feat: 因果链三层验证机制(P2) — 数据验证+人工确认+状态机

- kpi_causality 加列: source_type/verify_status/verified_at/verified_by/entity_id(回填)
- 核心服务: app/services/causality_verification.py (Pearson+滞后对齐+状态机)
- 数据验证脚本: scripts/correlation-check.py (月度cron, 输出JSON报告)
- API: create/update支持source_type, GET /verify-status, PUT /{id}/verify(人工确认)
- 全部端点按entity_id账套隔离, kpi/{id}/network/simulate补跨企业校验
- 前端: KPIDetail因果链页显示验证状态徽标(数据证实/存疑/待检)
- 测试: test_causality_verification.py 37用例 + 原因果链测试全过(59个)
- 50条因果链首轮验证: 1数据证实(#37渠补率到净利润lag1 r=-0.89), 4存疑, 45待检(数据不足)

.gitignore

@@ -11,3 +11,6 @@ __pycache__/
 *.tsbuildinfo
 venv/
 backend/logs/
+
+# 因果链验证报告（生成物）
+backend/scripts/reports/

backend/app/api/kpi_causality.py

@@ -1,6 +1,7 @@
 """KPI因果链建模 — 任务2
-KPI间因果关系网络 + 模拟推演
+KPI间因果关系网络 + 模拟推演 + 三层验证机制(数据/AI/人工) (2026-08-27 P2)
 """
+from datetime import datetime
 from fastapi import APIRouter, Depends, HTTPException, Query
 from sqlalchemy.orm import Session
 from sqlalchemy import text
@@ -11,6 +12,11 @@ from app.database import get_db
 from app.deps import get_entity_id
 from app.auth_middleware import require_auth, require_role
 from app.models import KPIDefinition, KPICausality, KPIValue, OperationLog
+from app.services.causality_verification import (
+    ALL_STATUSES,
+    ALL_SOURCE_TYPES,
+    STATUS_PENDING,
+)
 
 logger = logging.getLogger("kpi-causality")
 
@@ -30,8 +36,8 @@ def _to_dict(obj):
 
 @router.get("/full-network")
 def get_full_network(db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
-    """获取全局因果网络数据（用于力导向图）— 账套隔离: 仅当前企业KPI的因果链 (2026-08-23 P1b)"""
+    """获取全局因果网络数据（用于力导向图）— 账套隔离: 仅当前企业KPI的因果链 (2026-08-23 P1b, 2026-08-27 用entity_id列)"""
-    edges = db.query(KPICausality).join(KPIDefinition, KPIDefinition.id == KPICausality.source_kpi_id).filter(KPIDefinition.entity_id == entity_id).all()
+    edges = db.query(KPICausality).filter(KPICausality.entity_id == entity_id).all()
     node_ids = set()
     edge_list = []
     for e in edges:
@@ -61,14 +67,19 @@ def get_full_network(db: Session = Depends(get_db), entity_id: int = Depends(get
 
 
 @router.get("/kpi/{kpi_id}/network")
-def get_kpi_network(kpi_id: int, db: Session = Depends(get_db)):
+def get_kpi_network(kpi_id: int, db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
-    """获取KPI的因果网络（上游驱动 + 下游影响）"""
+    """获取KPI的因果网络（上游驱动 + 下游影响）— 账套隔离: 校验KPI属于当前企业"""
     kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
     if not kpi:
         raise HTTPException(404, "KPI不存在")
+    if kpi.entity_id != entity_id:
+        raise HTTPException(404, "KPI不存在")  # 跨企业不暴露存在性
 
