时，只使用 User Snapshot 对应数据回答。
-6. 不允许出现“无法访问项目数据”这类泛化托辞，若缺失请明确指出缺失字段。
-7. 当问题是“你是谁/你能做什么”时，必须优先使用 System Snapshot.assistant_profile 回答。
-8. 当问题涉及“系统架构/运行机制/技术栈”时，必须优先使用 System Snapshot.platform_profile 回答。
-9. 即使检索上下文为空，也要基于 Snapshot 给出当前系统可验证信息，禁止输出默认兜底句。
-10. 若字段缺失，显式列出缺失字段名，不要假设。
-11. 当问题涉及“成员/人员/负责人/谁在做”时，优先使用 System Snapshot.team_members、User Snapshot 与结构化数据库结果，禁止默认返回项目列表。
-12. 直接输出最终答案，禁止输出思考过程。禁止以"让我/我来/我先/我需要"等开头。
-    若信息不足直接说："当前信息不足以回答此问题，缺少：[具体缺失字段]"
+from .part_01 import JSON_OUTPUT_GUARDRAIL, PMFLOW_PMO_STYLE_BASE, TEXT_OUTPUT_GUARDRAIL, _compose_prompt
 
-当前项目ID：{project_id}
-问题：{question}
-检索最大相关度：{relevance_max}
+RESPONSE_SCOPE_BASE = """
+职责边界：
+1. 你是呈现层，只负责把已知事实转成可读答复。
+2. 不编造数据，不透露系统实现，不替用户做写操作。
+""".strip()
 
-System Snapshot（全局）：
+PLANNING_SCOPE_BASE = """
+职责边界：
+1. 你是规划层，只负责把上下文转成可执行方案。
+2. 不重复分类，不输出空泛管理话术。
+""".strip()
+
+GOVERNANCE_SCOPE_BASE = """
+职责边界：
+1. 你是治理层，只负责评估、约束与风控建议。
+2. 不替代业务决策，不伪造证据，不输出泛化结论。
+""".strip()
+
+PROMPT_RAG_ANSWER = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    RESPONSE_SCOPE_BASE,
+    """
+你是 PMFlow 的 RAG 问答助手。
+
+输入：
+- 当前项目ID：{project_id}
+- 问题：{question}
+- 检索最大相关度：{relevance_max}
+- System Snapshot：
 {system_snapshot}
-
-User Snapshot（当前用户）：
+- User Snapshot：
 {user_snapshot}
-
-最近会话（最多10轮）：
+- 最近会话：
 {conversation}
-
-检索上下文：
+- 检索上下文：
 {context_blocks}
+
+回答规则：
+1. 只基于给定上下文回答，不要编造。
+2. 若证据不足，明确说“不确定/信息不足”，并写出缺失项。
+3. 当问题涉及“我/当前用户/我的任务”时，只使用 User Snapshot 对应数据回答。
+4. 先给结论，再给关键依据，最后给下一步建议。
+5. 回答末尾用“来源：...”列出所依据的上下文片段名称或编号。
+6. 直接输出最终答案，不要输出思考过程。
+""",
+    TEXT_OUTPUT_GUARDRAIL,
+)
+
+PROMPT_DECISION_QA = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
     """
+你是 PMFlow 的产品决策顾问，负责处理产品取舍、优先级和方案比较类问题。
 
-PROMPT_DECISION_QA = """
-你是 PMFlow 的产品顾问，负责处理产品取舍、优先级、二选一和方案比较类问题。
+输入：
+- 问题：{question}
 
-规则：
-1. 直接给出明确选择，不要回避，不要反问。
-2. 如果用户给出两个或多个选项，必须明确选一个。
-3. 不依赖数据库、历史记录或外部实时数据，基于常识与办公协作场景给出最稳妥建议即可。
-4. 先给结论，再给 1-2 条理由，回答要简洁。
-5. 不输出思考过程，不要使用“让我/我来/我先/我需要”开头。
+回答规则：
+1. 对取舍、优先级、方案比较，必须明确选择一个最稳妥的方向。
+2. 先给结论，再给 1-2 条理由，回答要简洁。
+3. 若选项没有写全，基于项目管理与办公协作常识给出最稳妥建议。
+4. 不要回避，不要反问，不要输出内部推理过程。
+""",
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
-问题：{question}
+PROMPT_RAG_DB_PROJECT_QA = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    RESPONSE_SCOPE_BASE,
     """
-
-PROMPT_RAG_DB_PROJECT_QA = """
 你是 PMFlow 的项目数据语义检索助手。
-你会收到“用户问题”和“项目索引(JSON)”，请基于索引做语义匹配后回答。
+请基于“用户问题”和“项目索引(JSON)”做语义匹配后回答。
 
-规则：
-1. 优先语义理解，不要机械关键词匹配；要容错同义词、口语、轻微错别字（如“键身”≈“健身”）。
-2. 若用户问“有没有/是否存在某类项目”，必须给出明确结论（有/没有）并列出匹配项目。
-3. 若用户问“进度/阶段/完成率”，请综合 task_total/done_count/doing_count/pending_count 输出量化结论。
-4. 若证据不足，请明确说明不足点，不要编造。
-5. 若问题焦点是成员/人员/负责人/谁在做（人员管理），该问题不应由项目索引直接回答，必须返回 handled=false。
+输入：
+- 用户问题：{question}
+- 项目索引(JSON)：
+{project_index}
 
-输出 JSON（只输出 JSON，不要 Markdown）：
+输出 JSON：
 {{
   "handled": true,
   "answer": "给用户展示的最终中文回答",
@@ -66,65 +88,66 @@ PROMPT_RAG_DB_PROJECT_QA = """
   "confidence": 0.0
 }}
 
-用户问题：
-{question}
+规则：
+1. 若项目索引无法回答，返回 handled=false，answer 置空，matched_project_ids 置空数组。
+2. answer 只写对用户有用的最终回答，不要复述索引原文。
+3. confidence 用 0.0-1.0 的小数。
+4. 不要输出 Markdown、解释文本或思考过程。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-项目索引(JSON)：
-{project_index}
+PROMPT_GENERATE_CHARTER = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
     """
-
-PROMPT_GENERATE_CHARTER = """
-首字符必须是 {{，末字符必须是 }}。
 你是一位 PMFlow 的资深产品经理。根据当前项目上下文与输入信号，生成一份结构化的需求宪章草案。
 
 输入：
 - 当前项目上下文：{project_context}
 - 现有活跃项目（JSON）：{active_projects_json}
-- 信号内容：{signal_content}
+- 信号输入：{signal_content}
 
-请先判断这条需求更适合作为：
-1. 当前项目的延展；
-2. 现有活跃项目的复用/合并；
-3. 必须新建项目。
+请先判断是否存在同名或高度相似项目，再决定是否新建、复用、合并或暂缓。
 
-如果存在同名或高度相似项目，必须在背景、范围和风险里明确写出差异点、复用点与合并建议。
-
-请输出 JSON 格式，包含以下字段：
+请输出 JSON，包含：
 {{
   "title": "需求宪章标题",
   "summary": "一句话摘要",
   "background": "背景与动机",
   "objectives": ["目标1", "目标2"],
   "scope": "范围说明",
-  "success_metrics": ["指标1", "指标2"],
-  "risks": ["风险1", "风险2"]
+  "success_criteria": ["标准1", "标准2"],
+  "risks": ["风险1", "风险2"],
+  "dependencies": ["依赖1", "依赖2"],
+  "mvp_items": ["MVP项1", "MVP项2"],
+  "owner_suggestion": "负责人建议",
+  "followup_questions": ["待确认问题1", "待确认问题2"]
 }}
 
-只输出 JSON，不要其他文字。
+约束：
+1. 只围绕 PMFlow 的项目立项、复用、合并与宪章起草目标输出。
+2. 若信息不足，followup_questions 最多 3 条。
+3. MVP 优先最小可验证闭环，不要写空泛愿景。
+4. 输出必须可直接被 json.loads 解析。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
+
+PROMPT_DECOMPOSE_TASKS = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
     """
-
-PROMPT_DECOMPOSE_TASKS = """
 你是一位 AI 技术项目经理。根据以下需求宪章与项目上下文，拆解为可执行的任务列表。
-项目 ID：{project_id}。
 
-宪章内容：
-{charter_content}
+输入：
+- 项目 ID：{project_id}
+- 需求宪章（JSON）：{charter_content}
+- 可用技能：{available_skills}
 
-请识别每个任务所需的 AI 开发技能标签（仅从以下系统技能中匹配：{available_skills}）。
+请识别每个任务所需的 AI 开发技能标签（仅从系统技能中匹配）。
 
-规则：
-1. 如果宪章内容是结构化 JSON，请优先读取其中的 `request` / `project` / `charter` / `modules` 字段。
-2. 优先围绕 `objectives`、`scope`、`success_criteria`、`constraints`、`milestones`、`risks` 拆解任务。
-3. 任务要尽量贴近可执行产出，不要输出空泛的管理话术。
-4. 如果上下文同时给出项目与宪章信息，以宪章约束为主，项目信息用于补充边界。
-5. 每个任务必须生成稳定的 `task_key`，推荐格式为 `task-1`、`task-2`、`task-3`。
-6. `required_skill_names` 只写系统技能名称；`required_skill_tag_ids` 可留空，由后端按技能名称自动回填。
-7. `depends_on_task_keys` 用于描述任务依赖，必须引用前面任务的 `task_key`。
-8. `risk_level` 只能是 `low`、`medium`、`high` 或 `critical`。
-9. `confidence` 使用 0 到 1 的小数，越接近 1 表示越确定。
-10. `evidence_refs` 填写宪章或项目上下文里的证据位置，例如 `charter.objectives.0`、`charter.risks.1`。
-
-输出 JSON 格式：
+输出 JSON：
 {{
   "tasks": [
     {{
@@ -146,66 +169,89 @@ PROMPT_DECOMPOSE_TASKS = """
   "detected_skills": ["检测到的所有技能"]
 }}
 
-只输出 JSON，不要其他文字。
+要求：
+1. 任务要可执行、可验收，避免空泛管理语言。
+2. 优先体现与业务目标直接相关的最小闭环。
+3. evidence_refs 必须能对应到传入的 charter_content。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
+
+PROMPT_RECOMMEND_ASSIGNEES = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
     """
+你是一位项目协调员。根据以下任务内容、所需技能和候选人信息，推荐 Top3 最合适的负责人。
 
-PROMPT_RECOMMEND_ASSIGNEES = """
-你是一位项目协调员。根据以下任务内容、所需技能、以及候选人信息，推荐 Top3 最合适的负责人。
-
-任务内容：{task_content}
-
-所需技能标签 IDs：{required_skill_tag_ids}
-
-候选人信息（JSON 数组，每项包含 user_id, user_name, workload, proficiency_avg, skill_match_count）：
+输入：
+- 任务内容：{task_content}
+- 所需技能标签 IDs：{required_skill_tag_ids}
+- 候选人信息（JSON 数组）：
 {candidates_json}
 
-请考虑：当前负载、技能匹配度、历史表现。输出 JSON：
+输出 JSON：
 {{
   "recommendations": [
     {{
       "user_id": "用户ID",
       "user_name": "姓名",
-      "match_score": 0-100,
+      "match_score": 0,
       "reason": "推荐理由"
     }}
   ]
 }}
 
-只输出 JSON。
-"""
+规则：
+1. 至多推荐 3 人，按 match_score 从高到低排序。
+2. 推荐理由要同时解释技能匹配与负载/可用性判断。
+3. 不要编造候选人列表中不存在的用户。
+4. match_score 使用 0-100 整数或小数都可，但需保持一致。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_ALIGNMENT_SCORE = """
+PROMPT_ALIGNMENT_SCORE = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 计算用户对真理节点的认知对齐度（0-100 分）。
 
-用户 ID：{user_id}
-真理节点 ID：{truth_node_id}
-
-真理节点内容：
+输入：
+- 用户 ID：{user_id}
+- 真理节点 ID：{truth_node_id}
+- 真理节点内容：
 {truth_content}
-
-用户相关行为/产出（如有）：
+- 用户相关行为/产出：
 {user_context}
 
-请评估该用户对该真理节点的理解与践行程度。输出 JSON：
+输出 JSON：
 {{
-  "alignment_score": 0-100,
+  "alignment_score": 0,
   "explanation": "简要说明"
 }}
 
-只输出 JSON。
-"""
+规则：
+1. alignment_score 范围必须是 0-100。
+2. explanation 只写最关键的判断依据，不要写推理过程。
+3. 若证据不足，说明缺少什么。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_COGNITIVE_CONFLICTS = """
+PROMPT_COGNITIVE_CONFLICTS = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 识别真理节点下的隐性认知冲突。
 
-真理节点 ID：{truth_node_id}
-真理节点内容：
+输入：
+- 真理节点 ID：{truth_node_id}
+- 真理节点内容：
 {truth_content}
-
-相关用户的观点/产出：
+- 用户观点/反馈：
 {user_viewpoints}
 
-请识别冲突类型（如 definition/定义分歧、priority/优先级冲突、scope/范围争议），输出 JSON：
+输出 JSON：
 {{
   "conflicts": [
     {{
@@ -218,38 +264,54 @@ PROMPT_COGNITIVE_CONFLICTS = """
   ]
 }}
 
-只输出 JSON。
-"""
+规则：
+1. 只识别有明确证据支持的冲突。
+2. 没有冲突时返回空数组，不要编造。
+3. description 需要业务化表达，避免空泛术语。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_EVALUATE_MODEL_REQUIREMENT = """
+PROMPT_EVALUATE_MODEL_REQUIREMENT = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 评估需求与 AI 模型的匹配度。
 
-宪章内容：{charter_content}
-模型版本 ID：{model_version_id}
-模型信息（如有）：{model_info}
+输入：
+- 需求内容：{charter_content}
+- 模型版本 ID：{model_version_id}
+- 模型说明：{model_info}
 
-请评估兼容性、成本、风险。输出 JSON：
+输出 JSON：
 {{
-  "compatibility_score": 0-100,
-  "cost_estimate_usd": 预估日成本美元,
-  "cost_estimate_tokens_per_day": 预估日 token 量,
+  "compatibility_score": 0,
+  "cost_estimate_usd": 0.0,
+  "cost_estimate_tokens_per_day": 0,
   "risk_hints": ["风险1", "风险2"],
   "recommendations": ["建议1", "建议2"]
 }}
 
-只输出 JSON。
-"""
+规则：
+1. compatibility_score 使用 0-100。
+2. 风险和建议必须能直接指导模型选型或范围调整。
+3. 若信息不足，明确指出无法判断的关键缺口。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_ANALYZE_CAPABILITY_GAP = """
+PROMPT_ANALYZE_CAPABILITY_GAP = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 你是一位 AI 模型能力专家。分析以下需求与模型之间的能力差距。
 
-需求宪章：
-{charter_content}
+输入：
+- 需求描述：{requirement_description}
+- 模型能力说明：{model_capabilities}
+- 约束条件：{constraints}
 
-模型信息：
-{model_info}
-
-请识别并分类能力差距，输出 JSON：
+输出 JSON：
 {{
   "gaps": [
     {{
@@ -261,39 +323,50 @@ PROMPT_ANALYZE_CAPABILITY_GAP = """
     }}
   ],
   "overall_risk_level": "critical|high|medium|low",
-  "confidence": 0.0-1.0
+  "confidence": 0.0
 }}
 
-只输出 JSON。
-"""
+规则：
+1. gaps 只列出真正影响落地的差距。
+2. overall_risk_level 要与 gaps 的严重程度保持一致。
+3. confidence 使用 0-1 小数。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_ANALYZE_COST_ROI = """
+PROMPT_ANALYZE_COST_ROI = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 分析 AI 项目的成本效益。
 
-总成本：{total_cost}
-总 Token：{total_tokens}
-任务数：{task_count}
-项目内容：{truth_node_content}
-
-请计算：
-1. ROI 评分 (0-10)
-2. 成本效率评估
-3. 优化建议
+输入：
+- 总成本：{total_cost}
+- 总 token 数：{total_tokens}
+- 任务数：{task_count}
+- 项目内容：
+{truth_node_content}
 
 输出 JSON：
 {{
-  "roi_score": 0.0-10.0,
+  "roi_score": 0.0,
   "cost_efficiency": "excellent|good|fair|poor",
-  "cost_per_task": 数值,
+  "cost_per_task": 0.0,
   "optimization_suggestions": ["建议1", "建议2"],
   "benchmark_comparison": "与行业标准比较"
 }}
 
-只输出 JSON。
-"""
+规则：
+1. roi_score 使用 0.0-10.0。
+2. cost_per_task 必须可由输入推导，不能胡写。
+3. optimization_suggestions 要能实际指导降本或提效。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 __all__ = [
     "PROMPT_RAG_ANSWER",
+    "PROMPT_DECISION_QA",
     "PROMPT_RAG_DB_PROJECT_QA",
     "PROMPT_GENERATE_CHARTER",
     "PROMPT_DECOMPOSE_TASKS",

backend/app/config/ai_prompts_catalog/part_04.py

@@ -1,67 +1,118 @@
 from __future__ import annotations
 
-PROMPT_ANALYZE_COST_OPTIMIZATION = """
+from .part_01 import JSON_OUTPUT_GUARDRAIL, PMFLOW_PMO_STYLE_BASE, TEXT_OUTPUT_GUARDRAIL, _compose_prompt
+
+ANALYSIS_SCOPE_BASE = """
+职责边界：
+1. 你是分析层，只负责基于输入做测算、比较与建议。
+2. 不编造缺失数据，不替代人工验证。
+""".strip()
+
+PLANNING_SCOPE_BASE = """
+职责边界：
+1. 你是规划层，只负责给出可执行优化或重分配建议。
+2. 不重复分类判断，不解释系统内部实现。
+""".strip()
+
+GOVERNANCE_SCOPE_BASE = """
+职责边界：
+1. 你是治理层，只负责变更评估、同步决策与风控建议。
+2. 不替代业务决策，不伪造证据，不输出泛化结论。
+""".strip()
+
+PROMPT_ANALYZE_COST_OPTIMIZATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 分析成本优化机会。
 
-成本历史：{cost_history}
-项目内容：{truth_node_content}
+输入：
+- 成本历史：{cost_history}
+- 项目内容：
+{truth_node_content}
 
 请识别优化机会，输出 JSON：
 {{
-  "can_downgrade_model": true|false,
-  "downgrade_savings": 预计节省金额,
+  "can_downgrade_model": true,
+  "downgrade_savings": 0.0,
   "suggested_model": "建议的替代模型",
-  "high_cache_potential": true|false,
-  "cache_savings": 预计节省金额,
-  "batchable_operations": true|false,
-  "batch_savings": 预计节省金额,
-  "monthly_cost": 数值,
-  "total_potential_savings": 数值,
-  "roi_after_optimization": 数值
+  "high_cache_potential": true,
+  "cache_savings": 0.0,
+  "batchable_operations": true,
+  "batch_savings": 0.0,
+  "monthly_cost": 0.0,
+  "total_potential_savings": 0.0,
+  "roi_after_optimization": 0.0
 }}
 
-只输出 JSON。
-"""
+规则：
+1. 只基于输入中的成本历史与项目内容做判断，不要编造。
+2. 结果要能直接指导降本动作。
+3. 不要输出解释文本，只输出 JSON。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_ASSESS_MODEL_UPDATE_IMPACT = """
+PROMPT_ASSESS_MODEL_UPDATE_IMPACT = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 评估模型升级影响。
 
-旧模型信息：{old_model_info}
-新模型信息：{new_model_info}
-受影响宪章：{affected_truth_nodes}
+输入：
+- 旧模型信息：{old_model_info}
+- 新模型信息：{new_model_info}
+- 受影响宪章：{affected_truth_nodes}
 
 输出 JSON：
 {{
-  "cost_change_percentage": 数值,
+  "cost_change_percentage": 0.0,
   "performance_change": {{
-    "context_window_change_percent": 数值,
-    "quality_change_percent": 数值
+    "context_window_change_percent": 0.0,
+    "quality_change_percent": 0.0
   }},
   "compatibility_risks": [{{"type": "风险类型", "severity": "low|medium|high"}}],
   "recommendation": "immediate_upgrade|evaluate|defer"
 }}
 
-只输出 JSON。
-"""
+规则：
+1. 结论要明确是否适合立即升级。
+2. 风险说明必须落到兼容性、稳定性或成本变化。
+3. 不要输出解释文本，只输出 JSON。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_EXPERIMENT_SYNC_DECISION = """
+PROMPT_EXPERIMENT_SYNC_DECISION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    GOVERNANCE_SCOPE_BASE,
+    """
 评估实验结果是否可自动同步到宪章。
 
-实验信息：{experiment_info}
-宪章内容：{truth_content}
-阈值：{threshold}
+输入：
+- 实验信息：{experiment_info}
+- 宪章内容：{truth_content}
+- 阈值：{threshold}
 
 输出 JSON：
 {{
-  "confidence_score": 0.0-1.0,
-  "auto_apply": true|false,
+  "confidence_score": 0.0,
+  "auto_apply": true,
   "reason": "原因"
 }}
 
-只输出 JSON。
-"""
+规则：
+1. 只有实验结果与宪章目标高度一致时才允许 auto_apply=true。
+2. reason 要明确说明同步或拒绝同步的依据。
+3. 不要输出解释文本，只输出 JSON。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_SKILL_SCORE_UPDATE = """
+PROMPT_SKILL_SCORE_UPDATE = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
+    """
 你是 PMFlow 的技能评估引擎。
 请根据任务完成结果，给每个技能标签输出质量分与效率分的增量（delta）。
 
@@ -90,9 +141,14 @@ PROMPT_SKILL_SCORE_UPDATE = """
     }}
   ]
 }}
-"""
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_WORKLOAD_OPTIMIZATION = """
+PROMPT_WORKLOAD_OPTIMIZATION = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
+    """
 你是 PMFlow 的任务分配优化器。
 请基于团队成员技能与当前负载，输出任务重分配建议。
 
@@ -117,26 +173,29 @@ PROMPT_WORKLOAD_OPTIMIZATION = """
     }}
   ]
 }}
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
+
+PROMPT_ANALYZE_FOOD_CALORIES = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    ANALYSIS_SCOPE_BASE,
     """
+你是一位专业的营养分析师，擅长通过文本描述识别食物并估算热量和营养成分。
 
-PROMPT_ANALYZE_FOOD_CALORIES = """
-你是一位专业的营养分析师，擅长通过图片识别食物并估算热量和营养成分。
-
-请基于提供的食物描述或特征，识别食物类型并估算营养信息。
-
-输入信息：
+输入：
 {food_description}
 
 请输出 JSON 格式，包含以下字段：
 {{
   "food_name": "食物名称",
-  "confidence": 0.0-1.0,
+  "confidence": 0.0,
   "estimated_portion_size": "份量大小描述（如：一份、100克、半碗等）",
-  "calories": 数值,
-  "protein": 数值,
-  "fat": 数值,
-  "carbs": 数值,
-  "fiber": 数值,
+  "calories": 0.0,
+  "protein": 0.0,
+  "fat": 0.0,
+  "carbs": 0.0,
+  "fiber": 0.0,
   "category": "分类（如：主食、蔬菜、肉类、汤品、甜品、饮品等）",
   "cuisine": "菜系（如：中式、西式、日式、韩式等）",
   "description": "简短的食物描述",
@@ -145,15 +204,19 @@ PROMPT_ANALYZE_FOOD_CALORIES = """
 }}
 
 估算规则：
-1. 热量估算范围：考虑到份量不确定性，给出合理的中位值
-2. 营养成分：基于常见食物数据库估算
-3. confidence 根据描述清晰度和食物识别难度给出
-4. 若无法识别，confidence 应 < 0.5，并在 uncertainty_factors 中说明
+1. 热量估算考虑份量不确定性，给出合理中位值。
+2. 营养成分基于常见食物数据库估算。
+3. confidence 根据描述清晰度和食物识别难度给出。
+4. 若无法识别，confidence 应 < 0.5，并在 uncertainty_factors 中说明。
+5. 只输出 JSON，不要其他文字或 Markdown。
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
-只输出 JSON，不要其他文字或 Markdown。
+PROMPT_SIGNAL_PLANNER = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    PLANNING_SCOPE_BASE,
     """
-
-PROMPT_SIGNAL_PLANNER = """
 你是 PMFlow 的 Signal Planner。请基于信号内容、项目上下文和团队成员能力，给出可执行的规划建议。
 
 输入：
@@ -193,7 +256,9 @@ PROMPT_SIGNAL_PLANNER = """
 4. execution_plan 必须是 2-5 条可执行动作，不要空泛描述。
 5. confidence 范围 0-1，不能省略。
 6. 只输出 JSON，不要输出 Markdown 或解释文本。
-"""
+""",
+    JSON_OUTPUT_GUARDRAIL,
+)
 
 __all__ = [
     "PROMPT_ANALYZE_COST_OPTIMIZATION",

backend/app/config/ai_prompts_catalog/part_scenes_01.py

@@ -1,19 +1,22 @@
 from __future__ import annotations
 
-FEISHU_QA_RESPONSE_STYLE = """
+from .part_01 import PMFLOW_PMO_STYLE_BASE, TEXT_OUTPUT_GUARDRAIL, _compose_prompt
+
+FEISHU_QA_RESPONSE_STYLE = _compose_prompt(
+    PMFLOW_PMO_STYLE_BASE,
+    """
 回复风格要求：
 1. 先给结论，再给关键依据，最后再给下一步。
 2. 用正式、克制、面向协作的中文，像资深项目经理而不是聊天机器人。
 3. 如果能用数字、项目名、负责人、时间点表达，请直接写明。
 4. 若信息不足，必须明确说明缺少什么，最多列 3 项。
 5. 禁止自我介绍、寒暄、思考过程、数据库/模型/接口实现细节。
-""".strip()
-
-PROMPT_FEISHU_DIRECT_QA = "\n\n".join(
-    [
-        """
-你是 PMFlow 的飞书项目管理顾问，负责直接问答。
 """.strip(),
+    TEXT_OUTPUT_GUARDRAIL,
+)
+
+PROMPT_FEISHU_DIRECT_QA = _compose_prompt(
+    "你是 PMFlow 的飞书项目管理顾问，负责直接问答。",
     FEISHU_QA_RESPONSE_STYLE,
     """
 回答要求：
@@ -32,7 +35,6 @@ User Snapshot：{user_snapshot}
 最近会话：{conversation}
 检索上下文：{context_blocks}
 """.strip(),
-    ]
 )
 
 __all__ = ["FEISHU_QA_RESPONSE_STYLE", "PROMPT_FEISHU_DIRECT_QA"]

backend/app/config/ai_prompts_catalog/part_scenes_03.py

@@ -1,7 +1,10 @@
 from __future__ import annotations
 
-PROMPT_PROJECT_STATUS_QA = """
-你是 PMFlow 的项目状态分析助手。
+from .part_01 import TEXT_OUTPUT_GUARDRAIL, _compose_prompt
+
+PROMPT_PROJECT_STATUS_QA = _compose_prompt(
+    "你是 PMFlow 的项目状态分析助手。",
+    """
 用户关注项目进度、状态、完成率、阶段和卡点。
 
 规则：
@@ -21,6 +24,8 @@ PROMPT_PROJECT_STATUS_QA = """
 System Snapshot：{system_snapshot}
 User Snapshot：{user_snapshot}
 检索上下文：{context_blocks}
-""".strip()
+""".strip(),
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
 __all__ = ["PROMPT_PROJECT_STATUS_QA"]

backend/app/config/ai_prompts_catalog/part_scenes_04.py

@@ -1,22 +1,27 @@
 from __future__ import annotations
 
-PROMPT_MEMBER_WORKLOAD_QA = """
-你是 PMFlow 的团队协作助手。
+from .part_01 import TEXT_OUTPUT_GUARDRAIL, _compose_prompt
+
+PROMPT_MEMBER_WORKLOAD_QA = _compose_prompt(
+    "你是 PMFlow 的团队协作助手。",
+    """
 用户询问团队成员或负载相关信息。
 
 规则：
-1. 优先使用 System Snapshot.team_members 和 User Snapshot 数据
-2. 禁止默认返回项目列表
-3. 涉及负载用百分比表示
-4. 忽略与成员负载无关的项目列表和历史项目详情
-5. 推荐负责人时说明推荐理由
-6. 禁止输出思考过程
-7. 不超过150字
+1. 优先使用 System Snapshot.team_members 和 User Snapshot 数据。
+2. 禁止默认返回项目列表。
+3. 涉及负载用百分比表示。
+4. 忽略与成员负载无关的项目列表和历史项目详情。
+5. 推荐负责人时说明推荐理由。
+6. 禁止输出思考过程。
+7. 不超过 150 字。
 
 问题：{question}
 System Snapshot：{system_snapshot}
 User Snapshot：{user_snapshot}
 检索上下文：{context_blocks}
-""".strip()
+""".strip(),
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
 __all__ = ["PROMPT_MEMBER_WORKLOAD_QA"]

backend/app/config/ai_prompts_catalog/part_scenes_05.py

@@ -1,14 +1,17 @@
 from __future__ import annotations
 
-PROMPT_AI_PMO_QA = """
-你是 PMFlow 的 AI 项目管理官。
+from .part_01 import TEXT_OUTPUT_GUARDRAIL, _compose_prompt
+
+PROMPT_AI_PMO_QA = _compose_prompt(
+    "你是 PMFlow 的 AI 项目管理官。",
+    """
 用户通过指令入口提问。
 
 规则：
-1. 你既是执行者也是回答者
-2. 问题涉及操作时先回答再提示可用指令
-3. 只基于给定上下文回答
-4. 禁止输出思考过程
+1. 你既是执行者也是回答者，但只回答当前上下文明确支持的内容。
+2. 问题涉及操作时先回答再提示可用指令。
+3. 只基于给定上下文回答，不要编造系统状态。
+4. 禁止输出思考过程。
 5. 回答后附可操作建议：
    "你可以继续：[操作A]/[操作B]"
 
@@ -16,6 +19,8 @@ PROMPT_AI_PMO_QA = """
 当前项目：{project_id}
 World Model：{world_model}
 检索上下文：{context_blocks}
-""".strip()
+""".strip(),
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
 __all__ = ["PROMPT_AI_PMO_QA"]

backend/app/config/ai_prompts_catalog/part_scenes_06.py

@@ -1,29 +1,35 @@
 from __future__ import annotations
 
-PROMPT_EXTERNAL_TREND_QA = """
-你是 PMFlow 的市场情报助手。
+from .part_01 import TEXT_OUTPUT_GUARDRAIL, _compose_prompt
+
+PROMPT_EXTERNAL_TREND_QA = _compose_prompt(
+    "你是 PMFlow 的市场情报助手。",
+    """
 用户询问外部行业或市场信息。
 
 规则：
-1. 若有外部搜索结果优先使用
+1. 若有外部搜索结果优先使用。
 2. 若无外部数据明确说：
    "当前未接入实时数据，以下基于通用行业认知。"
 3. 结合项目实际给出应用建议：
    "对你们[项目名]的影响是..."
-4. 禁止输出思考过程
+4. 禁止输出思考过程。
 5. 分段输出：
    [行业动态] xxx
    [对项目的影响] xxx
    [建议] xxx
-6. 控制总回复长度不超过800字，优先保留[建议]段
+6. 控制总回复长度不超过 800 字，优先保留[建议]段。
 
 问题：{question}
 项目上下文：{project_context}
 外部搜索结果：{external_results}
-""".strip()
+""".strip(),
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
-PROMPT_INDUSTRY_TREND_QA = """
-你是 PMFlow 的行业趋势助手。
+PROMPT_INDUSTRY_TREND_QA = _compose_prompt(
+    "你是 PMFlow 的行业趋势助手。",
+    """
 用户在询问某个行业、市场或产品方向的通用趋势判断。
 
 规则：
@@ -35,6 +41,8 @@ PROMPT_INDUSTRY_TREND_QA = """
 6. 不要输出思考过程，不要使用“让我/我来/我先/我需要”开头。
 
 问题：{question}
-""".strip()
+""".strip(),
+    TEXT_OUTPUT_GUARDRAIL,
+)
 
 __all__ = ["PROMPT_EXTERNAL_TREND_QA", "PROMPT_INDUSTRY_TREND_QA"]

backend/app/models/__init__.py

@@ -45,6 +45,7 @@ from .knowledge_base_doc import KnowledgeBaseDoc
 from .knowledge_base_chunk import KnowledgeBaseChunk
 from .qa_record import QARecord
 from .qa_feedback import QAFeedback
+from .feedback_center import FeedbackCenterRecord, FeedbackCenterRecordRevision
 from .qa_session import QASession
 from .qa_session_state import QASessionState
 from .kg_entity import KGEntity
@@ -111,6 +112,8 @@ __all__ = [
     "KnowledgeBaseChunk",
     "QARecord",
     "QAFeedback",
+    "FeedbackCenterRecord",
+    "FeedbackCenterRecordRevision",
     "QASession",
     "QASessionState",
     "KGEntity",

backend/app/models/feedback_center.py

@@ -0,0 +1,79 @@
+"""统一反馈中心模型。"""
+
+from __future__ import annotations
+
+from typing import Any, Optional
+
+from sqlalchemy import BigInteger, Boolean, Float, ForeignKey, Integer, Text, TIMESTAMP, UniqueConstraint, VARCHAR
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from .base import Base
+from .types import JSONB
+
+
+class FeedbackCenterRecord(Base):
+    """统一反馈记录：承载任务、QA、信号三类学习样本。"""
+
+    __tablename__ = "feedback_center_records"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    project_id: Mapped[Optional[int]] = mapped_column(BigInteger, nullable=True, index=True)
+    source_type: Mapped[str] = mapped_column(VARCHAR(32), nullable=False, index=True)
+    source_id: Mapped[int] = mapped_column(BigInteger, nullable=False, index=True)
+    object_type: Mapped[str] = mapped_column(VARCHAR(32), nullable=False, index=True)
+    object_id: Mapped[int] = mapped_column(BigInteger, nullable=False, index=True)
+    field_name: Mapped[str] = mapped_column(VARCHAR(128), nullable=False, index=True)
+    source_title: Mapped[Optional[str]] = mapped_column(VARCHAR(500), nullable=True)
+    object_title: Mapped[Optional[str]] = mapped_column(VARCHAR(500), nullable=True)
+    before_value: Mapped[Any] = mapped_column(JSONB, nullable=True)
+    after_value: Mapped[Any] = mapped_column(JSONB, nullable=True)
+    ai_snapshot: Mapped[Any] = mapped_column(JSONB, nullable=True)
+    ai_confidence: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    reason_candidates: Mapped[Any] = mapped_column(JSONB, nullable=True)
+    reason_code: Mapped[Optional[str]] = mapped_column(VARCHAR(64), nullable=True, index=True)
+    reason_text: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    review_status: Mapped[str] = mapped_column(VARCHAR(16), nullable=False, default="pending", index=True)
+    reviewed_by: Mapped[Optional[int]] = mapped_column(BigInteger, nullable=True, index=True)
+    reviewed_at: Mapped[Optional[TIMESTAMP]] = mapped_column(TIMESTAMP, nullable=True)
+    auto_assigned: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+
+    __table_args__ = (
+        UniqueConstraint(
+            "tenant_id",
+            "source_type",
+            "source_id",
+            "object_type",
+            "object_id",
+            "field_name",
+            name="uq_feedback_center_records_source_object_field",
+        ),
+    )
+
+    revisions = relationship(
+        "FeedbackCenterRecordRevision",
+        back_populates="record",
+        cascade="all, delete-orphan",
+    )
+
+
+class FeedbackCenterRecordRevision(Base):
+    """统一反馈记录的修订历史。"""
+
+    __tablename__ = "feedback_center_record_revisions"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    record_id: Mapped[int] = mapped_column(
+        Integer,
+        ForeignKey("feedback_center_records.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+    before_snapshot: Mapped[Any] = mapped_column(JSONB, nullable=False)
+    after_snapshot: Mapped[Any] = mapped_column(JSONB, nullable=False)
+    reviewed_by: Mapped[Optional[int]] = mapped_column(BigInteger, nullable=True, index=True)
+    reviewed_at: Mapped[Optional[TIMESTAMP]] = mapped_column(TIMESTAMP, nullable=True)
+
+    record = relationship("FeedbackCenterRecord", back_populates="revisions")
+
+
+__all__ = ["FeedbackCenterRecord", "FeedbackCenterRecordRevision"]

backend/app/schemas/__init__.py

@@ -23,6 +23,21 @@ from .common import (
     TaskItem,
 )
 from .feedback import SignalFeedbackCreate, SignalFeedbackResponse
+from .feedback_center import (
+    FeedbackReasonCandidate,
+    FeedbackCenterRecordRevisionRead,
+    FeedbackCenterRecordRead,
+    FeedbackCenterSummaryReasonRead,
+    FeedbackCenterSummaryMetricsRead,
+    FeedbackCenterTrendDailyRead,
+    FeedbackCenterTrendWindowRead,
+    FeedbackCenterTrendDeltaRead,
+    FeedbackCenterTrendRead,
+    FeedbackCenterSummaryRead,
+    FeedbackCenterListRead,
+    FeedbackCenterReviewRequest,
+    FeedbackCenterReasonCatalogRead,
+)
 from .model_capability import (
     ValidateMatchRequest,
     LifecycleImpactRequest,
@@ -79,6 +94,19 @@ __all__ = [
     "TaskItem",
     "SignalFeedbackCreate",
     "SignalFeedbackResponse",
+    "FeedbackReasonCandidate",
+    "FeedbackCenterRecordRevisionRead",
+    "FeedbackCenterRecordRead",
+    "FeedbackCenterSummaryReasonRead",
+    "FeedbackCenterSummaryMetricsRead",
+    "FeedbackCenterTrendDailyRead",
+    "FeedbackCenterTrendWindowRead",
+    "FeedbackCenterTrendDeltaRead",
+    "FeedbackCenterTrendRead",
+    "FeedbackCenterSummaryRead",
+    "FeedbackCenterListRead",
+    "FeedbackCenterReviewRequest",
+    "FeedbackCenterReasonCatalogRead",
     "ValidateMatchRequest",
     "LifecycleImpactRequest",
     "GenerateMigrationTasksRequest",

backend/app/schemas/feedback_center.py

@@ -0,0 +1,209 @@
+"""统一反馈中心 Pydantic 模型。"""
+
+from __future__ import annotations
+
+from datetime import date, datetime
+from typing import Any, Literal, Optional
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class FeedbackReasonCandidate(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    code: str
+    label: str
+    confidence: float = 0.0
+    description: Optional[str] = None
+    source_type: Optional[str] = None
+    field_name: Optional[str] = None
+
+
+class FeedbackCenterRecordRevisionRead(BaseModel):
+    model_config = ConfigDict(from_attributes=True, extra="ignore")
+
+    id: int
+    record_id: int
+    before_snapshot: Any
+    after_snapshot: Any
+    reviewed_by: Optional[int] = None
+    reviewed_at: Optional[datetime] = None
+    created_at: Optional[datetime] = None
+
+
+class FeedbackCenterRecordRead(BaseModel):
+    model_config = ConfigDict(from_attributes=True, extra="ignore")
+
+    id: int
+    tenant_id: int
+    project_id: Optional[int] = None
+    source_type: str
+    source_id: int
+    object_type: str
+    object_id: int
+    field_name: str
+    source_title: Optional[str] = None
+    object_title: Optional[str] = None
+    before_value: Any = None
+    after_value: Any = None
+    ai_snapshot: Any = None
+    ai_confidence: Optional[float] = None
+    reason_candidates: list[FeedbackReasonCandidate] = Field(default_factory=list)
+    reason_code: Optional[str] = None
+    reason_text: Optional[str] = None
+    review_status: str
+    reviewed_by: Optional[int] = None
+    reviewed_at: Optional[datetime] = None
+    auto_assigned: bool = False
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+    revisions: list[FeedbackCenterRecordRevisionRead] = Field(default_factory=list)
+
+
+class FeedbackCenterSummaryReasonRead(BaseModel):
+    code: str
+    label: str
+    count: int
+
+
+class FeedbackCenterSummaryMetricsRead(BaseModel):
+    accepted_rate_reviewed: float
+    manual_override_rate: float
+    rework_rate: float
+    auto_assigned_rate: float
+    avg_review_latency_minutes: float
+    total_task_breakdown_count: int
+    total_task_override_count: int
+    total_qa_feedback_count: int
+    total_signal_feedback_count: int
+    total_revision_count: int
+
+
+class FeedbackCenterTrendDailyRead(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    date: date
+    total_records: int
+    accepted_count: int
+    rejected_count: int
+    reviewed_count: int
+    pending_count: int
+    task_breakdown_count: int
+    task_override_count: int
+    qa_feedback_count: int
+    signal_feedback_count: int
+    auto_assigned_count: int
+    override_count: int
+    rework_count: int
+    accepted_rate_reviewed: float
+    manual_override_rate: float
+    rework_rate: float
+    auto_assigned_rate: float
+    avg_review_latency_minutes: float
+    revision_count: int
+
+
+class FeedbackCenterTrendWindowRead(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    total_records: int
+    accepted_count: int
+    rejected_count: int
+    reviewed_count: int
+    pending_count: int
+    task_breakdown_count: int
+    task_override_count: int
+    qa_feedback_count: int
+    signal_feedback_count: int
+    auto_assigned_count: int
+    override_count: int
+    rework_count: int
+    accepted_rate_reviewed: float
+    manual_override_rate: float
+    rework_rate: float
+    auto_assigned_rate: float
+    avg_review_latency_minutes: float
+    revision_count: int
+
+
+class FeedbackCenterTrendDeltaRead(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    total_records_delta: int
+    accepted_rate_reviewed_delta: float
+    manual_override_rate_delta: float
+    rework_rate_delta: float
+    auto_assigned_rate_delta: float
+    avg_review_latency_minutes_delta: float
+
+
+class FeedbackCenterTrendRead(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    window_days: int
+    current_window_start: date
+    previous_window_start: date
+    daily: list[FeedbackCenterTrendDailyRead]
+    current: FeedbackCenterTrendWindowRead
+    previous: FeedbackCenterTrendWindowRead
+    deltas: FeedbackCenterTrendDeltaRead
+
+
+class FeedbackCenterSummaryRead(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    tenant_id: int
+    project_id: Optional[int] = None
+    source_group: str = "all"
+    total: int
+    backlog_count: int
+    pending_count: int
+    reviewed_count: int
+    accepted_count: int
+    rejected_count: int
+    accepted_rate: float
+    avg_confidence: float
+    by_status: dict[str, int]
+    by_source_type: dict[str, int]
+    top_reasons: list[FeedbackCenterSummaryReasonRead]
+    metrics: FeedbackCenterSummaryMetricsRead
+    trend: FeedbackCenterTrendRead
+
+
+class FeedbackCenterListRead(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    items: list[FeedbackCenterRecordRead]
+    total: int
+    limit: int
+    offset: int
+    has_more: bool
+
+
+class FeedbackCenterReviewRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    review_status: Literal["reviewed", "accepted", "rejected"] = "accepted"
+    reason_code: Optional[str] = None
+    reason_text: Optional[str] = None
+
+
+class FeedbackCenterReasonCatalogRead(BaseModel):
+    items: list[FeedbackReasonCandidate]
+
+
+__all__ = [
+    "FeedbackReasonCandidate",
+    "FeedbackCenterRecordRevisionRead",
+    "FeedbackCenterRecordRead",
+    "FeedbackCenterSummaryReasonRead",
+    "FeedbackCenterSummaryMetricsRead",
+    "FeedbackCenterTrendDailyRead",
+    "FeedbackCenterTrendWindowRead",
+    "FeedbackCenterTrendDeltaRead",
+    "FeedbackCenterTrendRead",
+    "FeedbackCenterSummaryRead",
+    "FeedbackCenterListRead",
+    "FeedbackCenterReviewRequest",
+    "FeedbackCenterReasonCatalogRead",
+]

backend/app/services/feedback_center/__init__.py

@@ -0,0 +1,20 @@
+"""统一反馈中心服务。"""
+
+from .mappers import (
+    map_qa_feedback_to_record_payload,
+    map_signal_feedback_to_record_payload,
+    map_task_breakdown_to_record_payload,
+    map_task_override_to_record_payloads,
+)
+from .reason_catalog import build_reason_candidates, list_reason_catalog
+from .service import FeedbackCenterService
+
+__all__ = [
+    "FeedbackCenterService",
+    "build_reason_candidates",
+    "list_reason_catalog",
+    "map_qa_feedback_to_record_payload",
+    "map_signal_feedback_to_record_payload",
+    "map_task_breakdown_to_record_payload",
+    "map_task_override_to_record_payloads",
+]

backend/app/services/feedback_center/mappers.py

@@ -0,0 +1,296 @@
+"""统一反馈中心数据映射器。"""
+
+from __future__ import annotations
+
+from datetime import date, datetime
+from decimal import Decimal
+from typing import Any
+
+from .reason_catalog import build_reason_candidates
+
+
+def _jsonable(value: Any) -> Any:
+    if value is None:
+        return None
+    if isinstance(value, (str, int, float, bool)):
+        return value
+    if isinstance(value, Decimal):
+        return float(value)
+    if isinstance(value, (datetime, date)):
+        return value.isoformat()
+    if isinstance(value, dict):
+        return {str(key): _jsonable(item) for key, item in value.items()}
+    if isinstance(value, (list, tuple, set)):
+        return [_jsonable(item) for item in value]
+    model_dump = getattr(value, "model_dump", None)
+    if callable(model_dump):
+        return _jsonable(model_dump(mode="json"))
+    if hasattr(value, "__dict__"):
+        raw = {key: item for key, item in vars(value).items() if not key.startswith("_")}
+        if raw:
+            return _jsonable(raw)
+    return str(value)
+
+
+def _safe_int(value: Any) -> int | None:
+    try:
+        if value is None or value == "":
+            return None
+        return int(str(value).strip())
+    except (TypeError, ValueError):
+        return None
+
+
+def _extract_task_breakdown_object(task: Any) -> dict[str, Any]:
+    payload = getattr(task, "ai_breakdown_payload", None)
+    if isinstance(payload, str):
+        try:
+            import json
+
+            payload = json.loads(payload)
+        except Exception:
+            payload = None
+    if isinstance(payload, dict):
+        if isinstance(payload.get("task"), dict):
+            return _jsonable(payload["task"])
+        tasks = payload.get("tasks")
+        if isinstance(tasks, list) and tasks:
+            current_title = str(getattr(task, "title", "") or "").strip().lower()
+            current_id = str(getattr(task, "id", "") or "").strip()
+            for item in tasks:
+                if not isinstance(item, dict):
+                    continue
+                title = str(item.get("title") or "").strip().lower()
+                task_key = str(item.get("task_key") or "").strip()
+                if (current_title and title == current_title) or (current_id and task_key == current_id):
+                    return _jsonable(item)
+            first = tasks[0]
+            if isinstance(first, dict):
+                return _jsonable(first)
+    return {
+        "task_key": str(getattr(task, "id", "") or "").strip() or str(getattr(task, "title", "") or "").strip(),
+        "title": getattr(task, "title", None),
+        "description": getattr(task, "description", None),
+        "type": "task",
+        "estimated_hours": _jsonable(getattr(task, "estimated_hours", None)),
+        "required_skill_names": [],
+        "required_skill_tag_ids": list(getattr(task, "required_skill_tag_ids", None) or []),
+        "depends_on_task_keys": [],
+        "risk_level": "medium",
+        "risk_notes": None,
+        "confidence": 0.0,
+        "evidence_refs": [],
+        "priority": getattr(task, "priority", "medium"),
+    }
+
+
+def map_task_breakdown_to_record_payload(
+    *,
+    task: Any,
+    project_title: str | None = None,
+    source_context: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    task_object = _extract_task_breakdown_object(task)
+    ai_snapshot = _jsonable(getattr(task, "ai_breakdown_payload", None))
+    reason_candidates = build_reason_candidates(
+        source_type="task_breakdown",
+        field_name="task_object",
+        before_value=None,
+        after_value=task_object,
+    )
+    return {
+        "tenant_id": int(getattr(task, "tenant_id", 1) or 1),
+        "project_id": int(getattr(task, "project_id", 0) or 0) or None,
+        "source_type": "task_breakdown",
+        "source_id": int(getattr(task, "id", 0) or 0),
+        "object_type": "task",
+        "object_id": int(getattr(task, "id", 0) or 0),
+        "field_name": "task_object",
+        "source_title": str(project_title or getattr(task, "title", "") or "")[:500] or None,
+        "object_title": str(getattr(task, "title", "") or "")[:500] or None,
+        "before_value": None,
+        "after_value": task_object,
+        "ai_snapshot": ai_snapshot,
+        "ai_confidence": float(task_object.get("confidence") or 0.0) if isinstance(task_object, dict) else None,
+        "reason_candidates": reason_candidates,
+        "reason_code": reason_candidates[0]["code"] if reason_candidates else None,
+        "reason_text": None,
+        "review_status": "pending",
+        "reviewed_by": None,
+        "reviewed_at": None,
+        "auto_assigned": bool(getattr(task, "assignee_id", None)),
+    }
+
+
+def map_task_override_to_record_payloads(*, task: Any) -> list[dict[str, Any]]:
+    ai_object = _extract_task_breakdown_object(task)
+    raw_ai_payload = getattr(task, "ai_breakdown_payload", None)
+    if isinstance(raw_ai_payload, str):
+        try:
+            import json
+
+            raw_ai_payload = json.loads(raw_ai_payload)
+        except Exception:
+            raw_ai_payload = None
+    raw_ai_map = raw_ai_payload if isinstance(raw_ai_payload, dict) else {}
+    overrides = getattr(task, "manual_overrides", None) or {}
+    if isinstance(overrides, str):
+        try:
+            import json
+
+            overrides = json.loads(overrides)
+        except Exception:
+            overrides = {}
+    if not isinstance(overrides, dict):
+        overrides = dict(overrides or {})
+
+    payloads: list[dict[str, Any]] = []
+    for field_name, after_value in overrides.items():
+        if field_name == "manual_overrides":
+            continue
+        before_value = raw_ai_map.get(field_name)
+        if before_value is None:
+            before_value = ai_object.get(field_name) if isinstance(ai_object, dict) else getattr(task, field_name, None)
+        if _jsonable(before_value) == _jsonable(after_value):
+            continue
+        field_candidates = build_reason_candidates(
+            source_type="task_override",
+            field_name=str(field_name),
+            before_value=before_value,
+            after_value=after_value,
+        )
+        payloads.append(
+            {
+                "tenant_id": int(getattr(task, "tenant_id", 1) or 1),
+                "project_id": int(getattr(task, "project_id", 0) or 0) or None,
+                "source_type": "task_override",
+                "source_id": int(getattr(task, "id", 0) or 0),
+                "object_type": "task",
+                "object_id": int(getattr(task, "id", 0) or 0),
+                "field_name": str(field_name),
+                "source_title": str(getattr(task, "title", "") or "")[:500] or None,
+                "object_title": str(getattr(task, "title", "") or "")[:500] or None,
+                "before_value": _jsonable(before_value),
+                "after_value": _jsonable(after_value),
+                "ai_snapshot": {
+                    "ai_breakdown_payload": _jsonable(getattr(task, "ai_breakdown_payload", None)),
+                    "manual_overrides": _jsonable(overrides),
+                },
+                "ai_confidence": float(ai_object.get("confidence") or 0.0) if isinstance(ai_object, dict) else None,
+                "reason_candidates": field_candidates,
+                "reason_code": field_candidates[0]["code"] if field_candidates else None,
+                "reason_text": None,
+                "review_status": "accepted",
+                "reviewed_by": None,
+                "reviewed_at": None,
+        "auto_assigned": bool(getattr(task, "assignee_id", None)),
+            }
+        )
+    return payloads
+
+
+def map_qa_feedback_to_record_payload(*, qa_feedback: Any, qa_record: Any) -> dict[str, Any]:
+    rating = str(getattr(qa_feedback, "rating", "") or "").strip().lower()
+    helpfulness = getattr(qa_feedback, "helpfulness", None)
+    error_types = list(getattr(qa_feedback, "error_types", None) or [])
+    correct_answer = getattr(qa_feedback, "correct_answer", None)
+    before_value = {
+        "question": getattr(qa_record, "question", None),
+        "answer": getattr(qa_record, "answer", None),
+        "sources": _jsonable(getattr(qa_record, "sources", None) or []),
+    }
+    after_value = {
+        "rating": rating,
+        "helpfulness": helpfulness,
+        "error_types": [str(item).strip() for item in error_types if str(item).strip()],
+        "correct_answer": correct_answer,
+    }
+    reason_candidates = build_reason_candidates(
+        source_type="qa_feedback",
+        field_name="qa_feedback",
+        before_value=before_value,
+        after_value=after_value,
+    )
+    return {
+        "tenant_id": int(getattr(qa_feedback, "tenant_id", getattr(qa_record, "tenant_id", 1)) or 1),
+        "project_id": int(getattr(qa_record, "project_id", 0) or 0) or None,
+        "source_type": "qa_feedback",
+        "source_id": int(getattr(qa_feedback, "id", 0) or 0),
+        "object_type": "qa_record",
+        "object_id": int(getattr(qa_feedback, "qa_id", 0) or 0),
+        "field_name": "qa_feedback",
+        "source_title": str(getattr(qa_record, "question", "") or "")[:500] or None,
+        "object_title": str(getattr(qa_record, "question", "") or "")[:500] or None,
+        "before_value": before_value,
+        "after_value": after_value,
+        "ai_snapshot": {
+            "question": getattr(qa_record, "question", None),
+            "answer": getattr(qa_record, "answer", None),
+            "sources": _jsonable(getattr(qa_record, "sources", None) or []),
+            "domains_used": _jsonable(getattr(qa_record, "domains_used", None) or []),
+        },
+        "ai_confidence": None,
+        "reason_candidates": reason_candidates,
+        "reason_code": reason_candidates[0]["code"] if reason_candidates else None,
+        "reason_text": correct_answer or None,
+        "review_status": "accepted",
+        "reviewed_by": int(getattr(qa_feedback, "submitted_by", 0) or 0) or None,
+        "reviewed_at": None,
+        "auto_assigned": False,
+    }
+
+
+def map_signal_feedback_to_record_payload(*, signal_feedback: Any, signal: Any) -> dict[str, Any]:
+    predicted = bool(getattr(signal_feedback, "ai_predicted_strategic", False))
+    actual = bool(getattr(signal_feedback, "was_strategic", False))
+    before_value = {
+        "ai_predicted_strategic": predicted,
+        "ai_confidence": float(getattr(signal_feedback, "ai_confidence", 0.0) or 0.0),
+    }
+    after_value = {
+        "was_strategic": actual,
+        "reason": getattr(signal_feedback, "reason", None),
+    }
+    reason_candidates = build_reason_candidates(
+        source_type="signal_feedback",
+        field_name="strategic_classification",
+        before_value=before_value,
+        after_value=after_value,
+    )
+    return {
+        "tenant_id": int(getattr(signal_feedback, "tenant_id", getattr(signal, "tenant_id", 1)) or 1),
+        "project_id": int(getattr(signal, "matched_project_id", 0) or 0) or None,
+        "source_type": "signal_feedback",
+        "source_id": int(getattr(signal_feedback, "id", 0) or 0),
+        "object_type": "signal",
+        "object_id": int(getattr(signal_feedback, "signal_id", 0) or 0),
+        "field_name": "strategic_classification",
+        "source_title": str(getattr(signal, "title", "") or getattr(signal, "source", "") or "")[:500] or None,
+        "object_title": str(getattr(signal, "title", "") or getattr(signal, "source", "") or "")[:500] or None,
+        "before_value": before_value,
+        "after_value": after_value,
+        "ai_snapshot": {
+            "signal_id": getattr(signal_feedback, "signal_id", None),
+            "title": getattr(signal, "title", None),
+            "content": getattr(signal, "content", None),
+            "impact_score": getattr(signal, "impact_score", None),
+            "priority": getattr(signal, "priority", None),
+            "strategic_analysis": _jsonable(getattr(signal, "strategic_analysis", None)),
+        },
+        "ai_confidence": float(getattr(signal_feedback, "ai_confidence", 0.0) or 0.0),
+        "reason_candidates": reason_candidates,
+        "reason_code": reason_candidates[0]["code"] if reason_candidates else None,
+        "reason_text": getattr(signal_feedback, "reason", None),
+        "review_status": "accepted",
+        "reviewed_by": _safe_int(getattr(signal_feedback, "user_id", None)),
+        "reviewed_at": None,
+        "auto_assigned": False,
+    }
+
+
+__all__ = [
+    "map_qa_feedback_to_record_payload",
+    "map_signal_feedback_to_record_payload",
+    "map_task_breakdown_to_record_payload",
+    "map_task_override_to_record_payloads",
+]

backend/app/services/feedback_center/reason_catalog.py

@@ -0,0 +1,364 @@
+"""统一反馈中心的原因池与候选生成。"""
+
+from __future__ import annotations
+
+from collections import OrderedDict
+from typing import Any
+
+
+_REASON_DEFINITIONS: list[dict[str, Any]] = [
+    {
+        "code": "owner_mismatch",
+        "label": "负责人不匹配",
+        "description": "AI 推荐、人工指派或协作责任边界需要复核。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"assignee_id"},
+        "confidence": 0.96,
+    },
+    {
+        "code": "capability_mismatch",
+        "label": "能力匹配偏差",
+        "description": "人员能力、技能覆盖或职责分配与任务要求不一致。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"assignee_id", "required_skill_tag_ids", "required_skill_names"},
+        "confidence": 0.91,
+    },
+    {
+        "code": "skill_gap",
+        "label": "技能要求不准",
+        "description": "任务技能标签、能力要求或技能覆盖范围需要修正。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"required_skill_tag_ids", "required_skill_names"},
+        "confidence": 0.94,
+    },
+    {
+        "code": "priority_misalignment",
+        "label": "优先级错配",
+        "description": "优先级建议与业务紧急度或协作节奏不匹配。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"priority"},
+        "confidence": 0.89,
+    },
+    {
+        "code": "dependency_wrong",
+        "label": "依赖关系错误",
+        "description": "任务之间的依赖、先后顺序或阻塞链路判断有误。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"depends_on_task_keys"},
+        "confidence": 0.92,
+    },
+    {
+        "code": "risk_underestimated",
+        "label": "风险低估",
+        "description": "任务风险等级、风险说明或执行风险识别偏低。",
+        "source_types": {"task_breakdown", "task_override", "signal_feedback"},
+        "field_names": {"risk_level", "risk_notes", "strategic_classification"},
+        "confidence": 0.9,
+    },
+    {
+        "code": "effort_estimation_bias",
+        "label": "工时预估偏差",
+        "description": "任务预估工时与人工判断差距较大。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"estimated_hours"},
+        "confidence": 0.95,
+    },
+    {
+        "code": "scope_overbroad",
+        "label": "范围过大",
+        "description": "任务标题、描述或业务范围切分不够清晰。",
+        "source_types": {"task_breakdown", "task_override"},
+        "field_names": {"title", "description"},
+        "confidence": 0.82,
+    },
+    {
+        "code": "incomplete_output",
+        "label": "输出信息不完整",
+        "description": "AI 输出缺少关键字段、上下文或业务约束。",
+        "source_types": {"task_breakdown", "task_override", "qa_feedback"},
+        "field_names": {"task_object", "qa_feedback"},
+        "confidence": 0.78,
+    },
+    {
+        "code": "answer_incorrect",
+        "label": "答案不正确",
+        "description": "QA 回复存在事实错误、遗漏或推断偏差。",
+        "source_types": {"qa_feedback"},
+        "field_names": {"qa_feedback"},
+        "confidence": 0.93,
+    },
+    {
+        "code": "source_issue",
+        "label": "来源问题",
+        "description": "QA 的来源引用、检索材料或证据组织存在问题。",
+        "source_types": {"qa_feedback"},
+        "field_names": {"qa_feedback"},
+        "confidence": 0.86,
+    },
+    {
+        "code": "strategic_judgement_mismatch",
+        "label": "战略判断偏差",
+        "description": "AI 对信号是否战略相关的判断与人工判断不一致。",
+        "source_types": {"signal_feedback"},
+        "field_names": {"strategic_classification"},
+        "confidence": 0.97,
+    },
+    {
+        "code": "signal_noise",
+        "label": "噪声信号",
+        "description": "信号本身噪声过高或不值得进入后续流程。",
+        "source_types": {"signal_feedback"},
+        "field_names": {"strategic_classification"},
+        "confidence": 0.8,
+    },
+    {
+        "code": "manual_override",
+        "label": "人工覆盖",
+        "description": "字段由人工介入修改后形成反馈样本。",
+        "source_types": {"task_breakdown", "task_override", "qa_feedback", "signal_feedback"},
+        "field_names": {"*", "task_object", "qa_feedback", "strategic_classification"},
+        "confidence": 0.75,
+    },
+    {
+        "code": "manual_review_needed",
+        "label": "需要人工复核",
+        "description": "当前样本仍需要人工确认，不足以自动通过。",
+        "source_types": {"task_breakdown", "task_override", "qa_feedback", "signal_feedback"},
+        "field_names": {"*"},
+        "confidence": 0.7,
+    },
+]
+
+
+def _candidate_payload(definition: dict[str, Any], *, source_type: str, field_name: str) -> dict[str, Any]:
+    return {
+        "code": definition["code"],
+        "label": definition["label"],
+        "description": definition["description"],
+        "source_type": source_type,
+        "field_name": field_name,
+        "confidence": float(definition.get("confidence") or 0.0),
+    }
+
+
+def list_reason_catalog(
+    *,
+    source_type: str | None = None,
+    field_name: str | None = None,
+) -> list[dict[str, Any]]:
+    items: list[dict[str, Any]] = []
+    for definition in _REASON_DEFINITIONS:
+        if source_type and source_type not in definition["source_types"]:
+            continue
+        if field_name and "*" not in definition["field_names"] and field_name not in definition["field_names"]:
+            continue
+        items.append(
+            {
+                "code": definition["code"],
+                "label": definition["label"],
+                "description": definition["description"],
+                "source_types": sorted(definition["source_types"]),
+                "field_names": sorted(definition["field_names"]),
+                "confidence": float(definition.get("confidence") or 0.0),
+            }
+        )
+    return items
+
+
+def _append_unique(candidate_map: OrderedDict[str, dict[str, Any]], candidate: dict[str, Any]) -> None:
+    code = str(candidate.get("code") or "").strip()
+    if not code or code in candidate_map:
+        return
+    candidate_map[code] = candidate
+
+
+def _normalize_text(value: Any) -> str:
+    return str(value or "").strip().lower()
+
+
+def _is_truthy_change(before_value: Any, after_value: Any) -> bool:
+    if before_value is None and after_value is None:
+        return False
+    return before_value != after_value
+
+
+def _estimated_hours_direction(before_value: Any, after_value: Any) -> str:
+    try:
+        before = float(before_value or 0)
+        after = float(after_value or 0)
+    except (TypeError, ValueError):
+        return "unknown"
+    if after > before:
+        return "increase"
+    if after < before:
+        return "decrease"
+    return "flat"
+
+
+def build_reason_candidates(
+    *,
+    source_type: str,
+    field_name: str,
+    before_value: Any = None,
+    after_value: Any = None,
+) -> list[dict[str, Any]]:
+    """基于字段差异构建稳定、可解释的候选原因。"""
+
+    source_key = _normalize_text(source_type)
+    field_key = _normalize_text(field_name)
+    candidates: OrderedDict[str, dict[str, Any]] = OrderedDict()
+
+    def add(code: str, label: str, confidence: float, description: str) -> None:
+        _append_unique(
+            candidates,
+            {
+                "code": code,
+                "label": label,
+                "description": description,
+                "source_type": source_key,
+                "field_name": field_key,
+                "confidence": float(confidence),
+            },
+        )
+
+    if source_key == "task_override" or source_key == "task_breakdown":
+        if field_key in {"assignee_id", "owner_id"}:
+            add(
+                "owner_mismatch",
+                "负责人不匹配",
+                0.96 if _is_truthy_change(before_value, after_value) else 0.88,
+                "负责人或责任归属与任务实际执行人不一致。",
+            )
+            add(
+                "capability_mismatch",
+                "能力匹配偏差",
+                0.91,
+                "人员能力、技能覆盖或职责分配与任务要求不一致。",
+            )
+        if field_key in {"required_skill_tag_ids", "required_skill_names"}:
+            add(
+                "skill_gap",
+                "技能要求不准",
+                0.94 if _is_truthy_change(before_value, after_value) else 0.82,
+                "任务所需技能标签需要复核。",
+            )
+            add(
+                "capability_mismatch",
+                "能力匹配偏差",
+                0.9,
+                "人员能力、技能覆盖或职责分配与任务要求不一致。",
+            )
+        if field_key in {"depends_on_task_keys", "dependency", "dependencies"}:
+            add(
+                "dependency_wrong",
+                "依赖关系错误",
+                0.92,
+                "任务依赖、前置关系或阻塞关系需要修正。",
+            )
+        if field_key == "priority":
+            add(
+                "priority_misalignment",
+                "优先级错配",
+                0.9,
+                "优先级建议与业务紧急度或协作节奏不匹配。",
+            )
+        if field_key == "estimated_hours":
+            direction = _estimated_hours_direction(before_value, after_value)
+            confidence = 0.95 if direction in {"increase", "decrease"} else 0.8
+            add(
+                "effort_estimation_bias",
+                "工时预估偏差",
+                confidence,
+                "AI 工时预估与人工判断存在明显差异。",
+            )
+            if direction == "increase":
+                add(
+                    "scope_overbroad",
+                    "范围过大",
+                    0.78,
+                    "任务范围可能需要进一步拆分。",
+                )
+        if field_key in {"risk_level", "risk_notes"}:
+            add(
+                "risk_underestimated",
+                "风险低估",
+                0.9,
+                "任务风险说明可能低估了实际执行复杂度。",
+            )
+        if field_key in {"title", "description"}:
+            add(
+                "scope_overbroad",
+                "范围过大",
+                0.84,
+                "任务标题或描述可能覆盖了过多内容。",
+            )
+        if field_key == "task_object":
+            add(
+                "incomplete_output",
+                "输出信息不完整",
+                0.76,
+                "模型输出仍可能缺失关键细节，需要人工补全。",
+            )
+            add("manual_override", "人工覆盖", 0.78, "AI 拆解结果经过人工修改后形成学习样本。")
+    elif source_key == "qa_feedback":
+        rating = _normalize_text((after_value or {}).get("rating") if isinstance(after_value, dict) else None)
+        error_types = []
+        if isinstance(after_value, dict):
+            error_types = [str(item).strip().lower() for item in after_value.get("error_types") or [] if str(item).strip()]
+        if rating == "negative":
+            if any(token in {"hallucination", "幻觉", "错误", "wrong"} for token in error_types):
+                add(
+                    "answer_incorrect",
+                    "答案不正确",
+                    0.95,
+                    "反馈显示回答内容存在事实错误或幻觉。",
+                )
+            add(
+                "source_issue",
+                "来源问题",
+                0.84,
+                "回答所依赖的来源或证据组织需要复核。",
+            )
+            add(
+                "incomplete_output",
+                "输出信息不完整",
+                0.78,
+                "回答缺少关键信息或上下文不够完整。",
+            )
+        else:
+            add("manual_review_needed", "需要人工复核", 0.72, "QA 反馈建议保留人工复核以确认样本质量。")
+    elif source_key == "signal_feedback":
+        predicted = None
+        actual = None
+        if isinstance(before_value, dict):
+            predicted = before_value.get("ai_predicted_strategic")
+        if isinstance(after_value, dict):
+            actual = after_value.get("was_strategic")
+        if predicted is not None and actual is not None and bool(predicted) != bool(actual):
+            add(
+                "strategic_judgement_mismatch",
+                "战略性判断偏差",
+                0.97,
+                "AI 对信号是否需要进入战略流程的判断与人工结果不一致。",
+            )
+        add(
+            "signal_noise",
+            "噪声信号",
+            0.82,
+            "当前信号可能缺少足够强的业务价值信号。",
+        )
+    else:
+        add("manual_review_needed", "需要人工复核", 0.7, "当前样本需要进一步人工确认。")
+
+    if not candidates:
+        add(
+            "manual_review_needed",
+            "需要人工复核",
+            0.7,
+            "当前样本需要进一步人工确认。",
+        )
+
+    return list(candidates.values())
+
+
+__all__ = ["build_reason_candidates", "list_reason_catalog"]

backend/app/services/feedback_center/service.py

@@ -0,0 +1,883 @@
+"""统一反馈中心业务服务。"""
+
+from __future__ import annotations
+
+from datetime import date, datetime, time, timedelta
+from typing import Any, Optional
+
+from sqlalchemy import and_, func, or_, select
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
+
+from ...models import FeedbackCenterRecord, FeedbackCenterRecordRevision
+from ...utils.timezone import now_cn_naive
+from .mappers import (
+    map_qa_feedback_to_record_payload,
+    map_signal_feedback_to_record_payload,
+    map_task_breakdown_to_record_payload,
+    map_task_override_to_record_payloads,
+)
+from .reason_catalog import build_reason_candidates, list_reason_catalog
+
+SOURCE_GROUP_VALUES = {"all", "task", "qa", "signal"}
+SOURCE_GROUP_TASK_TYPES = ("task_breakdown", "task_override")
+WINDOW_DAYS_VALUES = {7, 30}
+
+
+def _normalize_jsonable(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {str(key): _normalize_jsonable(item) for key, item in value.items()}
+    if isinstance(value, list):
+        return [_normalize_jsonable(item) for item in value]
+    if isinstance(value, tuple):
+        return [_normalize_jsonable(item) for item in value]
+    if hasattr(value, "model_dump"):
+        return _normalize_jsonable(value.model_dump(mode="json"))
+    return value
+
+
+def _normalize_source_group(value: Any) -> str:
+    normalized = str(value or "all").strip().lower()
+    if normalized not in SOURCE_GROUP_VALUES:
+        raise ValueError(f"unsupported source_group: {normalized}")
+    return normalized
+
+
+def _normalize_window_days(value: Any) -> int:
+    if value is None:
+        return 7
+    normalized = int(value)
+    if normalized not in WINDOW_DAYS_VALUES:
+        raise ValueError(f"unsupported window_days: {normalized}")
+    return normalized
+
+
+def _source_group_clause(source_group: str):
+    if source_group == "task":
+        return FeedbackCenterRecord.source_type.in_(SOURCE_GROUP_TASK_TYPES)
+    if source_group == "qa":
+        return FeedbackCenterRecord.source_type == "qa_feedback"
+    if source_group == "signal":
+        return FeedbackCenterRecord.source_type == "signal_feedback"
+    return None
+
+
+def _apply_source_group(stmt, source_group: str):
+    clause = _source_group_clause(source_group)
+    if clause is not None:
+        stmt = stmt.where(clause)
+    return stmt
+
+
+def _record_snapshot(record: FeedbackCenterRecord) -> dict[str, Any]:
+    # 避免异步会话下属性过期触发隐式 IO（MissingGreenlet）。
+    created_at = record.__dict__.get("created_at")
+    updated_at = record.__dict__.get("updated_at")
+    return {
+        "id": int(record.id),
+        "tenant_id": int(record.tenant_id),
+        "project_id": int(record.project_id) if record.project_id is not None else None,
+        "source_type": record.source_type,
+        "source_id": int(record.source_id),
+        "object_type": record.object_type,
+        "object_id": int(record.object_id),
+        "field_name": record.field_name,
+        "source_title": record.source_title,
+        "object_title": record.object_title,
+        "before_value": _normalize_jsonable(record.before_value),
+        "after_value": _normalize_jsonable(record.after_value),
+        "ai_snapshot": _normalize_jsonable(record.ai_snapshot),
+        "ai_confidence": float(record.ai_confidence or 0.0) if record.ai_confidence is not None else None,
+        "reason_candidates": _normalize_jsonable(record.reason_candidates or []),
+        "reason_code": record.reason_code,
+        "reason_text": record.reason_text,
+        "review_status": record.review_status,
+        "reviewed_by": int(record.reviewed_by) if record.reviewed_by is not None else None,
+        "reviewed_at": record.reviewed_at.isoformat() if record.reviewed_at else None,
+        "auto_assigned": bool(record.auto_assigned),
+        "created_at": created_at.isoformat() if created_at else None,
+        "updated_at": updated_at.isoformat() if updated_at else None,
+    }
+
+
+def _revision_snapshot(revision: FeedbackCenterRecordRevision) -> dict[str, Any]:
+    return {
+        "id": int(revision.id),
+        "record_id": int(revision.record_id),
+        "before_snapshot": _normalize_jsonable(revision.before_snapshot),
+        "after_snapshot": _normalize_jsonable(revision.after_snapshot),
+        "reviewed_by": int(revision.reviewed_by) if revision.reviewed_by is not None else None,
+        "reviewed_at": revision.reviewed_at.isoformat() if revision.reviewed_at else None,
+        "created_at": revision.created_at.isoformat() if revision.created_at else None,
+        "updated_at": revision.updated_at.isoformat() if revision.updated_at else None,
+    }
+
+
+def _trend_accumulator(date_value: date | None = None) -> dict[str, Any]:
+    return {
+        "date": date_value,
+        "total_records": 0,
+        "accepted_count": 0,
+        "rejected_count": 0,
+        "reviewed_count": 0,
+        "pending_count": 0,
+        "task_breakdown_count": 0,
+        "task_override_count": 0,
+        "qa_feedback_count": 0,
+        "signal_feedback_count": 0,
+        "auto_assigned_count": 0,
+        "override_count": 0,
+        "rework_count": 0,
+        "revision_count": 0,
+        "_breakdown_task_ids": set(),
+        "_override_task_ids": set(),
+        "_auto_assigned_task_ids": set(),
+        "_latencies": [],
+        "_reworked_record_ids": set(),
+    }
+
+
+def _finalize_trend_accumulator(accumulator: dict[str, Any]) -> dict[str, Any]:
+    breakdown_count = len(accumulator.pop("_breakdown_task_ids", set()))
+    override_count = len(accumulator.pop("_override_task_ids", set()))
+    auto_assigned_count = len(accumulator.pop("_auto_assigned_task_ids", set()))
+    rework_count = len(accumulator.pop("_reworked_record_ids", set()))
+    latencies = accumulator.pop("_latencies", [])
+    accepted_count = int(accumulator.get("accepted_count", 0))
+    rejected_count = int(accumulator.get("rejected_count", 0))
+    reviewed_count = int(accumulator.get("reviewed_count", 0))
+    pending_count = int(accumulator.get("pending_count", 0))
+    total_records = int(accumulator.get("total_records", 0))
+    avg_review_latency_minutes = sum(latencies) / len(latencies) if latencies else 0.0
+    reviewed_decisions = accepted_count + rejected_count
+
+    accumulator["task_breakdown_count"] = breakdown_count
+    accumulator["task_override_count"] = override_count
+    accumulator["qa_feedback_count"] = int(accumulator.get("qa_feedback_count", 0))
+    accumulator["signal_feedback_count"] = int(accumulator.get("signal_feedback_count", 0))
+    accumulator["auto_assigned_count"] = auto_assigned_count
+    accumulator["override_count"] = override_count
+    accumulator["rework_count"] = rework_count
+    accumulator["revision_count"] = int(accumulator.get("revision_count", 0))
+    accumulator["accepted_rate_reviewed"] = accepted_count / reviewed_decisions if reviewed_decisions else 0.0
+    accumulator["manual_override_rate"] = override_count / breakdown_count if breakdown_count else 0.0
+    accumulator["rework_rate"] = rework_count / total_records if total_records else 0.0
+    accumulator["auto_assigned_rate"] = auto_assigned_count / breakdown_count if breakdown_count else 0.0
+    accumulator["avg_review_latency_minutes"] = avg_review_latency_minutes
+    accumulator["reviewed_count"] = reviewed_count
+    accumulator["pending_count"] = pending_count
+    accumulator["accepted_count"] = accepted_count
+    accumulator["rejected_count"] = rejected_count
+    accumulator["total_records"] = total_records
+    if accumulator.get("date") is not None:
+        accumulator["date"] = accumulator["date"].isoformat()
+    return accumulator
+
+
+class FeedbackCenterService:
+    """统一反馈中心服务。"""
+
+    def __init__(self, db: AsyncSession):
+        self.db = db
+
+    @staticmethod
+    def _base_stmt(*, tenant_id: int, project_id: Optional[int] = None):
+        stmt = select(FeedbackCenterRecord).where(FeedbackCenterRecord.tenant_id == int(tenant_id))
+        if project_id is not None:
+            stmt = stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        return stmt
+
+    @staticmethod
+    def _identity_filters(payload: dict[str, Any]):
+        return (
+            FeedbackCenterRecord.tenant_id == int(payload["tenant_id"]),
+            FeedbackCenterRecord.source_type == str(payload["source_type"]),
+            FeedbackCenterRecord.source_id == int(payload["source_id"]),
+            FeedbackCenterRecord.object_type == str(payload["object_type"]),
+            FeedbackCenterRecord.object_id == int(payload["object_id"]),
+            FeedbackCenterRecord.field_name == str(payload["field_name"]),
+        )
+
+    async def _find_existing_record(self, payload: dict[str, Any]) -> FeedbackCenterRecord | None:
+        stmt = select(FeedbackCenterRecord).where(*self._identity_filters(payload))
+        return (await self.db.execute(stmt)).scalar_one_or_none()
+
+    def _merge_existing_record(self, record: FeedbackCenterRecord, payload: dict[str, Any]) -> bool:
+        mutable_fields = (
+            "project_id",
+            "source_title",
+            "object_title",
+            "before_value",
+            "after_value",
+            "ai_snapshot",
+            "ai_confidence",
+            "reason_candidates",
+            "reason_code",
+            "reason_text",
+            "review_status",
+            "reviewed_by",
+            "reviewed_at",
+            "auto_assigned",
+        )
+        changed = False
+        for field in mutable_fields:
+            next_value = payload.get(field)
+            current_value = getattr(record, field)
+            if _normalize_jsonable(current_value) == _normalize_jsonable(next_value):
+                continue
+            setattr(record, field, next_value)
+            changed = True
+        if changed:
+            record.updated_at = now_cn_naive()
+        return changed
+
+    @staticmethod
+    def _apply_filters(
+        stmt,
+        *,
+        source_type: Optional[str] = None,
+        review_status: Optional[str] = None,
+        field_name: Optional[str] = None,
+        reason_code: Optional[str] = None,
+        search: Optional[str] = None,
+    ):
+        if source_type:
+            stmt = stmt.where(FeedbackCenterRecord.source_type == str(source_type))
+        if review_status:
+            stmt = stmt.where(FeedbackCenterRecord.review_status == str(review_status))
+        if field_name:
+            stmt = stmt.where(FeedbackCenterRecord.field_name == str(field_name))
+        if reason_code:
+            stmt = stmt.where(FeedbackCenterRecord.reason_code == str(reason_code))
+        if search:
+            like = f"%{str(search).strip()}%"
+            stmt = stmt.where(
+                or_(
+                    func.coalesce(FeedbackCenterRecord.object_title, "").ilike(like),
+                    func.coalesce(FeedbackCenterRecord.source_title, "").ilike(like),
+                )
+            )
+        return stmt
+
+    @staticmethod
+    def _trend_add_record(accumulator: dict[str, Any], record: FeedbackCenterRecord, revision_count: int) -> None:
+        accumulator["total_records"] += 1
+        status = str(record.review_status or "pending")
+        if status == "accepted":
+            accumulator["accepted_count"] += 1
+        elif status == "rejected":
+            accumulator["rejected_count"] += 1
+        elif status == "reviewed":
+            accumulator["reviewed_count"] += 1
+        else:
+            accumulator["pending_count"] += 1
+
+        source_type = str(record.source_type or "")
+        object_type = str(record.object_type or "")
+        if source_type == "task_breakdown" and object_type == "task":
+            accumulator["_breakdown_task_ids"].add(int(record.object_id))
+            if bool(record.auto_assigned):
+                accumulator["_auto_assigned_task_ids"].add(int(record.object_id))
+        if source_type == "task_override" and object_type == "task":
+            accumulator["_override_task_ids"].add(int(record.object_id))
+        if source_type == "qa_feedback":
+            accumulator["qa_feedback_count"] += 1
+        if source_type == "signal_feedback":
+            accumulator["signal_feedback_count"] += 1
+
+        if revision_count > 0:
+            accumulator["revision_count"] += int(revision_count)
+        if revision_count > 1:
+            accumulator["_reworked_record_ids"].add(int(record.id))
+
+        if record.created_at and record.reviewed_at:
+            delta = (record.reviewed_at - record.created_at).total_seconds() / 60
+            if delta >= 0:
+                accumulator["_latencies"].append(delta)
+
+    async def _build_trend(
+        self,
+        *,
+        tenant_id: int,
+        project_id: Optional[int] = None,
+        window_days: int = 7,
+        source_group: str = "all",
+    ) -> dict[str, Any]:
+        window_days = _normalize_window_days(window_days)
+        source_group = _normalize_source_group(source_group)
+        today = now_cn_naive().date()
+        current_start = today - timedelta(days=window_days - 1)
+        previous_start = current_start - timedelta(days=window_days)
+        end_exclusive = today + timedelta(days=1)
+        start_dt = datetime.combine(previous_start, time.min)
+        end_dt = datetime.combine(end_exclusive, time.min)
+
+        stmt = (
+            select(FeedbackCenterRecord)
+            .options(selectinload(FeedbackCenterRecord.revisions))
+            .where(
+                and_(
+                    FeedbackCenterRecord.tenant_id == int(tenant_id),
+                    FeedbackCenterRecord.created_at.is_not(None),
+                    FeedbackCenterRecord.created_at >= start_dt,
+                    FeedbackCenterRecord.created_at < end_dt,
+                )
+            )
+        )
+        if project_id is not None:
+            stmt = stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        stmt = _apply_source_group(stmt, source_group)
+        records = (await self.db.execute(stmt)).scalars().all()
+
+        current_daily: dict[date, dict[str, Any]] = {
+            current_start + timedelta(days=index): _trend_accumulator(current_start + timedelta(days=index))
+            for index in range(window_days)
+        }
+        current_window = _trend_accumulator()
+        previous_window = _trend_accumulator()
+
+        for record in records:
+            created_at = getattr(record, "created_at", None)
+            if created_at is None:
+                continue
+            created_date = created_at.date()
+            revision_count = len(record.revisions or [])
+            if current_start <= created_date <= today:
+                bucket = current_daily.get(created_date)
+                if bucket is not None:
+                    self._trend_add_record(bucket, record, revision_count)
+                self._trend_add_record(current_window, record, revision_count)
+                continue
+            if previous_start <= created_date < current_start:
+                self._trend_add_record(previous_window, record, revision_count)
+
+        daily = [_finalize_trend_accumulator(current_daily[current_start + timedelta(days=index)]) for index in range(window_days)]
+        current = _finalize_trend_accumulator(current_window)
+        previous = _finalize_trend_accumulator(previous_window)
+
+        deltas = {
+            "total_records_delta": int(current["total_records"] - previous["total_records"]),
+            "accepted_rate_reviewed_delta": float(current["accepted_rate_reviewed"] - previous["accepted_rate_reviewed"]),
+            "manual_override_rate_delta": float(current["manual_override_rate"] - previous["manual_override_rate"]),
+            "rework_rate_delta": float(current["rework_rate"] - previous["rework_rate"]),
+            "auto_assigned_rate_delta": float(current["auto_assigned_rate"] - previous["auto_assigned_rate"]),
+            "avg_review_latency_minutes_delta": float(
+                current["avg_review_latency_minutes"] - previous["avg_review_latency_minutes"]
+            ),
+        }
+
+        return {
+            "window_days": window_days,
+            "current_window_start": current_start,
+            "previous_window_start": previous_start,
+            "daily": daily,
+            "current": current,
+            "previous": previous,
+            "deltas": deltas,
+        }
+
+    async def summary(
+        self,
+        *,
+        tenant_id: int,
+        project_id: Optional[int] = None,
+        window_days: int = 7,
+        source_group: str = "all",
+    ) -> dict[str, Any]:
+        window_days = _normalize_window_days(window_days)
+        source_group = _normalize_source_group(source_group)
+        stmt = self._base_stmt(tenant_id=tenant_id, project_id=project_id)
+        stmt = _apply_source_group(stmt, source_group)
+        total_stmt = select(func.count()).select_from(stmt.subquery())
+        total = int((await self.db.execute(total_stmt)).scalar_one() or 0)
+
+        status_stmt = select(FeedbackCenterRecord.review_status, func.count()).group_by(
+            FeedbackCenterRecord.review_status
+        )
+        status_stmt = status_stmt.where(FeedbackCenterRecord.tenant_id == int(tenant_id))
+        if project_id is not None:
+            status_stmt = status_stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        status_stmt = _apply_source_group(status_stmt, source_group)
+        status_rows = await self.db.execute(status_stmt)
+        by_status = {str(status or "pending"): int(count or 0) for status, count in status_rows.all()}
+
+        source_stmt = select(FeedbackCenterRecord.source_type, func.count()).group_by(FeedbackCenterRecord.source_type)
+        source_stmt = source_stmt.where(FeedbackCenterRecord.tenant_id == int(tenant_id))
+        if project_id is not None:
+            source_stmt = source_stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        source_stmt = _apply_source_group(source_stmt, source_group)
+        source_rows = await self.db.execute(source_stmt)
+        by_source_type = {str(source_type or "unknown"): int(count or 0) for source_type, count in source_rows.all()}
+
+        confidence_stmt = select(func.avg(FeedbackCenterRecord.ai_confidence))
+        confidence_stmt = confidence_stmt.where(FeedbackCenterRecord.tenant_id == int(tenant_id))
+        if project_id is not None:
+            confidence_stmt = confidence_stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        confidence_stmt = _apply_source_group(confidence_stmt, source_group)
+        avg_confidence = float((await self.db.execute(confidence_stmt)).scalar_one() or 0.0)
+
+        trend = await self._build_trend(
+            tenant_id=tenant_id,
+            project_id=project_id,
+            window_days=window_days,
+            source_group=source_group,
+        )
+
+        accepted_count = int(by_status.get("accepted", 0))
+        reviewed_count = int(by_status.get("reviewed", 0))
+        pending_count = int(by_status.get("pending", 0))
+        rejected_count = int(by_status.get("rejected", 0))
+        backlog_count = pending_count + reviewed_count
+        accepted_rate = (accepted_count / total) if total else 0.0
+
+        reason_stmt = select(FeedbackCenterRecord.reason_code, func.count())
+        reason_stmt = reason_stmt.where(FeedbackCenterRecord.tenant_id == int(tenant_id))
+        if project_id is not None:
+            reason_stmt = reason_stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        reason_stmt = _apply_source_group(reason_stmt, source_group)
+        reason_stmt = reason_stmt.where(FeedbackCenterRecord.reason_code.is_not(None))
+        reason_stmt = reason_stmt.group_by(FeedbackCenterRecord.reason_code).order_by(func.count().desc())
+        reason_rows = (await self.db.execute(reason_stmt)).all()
+        top_reasons: list[dict[str, Any]] = []
+        for reason_code, count in reason_rows[:5]:
+            if not reason_code:
+                continue
+            catalog = list_reason_catalog()
+            label = next((item["label"] for item in catalog if item["code"] == reason_code), reason_code)
+            top_reasons.append(
+                {
+                    "code": str(reason_code),
+                    "label": label,
+                    "count": int(count or 0),
+                }
+            )
+
+        async def _distinct_task_count(*, source_type: str, auto_assigned: Optional[bool] = None) -> int:
+            stmt = select(func.count(func.distinct(FeedbackCenterRecord.object_id))).where(
+                and_(
+                    FeedbackCenterRecord.tenant_id == int(tenant_id),
+                    FeedbackCenterRecord.source_type == source_type,
+                    FeedbackCenterRecord.object_type == "task",
+                )
+            )
+            if project_id is not None:
+                stmt = stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+            stmt = _apply_source_group(stmt, source_group)
+            if auto_assigned is not None:
+                stmt = stmt.where(FeedbackCenterRecord.auto_assigned.is_(auto_assigned))
+            return int((await self.db.execute(stmt)).scalar_one() or 0)
+
+        task_breakdown_count = int(by_source_type.get("task_breakdown", 0))
+        task_override_count = int(by_source_type.get("task_override", 0))
+        qa_feedback_count = int(by_source_type.get("qa_feedback", 0))
+        signal_feedback_count = int(by_source_type.get("signal_feedback", 0))
+
+        breakdown_task_count = await _distinct_task_count(source_type="task_breakdown")
+        override_task_count = await _distinct_task_count(source_type="task_override")
+        auto_assigned_task_count = await _distinct_task_count(source_type="task_breakdown", auto_assigned=True)
+
+        accepted_count_reviewed = accepted_count
+        reviewed_decision_total = accepted_count + rejected_count
+        accepted_rate_reviewed = accepted_count_reviewed / reviewed_decision_total if reviewed_decision_total else 0.0
+
+        manual_override_rate = override_task_count / breakdown_task_count if breakdown_task_count else 0.0
+        auto_assigned_rate = auto_assigned_task_count / breakdown_task_count if breakdown_task_count else 0.0
+
+        revision_scope = (
+            select(FeedbackCenterRecordRevision)
+            .join(
+                FeedbackCenterRecord,
+                FeedbackCenterRecord.id == FeedbackCenterRecordRevision.record_id,
+            )
+            .where(
+                and_(
+                    FeedbackCenterRecordRevision.tenant_id == int(tenant_id),
+                    FeedbackCenterRecord.tenant_id == int(tenant_id),
+                )
+            )
+        )
+        if project_id is not None:
+            revision_scope = revision_scope.where(FeedbackCenterRecord.project_id == int(project_id))
+        revision_scope = _apply_source_group(revision_scope, source_group)
+        revision_count_stmt = select(func.count()).select_from(revision_scope.subquery())
+        revision_count = int((await self.db.execute(revision_count_stmt)).scalar_one() or 0)
+
+        rework_stmt = (
+            select(FeedbackCenterRecordRevision.record_id, func.count().label("cnt"))
+            .join(
+                FeedbackCenterRecord,
+                FeedbackCenterRecord.id == FeedbackCenterRecordRevision.record_id,
+            )
+            .where(
+                and_(
+                    FeedbackCenterRecordRevision.tenant_id == int(tenant_id),
+                    FeedbackCenterRecord.tenant_id == int(tenant_id),
+                )
+            )
+            .group_by(FeedbackCenterRecordRevision.record_id)
+            .having(func.count() > 1)
+        )
+        if project_id is not None:
+            rework_stmt = rework_stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        rework_stmt = _apply_source_group(rework_stmt, source_group)
+        rework_count_stmt = select(func.count()).select_from(rework_stmt.subquery())
+        rework_record_count = int((await self.db.execute(rework_count_stmt)).scalar_one() or 0)
+        rework_rate = rework_record_count / total if total else 0.0
+
+        latency_stmt = select(FeedbackCenterRecord.created_at, FeedbackCenterRecord.reviewed_at).where(
+            and_(
+                FeedbackCenterRecord.tenant_id == int(tenant_id),
+                FeedbackCenterRecord.reviewed_at.is_not(None),
+            )
+        )
+        if project_id is not None:
+            latency_stmt = latency_stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        latency_stmt = _apply_source_group(latency_stmt, source_group)
+        latency_rows = (await self.db.execute(latency_stmt)).all()
+        latencies: list[float] = []
+        for created_at, reviewed_at in latency_rows:
+            if not created_at or not reviewed_at:
+                continue
+            delta = (reviewed_at - created_at).total_seconds() / 60
+            if delta >= 0:
+                latencies.append(delta)
+        avg_review_latency_minutes = sum(latencies) / len(latencies) if latencies else 0.0
+
+        return {
+            "tenant_id": int(tenant_id),
+            "project_id": int(project_id) if project_id is not None else None,
+            "source_group": source_group,
+            "total": total,
+            "backlog_count": backlog_count,
+            "pending_count": pending_count,
+            "reviewed_count": reviewed_count,
+            "accepted_count": accepted_count,
+            "rejected_count": rejected_count,
+            "accepted_rate": accepted_rate,
+            "avg_confidence": avg_confidence,
+            "by_status": by_status,
+            "by_source_type": by_source_type,
+            "top_reasons": top_reasons,
+            "metrics": {
+                "accepted_rate_reviewed": accepted_rate_reviewed,
+                "manual_override_rate": manual_override_rate,
+                "rework_rate": rework_rate,
+                "auto_assigned_rate": auto_assigned_rate,
+                "avg_review_latency_minutes": avg_review_latency_minutes,
+                "total_task_breakdown_count": task_breakdown_count,
+                "total_task_override_count": task_override_count,
+                "total_qa_feedback_count": qa_feedback_count,
+                "total_signal_feedback_count": signal_feedback_count,
+                "total_revision_count": revision_count,
+            },
+            "trend": trend,
+        }
+
+    async def list_records(
+        self,
+        *,
+        tenant_id: int,
+        project_id: Optional[int] = None,
+        source_type: Optional[str] = None,
+        review_status: Optional[str] = None,
+        field_name: Optional[str] = None,
+        reason_code: Optional[str] = None,
+        search: Optional[str] = None,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> dict[str, Any]:
+        limit = max(1, min(int(limit), 100))
+        offset = max(0, int(offset))
+        stmt = self._base_stmt(tenant_id=tenant_id, project_id=project_id)
+        stmt = self._apply_filters(
+            stmt,
+            source_type=source_type,
+            review_status=review_status,
+            field_name=field_name,
+            reason_code=reason_code,
+            search=search,
+        )
+        total_stmt = select(func.count()).select_from(stmt.subquery())
+        total = int((await self.db.execute(total_stmt)).scalar_one() or 0)
+
+        stmt = stmt.options(selectinload(FeedbackCenterRecord.revisions))
+        stmt = stmt.order_by(FeedbackCenterRecord.created_at.desc().nullslast(), FeedbackCenterRecord.id.desc())
+        rows = (await self.db.execute(stmt.offset(offset).limit(limit))).scalars().all()
+        items = [self._serialize_record(row, include_revisions=False) for row in rows]
+        return {
+            "items": items,
+            "total": total,
+            "limit": limit,
+            "offset": offset,
+            "has_more": offset + len(items) < total,
+        }
+
+    async def get_detail(
+        self,
+        *,
+        tenant_id: int,
+        record_id: int,
+        project_id: Optional[int] = None,
+    ) -> dict[str, Any] | None:
+        stmt = (
+            select(FeedbackCenterRecord)
+            .options(selectinload(FeedbackCenterRecord.revisions))
+            .where(
+                and_(
+                    FeedbackCenterRecord.tenant_id == int(tenant_id),
+                    FeedbackCenterRecord.id == int(record_id),
+                )
+            )
+        )
+        if project_id is not None:
+            stmt = stmt.where(FeedbackCenterRecord.project_id == int(project_id))
+        record = (await self.db.execute(stmt)).scalar_one_or_none()
+        if not record:
+            return None
+        return {
+            "record": self._serialize_record(record, include_revisions=True),
+            "revisions": [self._serialize_revision(revision) for revision in record.revisions or []],
+        }
+
+    async def review(
+        self,
+        *,
+        tenant_id: int,
+        record_id: int,
+        reviewer_id: int,
+        review_status: str,
+        reason_code: str | None = None,
+        reason_text: str | None = None,
+        reason_candidates: list[dict[str, Any]] | None = None,
+    ) -> dict[str, Any]:
+        stmt = (
+            select(FeedbackCenterRecord)
+            .options(selectinload(FeedbackCenterRecord.revisions))
+            .where(
+                and_(
+                    FeedbackCenterRecord.tenant_id == int(tenant_id),
+                    FeedbackCenterRecord.id == int(record_id),
+                )
+            )
+        )
+        record = (await self.db.execute(stmt)).scalar_one_or_none()
+        if not record:
+            raise ValueError("feedback record not found")
+
+        previous_snapshot = self._serialize_record(record, include_revisions=False)
+        record.review_status = str(review_status)
+        record.reason_code = str(reason_code).strip()[:64] if reason_code else record.reason_code
+        record.reason_text = str(reason_text).strip()[:4000] if reason_text else record.reason_text
+        if reason_candidates is not None:
+            record.reason_candidates = reason_candidates
+        elif not record.reason_candidates:
+            record.reason_candidates = build_reason_candidates(
+                source_type=record.source_type,
+                field_name=record.field_name,
+                before_value=record.before_value,
+                after_value=record.after_value,
+            )
+        record.reviewed_by = int(reviewer_id)
+        record.reviewed_at = now_cn_naive()
+        record.updated_at = record.reviewed_at
+        await self.db.flush()
+
+        current_snapshot = self._serialize_record(record, include_revisions=False)
+        self.db.add(
+            FeedbackCenterRecordRevision(
+                tenant_id=int(tenant_id),
+                record_id=int(record.id),
+                before_snapshot=previous_snapshot,
+                after_snapshot=current_snapshot,
+                reviewed_by=int(reviewer_id),
+                reviewed_at=record.reviewed_at,
+            )
+        )
+        await self.db.commit()
+        await self.db.refresh(record)
+        refreshed = await self.get_detail(tenant_id=tenant_id, record_id=record.id)
+        if refreshed is None:
+            raise ValueError("feedback record not found after review")
+        return refreshed["record"]
+
+    async def ingest_payloads(
+        self,
+        *,
+        payloads: list[dict[str, Any]],
+        commit: bool = False,
+    ) -> list[FeedbackCenterRecord]:
+        records: list[FeedbackCenterRecord] = []
+        for payload in payloads:
+            normalized = self._normalize_payload(payload)
+            existing = await self._find_existing_record(normalized)
+            if existing is None:
+                record = FeedbackCenterRecord(**normalized)
+                self.db.add(record)
+                records.append(record)
+                continue
+
+            previous_snapshot = self._serialize_record(existing, include_revisions=False)
+            changed = self._merge_existing_record(existing, normalized)
+            if changed:
+                current_snapshot = self._serialize_record(existing, include_revisions=False)
+                self.db.add(
+                    FeedbackCenterRecordRevision(
+                        tenant_id=int(existing.tenant_id),
+                        record_id=int(existing.id),
+                        before_snapshot=previous_snapshot,
+                        after_snapshot=current_snapshot,
+                        reviewed_by=normalized.get("reviewed_by"),
+                        reviewed_at=normalized.get("reviewed_at") or now_cn_naive(),
+                    )
+                )
+            records.append(existing)
+        if commit:
+            await self.db.commit()
+            for record in records:
+                await self.db.refresh(record)
+        else:
+            await self.db.flush()
+        return records
+
+    async def ingest_task_breakdown(
+        self,
+        *,
+        task: Any,
+        project_title: str | None = None,
+        source_context: dict[str, Any] | None = None,
+        commit: bool = False,
+    ) -> FeedbackCenterRecord | None:
+        payload = map_task_breakdown_to_record_payload(
+            task=task,
+            project_title=project_title,
+            source_context=source_context,
+        )
+        records = await self.ingest_payloads(payloads=[payload], commit=commit)
+        return records[0] if records else None
+
+    async def ingest_task_overrides(
+        self,
+        *,
+        task: Any,
+        commit: bool = False,
+    ) -> list[FeedbackCenterRecord]:
+        payloads = map_task_override_to_record_payloads(task=task)
+        if not payloads:
+            return []
+        return await self.ingest_payloads(payloads=payloads, commit=commit)
+
+    async def ingest_qa_feedback(
+        self,
+        *,
+        qa_feedback: Any,
+        qa_record: Any,
+        commit: bool = False,
+    ) -> FeedbackCenterRecord | None:
+        payload = map_qa_feedback_to_record_payload(qa_feedback=qa_feedback, qa_record=qa_record)
+        records = await self.ingest_payloads(payloads=[payload], commit=commit)
+        return records[0] if records else None
+
+    async def ingest_signal_feedback(
+        self,
+        *,
+        signal_feedback: Any,
+        signal: Any,
+        commit: bool = False,
+    ) -> FeedbackCenterRecord | None:
+        payload = map_signal_feedback_to_record_payload(signal_feedback=signal_feedback, signal=signal)
+        records = await self.ingest_payloads(payloads=[payload], commit=commit)
+        return records[0] if records else None
+
+    async def get_signal_feedback_stats(self, *, tenant_id: int) -> dict[str, Any]:
+        stmt = select(FeedbackCenterRecord).where(
+            and_(
+                FeedbackCenterRecord.tenant_id == int(tenant_id),
+                FeedbackCenterRecord.source_type == "signal_feedback",
+            )
+        )
+        rows = (await self.db.execute(stmt)).scalars().all()
+        total = len(rows)
+        if total == 0:
+            return {
+                "total_feedbacks": 0,
+                "accuracy": 0.0,
+                "true_positives": 0,
+                "false_positives": 0,
+                "true_negatives": 0,
+                "false_negatives": 0,
+            }
+
+        def _predicted_actual(row: FeedbackCenterRecord) -> tuple[bool, bool]:
+            before = row.before_value if isinstance(row.before_value, dict) else {}
+            after = row.after_value if isinstance(row.after_value, dict) else {}
+            predicted = bool(before.get("ai_predicted_strategic") if isinstance(before, dict) else False)
+            actual = bool(after.get("was_strategic") if isinstance(after, dict) else False)
+            return predicted, actual
+
+        correct = 0
+        tp = fp = tn = fn = 0
+        for row in rows:
+            predicted, actual = _predicted_actual(row)
+            if predicted == actual:
+                correct += 1
+            if predicted and actual:
+                tp += 1
+            elif predicted and not actual:
+                fp += 1
+            elif not predicted and not actual:
+                tn += 1
+            elif not predicted and actual:
+                fn += 1
+        return {
+            "total_feedbacks": total,
+            "accuracy": correct / total if total else 0.0,
+            "true_positives": tp,
+            "false_positives": fp,
+            "true_negatives": tn,
+            "false_negatives": fn,
+        }
+
+    def _serialize_record(self, record: FeedbackCenterRecord, *, include_revisions: bool) -> dict[str, Any]:
+        payload = _record_snapshot(record)
+        if include_revisions:
+            payload["revisions"] = [self._serialize_revision(revision) for revision in getattr(record, "revisions", [])]
+        else:
+            payload["revisions"] = []
+        return payload
+
+    def _serialize_revision(self, revision: FeedbackCenterRecordRevision) -> dict[str, Any]:
+        return _revision_snapshot(revision)
+
+    def _normalize_payload(self, payload: dict[str, Any]) -> dict[str, Any]:
+        normalized = dict(payload)
+        normalized["tenant_id"] = int(normalized.get("tenant_id") or 1)
+        normalized["project_id"] = int(normalized["project_id"]) if normalized.get("project_id") not in {None, ""} else None
+        normalized["source_id"] = int(normalized.get("source_id") or 0)
+        normalized["object_id"] = int(normalized.get("object_id") or 0)
+        normalized["source_type"] = str(normalized.get("source_type") or "").strip()[:32]
+        normalized["object_type"] = str(normalized.get("object_type") or "").strip()[:32]
+        normalized["field_name"] = str(normalized.get("field_name") or "").strip()[:128]
+        normalized["source_title"] = str(normalized.get("source_title") or "").strip()[:500] or None
+        normalized["object_title"] = str(normalized.get("object_title") or "").strip()[:500] or None
+        normalized["before_value"] = _normalize_jsonable(normalized.get("before_value"))
+        normalized["after_value"] = _normalize_jsonable(normalized.get("after_value"))
+        normalized["ai_snapshot"] = _normalize_jsonable(normalized.get("ai_snapshot"))
+        ai_confidence = normalized.get("ai_confidence")
+        normalized["ai_confidence"] = float(ai_confidence) if ai_confidence not in {None, ""} else None
+        reason_candidates = normalized.get("reason_candidates")
+        normalized["reason_candidates"] = _normalize_jsonable(reason_candidates or [])
+        normalized["reason_code"] = str(normalized.get("reason_code") or "").strip()[:64] or None
+        normalized["reason_text"] = str(normalized.get("reason_text") or "").strip()[:4000] or None
+        normalized["review_status"] = str(normalized.get("review_status") or "pending").strip()[:16]
+        reviewed_by = normalized.get("reviewed_by")
+        normalized["reviewed_by"] = int(reviewed_by) if reviewed_by not in {None, ""} else None
+        normalized["reviewed_at"] = normalized.get("reviewed_at") or None
+        normalized["auto_assigned"] = bool(normalized.get("auto_assigned", False))
+        normalized.pop("provider", None)
+        normalized.pop("model_name", None)
+        normalized.pop("prompt_version", None)
+        normalized.pop("schema_version", None)
+        return normalized
+
+
+__all__ = ["FeedbackCenterService"]

backend/requirements.txt

@@ -2,7 +2,9 @@ fastapi>=0.115.0
 uvicorn[standard]>=0.30.0
 sqlalchemy>=2.0.30
 alembic>=1.13.2
+greenlet>=3.0.0
 asyncpg>=0.29.0
+aiosqlite>=0.20.0
 psycopg[binary]>=3.2.0
 pgvector>=0.3.5
 python-dotenv>=1.0.1

backend/tests/test_ai_prompt_registry_contract.py

@@ -1,9 +1,16 @@
 from app.config.ai_prompt_registry import (
     PROMPT_REGISTRY_MAP,
+    PROMPT_FAMILY_ALLOWED,
     get_prompt_registry,
     get_prompt_registry_entry,
     iter_prompt_registry_keys,
 )
+from app.config.ai_prompts import (
+    PROMPT_DECOMPOSE_TASKS,
+    PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION,
+    PROMPT_FEISHU_REQUIREMENT_ANALYSIS,
+    PROMPT_TASK_BREAKDOWN,
+)
 from app.config.ai_prompts_catalog import __all__ as PROMPT_CATALOG_EXPORTS
 
 
@@ -18,6 +25,10 @@ def test_prompt_registry_covers_all_catalog_exports() -> None:
         entry = get_prompt_registry_entry(key)
         assert entry is not None
         assert entry["prompt_key"] == key
+        assert entry["prompt_family"] in PROMPT_FAMILY_ALLOWED
+        assert entry["responsibility_scope"]
+        assert entry["priority"] in {"high", "medium", "low"}
+        assert entry["audience"]
 
 
 def test_prompt_registry_returns_detached_copies() -> None:
@@ -30,18 +41,22 @@ def test_prompt_registry_returns_detached_copies() -> None:
 
 
 def test_high_frequency_registry_entries_have_complete_metadata() -> None:
-    required_keys = (
-        "PROMPT_SIGNAL_PLANNER",
-        "PROMPT_GENERATE_CHARTER",
-        "PROMPT_DECOMPOSE_TASKS",
-        "PROMPT_PROJECT_STATUS_QA",
-        "PROMPT_MEMBER_WORKLOAD_QA",
-        "PROMPT_AI_PMO_QA",
-        "PROMPT_EXTERNAL_TREND_QA",
-        "PROMPT_INDUSTRY_TREND_QA",
-    )
+    expected_families = {
+        "PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION": "classification",
+        "PROMPT_FEISHU_LEVEL_CLASSIFICATION": "classification",
+        "PROMPT_FEISHU_SIGNAL_EXTRACTION": "extraction",
+        "PROMPT_FEISHU_TASK_ORCHESTRATION": "planning",
+        "PROMPT_SIGNAL_PLANNER": "planning",
+        "PROMPT_GENERATE_CHARTER": "planning",
+        "PROMPT_DECOMPOSE_TASKS": "planning",
+        "PROMPT_PROJECT_STATUS_QA": "response",
+        "PROMPT_MEMBER_WORKLOAD_QA": "response",
+        "PROMPT_AI_PMO_QA": "response",
+        "PROMPT_EXTERNAL_TREND_QA": "response",
+        "PROMPT_INDUSTRY_TREND_QA": "response",
+    }
 
-    for prompt_key in required_keys:
+    for prompt_key, family in expected_families.items():
         entry = get_prompt_registry_entry(prompt_key)
         assert entry is not None
         assert entry["module"]
@@ -50,9 +65,21 @@ def test_high_frequency_registry_entries_have_complete_metadata() -> None:
         assert entry["output_contract"]
         assert isinstance(entry["call_sites"], tuple)
         assert entry["call_sites"], prompt_key
+        assert entry["prompt_family"] == family
+        assert entry["responsibility_scope"]
 
     signal_planner_entry = get_prompt_registry_entry("PROMPT_SIGNAL_PLANNER")
     assert signal_planner_entry is not None
     assert "charter_json" in signal_planner_entry["input_contract"]
     assert any("mixin_prompt.py" in site for site in signal_planner_entry["call_sites"])
     assert any("mixin_analyze.py" in site for site in signal_planner_entry["call_sites"])
+    assert "规划" in signal_planner_entry["responsibility_scope"]
+
+
+def test_deprecated_prompt_aliases_point_to_canonical_prompts() -> None:
+    registry_keys = set(iter_prompt_registry_keys())
+
+    assert "PROMPT_FEISHU_REQUIREMENT_ANALYSIS" not in registry_keys
+    assert "PROMPT_TASK_BREAKDOWN" not in registry_keys
+    assert PROMPT_FEISHU_REQUIREMENT_ANALYSIS == PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION
+    assert PROMPT_TASK_BREAKDOWN == PROMPT_DECOMPOSE_TASKS

backend/tests/test_feedback_center_api.py

@@ -0,0 +1,353 @@
+from __future__ import annotations
+
+import os
+from datetime import datetime
+from types import SimpleNamespace
+
+import pytest
+import pytest_asyncio
+from fastapi import FastAPI
+from httpx import ASGITransport, AsyncClient
+from sqlalchemy import JSON
+from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
+
+os.environ["DATABASE_URL"] = "postgresql+asyncpg://tester:tester@localhost:5432/pmflow_test"
+
+from app.api.ai_steward_api import routes_feedback_center
+from app.core.database import get_db
+from app.dependencies.auth import get_current_user
+from app.dependencies.tenant import get_tenant_id
+from app.models.base import Base
+from app.models.feedback_center import FeedbackCenterRecord, FeedbackCenterRecordRevision
+import app.services.feedback_center.service as feedback_center_service_module
+
+
+def _use_sqlite_json_types() -> None:
+    for table in (FeedbackCenterRecord.__table__, FeedbackCenterRecordRevision.__table__):
+        for column in table.columns:
+            if column.name in {"before_value", "after_value", "ai_snapshot", "reason_candidates", "before_snapshot", "after_snapshot"}:
+                column.type = JSON()
+
+
+@pytest_asyncio.fixture
+async def db_session():
+    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    _use_sqlite_json_types()
+    async with engine.begin() as conn:
+        await conn.run_sync(
+            lambda sync_conn: Base.metadata.create_all(
+                sync_conn,
+                tables=[
+                    FeedbackCenterRecord.__table__,
+                    FeedbackCenterRecordRevision.__table__,
+                ],
+            )
+        )
+    maker = async_sessionmaker(engine, expire_on_commit=False)
+    async with maker() as session:
+        yield session
+    await engine.dispose()
+
+
+def _build_app(db_session):
+    app = FastAPI()
+    app.include_router(routes_feedback_center.router)
+
+    async def _fake_get_db():
+        yield db_session
+
+    app.dependency_overrides[get_db] = _fake_get_db
+    app.dependency_overrides[get_tenant_id] = lambda: 1
+    app.dependency_overrides[get_current_user] = lambda: SimpleNamespace(
+        id=9001,
+        tenant_id=1,
+        name="管理员",
+        username="admin",
+        role="ADMIN",
+    )
+    return app
+
+
+@pytest.mark.asyncio
+async def test_feedback_center_summary_list_detail_and_catalog(db_session) -> None:
+    db_session.add_all(
+        [
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=101,
+                object_type="task",
+                object_id=201,
+                field_name="task_object",
+                source_title="任务拆解",
+                object_title="任务拆解",
+                before_value=None,
+                after_value={"title": "任务拆解"},
+                ai_confidence=0.91,
+                reason_candidates=[{"code": "manual_override", "label": "人工覆盖"}],
+                reason_code="manual_override",
+                review_status="pending",
+                auto_assigned=False,
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=102,
+                object_type="qa_record",
+                object_id=202,
+                field_name="qa_feedback",
+                source_title="Q&A",
+                object_title="Q&A",
+                before_value={"answer": "旧答案"},
+                after_value={"rating": "negative", "correct_answer": "新答案"},
+                ai_confidence=None,
+                reason_candidates=[{"code": "answer_incorrect", "label": "答案不正确"}],
+                reason_code="answer_incorrect",
+                review_status="reviewed",
+                auto_assigned=False,
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=22,
+                source_type="signal_feedback",
+                source_id=103,
+                object_type="signal",
+                object_id=203,
+                field_name="strategic_classification",
+                source_title="上线异常告警",
+                object_title="上线异常告警",
+                before_value={"ai_predicted_strategic": True},
+                after_value={"was_strategic": False},
+                ai_confidence=0.8,
+                reason_candidates=[{"code": "strategic_judgement_mismatch", "label": "战略判断偏差"}],
+                reason_code="strategic_judgement_mismatch",
+                review_status="accepted",
+                auto_assigned=False,
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    app = _build_app(db_session)
+    transport = ASGITransport(app=app)
+    async with AsyncClient(transport=transport, base_url="http://test") as client:
+        summary_resp = await client.get("/ai-steward/feedback-center/summary", params={"project_id": 11})
+        list_resp = await client.get("/ai-steward/feedback-center/items", params={"project_id": 11, "limit": 20})
+        detail_resp = await client.get("/ai-steward/feedback-center/items/1", params={"project_id": 11})
+        catalog_resp = await client.get(
+            "/ai-steward/feedback-center/reason-catalog",
+            params={"source_type": "task_override", "field_name": "priority"},
+        )
+
+    assert summary_resp.status_code == 200
+    summary_payload = summary_resp.json()
+    assert summary_payload["code"] == 0
+    assert summary_payload["data"]["total"] == 2
+    assert summary_payload["data"]["by_status"]["pending"] == 1
+    assert summary_payload["data"]["by_status"]["reviewed"] == 1
+
+    assert list_resp.status_code == 200
+    list_payload = list_resp.json()
+    assert list_payload["code"] == 0
+    assert len(list_payload["data"]["items"]) == 2
+    assert all(item["project_id"] == 11 for item in list_payload["data"]["items"])
+
+    assert detail_resp.status_code == 200
+    detail_payload = detail_resp.json()
+    assert detail_payload["code"] == 0
+    assert detail_payload["data"]["record"]["id"] == 1
+    assert detail_payload["data"]["record"]["source_type"] == "task_breakdown"
+    assert detail_payload["data"]["revisions"] == []
+
+    assert catalog_resp.status_code == 200
+    catalog_payload = catalog_resp.json()
+    assert catalog_payload["code"] == 0
+    codes = [item["code"] for item in catalog_payload["data"]["items"]]
+    assert "priority_misalignment" in codes
+
+
+@pytest.mark.asyncio
+async def test_feedback_center_summary_honors_window_and_scope_query_params(db_session, monkeypatch) -> None:
+    fixed_now = datetime(2025, 1, 15, 12, 0, 0)
+    monkeypatch.setattr(feedback_center_service_module, "now_cn_naive", lambda: fixed_now)
+
+    db_session.add_all(
+        [
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=801,
+                object_type="task",
+                object_id=901,
+                field_name="task_breakdown",
+                source_title="任务拆解",
+                object_title="任务拆解",
+                review_status="accepted",
+                auto_assigned=True,
+                created_at=datetime(2025, 1, 15, 8, 0, 0),
+                reviewed_at=datetime(2025, 1, 15, 8, 30, 0),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_override",
+                source_id=802,
+                object_type="task",
+                object_id=901,
+                field_name="priority",
+                source_title="优先级修正",
+                object_title="优先级修正",
+                review_status="reviewed",
+                created_at=datetime(2025, 1, 14, 8, 0, 0),
+                reviewed_at=datetime(2025, 1, 14, 8, 50, 0),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=803,
+                object_type="qa_record",
+                object_id=902,
+                field_name="qa_feedback",
+                source_title="QA 反馈",
+                object_title="QA 反馈",
+                review_status="accepted",
+                created_at=datetime(2025, 1, 13, 8, 0, 0),
+                reviewed_at=datetime(2025, 1, 13, 8, 20, 0),
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    app = _build_app(db_session)
+    transport = ASGITransport(app=app)
+    async with AsyncClient(transport=transport, base_url="http://test") as client:
+        response = await client.get(
+            "/ai-steward/feedback-center/summary",
+            params={"project_id": 11, "window_days": 30, "source_group": "task"},
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["code"] == 0
+    assert payload["data"]["source_group"] == "task"
+    assert payload["data"]["trend"]["window_days"] == 30
+    assert payload["data"]["trend"]["current"]["total_records"] == 2
+    assert payload["data"]["trend"]["current"]["task_breakdown_count"] == 1
+    assert payload["data"]["trend"]["current"]["task_override_count"] == 1
+    assert payload["data"]["trend"]["current"]["qa_feedback_count"] == 0
+
+
+@pytest.mark.asyncio
+async def test_feedback_center_summary_accepts_scope_params(db_session) -> None:
+    db_session.add_all(
+        [
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=201,
+                object_type="task",
+                object_id=301,
+                field_name="task_object",
+                review_status="pending",
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=202,
+                object_type="qa_record",
+                object_id=302,
+                field_name="qa_feedback",
+                review_status="accepted",
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    app = _build_app(db_session)
+    transport = ASGITransport(app=app)
+    async with AsyncClient(transport=transport, base_url="http://test") as client:
+        scoped_resp = await client.get(
+            "/ai-steward/feedback-center/summary",
+            params={"project_id": 11, "window_days": 30, "source_group": "qa"},
+        )
+        invalid_window = await client.get(
+            "/ai-steward/feedback-center/summary",
+            params={"project_id": 11, "window_days": 14},
+        )
+        invalid_group = await client.get(
+            "/ai-steward/feedback-center/summary",
+            params={"project_id": 11, "source_group": "unknown"},
+        )
+
+    assert scoped_resp.status_code == 200
+    scoped_payload = scoped_resp.json()
+    assert scoped_payload["code"] == 0
+    assert scoped_payload["data"]["trend"]["window_days"] == 30
+    assert scoped_payload["data"]["by_source_type"] == {"qa_feedback": 1}
+
+    assert invalid_window.status_code == 400
+    assert invalid_group.status_code == 400
+
+
+@pytest.mark.asyncio
+async def test_feedback_center_review_updates_record_and_revision(db_session) -> None:
+    db_session.add(
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_override",
+            source_id=500,
+            object_type="task",
+            object_id=600,
+            field_name="priority",
+            source_title="优先级修正",
+            object_title="优先级修正",
+            before_value="P2",
+            after_value="P1",
+            ai_confidence=0.62,
+            reason_candidates=[{"code": "priority_misalignment", "label": "优先级错配"}],
+            reason_code="priority_misalignment",
+            review_status="pending",
+            auto_assigned=False,
+        )
+    )
+    await db_session.commit()
+
+    app = _build_app(db_session)
+    transport = ASGITransport(app=app)
+    async with AsyncClient(transport=transport, base_url="http://test") as client:
+        response = await client.post(
+            "/ai-steward/feedback-center/items/1/review",
+            json={
+                "review_status": "accepted",
+                "reason_code": "priority_misalignment",
+                "reason_text": "优先级确认需要上调",
+            },
+        )
+
+    assert response.status_code == 200
+    payload = response.json()
+    assert payload["code"] == 0
+    assert payload["data"]["review_status"] == "accepted"
+    assert payload["data"]["reason_code"] == "priority_misalignment"
+    assert payload["data"]["reviewed_by"] == 9001
+
+    refreshed = await db_session.get(FeedbackCenterRecord, 1)
+    assert refreshed is not None
+    assert refreshed.review_status == "accepted"
+    assert refreshed.reason_text == "优先级确认需要上调"
+
+    revisions = (
+        await db_session.execute(
+            FeedbackCenterRecordRevision.__table__.select().where(FeedbackCenterRecordRevision.record_id == 1)
+        )
+    ).mappings().all()
+    assert len(revisions) == 1
+    assert revisions[0]["before_snapshot"]["review_status"] == "pending"
+    assert revisions[0]["after_snapshot"]["review_status"] == "accepted"

backend/tests/test_feedback_center_service.py

@@ -0,0 +1,787 @@
+from __future__ import annotations
+
+import os
+from datetime import datetime, timedelta
+from types import SimpleNamespace
+
+import pytest
+import pytest_asyncio
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
+
+os.environ["DATABASE_URL"] = "sqlite+aiosqlite:///:memory:"
+
+from app.models.base import Base
+from app.models.feedback_center import FeedbackCenterRecord, FeedbackCenterRecordRevision
+from app.services.feedback_center.mappers import (
+    map_qa_feedback_to_record_payload,
+    map_signal_feedback_to_record_payload,
+    map_task_override_to_record_payloads,
+)
+from app.services.feedback_center.reason_catalog import build_reason_candidates
+import app.services.feedback_center.service as feedback_center_service_module
+from app.services.feedback_center.service import FeedbackCenterService
+
+
+@pytest_asyncio.fixture
+async def db_session():
+    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    async with engine.begin() as conn:
+        await conn.run_sync(
+            lambda sync_conn: Base.metadata.create_all(
+                sync_conn,
+                tables=[
+                    FeedbackCenterRecord.__table__,
+                    FeedbackCenterRecordRevision.__table__,
+                ],
+            )
+        )
+    maker = async_sessionmaker(engine, expire_on_commit=False)
+    async with maker() as session:
+        yield session
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_summary_list_and_detail_respect_tenant_and_project_scope(db_session) -> None:
+    db_session.add_all(
+        [
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=101,
+                object_type="qa_record",
+                object_id=1,
+                field_name="qa_feedback",
+                before_value={"answer": "A"},
+                after_value={"rating": "negative"},
+                review_status="pending",
+                reason_code="answer_incorrect",
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="signal_feedback",
+                source_id=102,
+                object_type="signal",
+                object_id=2,
+                field_name="strategic_classification",
+                before_value={"predicted": True},
+                after_value={"actual": False},
+                review_status="reviewed",
+                reason_code="answer_incorrect",
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=22,
+                source_type="task_override",
+                source_id=103,
+                object_type="task",
+                object_id=3,
+                field_name="estimated_hours",
+                before_value=8,
+                after_value=13,
+                review_status="accepted",
+                reason_code="effort_estimation_bias",
+            ),
+            FeedbackCenterRecord(
+                tenant_id=2,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=201,
+                object_type="qa_record",
+                object_id=9,
+                field_name="qa_feedback",
+                before_value={"answer": "B"},
+                after_value={"rating": "negative"},
+                review_status="pending",
+                reason_code="answer_incorrect",
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    service = FeedbackCenterService(db_session)
+    summary = await service.summary(tenant_id=1, project_id=11)
+    records = await service.list_records(tenant_id=1, project_id=11, limit=50, offset=0)
+
+    assert summary["total"] == 2
+    assert summary["by_status"]["pending"] == 1
+    assert summary["by_status"]["reviewed"] == 1
+    assert summary["by_source_type"]["qa_feedback"] == 1
+    assert summary["by_source_type"]["signal_feedback"] == 1
+    top_reason_code = summary["top_reasons"][0].get("reason_code") or summary["top_reasons"][0].get("code")
+    assert top_reason_code == "answer_incorrect"
+    assert summary["top_reasons"][0]["count"] == 2
+    assert records["total"] == 2
+    assert len(records["items"]) == 2
+    assert all(item["tenant_id"] == 1 for item in records["items"])
+    assert all(item["project_id"] == 11 for item in records["items"])
+
+    detail = await service.get_detail(
+        tenant_id=1,
+        project_id=11,
+        record_id=records["items"][0]["id"],
+    )
+    assert detail is not None
+    assert detail["record"]["project_id"] == 11
+    assert detail["revisions"] == []
+
+
+@pytest.mark.asyncio
+async def test_review_updates_record_and_appends_revision_in_single_flow(db_session) -> None:
+    record = FeedbackCenterRecord(
+        tenant_id=1,
+        project_id=31,
+        source_type="task_override",
+        source_id=501,
+        object_type="task",
+        object_id=88,
+        field_name="priority",
+        before_value="P2",
+        after_value="P0",
+        reason_code="manual_override",
+        reason_text="初始记录",
+        review_status="pending",
+    )
+    db_session.add(record)
+    await db_session.commit()
+
+    service = FeedbackCenterService(db_session)
+    reviewed = await service.review(
+        tenant_id=1,
+        record_id=record.id,
+        reviewer_id=9001,
+        review_status="accepted",
+        reason_code="priority_misalignment",
+        reason_text="已确认优先级应提升",
+        reason_candidates=[{"code": "priority_misalignment", "label": "优先级偏差"}],
+    )
+
+    assert reviewed["id"] == record.id
+    assert reviewed["review_status"] == "accepted"
+    assert reviewed["reason_code"] == "priority_misalignment"
+    assert reviewed["reviewed_by"] == 9001
+    assert reviewed["reviewed_at"] is not None
+
+    revisions = (
+        await db_session.execute(
+            select(FeedbackCenterRecordRevision).where(
+                FeedbackCenterRecordRevision.tenant_id == 1,
+                FeedbackCenterRecordRevision.record_id == record.id,
+            )
+        )
+    ).scalars().all()
+    assert len(revisions) == 1
+    assert revisions[0].before_snapshot["review_status"] == "pending"
+    assert revisions[0].after_snapshot["review_status"] == "accepted"
+    assert revisions[0].reviewed_by == 9001
+
+
+@pytest.mark.asyncio
+async def test_ingest_payloads_upsert_same_identity_and_append_revision(db_session) -> None:
+    service = FeedbackCenterService(db_session)
+    initial = await service.ingest_payloads(
+        payloads=[
+            {
+                "tenant_id": 1,
+                "project_id": 44,
+                "source_type": "task_override",
+                "source_id": 808,
+                "object_type": "task",
+                "object_id": 909,
+                "field_name": "priority",
+                "source_title": "优先级调整",
+                "object_title": "优先级调整",
+                "before_value": "P2",
+                "after_value": "P1",
+                "ai_snapshot": {"priority": "P2"},
+                "ai_confidence": 0.61,
+                "reason_candidates": [{"code": "priority_misalignment", "label": "优先级错配"}],
+                "reason_code": "priority_misalignment",
+                "review_status": "pending",
+                "reviewed_by": None,
+                "reviewed_at": None,
+                "auto_assigned": False,
+            }
+        ],
+        commit=True,
+    )
+    assert len(initial) == 1
+    record_id = initial[0].id
+
+    updated = await service.ingest_payloads(
+        payloads=[
+            {
+                "tenant_id": 1,
+                "project_id": 44,
+                "source_type": "task_override",
+                "source_id": 808,
+                "object_type": "task",
+                "object_id": 909,
+                "field_name": "priority",
+                "source_title": "优先级调整（人工确认）",
+                "object_title": "优先级调整（人工确认）",
+                "before_value": "P2",
+                "after_value": "P0",
+                "ai_snapshot": {"priority": "P2"},
+                "ai_confidence": 0.72,
+                "reason_candidates": [{"code": "priority_misalignment", "label": "优先级错配"}],
+                "reason_code": "priority_misalignment",
+                "review_status": "accepted",
+                "reviewed_by": 42,
+                "reviewed_at": None,
+                "auto_assigned": False,
+            }
+        ],
+        commit=True,
+    )
+
+    assert len(updated) == 1
+    assert updated[0].id == record_id
+
+    detail = await service.get_detail(tenant_id=1, record_id=record_id)
+    assert detail is not None
+    assert detail["record"]["after_value"] == "P0"
+    assert detail["record"]["review_status"] == "accepted"
+    assert len(detail["revisions"]) == 1
+    assert detail["revisions"][0]["before_snapshot"]["after_value"] == "P1"
+    assert detail["revisions"][0]["after_snapshot"]["after_value"] == "P0"
+
+
+def test_reason_catalog_is_deterministic_for_field_diff() -> None:
+    first = build_reason_candidates(
+        source_type="task_override",
+        field_name="estimated_hours",
+        before_value=5,
+        after_value=13,
+    )
+    second = build_reason_candidates(
+        source_type="task_override",
+        field_name="estimated_hours",
+        before_value=5,
+        after_value=13,
+    )
+
+    assert first == second
+    assert first[0]["code"] == "effort_estimation_bias"
+
+
+def test_source_mappers_generate_unified_payloads() -> None:
+    qa_payload = map_qa_feedback_to_record_payload(
+        qa_feedback=SimpleNamespace(
+            id=1,
+            tenant_id=1,
+            qa_id=7,
+            rating="negative",
+            helpfulness="low",
+            error_types=["hallucination"],
+            correct_answer="正确答案",
+            submitted_by=100,
+            created_at=None,
+        ),
+        qa_record=SimpleNamespace(
+            project_id=99,
+            question="问题",
+            answer="旧答案",
+        ),
+    )
+    assert qa_payload["source_type"] == "qa_feedback"
+    assert qa_payload["project_id"] == 99
+    assert qa_payload["object_id"] == 7
+
+    signal_payload = map_signal_feedback_to_record_payload(
+        signal_feedback=SimpleNamespace(
+            id=2,
+            tenant_id=1,
+            signal_id=33,
+            was_strategic=False,
+            ai_predicted_strategic=True,
+            ai_confidence=0.81,
+            reason="噪音信号",
+            user_id="u-1",
+            created_at=None,
+        ),
+        signal=SimpleNamespace(
+            matched_project_id=55,
+            title="上线异常告警",
+        ),
+    )
+    assert signal_payload["source_type"] == "signal_feedback"
+    assert signal_payload["project_id"] == 55
+    assert signal_payload["field_name"] == "strategic_classification"
+
+    task_payloads = map_task_override_to_record_payloads(
+        task=SimpleNamespace(
+            id=66,
+            tenant_id=1,
+            project_id=77,
+            title="实现回放",
+            ai_breakdown_payload={"estimated_hours": 5, "priority": "P2"},
+            manual_overrides={"estimated_hours": 8},
+        )
+    )
+    assert len(task_payloads) == 1
+    assert task_payloads[0]["source_type"] == "task_override"
+    assert task_payloads[0]["before_value"] == 5
+    assert task_payloads[0]["after_value"] == 8
+
+
+@pytest.mark.asyncio
+async def test_summary_metrics_cover_learning_kpis(db_session) -> None:
+    base_time = datetime(2025, 1, 1, 10, 0, 0)
+    db_session.add_all(
+        [
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=201,
+                object_type="task",
+                object_id=1,
+                field_name="task_breakdown",
+                review_status="accepted",
+                auto_assigned=True,
+                created_at=base_time,
+                reviewed_at=base_time + timedelta(minutes=60),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=202,
+                object_type="task",
+                object_id=2,
+                field_name="task_breakdown",
+                review_status="rejected",
+                auto_assigned=False,
+                created_at=base_time + timedelta(minutes=10),
+                reviewed_at=base_time + timedelta(minutes=40),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_override",
+                source_id=301,
+                object_type="task",
+                object_id=1,
+                field_name="priority",
+                review_status="accepted",
+                created_at=base_time + timedelta(minutes=5),
+                reviewed_at=base_time + timedelta(minutes=20),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_override",
+                source_id=302,
+                object_type="task",
+                object_id=1,
+                field_name="estimated_hours",
+                review_status="pending",
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=401,
+                object_type="qa_record",
+                object_id=7,
+                field_name="qa_feedback",
+                review_status="pending",
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="signal_feedback",
+                source_id=501,
+                object_type="signal",
+                object_id=9,
+                field_name="strategic_classification",
+                review_status="reviewed",
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    target_record = (
+        await db_session.execute(
+            select(FeedbackCenterRecord).where(
+                FeedbackCenterRecord.tenant_id == 1,
+                FeedbackCenterRecord.project_id == 11,
+                FeedbackCenterRecord.source_type == "task_override",
+                FeedbackCenterRecord.field_name == "priority",
+            )
+        )
+    ).scalar_one()
+    db_session.add_all(
+        [
+            FeedbackCenterRecordRevision(
+                tenant_id=1,
+                record_id=target_record.id,
+                before_snapshot={"review_status": "pending"},
+                after_snapshot={"review_status": "reviewed"},
+                reviewed_by=9001,
+                reviewed_at=base_time + timedelta(minutes=25),
+            ),
+            FeedbackCenterRecordRevision(
+                tenant_id=1,
+                record_id=target_record.id,
+                before_snapshot={"review_status": "reviewed"},
+                after_snapshot={"review_status": "accepted"},
+                reviewed_by=9001,
+                reviewed_at=base_time + timedelta(minutes=30),
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    summary = await FeedbackCenterService(db_session).summary(tenant_id=1, project_id=11)
+    metrics = summary["metrics"]
+
+    assert metrics["accepted_rate_reviewed"] == pytest.approx(2 / 3)
+    assert metrics["manual_override_rate"] == pytest.approx(0.5)
+    assert metrics["auto_assigned_rate"] == pytest.approx(0.5)
+    assert metrics["rework_rate"] == pytest.approx(1 / 6)
+    assert metrics["avg_review_latency_minutes"] == pytest.approx(35.0)
+    assert metrics["total_task_breakdown_count"] == 2
+    assert metrics["total_task_override_count"] == 2
+    assert metrics["total_qa_feedback_count"] == 1
+    assert metrics["total_signal_feedback_count"] == 1
+    assert metrics["total_revision_count"] == 2
+
+
+@pytest.mark.asyncio
+async def test_summary_trend_cover_daily_windows_and_deltas(db_session, monkeypatch) -> None:
+    fixed_now = datetime(2025, 1, 15, 12, 0, 0)
+    monkeypatch.setattr(feedback_center_service_module, "now_cn_naive", lambda: fixed_now)
+
+    records = [
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_breakdown",
+            source_id=601,
+            object_type="task",
+            object_id=100,
+            field_name="task_breakdown",
+            review_status="accepted",
+            auto_assigned=True,
+            created_at=datetime(2025, 1, 15, 8, 0, 0),
+            reviewed_at=datetime(2025, 1, 15, 8, 30, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_breakdown",
+            source_id=602,
+            object_type="task",
+            object_id=101,
+            field_name="task_breakdown",
+            review_status="rejected",
+            auto_assigned=False,
+            created_at=datetime(2025, 1, 14, 8, 0, 0),
+            reviewed_at=datetime(2025, 1, 14, 8, 40, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_override",
+            source_id=603,
+            object_type="task",
+            object_id=100,
+            field_name="priority",
+            review_status="pending",
+            created_at=datetime(2025, 1, 14, 9, 0, 0),
+            reviewed_at=datetime(2025, 1, 14, 9, 50, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="qa_feedback",
+            source_id=604,
+            object_type="qa_record",
+            object_id=401,
+            field_name="qa_feedback",
+            review_status="reviewed",
+            created_at=datetime(2025, 1, 13, 9, 0, 0),
+            reviewed_at=datetime(2025, 1, 13, 9, 20, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="signal_feedback",
+            source_id=605,
+            object_type="signal",
+            object_id=501,
+            field_name="strategic_classification",
+            review_status="pending",
+            created_at=datetime(2025, 1, 12, 9, 0, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_breakdown",
+            source_id=606,
+            object_type="task",
+            object_id=200,
+            field_name="task_breakdown",
+            review_status="accepted",
+            auto_assigned=False,
+            created_at=datetime(2025, 1, 8, 8, 0, 0),
+            reviewed_at=datetime(2025, 1, 8, 8, 20, 0),
+        ),
+    ]
+    db_session.add_all(records)
+    await db_session.commit()
+
+    target_record = (
+        await db_session.execute(
+            select(FeedbackCenterRecord).where(
+                FeedbackCenterRecord.tenant_id == 1,
+                FeedbackCenterRecord.project_id == 11,
+                FeedbackCenterRecord.source_type == "task_override",
+                FeedbackCenterRecord.field_name == "priority",
+            )
+        )
+    ).scalar_one()
+    db_session.add_all(
+        [
+            FeedbackCenterRecordRevision(
+                tenant_id=1,
+                record_id=target_record.id,
+                before_snapshot={"review_status": "pending"},
+                after_snapshot={"review_status": "reviewed"},
+                reviewed_by=9001,
+                reviewed_at=datetime(2025, 1, 14, 10, 0, 0),
+            ),
+            FeedbackCenterRecordRevision(
+                tenant_id=1,
+                record_id=target_record.id,
+                before_snapshot={"review_status": "reviewed"},
+                after_snapshot={"review_status": "accepted"},
+                reviewed_by=9001,
+                reviewed_at=datetime(2025, 1, 14, 10, 10, 0),
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    summary = await FeedbackCenterService(db_session).summary(tenant_id=1, project_id=11)
+    trend = summary["trend"]
+
+    assert trend["window_days"] == 7
+    assert trend["current_window_start"] == datetime(2025, 1, 9).date()
+    assert trend["previous_window_start"] == datetime(2025, 1, 2).date()
+    assert len(trend["daily"]) == 7
+
+    jan14 = next(item for item in trend["daily"] if item["date"] == "2025-01-14")
+    assert jan14["total_records"] == 2
+    assert jan14["task_breakdown_count"] == 1
+    assert jan14["task_override_count"] == 1
+    assert jan14["rework_count"] == 1
+    assert jan14["avg_review_latency_minutes"] == pytest.approx(45.0)
+
+    current = trend["current"]
+    previous = trend["previous"]
+    deltas = trend["deltas"]
+
+    assert current["total_records"] == 5
+    assert current["accepted_rate_reviewed"] == pytest.approx(0.5)
+    assert current["manual_override_rate"] == pytest.approx(0.5)
+    assert current["auto_assigned_rate"] == pytest.approx(0.5)
+    assert current["rework_rate"] == pytest.approx(1 / 5)
+    assert current["avg_review_latency_minutes"] == pytest.approx(35.0)
+    assert current["revision_count"] == 2
+
+    assert previous["total_records"] == 1
+    assert previous["accepted_rate_reviewed"] == pytest.approx(1.0)
+    assert previous["manual_override_rate"] == pytest.approx(0.0)
+    assert previous["auto_assigned_rate"] == pytest.approx(0.0)
+    assert previous["rework_rate"] == pytest.approx(0.0)
+    assert previous["avg_review_latency_minutes"] == pytest.approx(20.0)
+
+    assert deltas["total_records_delta"] == 4
+    assert deltas["accepted_rate_reviewed_delta"] == pytest.approx(-0.5)
+    assert deltas["manual_override_rate_delta"] == pytest.approx(0.5)
+    assert deltas["auto_assigned_rate_delta"] == pytest.approx(0.5)
+    assert deltas["rework_rate_delta"] == pytest.approx(1 / 5)
+    assert deltas["avg_review_latency_minutes_delta"] == pytest.approx(15.0)
+
+
+@pytest.mark.asyncio
+async def test_summary_trend_respects_source_group_scope(db_session, monkeypatch) -> None:
+    fixed_now = datetime(2025, 1, 15, 12, 0, 0)
+    monkeypatch.setattr(feedback_center_service_module, "now_cn_naive", lambda: fixed_now)
+
+    records = [
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="qa_feedback",
+            source_id=701,
+            object_type="qa_record",
+            object_id=301,
+            field_name="qa_feedback",
+            review_status="accepted",
+            created_at=datetime(2025, 1, 14, 9, 0, 0),
+            reviewed_at=datetime(2025, 1, 14, 9, 20, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="signal_feedback",
+            source_id=702,
+            object_type="signal",
+            object_id=401,
+            field_name="strategic_classification",
+            review_status="pending",
+            created_at=datetime(2025, 1, 14, 10, 0, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_breakdown",
+            source_id=703,
+            object_type="task",
+            object_id=501,
+            field_name="task_breakdown",
+            review_status="accepted",
+            auto_assigned=True,
+            created_at=datetime(2025, 1, 13, 8, 0, 0),
+            reviewed_at=datetime(2025, 1, 13, 8, 25, 0),
+        ),
+        FeedbackCenterRecord(
+            tenant_id=1,
+            project_id=11,
+            source_type="task_override",
+            source_id=704,
+            object_type="task",
+            object_id=501,
+            field_name="priority",
+            review_status="reviewed",
+            created_at=datetime(2025, 1, 13, 9, 0, 0),
+            reviewed_at=datetime(2025, 1, 13, 9, 30, 0),
+        ),
+    ]
+    db_session.add_all(records)
+    await db_session.commit()
+
+    service = FeedbackCenterService(db_session)
+    summary_qa = await service.summary(tenant_id=1, project_id=11, window_days=30, source_group="qa")
+
+    assert summary_qa["total"] == 1
+    assert summary_qa["by_source_type"] == {"qa_feedback": 1}
+    assert summary_qa["metrics"]["total_qa_feedback_count"] == 1
+    assert summary_qa["metrics"]["total_task_breakdown_count"] == 0
+    assert summary_qa["metrics"]["total_task_override_count"] == 0
+    assert summary_qa["metrics"]["total_signal_feedback_count"] == 0
+
+    trend = summary_qa["trend"]
+    assert trend["window_days"] == 30
+    assert len(trend["daily"]) == 30
+    jan14 = next(item for item in trend["daily"] if item["date"] == "2025-01-14")
+    assert jan14["total_records"] == 1
+    assert jan14["qa_feedback_count"] == 1
+    assert jan14["task_breakdown_count"] == 0
+
+    summary_task = await service.summary(tenant_id=1, project_id=11, window_days=7, source_group="task")
+    assert summary_task["total"] == 2
+    assert summary_task["by_source_type"]["task_breakdown"] == 1
+    assert summary_task["by_source_type"]["task_override"] == 1
+
+
+@pytest.mark.asyncio
+async def test_summary_supports_30_day_task_scope(db_session, monkeypatch) -> None:
+    fixed_now = datetime(2025, 1, 15, 12, 0, 0)
+    monkeypatch.setattr(feedback_center_service_module, "now_cn_naive", lambda: fixed_now)
+
+    db_session.add_all(
+        [
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=701,
+                object_type="task",
+                object_id=300,
+                field_name="task_breakdown",
+                review_status="accepted",
+                auto_assigned=True,
+                created_at=datetime(2025, 1, 15, 8, 0, 0),
+                reviewed_at=datetime(2025, 1, 15, 8, 30, 0),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_breakdown",
+                source_id=702,
+                object_type="task",
+                object_id=301,
+                field_name="task_breakdown",
+                review_status="rejected",
+                auto_assigned=False,
+                created_at=datetime(2025, 1, 14, 8, 0, 0),
+                reviewed_at=datetime(2025, 1, 14, 8, 40, 0),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="task_override",
+                source_id=703,
+                object_type="task",
+                object_id=300,
+                field_name="priority",
+                review_status="reviewed",
+                created_at=datetime(2025, 1, 14, 9, 0, 0),
+                reviewed_at=datetime(2025, 1, 14, 9, 50, 0),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="qa_feedback",
+                source_id=704,
+                object_type="qa_record",
+                object_id=401,
+                field_name="qa_feedback",
+                review_status="accepted",
+                created_at=datetime(2025, 1, 13, 9, 0, 0),
+                reviewed_at=datetime(2025, 1, 13, 9, 20, 0),
+            ),
+            FeedbackCenterRecord(
+                tenant_id=1,
+                project_id=11,
+                source_type="signal_feedback",
+                source_id=705,
+                object_type="signal",
+                object_id=501,
+                field_name="strategic_classification",
+                review_status="pending",
+                created_at=datetime(2025, 1, 12, 9, 0, 0),
+            ),
+        ]
+    )
+    await db_session.commit()
+
+    summary = await FeedbackCenterService(db_session).summary(
+        tenant_id=1,
+        project_id=11,
+        window_days=30,
+        source_group="task",
+    )
+    assert summary["source_group"] == "task"
+    trend = summary["trend"]
+
+    assert trend["window_days"] == 30
+    assert len(trend["daily"]) == 30
+    assert trend["current"]["total_records"] == 3
+    assert trend["current"]["task_breakdown_count"] == 2
+    assert trend["current"]["task_override_count"] == 1
+    assert trend["current"]["qa_feedback_count"] == 0
+    assert trend["current"]["signal_feedback_count"] == 0
+    assert trend["current"]["accepted_rate_reviewed"] == pytest.approx(0.5)
+    assert trend["current"]["manual_override_rate"] == pytest.approx(0.5)
+    assert trend["current"]["auto_assigned_rate"] == pytest.approx(0.5)
+    assert trend["previous"]["total_records"] == 0
+    assert summary["by_source_type"] == {"task_breakdown": 2, "task_override": 1}

backend/tests/test_prompt_profile_bundle_contract.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from app.config.ai_prompts import build_default_role_prompt_bundle
+
+
+def test_default_role_prompt_bundle_uses_pmflow_pmo_voice() -> None:
+    bundle = build_default_role_prompt_bundle()
+    roles = {str(item.get("role_key") or ""): item for item in bundle["roles"]}
+
+    dispatcher = roles.get("pm_dispatcher", {})
+    assert "PMFlow 的 PMO 协作中枢" in str(dispatcher.get("system_prompt") or "")
+    assert "查重、复用/合并、立项评审" in str(dispatcher.get("system_prompt") or "")
+    assert "MVP" in str(dispatcher.get("system_prompt") or "")
+
+    architect = roles.get("tech_architect", {})
+    assert "可行性、依赖、风险边界" in str(architect.get("system_prompt") or "")
+    assert "最小可行步骤" in str(architect.get("system_prompt") or "")
+
+    coach = roles.get("delivery_coach", {})
+    assert "节奏、排期、负责人、推进动作" in str(coach.get("system_prompt") or "")
+    assert "不超过 3 条" in str(coach.get("system_prompt") or "")

frontend/src/api/feedbackCenter.ts

@@ -0,0 +1,278 @@
+import { apiRequest } from "./base";
+
+type ApiEnvelope<T> = { code: number; message: string; data: T };
+
+function unwrap<T>(raw: ApiEnvelope<T> | T): T {
+  if (raw && typeof raw === "object" && "code" in (raw as ApiEnvelope<T>) && "data" in (raw as ApiEnvelope<T>)) {
+    return (raw as ApiEnvelope<T>).data;
+  }
+  return raw as T;
+}
+
+export type FeedbackCenterSourceType = "task_breakdown" | "task_override" | "qa_feedback" | "signal_feedback";
+export type FeedbackCenterReviewStatus = "pending" | "reviewed" | "accepted" | "rejected";
+export type FeedbackCenterSection = "overview" | "metrics" | "trend" | "task" | "qa" | "signal";
+export type FeedbackCenterTrendWindowDays = 7 | 30;
+export type FeedbackCenterSourceGroup = "all" | "task" | "qa" | "signal";
+
+export interface FeedbackReasonCandidate {
+  code: string;
+  label: string;
+  description?: string | null;
+  confidence?: number | null;
+  source_type?: string | null;
+  field_name?: string | null;
+}
+
+export interface FeedbackCenterRecordRevision {
+  id: number;
+  record_id: number;
+  before_snapshot: unknown;
+  after_snapshot: unknown;
+  reviewed_by?: number | null;
+  reviewed_at?: string | null;
+  created_at?: string | null;
+  updated_at?: string | null;
+}
+
+export interface FeedbackCenterRecord {
+  id: number;
+  tenant_id: number;
+  project_id?: number | null;
+  source_type: FeedbackCenterSourceType | string;
+  source_id: number;
+  object_type: string;
+  object_id: number;
+  field_name: string;
+  source_title?: string | null;
+  object_title?: string | null;
+  before_value?: unknown;
+  after_value?: unknown;
+  ai_snapshot?: unknown;
+  ai_confidence?: number | null;
+  reason_candidates: FeedbackReasonCandidate[];
+  reason_code?: string | null;
+  reason_text?: string | null;
+  review_status: FeedbackCenterReviewStatus | string;
+  reviewed_by?: number | null;
+  reviewed_at?: string | null;
+  auto_assigned: boolean;
+  created_at?: string | null;
+  updated_at?: string | null;
+  revisions?: FeedbackCenterRecordRevision[];
+}
+
+export interface FeedbackCenterSummaryReason {
+  code: string;
+  label: string;
+  count: number;
+}
+
+export interface FeedbackCenterSummaryMetrics {
+  accepted_rate_reviewed: number;
+  manual_override_rate: number;
+  rework_rate: number;
+  auto_assigned_rate: number;
+  avg_review_latency_minutes: number;
+  total_task_breakdown_count: number;
+  total_task_override_count: number;
+  total_qa_feedback_count: number;
+  total_signal_feedback_count: number;
+  total_revision_count: number;
+}
+
+export interface FeedbackCenterTrendDaily {
+  date: string;
+  total_records: number;
+  accepted_count: number;
+  rejected_count: number;
+  reviewed_count: number;
+  pending_count: number;
+  task_breakdown_count: number;
+  task_override_count: number;
+  qa_feedback_count: number;
+  signal_feedback_count: number;
+  auto_assigned_count: number;
+  override_count: number;
+  rework_count: number;
+  accepted_rate_reviewed: number;
+  manual_override_rate: number;
+  rework_rate: number;
+  auto_assigned_rate: number;
+  avg_review_latency_minutes: number;
+  revision_count: number;
+}
+
+export interface FeedbackCenterTrendWindow {
+  total_records: number;
+  accepted_count: number;
+  rejected_count: number;
+  reviewed_count: number;
+  pending_count: number;
+  task_breakdown_count: number;
+  task_override_count: number;
+  qa_feedback_count: number;
+  signal_feedback_count: number;
+  auto_assigned_count: number;
+  override_count: number;
+  rework_count: number;
+  accepted_rate_reviewed: number;
+  manual_override_rate: number;
+  rework_rate: number;
+  auto_assigned_rate: number;
+  avg_review_latency_minutes: number;
+  revision_count: number;
+}
+
+export interface FeedbackCenterTrendDelta {
+  total_records_delta: number;
+  accepted_rate_reviewed_delta: number;
+  manual_override_rate_delta: number;
+  rework_rate_delta: number;
+  auto_assigned_rate_delta: number;
+  avg_review_latency_minutes_delta: number;
+}
+
+export interface FeedbackCenterTrend {
+  window_days: number;
+  current_window_start: string;
+  previous_window_start: string;
+  daily: FeedbackCenterTrendDaily[];
+  current: FeedbackCenterTrendWindow;
+  previous: FeedbackCenterTrendWindow;
+  deltas: FeedbackCenterTrendDelta;
+}
+
+export interface FeedbackCenterSummary {
+  tenant_id: number;
+  project_id?: number | null;
+  source_group: FeedbackCenterSourceGroup | string;
+  total: number;
+  backlog_count: number;
+  pending_count: number;
+  reviewed_count: number;
+  accepted_count: number;
+  rejected_count: number;
+  accepted_rate: number;
+  avg_confidence: number;
+  by_status: Record<string, number>;
+  by_source_type: Record<string, number>;
+  top_reasons: FeedbackCenterSummaryReason[];
+  metrics: FeedbackCenterSummaryMetrics;
+  trend: FeedbackCenterTrend;
+}
+
+export interface FeedbackCenterSummaryParams {
+  project_id?: number | string;
+  window_days?: FeedbackCenterTrendWindowDays;
+  source_group?: FeedbackCenterSourceGroup;
+}
+
+export interface FeedbackCenterListResponse {
+  items: FeedbackCenterRecord[];
+  total: number;
+  limit: number;
+  offset: number;
+  has_more: boolean;
+}
+
+export interface FeedbackCenterDetailResponse {
+  record: FeedbackCenterRecord;
+  revisions: FeedbackCenterRecordRevision[];
+}
+
+export interface FeedbackCenterReasonCatalogResponse {
+  items: FeedbackReasonCandidate[];
+}
+
+export interface FeedbackCenterListParams {
+  project_id?: number | string;
+  source_type?: string;
+  review_status?: string;
+  field_name?: string;
+  reason_code?: string;
+  search?: string;
+  limit?: number;
+  offset?: number;
+}
+
+export interface FeedbackCenterReviewPayload {
+  review_status: Exclude<FeedbackCenterReviewStatus, "pending">;
+  reason_code?: string;
+  reason_text?: string;
+}
+
+function toQuery(params: Record<string, string | number | undefined | null>): string {
+  const query = new URLSearchParams();
+  Object.entries(params).forEach(([key, value]) => {
+    if (value === undefined || value === null || value === "") return;
+    query.set(key, String(value));
+  });
+  const text = query.toString();
+  return text ? `?${text}` : "";
+}
+
+function isFeedbackCenterRecord(value: unknown): value is FeedbackCenterRecord {
+  if (!value || typeof value !== "object") return false;
+  const raw = value as Partial<FeedbackCenterRecord>;
+  return typeof raw.id === "number" && typeof raw.source_type === "string" && typeof raw.review_status === "string";
+}
+
+function normalizeListResponse(raw: FeedbackCenterListResponse): FeedbackCenterListResponse {
+  const safeItems = Array.isArray(raw.items) ? raw.items.filter(isFeedbackCenterRecord) : [];
+  return {
+    items: safeItems,
+    total: Number(raw.total || safeItems.length),
+    limit: Number(raw.limit || 40),
+    offset: Number(raw.offset || 0),
+    has_more: Boolean(raw.has_more),
+  };
+}
+
+export const feedbackCenterApi = {
+  async getSummary(params: FeedbackCenterSummaryParams = {}): Promise<FeedbackCenterSummary> {
+    const raw = await apiRequest<ApiEnvelope<FeedbackCenterSummary>>(
+      `/ai-steward/feedback-center/summary${toQuery({
+        project_id: params.project_id,
+        window_days: params.window_days,
+        source_group: params.source_group,
+      })}`
+    );
+    return unwrap(raw);
+  },
+  async listItems(params: FeedbackCenterListParams = {}): Promise<FeedbackCenterListResponse> {
+    const queryParams: Record<string, string | number | undefined | null> = {
+      project_id: params.project_id,
+      source_type: params.source_type,
+      review_status: params.review_status,
+      field_name: params.field_name,
+      reason_code: params.reason_code,
+      search: params.search,
+      limit: params.limit,
+      offset: params.offset,
+    };
+    const raw = await apiRequest<ApiEnvelope<FeedbackCenterListResponse>>(
+      `/ai-steward/feedback-center/items${toQuery(queryParams)}`
+    );
+    return normalizeListResponse(unwrap(raw));
+  },
+  async getDetail(recordId: number, projectId?: number | string): Promise<FeedbackCenterDetailResponse> {
+    const raw = await apiRequest<ApiEnvelope<FeedbackCenterDetailResponse>>(
+      `/ai-steward/feedback-center/items/${recordId}${toQuery({ project_id: projectId })}`
+    );
+    return unwrap(raw);
+  },
+  async reviewItem(recordId: number, payload: FeedbackCenterReviewPayload): Promise<FeedbackCenterRecord> {
+    const raw = await apiRequest<ApiEnvelope<FeedbackCenterRecord>>(`/ai-steward/feedback-center/items/${recordId}/review`, {
+      method: "POST",
+      body: JSON.stringify(payload),
+    });
+    return unwrap(raw);
+  },
+  async getReasonCatalog(sourceType?: string, fieldName?: string): Promise<FeedbackCenterReasonCatalogResponse> {
+    const raw = await apiRequest<ApiEnvelope<FeedbackCenterReasonCatalogResponse>>(
+      `/ai-steward/feedback-center/reason-catalog${toQuery({ source_type: sourceType, field_name: fieldName })}`
+    );
+    return unwrap(raw);
+  },
+};

frontend/src/components/sstc/FeedbackSummaryCard.tsx

@@ -0,0 +1,125 @@
+import { useMemo } from "react";
+import { useNavigate } from "react-router-dom";
+import { useQuery } from "@tanstack/react-query";
+import { feedbackCenterApi } from "@/api/feedbackCenter";
+import type { SSTCTask } from "@/api/sstc";
+import { Badge } from "@/components/ui/badge";
+import { Button } from "@/components/ui/button";
+import { Card, CardContent } from "@/components/ui/card";
+
+interface FeedbackSummaryCardProps {
+  projectId: string;
+  compact?: boolean;
+  tasks?: SSTCTask[];
+}
+
+function hasAIBreakdown(task: SSTCTask): boolean {
+  return !!task.ai_breakdown_payload && typeof task.ai_breakdown_payload === "object";
+}
+
+function getOverrideFields(task: SSTCTask): string[] {
+  const overrides = task.manual_overrides;
+  if (!overrides || typeof overrides !== "object") return [];
+  return Object.keys(overrides);
+}
+
+export function FeedbackSummaryCard({ projectId, compact = false, tasks = [] }: FeedbackSummaryCardProps) {
+  const navigate = useNavigate();
+  const numericProjectId = Number(projectId);
+  const localSummary = useMemo(() => {
+    const aiTasks = tasks.filter((task) => hasAIBreakdown(task));
+    if (!aiTasks.length) {
+      return {
+        pending: 0,
+        acceptedRate: 0,
+        topReasons: [] as Array<{ label: string; count: number }>,
+      };
+    }
+    let accepted = 0;
+    let rejected = 0;
+    let pending = 0;
+    const reasonCountMap = new Map<string, number>();
+    for (const task of aiTasks) {
+      const overrides = getOverrideFields(task);
+      if (overrides.length > 0) {
+        rejected += 1;
+        overrides.forEach((field) => {
+          reasonCountMap.set(field, Number(reasonCountMap.get(field) || 0) + 1);
+        });
+        continue;
+      }
+      if (task.auto_assigned) {
+        accepted += 1;
+      } else {
+        pending += 1;
+      }
+    }
+    const reviewed = accepted + rejected;
+    return {
+      pending,
+      acceptedRate: reviewed > 0 ? accepted / reviewed : 0,
+      topReasons: Array.from(reasonCountMap.entries())
+        .map(([label, count]) => ({ label, count }))
+        .sort((a, b) => b.count - a.count)
+        .slice(0, 3),
+    };
+  }, [tasks]);
+
+  const summaryQuery = useQuery({
+    queryKey: ["feedback-center", "summary", numericProjectId],
+    queryFn: () => feedbackCenterApi.getSummary({ project_id: numericProjectId }),
+    enabled: Number.isFinite(numericProjectId) && numericProjectId > 0,
+  });
+
+  const pendingCount = localSummary.pending > 0 ? localSummary.pending : summaryQuery.data?.backlog_count ?? 0;
+  const acceptedRate = localSummary.acceptedRate > 0 ? localSummary.acceptedRate : Number(summaryQuery.data?.accepted_rate || 0);
+  const topReasons = localSummary.topReasons.length
+    ? localSummary.topReasons
+    : (summaryQuery.data?.top_reasons || []).map((item) => ({ label: item.label, count: item.count }));
+
+  return (
+    <Card className="rounded-2xl border border-slate-200/80 bg-[linear-gradient(135deg,rgba(248,250,252,0.96),rgba(255,255,255,0.98))] shadow-[0_10px_30px_rgba(15,23,42,0.06)]">
+      <CardContent className={compact ? "p-4" : "p-5"}>
+        <div className="flex flex-col gap-4 sm:flex-row sm:items-end sm:justify-between">
+          <div>
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Learning Signal</p>
+            <h3 className={`${compact ? "text-base" : "text-lg"} mt-1 font-semibold text-slate-900`}>统一反馈中心摘要</h3>
+            <p className="mt-1 text-sm text-slate-500">
+              这里不做复核，只告诉你当前自动化链路里哪些地方最常被人工纠正。
+            </p>
+          </div>
+          <Button
+            size="sm"
+            variant="outline"
+            className="border-slate-200 bg-white text-slate-700 hover:bg-slate-50"
+            onClick={() => navigate(`/ai-steward?tab=feedback-center&section=task&project_id=${numericProjectId}`)}
+          >
+            打开反馈中心
+          </Button>
+        </div>
+
+        <div className="mt-4 grid gap-3 md:grid-cols-3">
+          <div className="rounded-xl border border-slate-200 bg-white px-4 py-3">
+            <p className="text-xs uppercase tracking-[0.16em] text-slate-400">待复核</p>
+            <p className="mt-2 text-2xl font-semibold text-slate-900">{pendingCount}</p>
+          </div>
+          <div className="rounded-xl border border-slate-200 bg-white px-4 py-3">
+            <p className="text-xs uppercase tracking-[0.16em] text-slate-400">采纳率</p>
+            <p className="mt-2 text-2xl font-semibold text-slate-900">{Math.round(acceptedRate * 100)}%</p>
+          </div>
+          <div className="rounded-xl border border-slate-200 bg-white px-4 py-3">
+            <p className="text-xs uppercase tracking-[0.16em] text-slate-400">高频原因</p>
+            <div className="mt-2 flex flex-wrap gap-1">
+              {topReasons.slice(0, 3).map((item) => (
+                <Badge key={item.label} variant="outline" className="border-slate-200 bg-slate-50 text-[10px] text-slate-600">
+                  {item.label} · {item.count}
+                </Badge>
+              ))}
+              {topReasons.length === 0 && <span className="text-xs text-slate-500">暂无</span>}
+            </div>
+          </div>
+        </div>
+      </CardContent>
+    </Card>
+  );
+}

frontend/src/components/sstc/SSTCExecutionBoardPanel.tsx

@@ -5,6 +5,7 @@ import type { User } from "@/api/users";
 import { Badge } from "@/components/ui/badge";
 import { Button } from "@/components/ui/button";
 import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
+import { FeedbackSummaryCard } from "@/components/sstc/FeedbackSummaryCard";
 import {
   Dialog,
   DialogContent,
@@ -26,6 +27,7 @@ interface PatchTaskStatusMutationLike {
 }
 
 interface SSTCExecutionBoardPanelProps {
+  projectId: string;
   stageLocked: boolean;
   projectUsers: User[];
   optimizeFeedback: string;
@@ -62,6 +64,7 @@ function getOverrideFields(task: SSTCTask): string[] {
 }
 
 export function SSTCExecutionBoardPanel({
+  projectId,
   stageLocked,
   projectUsers,
   optimizeFeedback,
@@ -78,6 +81,10 @@ export function SSTCExecutionBoardPanel({
   );
   const detailBreakdown = detailTask ? getTaskBreakdownObject(detailTask) : null;
   const detailOverrides = detailTask ? getOverrideFields(detailTask) : [];
+  const laneTasks = useMemo(
+    () => EXECUTION_LANES.flatMap((lane) => executionLanes[lane.key] || []),
+    [executionLanes]
+  );
 
   return (
     <Card>
@@ -121,6 +128,9 @@ export function SSTCExecutionBoardPanel({
             {optimizeFeedback}
           </p>
         )}
+        <div className="mb-3">
+          <FeedbackSummaryCard projectId={projectId} tasks={laneTasks} />
+        </div>
         <div className="-mx-1 overflow-x-auto pb-2 [-webkit-overflow-scrolling:touch]">
           <div className="grid min-w-[920px] grid-cols-5 gap-3 px-1">
             {EXECUTION_LANES.map((lane) => (

frontend/src/components/sstc/SSTCTaskStagePanel.tsx

@@ -9,6 +9,7 @@ import type {
   SSTCTaskRecommendation,
 } from "@/api/sstc";
 import type { User } from "@/api/users";
+import { FeedbackSummaryCard } from "@/components/sstc/FeedbackSummaryCard";
 import { SSTCTaskCreateDialog } from "@/components/sstc/SSTCTaskCreateDialog";
 import { Badge } from "@/components/ui/badge";
 import { Button } from "@/components/ui/button";
@@ -247,6 +248,9 @@ export function SSTCTaskStagePanel({
             {optimizeFeedback}
           </div>
         )}
+        <div className="mb-3">
+          <FeedbackSummaryCard projectId={projectId} tasks={charterTasks} compact />
+        </div>
         {selectedCount > 0 && (
           <div className="mb-3 flex flex-wrap items-center justify-between gap-2 rounded-xl bg-slate-50 px-3 py-2 text-xs text-slate-600">
             <span>已选择 {selectedCount} 项任务</span>

frontend/src/components/sstc/SSTCWorkspace.tsx

@@ -113,6 +113,7 @@ export function SSTCWorkspace(props: SSTCWorkspaceViewProps) {
       )}
       {props.activeStage === "execution" && (
         <SSTCExecutionBoardPanel
+          projectId={props.projectId}
           stageLocked={orchestration.stageLocked}
           projectUsers={orchestration.projectUsers}
           optimizeFeedback={orchestration.optimizeFeedback}

frontend/src/pages/ai-steward/AIStewardPage.tsx

@@ -1,5 +1,5 @@
 import { useState, useEffect } from "react";
-import { useNavigate } from "react-router-dom";
+import { useNavigate, useSearchParams } from "react-router-dom";
 import { Sparkles } from "lucide-react";
 import { apiBaseURL, getAuthToken } from "@/lib/api";
 import {
@@ -8,6 +8,7 @@ import {
 import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
 import { useAuthStore } from "@/stores/auth";
 import { QAHistoryPanel } from "@/components/ai-steward/QAHistoryPanel";
+import { parseUserRoles } from "@/api/users";
 import {
   aiStewardApi,
   type LLMEngine,
@@ -21,11 +22,15 @@ import type {
   Blocker,
   CognitiveConflict,
   DailyBriefing,
+  FeedbackCenterSourceGroup,
+  FeedbackCenterSection,
+  FeedbackCenterTrendWindowDays,
   EngineDataState,
   EngineForm,
   EngineMessage,
   EngineProviderPresets,
   ProjectFromTextResponse,
+  AIStewardTab,
 } from "./types";
 import FeatureOverviewCards from "./components/FeatureOverviewCards";
 import BlockersTab from "./sections/BlockersTab";
@@ -33,12 +38,54 @@ import BriefingTab from "./sections/BriefingTab";
 import ConflictsTab from "./sections/ConflictsTab";
 import CreateProjectTab from "./sections/CreateProjectTab";
 import EngineTab from "./sections/EngineTab";
+import FeedbackCenterTab from "./sections/FeedbackCenterTab";
 import OverviewTab from "./sections/OverviewTab";
 import SignalsTab from "./sections/SignalsTab";
 
+const AIS_STEWARD_TABS: AIStewardTab[] = [
+  "overview",
+  "create",
+  "signals",
+  "conflicts",
+  "blockers",
+  "briefing",
+  "qa",
+  "feedback-center",
+  "engine",
+];
+
+const FEEDBACK_SECTIONS: FeedbackCenterSection[] = ["overview", "metrics", "trend", "task", "qa", "signal"];
+
+function normalizeTab(value: string | null): AIStewardTab {
+  if (value && AIS_STEWARD_TABS.includes(value as AIStewardTab)) {
+    return value as AIStewardTab;
+  }
+  return "overview";
+}
+
+function normalizeSection(value: string | null): FeedbackCenterSection {
+  if (value && FEEDBACK_SECTIONS.includes(value as FeedbackCenterSection)) {
+    return value as FeedbackCenterSection;
+  }
+  return "overview";
+}
+
+function normalizeTrendWindow(value: string | null): FeedbackCenterTrendWindowDays {
+  return value === "30" ? 30 : 7;
+}
+
+function normalizeTrendSourceGroup(value: string | null): FeedbackCenterSourceGroup {
+  if (value === "task" || value === "qa" || value === "signal") {
+    return value;
+  }
+  return "all";
+}
+
 export default function AIStewardPage() {
   const navigate = useNavigate();
+  const [searchParams, setSearchParams] = useSearchParams();
   const user = useAuthStore((s) => s.user);
+  const canViewFeedbackCenter = parseUserRoles(user?.role).some((role) => role === "ADMIN" || role === "BOSS");
 
   // Project creation state
   const [input, setInput] = useState("");
@@ -47,7 +94,16 @@ export default function AIStewardPage() {
   const [error, setError] = useState<string | null>(null);
 
   // AI Steward features state
-  const [activeTab, setActiveTab] = useState("overview");
+  const [activeTab, setActiveTab] = useState<AIStewardTab>(() => normalizeTab(searchParams.get("tab")));
+  const [feedbackSection, setFeedbackSection] = useState<FeedbackCenterSection>(() =>
+    normalizeSection(searchParams.get("section"))
+  );
+  const [trendWindowDays, setTrendWindowDays] = useState<FeedbackCenterTrendWindowDays>(() =>
+    normalizeTrendWindow(searchParams.get("window"))
+  );
+  const [trendSourceGroup, setTrendSourceGroup] = useState<FeedbackCenterSourceGroup>(() =>
+    normalizeTrendSourceGroup(searchParams.get("scope"))
+  );
   const [conflicts, setConflicts] = useState<CognitiveConflict[]>([]);
   const [blockers, setBlockers] = useState<Blocker[]>([]);
   const [briefing, setBriefing] = useState<DailyBriefing | null>(null);
@@ -422,8 +478,68 @@ export default function AIStewardPage() {
     }
   };
 
+  const syncUrlParams = (
+    tab: AIStewardTab,
+    section: FeedbackCenterSection,
+    nextWindowDays: FeedbackCenterTrendWindowDays = trendWindowDays,
+    nextSourceGroup: FeedbackCenterSourceGroup = trendSourceGroup
+  ) => {
+    const next = new URLSearchParams(searchParams);
+    next.set("tab", tab);
+    next.set("section", section);
+    next.set("window", String(nextWindowDays));
+    next.set("scope", nextSourceGroup);
+    setSearchParams(next, { replace: true });
+  };
+
+  const handleTabChange = (value: string) => {
+    const nextTab = normalizeTab(value);
+    if (nextTab === "feedback-center" && !canViewFeedbackCenter) {
+      setActiveTab("overview");
+      syncUrlParams("overview", "overview");
+      return;
+    }
+    setActiveTab(nextTab);
+    syncUrlParams(nextTab, feedbackSection);
+  };
+
+  const handleFeedbackSectionChange = (nextSection: FeedbackCenterSection) => {
+    setFeedbackSection(nextSection);
+    syncUrlParams(activeTab, nextSection);
+  };
+
+  const handleTrendWindowDaysChange = (nextWindowDays: FeedbackCenterTrendWindowDays) => {
+    setTrendWindowDays(nextWindowDays);
+    syncUrlParams(activeTab, feedbackSection, nextWindowDays, trendSourceGroup);
+  };
+
+  const handleTrendSourceGroupChange = (nextSourceGroup: FeedbackCenterSourceGroup) => {
+    setTrendSourceGroup(nextSourceGroup);
+    syncUrlParams(activeTab, feedbackSection, trendWindowDays, nextSourceGroup);
+  };
+
   // ==================== Initial Load ====================
 
+  useEffect(() => {
+    const urlTab = normalizeTab(searchParams.get("tab"));
+    const urlSection = normalizeSection(searchParams.get("section"));
+    const safeTab = urlTab === "feedback-center" && !canViewFeedbackCenter ? "overview" : urlTab;
+    if (safeTab !== activeTab) {
+      setActiveTab(safeTab);
+    }
+    if (urlSection !== feedbackSection) {
+      setFeedbackSection(urlSection);
+    }
+    const urlWindow = normalizeTrendWindow(searchParams.get("window"));
+    if (urlWindow !== trendWindowDays) {
+      setTrendWindowDays(urlWindow);
+    }
+    const urlSourceGroup = normalizeTrendSourceGroup(searchParams.get("scope"));
+    if (urlSourceGroup !== trendSourceGroup) {
+      setTrendSourceGroup(urlSourceGroup);
+    }
+  }, [searchParams, activeTab, feedbackSection, trendWindowDays, trendSourceGroup, canViewFeedbackCenter]);
+
   useEffect(() => {
     if (activeTab === "conflicts") loadConflicts();
     if (activeTab === "blockers") loadBlockers();
@@ -452,12 +568,12 @@ export default function AIStewardPage() {
           </p>
         </div>
 
-        <FeatureOverviewCards onSelectTab={(tab) => setActiveTab(tab)} />
+        <FeatureOverviewCards onSelectTab={(tab) => handleTabChange(tab)} />
 
         {/* Main Content */}
         <Card>
           <CardContent className="pt-6">
-            <Tabs value={activeTab} onValueChange={setActiveTab}>
+            <Tabs value={activeTab} onValueChange={handleTabChange}>
               <TabsList className="flex w-full min-w-0 flex-nowrap gap-1 overflow-x-auto rounded-lg p-1 [-webkit-overflow-scrolling:touch]">
                 <TabsTrigger value="overview" className="shrink-0 whitespace-nowrap">总览</TabsTrigger>
                 <TabsTrigger value="create" className="shrink-0 whitespace-nowrap">零配置创建</TabsTrigger>
@@ -466,6 +582,9 @@ export default function AIStewardPage() {
                 <TabsTrigger value="blockers" className="shrink-0 whitespace-nowrap">阻塞检测</TabsTrigger>
                 <TabsTrigger value="briefing" className="shrink-0 whitespace-nowrap">每日简报</TabsTrigger>
                 <TabsTrigger value="qa" className="shrink-0 whitespace-nowrap">问答记录</TabsTrigger>
+                {canViewFeedbackCenter && (
+                  <TabsTrigger value="feedback-center" className="shrink-0 whitespace-nowrap">反馈中心</TabsTrigger>
+                )}
                 <TabsTrigger value="engine" className="shrink-0 whitespace-nowrap">引擎配置</TabsTrigger>
               </TabsList>
 
@@ -511,6 +630,18 @@ export default function AIStewardPage() {
                 <QAHistoryPanel />
               </TabsContent>
 
+              {canViewFeedbackCenter && (
+              <FeedbackCenterTab
+                  section={feedbackSection}
+                  projectId={Number(searchParams.get("project_id") || "0") || null}
+                  onSectionChange={handleFeedbackSectionChange}
+                  trendWindowDays={trendWindowDays}
+                  trendSourceGroup={trendSourceGroup}
+                  onTrendWindowDaysChange={handleTrendWindowDaysChange}
+                  onTrendSourceGroupChange={handleTrendSourceGroupChange}
+                />
+              )}
+
               <EngineTab
                 featureLoading={featureLoading}
                 engineData={engineData}

frontend/src/pages/ai-steward/components/feedback-center/FeedbackDetail.tsx

@@ -0,0 +1,125 @@
+import type { FeedbackCenterRecord, FeedbackCenterRecordRevision } from "@/api/feedbackCenter";
+import { Badge } from "@/components/ui/badge";
+
+interface FeedbackDetailProps {
+  record: FeedbackCenterRecord | null;
+  revisions: FeedbackCenterRecordRevision[];
+}
+
+function formatJson(value: unknown): string {
+  if (value === null || value === undefined) return "—";
+  if (typeof value === "string") return value;
+  try {
+    return JSON.stringify(value, null, 2);
+  } catch {
+    return String(value);
+  }
+}
+
+function diffCard(title: string, value: unknown, tone: "before" | "after") {
+  const toneClass =
+    tone === "before"
+      ? "border-rose-100 bg-rose-50/70 text-rose-900"
+      : "border-emerald-100 bg-emerald-50/70 text-emerald-900";
+  return (
+    <div className={`rounded-2xl border p-4 ${toneClass}`}>
+      <p className="text-xs font-semibold uppercase tracking-[0.16em] opacity-70">{title}</p>
+      <pre className="mt-3 overflow-x-auto whitespace-pre-wrap break-words text-xs leading-6">{formatJson(value)}</pre>
+    </div>
+  );
+}
+
+export function FeedbackDetail({ record, revisions }: FeedbackDetailProps) {
+  if (!record) {
+    return (
+      <div className="rounded-2xl border border-dashed border-slate-200 bg-slate-50/80 p-5 text-sm text-slate-500">
+        从左侧选择一条记录，这里会展示 AI 原始结果、人工修正 diff 和修订历史。
+      </div>
+    );
+  }
+
+  return (
+    <div className="space-y-4 rounded-2xl border border-slate-200 bg-white p-5 shadow-sm">
+      <div className="flex flex-wrap items-start justify-between gap-3">
+        <div className="min-w-0">
+          <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Diff View</p>
+          <h3 className="mt-1 text-lg font-semibold text-slate-900">
+            {record.object_title || record.source_title || `记录 #${record.id}`}
+          </h3>
+          <p className="mt-1 text-sm text-slate-500">
+            {record.source_type} · 字段 {record.field_name}
+          </p>
+        </div>
+        <div className="flex flex-wrap gap-2">
+          <Badge className="border-slate-200 bg-slate-100 text-slate-700">状态 {record.review_status}</Badge>
+          {record.auto_assigned && (
+            <Badge className="border-emerald-200 bg-emerald-50 text-emerald-700">AI 自动指派</Badge>
+          )}
+        </div>
+      </div>
+
+      <div className="grid gap-3 md:grid-cols-2">
+        <div className="rounded-2xl border border-slate-200 bg-slate-50 p-4">
+          <p className="text-xs font-semibold uppercase tracking-[0.16em] text-slate-400">Meta</p>
+          <div className="mt-3 grid gap-3 text-sm text-slate-700 sm:grid-cols-2">
+            <div>
+              <p className="text-xs text-slate-500">项目</p>
+              <p>{record.project_id ?? "—"}</p>
+            </div>
+            <div>
+              <p className="text-xs text-slate-500">对象</p>
+              <p>{record.object_type} #{record.object_id}</p>
+            </div>
+            <div>
+              <p className="text-xs text-slate-500">原因码</p>
+              <p>{record.reason_code || "—"}</p>
+            </div>
+            <div>
+              <p className="text-xs text-slate-500">置信度</p>
+              <p>{record.ai_confidence !== null && record.ai_confidence !== undefined ? `${Math.round(Number(record.ai_confidence) * 100)}%` : "—"}</p>
+            </div>
+          </div>
+        </div>
+        <div className="rounded-2xl border border-slate-200 bg-slate-50 p-4">
+          <p className="text-xs font-semibold uppercase tracking-[0.16em] text-slate-400">AI Snapshot</p>
+          <pre className="mt-3 max-h-[210px] overflow-auto whitespace-pre-wrap break-words text-xs leading-6 text-slate-700">
+            {formatJson(record.ai_snapshot)}
+          </pre>
+        </div>
+      </div>
+
+      <div className="grid gap-3 xl:grid-cols-2">
+        {diffCard("AI Before", record.before_value, "before")}
+        {diffCard("Human After", record.after_value, "after")}
+      </div>
+
+      <div className="rounded-2xl border border-slate-200 bg-slate-50 p-4">
+        <div className="flex items-center justify-between gap-2">
+          <p className="text-xs font-semibold uppercase tracking-[0.16em] text-slate-400">Revisions</p>
+          <span className="text-xs text-slate-500">{revisions.length} 条</span>
+        </div>
+        {!revisions.length && <p className="mt-3 text-sm text-slate-500">暂无修订历史。</p>}
+        {!!revisions.length && (
+          <div className="mt-3 space-y-3">
+            {revisions.map((revision) => (
+              <div key={revision.id} className="rounded-xl border border-slate-200 bg-white p-3">
+                <p className="text-xs text-slate-500">
+                  修订 #{revision.id}
+                  {revision.reviewed_at ? ` · ${revision.reviewed_at}` : ""}
+                </p>
+                <div className="mt-2 grid gap-3 md:grid-cols-2">
+                  <pre className="overflow-x-auto whitespace-pre-wrap break-words rounded-lg bg-slate-50 p-2 text-[11px] leading-5 text-slate-700">
+                    {formatJson(revision.before_snapshot)}
+                  </pre>
+                  <pre className="overflow-x-auto whitespace-pre-wrap break-words rounded-lg bg-slate-50 p-2 text-[11px] leading-5 text-slate-700">
+                    {formatJson(revision.after_snapshot)}
+                  </pre>
+                </div>
+              </div>
+            ))}
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}

frontend/src/pages/ai-steward/components/feedback-center/FeedbackList.tsx

@@ -0,0 +1,131 @@
+import type { FeedbackCenterRecord } from "@/api/feedbackCenter";
+import { Badge } from "@/components/ui/badge";
+import { Input } from "@/components/ui/input";
+
+interface FeedbackListProps {
+  items: FeedbackCenterRecord[];
+  selectedId?: number | null;
+  search: string;
+  onSearchChange: (value: string) => void;
+  sourceType: string;
+  reviewStatus: string;
+  onSourceTypeChange: (value: string) => void;
+  onReviewStatusChange: (value: string) => void;
+  onSelect: (item: FeedbackCenterRecord) => void;
+  total: number;
+  isLoading: boolean;
+}
+
+function statusTone(status: string): string {
+  if (status === "accepted") return "border-emerald-200 bg-emerald-50 text-emerald-700";
+  if (status === "rejected") return "border-rose-200 bg-rose-50 text-rose-700";
+  if (status === "reviewed") return "border-sky-200 bg-sky-50 text-sky-700";
+  return "border-amber-200 bg-amber-50 text-amber-700";
+}
+
+function sourceLabel(sourceType: string): string {
+  if (sourceType === "task_breakdown" || sourceType === "task_override") return "任务";
+  if (sourceType === "qa_feedback") return "QA";
+  if (sourceType === "signal_feedback") return "信号";
+  return sourceType || "未知";
+}
+
+export function FeedbackList({
+  items,
+  selectedId,
+  search,
+  onSearchChange,
+  sourceType,
+  reviewStatus,
+  onSourceTypeChange,
+  onReviewStatusChange,
+  onSelect,
+  total,
+  isLoading,
+}: FeedbackListProps) {
+  return (
+    <div className="rounded-2xl border border-slate-200 bg-white shadow-sm">
+      <div className="border-b border-slate-100 p-4">
+        <div className="space-y-3">
+          <div>
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Queue</p>
+            <h3 className="text-lg font-semibold text-slate-900">待复核记录</h3>
+          </div>
+          <Input
+            value={search}
+            onChange={(event) => onSearchChange(event.target.value)}
+            placeholder="搜索任务标题、问题、信号..."
+            className="border-slate-200"
+          />
+          <div className="grid grid-cols-2 gap-2">
+            <select
+              value={sourceType}
+              onChange={(event) => onSourceTypeChange(event.target.value)}
+              className="h-10 rounded-md border border-slate-200 bg-white px-3 text-sm text-slate-700"
+            >
+              <option value="">全部来源</option>
+              <option value="task_breakdown">任务拆解</option>
+              <option value="task_override">任务覆盖</option>
+              <option value="qa_feedback">QA 反馈</option>
+              <option value="signal_feedback">信号反馈</option>
+            </select>
+            <select
+              value={reviewStatus}
+              onChange={(event) => onReviewStatusChange(event.target.value)}
+              className="h-10 rounded-md border border-slate-200 bg-white px-3 text-sm text-slate-700"
+            >
+              <option value="">全部状态</option>
+              <option value="pending">待复核</option>
+              <option value="reviewed">已复核</option>
+              <option value="accepted">已采纳</option>
+              <option value="rejected">已拒绝</option>
+            </select>
+          </div>
+          <p className="text-xs text-slate-500">共 {total} 条记录</p>
+        </div>
+      </div>
+
+      <div className="max-h-[720px] space-y-2 overflow-y-auto p-3">
+        {isLoading && <p className="px-2 py-4 text-sm text-slate-500">加载中...</p>}
+        {!isLoading && !items.length && <p className="px-2 py-6 text-sm text-slate-500">当前筛选下还没有反馈记录。</p>}
+        {items.map((item) => {
+          const selected = selectedId === item.id;
+          return (
+            <button
+              key={item.id}
+              type="button"
+              className={`w-full rounded-xl border p-3 text-left transition ${
+                selected
+                  ? "border-slate-900 bg-slate-900 text-white shadow-lg"
+                  : "border-slate-200 bg-slate-50/80 text-slate-800 hover:border-slate-300 hover:bg-white"
+              }`}
+              onClick={() => onSelect(item)}
+            >
+              <div className="flex flex-wrap items-start justify-between gap-2">
+                <div className="min-w-0 flex-1">
+                  <p className={`line-clamp-2 text-sm font-semibold ${selected ? "text-white" : "text-slate-900"}`}>
+                    {item.object_title || item.source_title || `${sourceLabel(item.source_type)} #${item.object_id}`}
+                  </p>
+                  <p className={`mt-1 text-xs ${selected ? "text-slate-200" : "text-slate-500"}`}>
+                    {sourceLabel(item.source_type)} · 字段 {item.field_name}
+                  </p>
+                </div>
+                <Badge className={selected ? "border-white/20 bg-white/10 text-white" : statusTone(item.review_status)}>
+                  {item.review_status}
+                </Badge>
+              </div>
+              <div className={`mt-3 flex flex-wrap gap-2 text-[11px] ${selected ? "text-slate-200" : "text-slate-500"}`}>
+                <span>对象 ID {item.object_id}</span>
+                <span>来源 ID {item.source_id}</span>
+                {item.auto_assigned && <span>自动指派</span>}
+                {item.ai_confidence !== null && item.ai_confidence !== undefined && (
+                  <span>置信度 {Math.round(Number(item.ai_confidence) * 100)}%</span>
+                )}
+              </div>
+            </button>
+          );
+        })}
+      </div>
+    </div>
+  );
+}

frontend/src/pages/ai-steward/components/feedback-center/MetricsPanel.tsx

@@ -0,0 +1,268 @@
+import type { FeedbackCenterSummary } from "@/api/feedbackCenter";
+import { Badge } from "@/components/ui/badge";
+import { Card, CardContent } from "@/components/ui/card";
+
+interface MetricsPanelProps {
+  summary?: FeedbackCenterSummary | null;
+}
+
+const EMPTY_METRICS = {
+  accepted_rate_reviewed: 0,
+  manual_override_rate: 0,
+  rework_rate: 0,
+  auto_assigned_rate: 0,
+  avg_review_latency_minutes: 0,
+  total_task_breakdown_count: 0,
+  total_task_override_count: 0,
+  total_qa_feedback_count: 0,
+  total_signal_feedback_count: 0,
+  total_revision_count: 0,
+};
+
+const SOURCE_LABELS: Record<string, string> = {
+  task_breakdown: "任务拆解",
+  task_override: "任务覆盖",
+  qa_feedback: "QA 反馈",
+  signal_feedback: "信号反馈",
+};
+
+const STATUS_LABELS: Record<string, string> = {
+  pending: "待复核",
+  reviewed: "已复核",
+  accepted: "已采纳",
+  rejected: "已拒绝",
+};
+
+function formatPercent(value: number): string {
+  return `${Math.round(value * 100)}%`;
+}
+
+function formatMinutes(value: number): string {
+  if (!Number.isFinite(value) || value <= 0) return "0m";
+  if (value < 60) return `${Math.round(value)}m`;
+  const hours = Math.floor(value / 60);
+  const minutes = Math.round(value % 60);
+  return `${hours}h ${minutes}m`;
+}
+
+function StatGridCard({
+  title,
+  value,
+  helper,
+}: {
+  title: string;
+  value: string;
+  helper: string;
+}) {
+  return (
+    <div className="rounded-2xl border border-slate-200 bg-white p-4 shadow-sm">
+      <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">{title}</p>
+      <p className="mt-3 text-2xl font-semibold text-slate-900">{value}</p>
+      <p className="mt-2 text-sm text-slate-500">{helper}</p>
+    </div>
+  );
+}
+
+function ProgressRows({
+  title,
+  items,
+  tone = "slate",
+  totalLabel,
+}: {
+  title: string;
+  items: Array<{ label: string; count: number; percent: number }>;
+  tone?: "slate" | "emerald" | "amber" | "rose" | "sky";
+  totalLabel?: string;
+}) {
+  const filledClass = {
+    slate: "bg-slate-900",
+    emerald: "bg-emerald-500",
+    amber: "bg-amber-500",
+    rose: "bg-rose-500",
+    sky: "bg-sky-500",
+  }[tone];
+  const emptyClass = {
+    slate: "bg-slate-200",
+    emerald: "bg-emerald-100",
+    amber: "bg-amber-100",
+    rose: "bg-rose-100",
+    sky: "bg-sky-100",
+  }[tone];
+
+  return (
+    <Card className="rounded-2xl border-slate-200 bg-white shadow-sm">
+      <CardContent className="p-5">
+        <div className="flex items-center justify-between gap-3">
+          <div>
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">{title}</p>
+            {totalLabel && <p className="mt-1 text-sm text-slate-500">{totalLabel}</p>}
+          </div>
+          <Badge variant="outline" className="border-slate-200 bg-slate-50 text-slate-600">
+            {items.length} 项
+          </Badge>
+        </div>
+        <div className="mt-4 space-y-4">
+          {items.length > 0 ? (
+            items.map((item) => {
+              const filled = item.percent <= 0 ? 0 : Math.max(1, Math.min(10, Math.round(item.percent * 10)));
+              return (
+                <div key={item.label} className="space-y-2">
+                  <div className="flex items-center justify-between gap-3 text-sm">
+                    <span className="font-medium text-slate-700">{item.label}</span>
+                    <span className="text-slate-500">{item.count}</span>
+                  </div>
+                  <div className="grid grid-cols-10 gap-1">
+                    {Array.from({ length: 10 }).map((_, index) => (
+                      <span
+                        key={`${item.label}-${index}`}
+                        className={`h-2 rounded-full ${index < filled ? filledClass : emptyClass}`}
+                      />
+                    ))}
+                  </div>
+                  <p className="text-xs text-slate-500">{formatPercent(item.percent)}</p>
+                </div>
+              );
+            })
+          ) : (
+            <p className="text-sm text-slate-500">暂无可展示的分布数据。</p>
+          )}
+        </div>
+      </CardContent>
+    </Card>
+  );
+}
+
+export function FeedbackMetricsPanel({ summary }: MetricsPanelProps) {
+  const metrics = summary?.metrics ?? EMPTY_METRICS;
+  const total = summary?.total ?? 0;
+  const bySourceType = summary?.by_source_type || {};
+  const byStatus = summary?.by_status || {};
+  const sourceTotal = Object.values(bySourceType).reduce((acc, value) => acc + value, 0) || 0;
+  const statusTotal = Object.values(byStatus).reduce((acc, value) => acc + value, 0) || 0;
+
+  const sourceRows = Object.entries(bySourceType)
+    .map(([label, count]) => ({
+      label: SOURCE_LABELS[label] || label,
+      count,
+      percent: sourceTotal > 0 ? count / sourceTotal : 0,
+    }))
+    .sort((a, b) => b.count - a.count);
+
+  const statusRows = Object.entries(byStatus)
+    .map(([label, count]) => ({
+      label: STATUS_LABELS[label] || label,
+      count,
+      percent: statusTotal > 0 ? count / statusTotal : 0,
+    }))
+    .sort((a, b) => b.count - a.count);
+
+  const reviewDecisionTotal = (summary?.accepted_count || 0) + (summary?.rejected_count || 0);
+
+  return (
+    <div className="space-y-5">
+      <div className="grid gap-4 md:grid-cols-2 xl:grid-cols-3">
+          <StatGridCard
+          title="采纳率"
+          value={formatPercent(metrics.accepted_rate_reviewed)}
+          helper={`已决策样本 ${reviewDecisionTotal} 条中进入学习池的比例`}
+        />
+        <StatGridCard
+          title="人工覆盖率"
+          value={formatPercent(metrics.manual_override_rate)}
+          helper={`任务覆盖样本 ${metrics.total_task_override_count} 条 / 任务拆解 ${metrics.total_task_breakdown_count} 条`}
+        />
+        <StatGridCard
+          title="返工率"
+          value={formatPercent(metrics.rework_rate)}
+          helper={`有二次修订的样本占比，累计修订历史 ${metrics.total_revision_count} 条`}
+        />
+        <StatGridCard
+          title="自动指派率"
+          value={formatPercent(metrics.auto_assigned_rate)}
+          helper={`任务拆解中被自动落负责人/自动完成的比例`}
+        />
+        <StatGridCard
+          title="平均复核耗时"
+          value={formatMinutes(metrics.avg_review_latency_minutes)}
+          helper="从样本入库到人工复核的平均耗时"
+        />
+        <StatGridCard title="样本总量" value={`${total}`} helper="当前反馈中心累计可学习样本数" />
+      </div>
+
+      <div className="grid gap-4 xl:grid-cols-2">
+        <Card className="rounded-2xl border-slate-200 bg-white shadow-sm">
+          <CardContent className="p-5">
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Learning Health</p>
+            <h3 className="mt-2 text-lg font-semibold text-slate-900">学习健康度</h3>
+            <p className="mt-1 text-sm text-slate-500">把关键采纳指标压成一组可扫读的条形信号，方便快速判断自动化是否在变好。</p>
+            <div className="mt-4 grid gap-4 md:grid-cols-2">
+              <div className="rounded-2xl border border-slate-100 bg-slate-50 p-4">
+                <p className="text-sm font-medium text-slate-700">采纳率</p>
+                <div className="mt-3 grid grid-cols-10 gap-1">
+                  {Array.from({ length: 10 }).map((_, index) => (
+                    <span
+                      key={`accept-${index}`}
+                      className={`h-2 rounded-full ${index < Math.max(1, Math.round(metrics.accepted_rate_reviewed * 10)) ? "bg-emerald-500" : "bg-emerald-100"}`}
+                    />
+                  ))}
+                </div>
+                <p className="mt-2 text-xs text-slate-500">{formatPercent(metrics.accepted_rate_reviewed)}</p>
+              </div>
+              <div className="rounded-2xl border border-slate-100 bg-slate-50 p-4">
+                <p className="text-sm font-medium text-slate-700">人工覆盖</p>
+                <div className="mt-3 grid grid-cols-10 gap-1">
+                  {Array.from({ length: 10 }).map((_, index) => (
+                    <span
+                      key={`override-${index}`}
+                      className={`h-2 rounded-full ${index < Math.max(1, Math.round(metrics.manual_override_rate * 10)) ? "bg-amber-500" : "bg-amber-100"}`}
+                    />
+                  ))}
+                </div>
+                <p className="mt-2 text-xs text-slate-500">{formatPercent(metrics.manual_override_rate)}</p>
+              </div>
+              <div className="rounded-2xl border border-slate-100 bg-slate-50 p-4">
+                <p className="text-sm font-medium text-slate-700">返工</p>
+                <div className="mt-3 grid grid-cols-10 gap-1">
+                  {Array.from({ length: 10 }).map((_, index) => (
+                    <span
+                      key={`rework-${index}`}
+                      className={`h-2 rounded-full ${index < Math.max(1, Math.round(metrics.rework_rate * 10)) ? "bg-rose-500" : "bg-rose-100"}`}
+                    />
+                  ))}
+                </div>
+                <p className="mt-2 text-xs text-slate-500">{formatPercent(metrics.rework_rate)}</p>
+              </div>
+              <div className="rounded-2xl border border-slate-100 bg-slate-50 p-4">
+                <p className="text-sm font-medium text-slate-700">自动指派</p>
+                <div className="mt-3 grid grid-cols-10 gap-1">
+                  {Array.from({ length: 10 }).map((_, index) => (
+                    <span
+                      key={`auto-${index}`}
+                      className={`h-2 rounded-full ${index < Math.max(1, Math.round(metrics.auto_assigned_rate * 10)) ? "bg-sky-500" : "bg-sky-100"}`}
+                    />
+                  ))}
+                </div>
+                <p className="mt-2 text-xs text-slate-500">{formatPercent(metrics.auto_assigned_rate)}</p>
+              </div>
+            </div>
+          </CardContent>
+        </Card>
+
+        <div className="space-y-4">
+          <ProgressRows
+            title="样本来源构成"
+            totalLabel={`总计 ${sourceTotal} 条`}
+            items={sourceRows}
+            tone="slate"
+          />
+          <ProgressRows
+            title="复核状态分布"
+            totalLabel={`总计 ${statusTotal} 条`}
+            items={statusRows}
+            tone="emerald"
+          />
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/pages/ai-steward/components/feedback-center/ReasonPicker.tsx

@@ -0,0 +1,142 @@
+import { useEffect, useMemo, useState } from "react";
+import type {
+  FeedbackCenterRecord,
+  FeedbackCenterReviewStatus,
+  FeedbackReasonCandidate,
+} from "@/api/feedbackCenter";
+import { Badge } from "@/components/ui/badge";
+import { Button } from "@/components/ui/button";
+import { Textarea } from "@/components/ui/textarea";
+
+interface ReasonPickerProps {
+  record: FeedbackCenterRecord | null;
+  catalog: FeedbackReasonCandidate[];
+  isPending: boolean;
+  onSubmit: (payload: {
+    review_status: Exclude<FeedbackCenterReviewStatus, "pending">;
+    reason_code?: string;
+    reason_text?: string;
+  }) => Promise<void> | void;
+}
+
+export function ReasonPicker({ record, catalog, isPending, onSubmit }: ReasonPickerProps) {
+  const [selectedReason, setSelectedReason] = useState<string>("");
+  const [reasonText, setReasonText] = useState("");
+  const [targetStatus, setTargetStatus] = useState<Exclude<FeedbackCenterReviewStatus, "pending">>("accepted");
+
+  const candidates = useMemo(() => {
+    if (!record) return [];
+    const base = Array.isArray(record.reason_candidates) ? record.reason_candidates : [];
+    const merged = [...base];
+    catalog.forEach((item) => {
+      if (merged.some((candidate) => candidate.code === item.code)) return;
+      merged.push(item);
+    });
+    return merged;
+  }, [catalog, record]);
+
+  useEffect(() => {
+    setSelectedReason(record?.reason_code || "");
+    setReasonText(record?.reason_text || "");
+    if (record?.review_status === "reviewed" || record?.review_status === "rejected") {
+      setTargetStatus(record.review_status);
+      return;
+    }
+    setTargetStatus("accepted");
+  }, [record?.id, record?.reason_code, record?.reason_text, record?.review_status]);
+
+  if (!record) {
+    return (
+      <div className="rounded-2xl border border-dashed border-slate-200 bg-slate-50/80 p-5 text-sm text-slate-500">
+        选择左侧记录后，这里会显示候选原因与学习确认操作。
+      </div>
+    );
+  }
+
+  return (
+    <div className="rounded-2xl border border-slate-200 bg-white p-5 shadow-sm">
+      <div className="space-y-1">
+        <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Review Action</p>
+        <h3 className="text-lg font-semibold text-slate-900">确认学习原因</h3>
+        <p className="text-sm text-slate-500">AI 已经先给出候选原因，人只需要确认、修正，或拒绝纳入学习。</p>
+      </div>
+
+      <div className="mt-5 space-y-3">
+        <div className="flex flex-wrap gap-2">
+          {(["reviewed", "accepted", "rejected"] as const).map((status) => (
+            <Button
+              key={status}
+              type="button"
+              size="sm"
+              variant={targetStatus === status ? "default" : "outline"}
+              className={targetStatus === status ? "bg-slate-900 text-white hover:bg-slate-800" : ""}
+              onClick={() => setTargetStatus(status)}
+            >
+              {status === "reviewed" ? "仅复核" : status === "accepted" ? "确认学习" : "拒绝样本"}
+            </Button>
+          ))}
+        </div>
+
+        <div className="space-y-2">
+          <p className="text-sm font-medium text-slate-700">候选原因</p>
+          <div className="flex flex-wrap gap-2">
+            {candidates.map((candidate) => {
+              const active = selectedReason === candidate.code;
+              return (
+                <button
+                  key={candidate.code}
+                  type="button"
+                  className={`rounded-full border px-3 py-1 text-xs transition ${
+                    active
+                      ? "border-slate-900 bg-slate-900 text-white"
+                      : "border-slate-200 bg-slate-50 text-slate-600 hover:border-slate-300 hover:bg-slate-100"
+                  }`}
+                  onClick={() => setSelectedReason(candidate.code)}
+                >
+                  {candidate.label}
+                  {candidate.confidence !== undefined && candidate.confidence !== null && (
+                    <span className={`ml-1 ${active ? "text-slate-200" : "text-slate-400"}`}>
+                      {Math.round(Number(candidate.confidence) * 100)}%
+                    </span>
+                  )}
+                </button>
+              );
+            })}
+          </div>
+          {!candidates.length && (
+            <Badge variant="outline" className="border-amber-200 bg-amber-50 text-amber-700">
+              当前没有自动候选，请手工补充原因
+            </Badge>
+          )}
+        </div>
+
+        <div className="space-y-2">
+          <p className="text-sm font-medium text-slate-700">补充备注</p>
+          <Textarea
+            value={reasonText}
+            onChange={(event) => setReasonText(event.target.value)}
+            placeholder="例如：依赖链路还没锁定，所以负责人不该提前自动落库。"
+            className="min-h-[110px] resize-none border-slate-200"
+          />
+        </div>
+
+        <Button
+          type="button"
+          className="w-full bg-blue-600 text-white hover:bg-blue-700"
+          disabled={isPending}
+          onClick={() =>
+            Promise.resolve(
+              onSubmit({
+                review_status: targetStatus,
+                reason_code: selectedReason || undefined,
+                reason_text: reasonText.trim() || undefined,
+              })
+            )
+          }
+        >
+          {isPending ? "提交中..." : targetStatus === "accepted" ? "确认进入学习池" : targetStatus === "rejected" ? "拒绝该样本" : "保存复核结果"}
+        </Button>
+      </div>
+    </div>
+  );
+}

frontend/src/pages/ai-steward/components/feedback-center/TrendPanel.tsx

@@ -0,0 +1,382 @@
+import type {
+  FeedbackCenterSourceGroup,
+  FeedbackCenterSummary,
+  FeedbackCenterTrendWindowDays,
+} from "@/api/feedbackCenter";
+import { Badge } from "@/components/ui/badge";
+import { Button } from "@/components/ui/button";
+import { Card, CardContent } from "@/components/ui/card";
+
+interface TrendPanelProps {
+  summary?: FeedbackCenterSummary | null;
+  isLoading?: boolean;
+  windowDays: FeedbackCenterTrendWindowDays;
+  sourceGroup: FeedbackCenterSourceGroup;
+  onWindowDaysChange: (value: FeedbackCenterTrendWindowDays) => void;
+  onSourceGroupChange: (value: FeedbackCenterSourceGroup) => void;
+}
+
+const STATUS_COLORS: Record<string, string> = {
+  accepted: "bg-emerald-500",
+  rejected: "bg-rose-500",
+  reviewed: "bg-sky-500",
+  pending: "bg-amber-500",
+};
+
+const WINDOW_OPTIONS: FeedbackCenterTrendWindowDays[] = [7, 30];
+const SOURCE_GROUP_OPTIONS: FeedbackCenterSourceGroup[] = ["all", "task", "qa", "signal"];
+
+const SOURCE_GROUP_LABELS: Record<FeedbackCenterSourceGroup, string> = {
+  all: "全部",
+  task: "任务",
+  qa: "QA",
+  signal: "信号",
+};
+
+function formatPercent(value: number): string {
+  return `${Math.round(value * 100)}%`;
+}
+
+function formatDelta(value: number): string {
+  const sign = value > 0 ? "+" : "";
+  return `${sign}${Math.round(value * 100)}%`;
+}
+
+function formatMinutes(value: number): string {
+  if (!Number.isFinite(value) || value <= 0) return "0m";
+  if (value < 60) return `${Math.round(value)}m`;
+  const hours = Math.floor(value / 60);
+  const minutes = Math.round(value % 60);
+  return `${hours}h ${minutes}m`;
+}
+
+function dayLabel(value: string): string {
+  const date = new Date(`${value}T00:00:00`);
+  if (Number.isNaN(date.getTime())) return value;
+  return new Intl.DateTimeFormat("zh-CN", { month: "2-digit", day: "2-digit" }).format(date);
+}
+
+function scopeLabel(value: FeedbackCenterSourceGroup): string {
+  if (value === "task") return "任务（拆解 + 覆盖）";
+  if (value === "qa") return "QA";
+  if (value === "signal") return "信号";
+  return "全部来源";
+}
+
+function MetricCard({
+  title,
+  value,
+  previous,
+  deltaLabel,
+  deltaValue,
+  trendDirection = "up",
+  helper,
+}: {
+  title: string;
+  value: string;
+  previous: string;
+  deltaLabel: string;
+  deltaValue: number;
+  trendDirection?: "up" | "down";
+  helper: string;
+}) {
+  const positive = deltaValue >= 0;
+  const positiveIsGood = trendDirection === "up";
+  const goodTone = positiveIsGood ? "border-emerald-200 bg-emerald-50 text-emerald-700" : "border-rose-200 bg-rose-50 text-rose-700";
+  const badTone = positiveIsGood ? "border-rose-200 bg-rose-50 text-rose-700" : "border-emerald-200 bg-emerald-50 text-emerald-700";
+
+  return (
+    <div className="rounded-2xl border border-slate-200 bg-white p-4 shadow-sm">
+      <div className="flex items-start justify-between gap-3">
+        <div>
+          <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">{title}</p>
+          <p className="mt-3 text-2xl font-semibold text-slate-900">{value}</p>
+        </div>
+        <Badge variant="outline" className={positive ? goodTone : badTone}>
+          {deltaLabel}
+        </Badge>
+      </div>
+      <p className="mt-2 text-sm text-slate-500">上期 {previous}</p>
+      <p className="mt-1 text-xs text-slate-500">{helper}</p>
+    </div>
+  );
+}
+
+function ToggleGroup<T extends string | number>({
+  title,
+  options,
+  value,
+  onChange,
+  renderLabel,
+}: {
+  title: string;
+  options: T[];
+  value: T;
+  onChange: (next: T) => void;
+  renderLabel: (value: T) => string;
+}) {
+  return (
+    <div className="space-y-2">
+      <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">{title}</p>
+      <div className="flex flex-wrap gap-2">
+        {options.map((option) => {
+          const active = option === value;
+          return (
+            <Button
+              key={String(option)}
+              type="button"
+              size="sm"
+              variant={active ? "default" : "outline"}
+              className={active ? "bg-slate-900 text-white hover:bg-slate-800" : "border-slate-200 bg-white text-slate-700 hover:bg-slate-50"}
+            onClick={() => onChange(option)}
+          >
+              {renderLabel(option)}
+            </Button>
+          );
+        })}
+      </div>
+    </div>
+  );
+}
+
+export function FeedbackTrendPanel({
+  summary,
+  isLoading = false,
+  windowDays,
+  sourceGroup,
+  onWindowDaysChange,
+  onSourceGroupChange,
+}: TrendPanelProps) {
+  const trend = summary?.trend;
+
+  if (isLoading && !trend) {
+    return (
+      <div className="rounded-2xl border border-dashed border-slate-200 bg-slate-50/80 p-5 text-sm text-slate-500">
+        趋势数据加载中...
+      </div>
+    );
+  }
+
+  if (!trend) {
+    return (
+      <div className="rounded-2xl border border-dashed border-slate-200 bg-slate-50/80 p-5 text-sm text-slate-500">
+        当前还没有趋势数据，先累计几天反馈后，这里会显示日度变化和前后窗口对比。
+      </div>
+    );
+  }
+
+  const daily = trend.daily || [];
+  const maxTotal = Math.max(...daily.map((item) => item.total_records), 1);
+  const maxRate = Math.max(
+    ...daily.flatMap((item) => [item.manual_override_rate, item.rework_rate, item.auto_assigned_rate, item.accepted_rate_reviewed]),
+    0.01
+  );
+  const dailyGridClass = windowDays === 30 ? "grid min-w-[1200px] grid-cols-[repeat(30,minmax(0,1fr))]" : "grid grid-cols-7";
+  const sparklineGridClass = windowDays === 30 ? "grid min-w-[1200px] grid-cols-[repeat(30,minmax(0,1fr))]" : "grid grid-cols-7";
+
+  return (
+    <div className="space-y-5">
+      <div className="rounded-2xl border border-slate-200 bg-white p-5 shadow-sm">
+        <div className="flex flex-col gap-4 lg:flex-row lg:items-end lg:justify-between">
+          <div>
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Trend Scope</p>
+            <h3 className="mt-1 text-lg font-semibold text-slate-900">趋势分析范围</h3>
+            <p className="mt-1 text-sm text-slate-500">
+              这里的筛选只影响趋势页，不会改动总览、指标页或任务复核列表。
+            </p>
+          </div>
+          <div className="flex flex-wrap items-center gap-2">
+            <Badge variant="outline" className="border-slate-200 bg-slate-50 text-slate-600">
+              当前 {windowDays} 天 · {scopeLabel(sourceGroup)}
+            </Badge>
+            <Badge variant="outline" className="border-slate-200 bg-slate-50 text-slate-600">
+              {trend.window_days} 天窗口
+            </Badge>
+          </div>
+        </div>
+
+        <div className="mt-4 grid gap-4 xl:grid-cols-[1fr_1fr]">
+          <ToggleGroup
+            title="时间窗口"
+            options={WINDOW_OPTIONS}
+            value={windowDays}
+            onChange={onWindowDaysChange}
+            renderLabel={(value) => `${value} 天`}
+          />
+          <ToggleGroup
+            title="来源范围"
+            options={SOURCE_GROUP_OPTIONS}
+            value={sourceGroup}
+            onChange={onSourceGroupChange}
+            renderLabel={(value) => SOURCE_GROUP_LABELS[value]}
+          />
+        </div>
+      </div>
+
+      <div className="grid gap-4 md:grid-cols-2 xl:grid-cols-3">
+        <MetricCard
+          title="样本量"
+          value={`${trend.current.total_records}`}
+          previous={`${trend.previous.total_records}`}
+          deltaLabel={`${trend.deltas.total_records_delta > 0 ? "+" : ""}${trend.deltas.total_records_delta}`}
+          deltaValue={trend.deltas.total_records_delta}
+          helper={`最近 ${trend.window_days} 天累计样本数`}
+        />
+        <MetricCard
+          title="采纳率"
+          value={formatPercent(trend.current.accepted_rate_reviewed)}
+          previous={formatPercent(trend.previous.accepted_rate_reviewed)}
+          deltaLabel={formatDelta(trend.deltas.accepted_rate_reviewed_delta)}
+          deltaValue={trend.deltas.accepted_rate_reviewed_delta}
+          helper="已决策样本的进入学习池比例"
+        />
+        <MetricCard
+          title="人工覆盖率"
+          value={formatPercent(trend.current.manual_override_rate)}
+          previous={formatPercent(trend.previous.manual_override_rate)}
+          deltaLabel={formatDelta(trend.deltas.manual_override_rate_delta)}
+          deltaValue={trend.deltas.manual_override_rate_delta}
+          helper="任务拆解后被人工改动的比例"
+        />
+        <MetricCard
+          title="返工率"
+          value={formatPercent(trend.current.rework_rate)}
+          previous={formatPercent(trend.previous.rework_rate)}
+          deltaLabel={formatDelta(trend.deltas.rework_rate_delta)}
+          deltaValue={trend.deltas.rework_rate_delta}
+          helper="存在二次修订的样本占比"
+        />
+        <MetricCard
+          title="自动指派率"
+          value={formatPercent(trend.current.auto_assigned_rate)}
+          previous={formatPercent(trend.previous.auto_assigned_rate)}
+          deltaLabel={formatDelta(trend.deltas.auto_assigned_rate_delta)}
+          deltaValue={trend.deltas.auto_assigned_rate_delta}
+          helper="任务拆解直接落负责人比例"
+        />
+        <MetricCard
+          title="平均复核耗时"
+          value={formatMinutes(trend.current.avg_review_latency_minutes)}
+          previous={formatMinutes(trend.previous.avg_review_latency_minutes)}
+          deltaLabel={`${trend.deltas.avg_review_latency_minutes_delta > 0 ? "+" : ""}${Math.round(trend.deltas.avg_review_latency_minutes_delta)}m`}
+          deltaValue={trend.deltas.avg_review_latency_minutes_delta}
+          trendDirection="down"
+          helper="样本从生成到复核的平均耗时"
+        />
+      </div>
+
+      <div className="grid gap-4 xl:grid-cols-[1.4fr_0.9fr]">
+        <Card className="rounded-2xl border-slate-200 bg-white shadow-sm">
+          <CardContent className="p-5">
+            <div className="flex flex-col gap-2 sm:flex-row sm:items-end sm:justify-between">
+              <div>
+                <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Daily Volume</p>
+                <h3 className="mt-1 text-lg font-semibold text-slate-900">最近 {trend.window_days} 天样本量与状态分布</h3>
+              </div>
+              <div className="flex flex-wrap gap-2">
+                {Object.entries(STATUS_COLORS).map(([status, color]) => (
+                  <span key={status} className="flex items-center gap-1 text-xs text-slate-500">
+                    <span className={`h-2.5 w-2.5 rounded-full ${color}`} />
+                    {status === "accepted" ? "采纳" : status === "rejected" ? "拒绝" : status === "reviewed" ? "复核" : "待复核"}
+                  </span>
+                ))}
+              </div>
+            </div>
+
+            <div className="mt-6 overflow-x-auto rounded-2xl bg-slate-50/80 p-4">
+              <div className={`gap-3 ${dailyGridClass}`}>
+                {daily.map((item) => {
+                  const totalHeight = Math.max((item.total_records / maxTotal) * 100, item.total_records > 0 ? 8 : 2);
+                  const segments = [
+                    { key: "accepted", count: item.accepted_count },
+                    { key: "rejected", count: item.rejected_count },
+                    { key: "reviewed", count: item.reviewed_count },
+                    { key: "pending", count: item.pending_count },
+                  ].filter((segment) => segment.count > 0);
+                  return (
+                    <div key={item.date} className="flex flex-col items-center justify-end gap-2">
+                      <div className="flex h-44 w-full items-end justify-center">
+                        <div
+                          className="flex w-full max-w-[42px] flex-col overflow-hidden rounded-t-2xl border border-slate-200 bg-white shadow-sm"
+                          style={{ height: `${Math.max(totalHeight, 4)}%` }}
+                        >
+                          {segments.map((segment) => {
+                            const segmentHeight = Math.max((segment.count / Math.max(item.total_records, 1)) * 100, segment.count > 0 ? 12 : 0);
+                            return (
+                              <div
+                                key={`${item.date}-${segment.key}`}
+                                className={`${STATUS_COLORS[segment.key]} flex items-center justify-center text-[10px] font-semibold text-white`}
+                                style={{ height: `${segmentHeight}%` }}
+                              >
+                                {segment.count > 1 ? segment.count : ""}
+                              </div>
+                            );
+                          })}
+                        </div>
+                      </div>
+                      <div className="text-center">
+                        <p className="text-xs font-medium text-slate-700">{dayLabel(item.date)}</p>
+                        <p className="text-[10px] text-slate-500">{item.total_records} 条</p>
+                      </div>
+                    </div>
+                  );
+                })}
+              </div>
+            </div>
+          </CardContent>
+        </Card>
+
+        <Card className="rounded-2xl border-slate-200 bg-white shadow-sm">
+          <CardContent className="p-5">
+            <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Rate Sparklines</p>
+            <h3 className="mt-1 text-lg font-semibold text-slate-900">关键比率走势</h3>
+            <p className="mt-1 text-sm text-slate-500">
+              每一行都展示最近 {trend.window_days} 天的节奏变化，方便快速看出自动化是不是在稳定下来。
+            </p>
+
+            <div className="mt-5 space-y-5">
+              {[
+                { label: "采纳率", key: "accepted_rate_reviewed", color: "bg-emerald-500", value: trend.current.accepted_rate_reviewed, delta: trend.deltas.accepted_rate_reviewed_delta },
+                { label: "人工覆盖率", key: "manual_override_rate", color: "bg-amber-500", value: trend.current.manual_override_rate, delta: trend.deltas.manual_override_rate_delta },
+                { label: "返工率", key: "rework_rate", color: "bg-rose-500", value: trend.current.rework_rate, delta: trend.deltas.rework_rate_delta },
+                { label: "自动指派率", key: "auto_assigned_rate", color: "bg-sky-500", value: trend.current.auto_assigned_rate, delta: trend.deltas.auto_assigned_rate_delta },
+              ].map((metric) => (
+                <div key={metric.key} className="rounded-2xl border border-slate-100 bg-slate-50 p-4">
+                  <div className="flex items-center justify-between gap-2">
+                    <div>
+                      <p className="text-sm font-medium text-slate-700">{metric.label}</p>
+                      <p className="mt-1 text-xs text-slate-500">
+                        当前 {formatPercent(metric.value)} · {metric.delta > 0 ? "上升" : metric.delta < 0 ? "下降" : "持平"}
+                      </p>
+                    </div>
+                    <Badge
+                      variant="outline"
+                      className={metric.delta >= 0 ? "border-emerald-200 bg-emerald-50 text-emerald-700" : "border-rose-200 bg-rose-50 text-rose-700"}
+                    >
+                      {metric.delta > 0 ? "+" : ""}
+                      {Math.round(metric.delta * 100)}%
+                    </Badge>
+                  </div>
+                  <div className={`mt-3 gap-2 overflow-x-auto ${sparklineGridClass}`}>
+                    {daily.map((item) => {
+                      const rate = item[metric.key as keyof typeof item] as number;
+                      const height = Math.max((rate / maxRate) * 100, rate > 0 ? 16 : 4);
+                      return (
+                        <div key={`${metric.key}-${item.date}`} className="flex flex-col items-center gap-2">
+                          <div className="flex h-20 w-full items-end justify-center rounded-xl bg-white px-1 py-1 shadow-sm">
+                            <span className={`w-full rounded-lg ${metric.color}`} style={{ height: `${height}%` }} />
+                          </div>
+                          <span className="text-[10px] text-slate-500">{dayLabel(item.date)}</span>
+                        </div>
+                      );
+                    })}
+                  </div>
+                </div>
+              ))}
+            </div>
+          </CardContent>
+        </Card>
+      </div>
+    </div>
+  );
+}

frontend/src/pages/ai-steward/sections/FeedbackCenterTab.tsx

@@ -0,0 +1,306 @@
+import { useEffect, useMemo, useState } from "react";
+import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query";
+import {
+  feedbackCenterApi,
+  type FeedbackCenterSourceGroup,
+  type FeedbackCenterRecord,
+  type FeedbackCenterSection,
+  type FeedbackCenterTrendWindowDays,
+} from "@/api/feedbackCenter";
+import { FeedbackDetail } from "@/pages/ai-steward/components/feedback-center/FeedbackDetail";
+import { FeedbackList } from "@/pages/ai-steward/components/feedback-center/FeedbackList";
+import { FeedbackMetricsPanel } from "@/pages/ai-steward/components/feedback-center/MetricsPanel";
+import { FeedbackTrendPanel } from "@/pages/ai-steward/components/feedback-center/TrendPanel";
+import { ReasonPicker } from "@/pages/ai-steward/components/feedback-center/ReasonPicker";
+import { Card, CardContent } from "@/components/ui/card";
+import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
+import { useToast } from "@/components/ui/toast";
+
+interface FeedbackCenterTabProps {
+  section: FeedbackCenterSection;
+  onSectionChange: (section: FeedbackCenterSection) => void;
+  projectId?: number | string | null;
+  trendWindowDays: FeedbackCenterTrendWindowDays;
+  trendSourceGroup: FeedbackCenterSourceGroup;
+  onTrendWindowDaysChange: (value: FeedbackCenterTrendWindowDays) => void;
+  onTrendSourceGroupChange: (value: FeedbackCenterSourceGroup) => void;
+}
+
+function matchesSection(record: FeedbackCenterRecord, section: FeedbackCenterSection): boolean {
+  if (section === "metrics" || section === "trend") {
+    return false;
+  }
+  if (section === "task") {
+    return record.source_type === "task_breakdown" || record.source_type === "task_override";
+  }
+  if (section === "qa") {
+    return record.source_type === "qa_feedback";
+  }
+  if (section === "signal") {
+    return record.source_type === "signal_feedback";
+  }
+  return true;
+}
+
+function sourceTypeForSection(section: FeedbackCenterSection): string {
+  if (section === "task") return "task_override";
+  if (section === "qa") return "qa_feedback";
+  if (section === "signal") return "signal_feedback";
+  return "";
+}
+
+export default function FeedbackCenterTab({
+  section,
+  onSectionChange,
+  projectId,
+  trendWindowDays,
+  trendSourceGroup,
+  onTrendWindowDaysChange,
+  onTrendSourceGroupChange,
+}: FeedbackCenterTabProps) {
+  const queryClient = useQueryClient();
+  const { toast } = useToast();
+  const [selectedId, setSelectedId] = useState<number | null>(null);
+  const [search, setSearch] = useState("");
+  const [sourceType, setSourceType] = useState("");
+  const [reviewStatus, setReviewStatus] = useState("");
+
+  const isAnalyticsSection = section === "metrics" || section === "trend";
+  const effectiveSourceType = sourceType || sourceTypeForSection(section);
+
+  const summaryQuery = useQuery({
+    queryKey: ["feedback-center", "summary", projectId ?? "all"],
+    queryFn: () => feedbackCenterApi.getSummary({ project_id: projectId ?? undefined }),
+  });
+
+  const trendQuery = useQuery({
+    queryKey: ["feedback-center", "trend-summary", projectId ?? "all", trendWindowDays, trendSourceGroup],
+    queryFn: () =>
+      feedbackCenterApi.getSummary({
+        project_id: projectId ?? undefined,
+        window_days: trendWindowDays,
+        source_group: trendSourceGroup,
+      }),
+    enabled: section === "trend",
+  });
+
+  const listQuery = useQuery({
+    queryKey: ["feedback-center", "items", projectId ?? "all", effectiveSourceType, reviewStatus, search],
+    queryFn: () =>
+      feedbackCenterApi.listItems({
+        project_id: projectId ?? undefined,
+        source_type: effectiveSourceType || undefined,
+        review_status: reviewStatus || undefined,
+        search: search.trim() || undefined,
+        limit: 40,
+        offset: 0,
+      }),
+    enabled: !isAnalyticsSection,
+  });
+
+  const displayItems = useMemo(
+    () => (listQuery.data?.items || []).filter((item) => matchesSection(item, section)),
+    [listQuery.data?.items, section]
+  );
+
+  const selectedRecord = useMemo<FeedbackCenterRecord | null>(() => {
+    if (isAnalyticsSection) return null;
+    if (!displayItems.length) return null;
+    if (selectedId === null) return displayItems[0] || null;
+    return displayItems.find((item) => item.id === selectedId) || displayItems[0] || null;
+  }, [displayItems, selectedId, isAnalyticsSection]);
+
+  const detailQuery = useQuery({
+    queryKey: ["feedback-center", "detail", selectedRecord?.id ?? "none", projectId ?? "all"],
+    queryFn: () => feedbackCenterApi.getDetail(selectedRecord!.id, projectId ?? undefined),
+    enabled: !!selectedRecord?.id && !isAnalyticsSection,
+  });
+
+  const reasonCatalogQuery = useQuery({
+    queryKey: [
+      "feedback-center",
+      "reason-catalog",
+      selectedRecord?.source_type ?? "none",
+      selectedRecord?.field_name ?? "none",
+    ],
+    queryFn: () =>
+      feedbackCenterApi.getReasonCatalog(selectedRecord?.source_type, selectedRecord?.field_name),
+    enabled: !!selectedRecord && !isAnalyticsSection,
+  });
+
+  const reviewMutation = useMutation({
+    mutationFn: (payload: { review_status: "reviewed" | "accepted" | "rejected"; reason_code?: string; reason_text?: string }) =>
+      feedbackCenterApi.reviewItem(selectedRecord!.id, payload),
+    onSuccess: async () => {
+      toast({ title: "反馈记录已更新", variant: "success" });
+      await Promise.all([
+        queryClient.invalidateQueries({ queryKey: ["feedback-center"] }),
+      ]);
+    },
+    onError: (error) => {
+      toast({
+        title: error instanceof Error ? error.message : "更新反馈记录失败",
+        variant: "error",
+      });
+    },
+  });
+
+  useEffect(() => {
+    setSelectedId(null);
+  }, [effectiveSourceType, reviewStatus, search, projectId, section]);
+
+  useEffect(() => {
+    setSourceType("");
+  }, [section]);
+
+  const summary = summaryQuery.data;
+  const detail = detailQuery.data;
+
+  return (
+    <TabsContent value="feedback-center" className="mt-6">
+      <div className="space-y-5">
+        <div className="grid gap-4 md:grid-cols-2 xl:grid-cols-4">
+          <Card className="rounded-2xl border-slate-200 bg-white/95 shadow-sm">
+            <CardContent className="p-5">
+              <p className="text-xs uppercase tracking-[0.18em] text-slate-400">Backlog</p>
+              <p className="mt-3 text-3xl font-semibold text-slate-900">{summary?.backlog_count ?? 0}</p>
+              <p className="mt-2 text-sm text-slate-500">待复核 + 已复核未入样本</p>
+            </CardContent>
+          </Card>
+          <Card className="rounded-2xl border-slate-200 bg-white/95 shadow-sm">
+            <CardContent className="p-5">
+              <p className="text-xs uppercase tracking-[0.18em] text-slate-400">Accepted</p>
+              <p className="mt-3 text-3xl font-semibold text-slate-900">
+                {Math.round(Number(summary?.accepted_rate || 0) * 100)}%
+              </p>
+              <p className="mt-2 text-sm text-slate-500">已进入学习池的样本占比</p>
+            </CardContent>
+          </Card>
+          <Card className="rounded-2xl border-slate-200 bg-white/95 shadow-sm">
+            <CardContent className="p-5">
+              <p className="text-xs uppercase tracking-[0.18em] text-slate-400">Confidence</p>
+              <p className="mt-3 text-3xl font-semibold text-slate-900">
+                {Math.round(Number(summary?.avg_confidence || 0) * 100)}%
+              </p>
+              <p className="mt-2 text-sm text-slate-500">当前反馈样本平均 AI 置信度</p>
+            </CardContent>
+          </Card>
+          <Card className="rounded-2xl border-slate-200 bg-white/95 shadow-sm">
+            <CardContent className="p-5">
+              <p className="text-xs uppercase tracking-[0.18em] text-slate-400">Top Reason</p>
+              <p className="mt-3 text-lg font-semibold text-slate-900">
+                {summary?.top_reasons?.[0]?.label || "暂无"}
+              </p>
+              <p className="mt-2 text-sm text-slate-500">
+                {summary?.top_reasons?.[0] ? `${summary.top_reasons[0].count} 次` : "等待样本累计"}
+              </p>
+            </CardContent>
+          </Card>
+        </div>
+
+        <Card className="rounded-3xl border border-slate-200/80 bg-[linear-gradient(135deg,rgba(255,255,255,0.98),rgba(248,250,252,0.98))] shadow-[0_18px_50px_rgba(15,23,42,0.08)]">
+          <CardContent className="p-4 sm:p-6">
+            <div className="mb-5 flex flex-col gap-4 lg:flex-row lg:items-end lg:justify-between">
+              <div>
+                <p className="text-xs font-semibold uppercase tracking-[0.18em] text-slate-400">Feedback Center</p>
+                <h2 className="mt-1 text-2xl font-semibold text-slate-900">统一反馈学习中心</h2>
+                <p className="mt-2 max-w-2xl text-sm text-slate-500">
+                  把任务、QA、信号三类人工修正收口成可学习样本，当前只做复核与归因，不在这里直接改业务对象。
+                </p>
+              </div>
+            </div>
+
+            <Tabs value={section} onValueChange={(value) => onSectionChange(value as FeedbackCenterSection)}>
+              <TabsList className="flex w-full flex-nowrap gap-1 overflow-x-auto rounded-xl bg-slate-100 p-1">
+                <TabsTrigger value="trend" className="shrink-0 whitespace-nowrap">趋势页</TabsTrigger>
+                <TabsTrigger value="metrics" className="shrink-0 whitespace-nowrap">指标页</TabsTrigger>
+                <TabsTrigger value="overview" className="shrink-0 whitespace-nowrap">总览</TabsTrigger>
+                <TabsTrigger value="task" className="shrink-0 whitespace-nowrap">任务反馈</TabsTrigger>
+                <TabsTrigger value="qa" className="shrink-0 whitespace-nowrap">QA 反馈</TabsTrigger>
+                <TabsTrigger value="signal" className="shrink-0 whitespace-nowrap">信号反馈</TabsTrigger>
+              </TabsList>
+
+              <TabsContent value="trend" className="mt-5">
+                <FeedbackTrendPanel
+                  summary={trendQuery.data || null}
+                  isLoading={trendQuery.isLoading}
+                  windowDays={trendWindowDays}
+                  sourceGroup={trendSourceGroup}
+                  onWindowDaysChange={onTrendWindowDaysChange}
+                  onSourceGroupChange={onTrendSourceGroupChange}
+                />
+              </TabsContent>
+
+              <TabsContent value="metrics" className="mt-5">
+                <FeedbackMetricsPanel summary={summary} />
+              </TabsContent>
+
+              <TabsContent value="overview" className="mt-5">
+                <div className="grid gap-4 xl:grid-cols-[1.3fr_1fr]">
+                  <div className="rounded-2xl border border-slate-200 bg-white p-5 shadow-sm">
+                    <p className="text-xs font-semibold uppercase tracking-[0.16em] text-slate-400">By Source</p>
+                    <div className="mt-4 space-y-3">
+                      {Object.entries(summary?.by_source_type || {}).map(([key, value]) => (
+                        <div key={key} className="flex items-center justify-between rounded-xl bg-slate-50 px-4 py-3">
+                          <span className="text-sm text-slate-600">{key}</span>
+                          <span className="text-sm font-semibold text-slate-900">{value}</span>
+                        </div>
+                      ))}
+                      {!Object.keys(summary?.by_source_type || {}).length && (
+                        <p className="text-sm text-slate-500">还没有可展示的来源分布。</p>
+                      )}
+                    </div>
+                  </div>
+                  <div className="rounded-2xl border border-slate-200 bg-white p-5 shadow-sm">
+                    <p className="text-xs font-semibold uppercase tracking-[0.16em] text-slate-400">Top Reasons</p>
+                    <div className="mt-4 space-y-3">
+                      {(summary?.top_reasons || []).map((item) => (
+                        <div key={item.code} className="rounded-xl bg-slate-50 px-4 py-3">
+                          <div className="flex items-center justify-between gap-2">
+                            <span className="text-sm font-medium text-slate-800">{item.label}</span>
+                            <span className="text-xs text-slate-500">{item.count} 次</span>
+                          </div>
+                          <p className="mt-1 text-xs text-slate-500">{item.code}</p>
+                        </div>
+                      ))}
+                      {!summary?.top_reasons?.length && <p className="text-sm text-slate-500">原因榜单会随着样本增加自动出现。</p>}
+                    </div>
+                  </div>
+                </div>
+              </TabsContent>
+
+              {(["task", "qa", "signal"] as const).map((pane) => (
+                <TabsContent key={pane} value={pane} className="mt-5">
+                  <div className="grid gap-4 xl:grid-cols-[320px_minmax(0,1fr)_320px]">
+                    <FeedbackList
+                      items={displayItems}
+                      selectedId={selectedRecord?.id ?? null}
+                      search={search}
+                      onSearchChange={setSearch}
+                      sourceType={sourceType}
+                      reviewStatus={reviewStatus}
+                      onSourceTypeChange={setSourceType}
+                      onReviewStatusChange={setReviewStatus}
+                      onSelect={(item) => setSelectedId(item.id)}
+                      total={displayItems.length}
+                      isLoading={listQuery.isLoading}
+                    />
+                    <FeedbackDetail record={detail?.record || selectedRecord} revisions={detail?.revisions || []} />
+                    <ReasonPicker
+                      record={detail?.record || selectedRecord}
+                      catalog={reasonCatalogQuery.data?.items || []}
+                      isPending={reviewMutation.isPending}
+                      onSubmit={async (payload) => {
+                        await reviewMutation.mutateAsync(payload);
+                      }}
+                    />
+                  </div>
+                </TabsContent>
+              ))}
+            </Tabs>
+          </CardContent>
+        </Card>
+      </div>
+    </TabsContent>
+  );
+}

frontend/src/pages/ai-steward/types.ts

@@ -1,4 +1,17 @@
 import type { LLMEngine, LLMEngineListResponse } from "@/api/aiSteward";
+export type { FeedbackCenterTrendWindowDays, FeedbackCenterSourceGroup } from "@/api/feedbackCenter";
+
+export type FeedbackCenterSection = "overview" | "metrics" | "trend" | "task" | "qa" | "signal";
+export type AIStewardTab =
+  | "overview"
+  | "create"
+  | "signals"
+  | "conflicts"
+  | "blockers"
+  | "briefing"
+  | "qa"
+  | "feedback-center"
+  | "engine";
 
 export interface ProjectFromTextResponse {
   project: { id: string; name: string; title: string; description: string; status: string };