     # 上游（指向当前KPI的因果）
-    upstream = db.query(KPICausality).filter(KPICausality.target_kpi_id == kpi_id).all()
+    upstream = db.query(KPICausality).filter(
+        KPICausality.target_kpi_id == kpi_id,
+        KPICausality.entity_id == entity_id,
+    ).all()
     upstream_list = []
     for c in upstream:
         src = db.query(KPIDefinition).filter(KPIDefinition.id == c.source_kpi_id).first()
@@ -78,10 +89,16 @@ def get_kpi_network(kpi_id: int, db: Session = Depends(get_db)):
                 "kpi_id": src.id, "kpi_code": src.kpi_code, "kpi_name": src.kpi_name,
                 "strength": c.strength, "lag_months": c.lag_months,
                 "direction": c.direction, "formula": c.formula,
+                "source_type": c.source_type, "verify_status": c.verify_status,
+                "verified_at": c.verified_at.isoformat() if c.verified_at else None,
+                "verified_by": c.verified_by,
             })
 
     # 下游（当前KPI指向的因果）
-    downstream = db.query(KPICausality).filter(KPICausality.source_kpi_id == kpi_id).all()
+    downstream = db.query(KPICausality).filter(
+        KPICausality.source_kpi_id == kpi_id,
+        KPICausality.entity_id == entity_id,
+    ).all()
     downstream_list = []
     for c in downstream:
         tgt = db.query(KPIDefinition).filter(KPIDefinition.id == c.target_kpi_id).first()
@@ -91,6 +108,9 @@ def get_kpi_network(kpi_id: int, db: Session = Depends(get_db)):
                 "kpi_id": tgt.id, "kpi_code": tgt.kpi_code, "kpi_name": tgt.kpi_name,
                 "strength": c.strength, "lag_months": c.lag_months,
                 "direction": c.direction, "formula": c.formula,
+                "source_type": c.source_type, "verify_status": c.verify_status,
+                "verified_at": c.verified_at.isoformat() if c.verified_at else None,
+                "verified_by": c.verified_by,
             })
 
     return {
@@ -101,7 +121,7 @@ def get_kpi_network(kpi_id: int, db: Session = Depends(get_db)):
 
 
 @router.post("/simulate")
-def simulate_causality(data: dict, db: Session = Depends(get_db)):
+def simulate_causality(data: dict, db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
     """模拟推演: 修改一个KPI的值，预测对其他KPI的影响
     Body: { kpi_id: int, new_value: float, period: str }
     """
@@ -115,6 +135,8 @@ def simulate_causality(data: dict, db: Session = Depends(get_db)):
     source_kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
     if not source_kpi:
         raise HTTPException(404, "KPI不存在")
+    if source_kpi.entity_id != entity_id:
+        raise HTTPException(404, "KPI不存在")
 
     # 获取当前值
     current_value = None
@@ -142,9 +164,10 @@ def simulate_causality(data: dict, db: Session = Depends(get_db)):
             continue
         visited.add(current_kpi_id)
 
-        # 查找从current_kpi_id出发的下游因果链
+        # 查找从current_kpi_id出发的下游因果链（账套隔离: 仅本企业链）
         downstream = db.query(KPICausality).filter(
-            KPICausality.source_kpi_id == current_kpi_id
+            KPICausality.source_kpi_id == current_kpi_id,
+            KPICausality.entity_id == entity_id,
         ).all()
 
         for edge in downstream:
@@ -212,15 +235,18 @@ def simulate_causality(data: dict, db: Session = Depends(get_db)):
 def list_causalities(
     source_kpi_id: Optional[int] = None,
     target_kpi_id: Optional[int] = None,
+    verify_status: Optional[str] = None,
     db: Session = Depends(get_db),
     entity_id: int = Depends(get_entity_id),
 ):
-    """获取因果链列表（账套隔离: 仅当前企业KPI, 2026-08-23 P1b）"""
+    """获取因果链列表（账套隔离: 仅当前企业KPI, 2026-08-23 P1b, 2026-08-27 支持verify_status筛选）"""
-    query = db.query(KPICausality).join(KPIDefinition, KPIDefinition.id == KPICausality.source_kpi_id).filter(KPIDefinition.entity_id == entity_id)
+    query = db.query(KPICausality).filter(KPICausality.entity_id == entity_id)
     if source_kpi_id:
         query = query.filter(KPICausality.source_kpi_id == source_kpi_id)
     if target_kpi_id:
         query = query.filter(KPICausality.target_kpi_id == target_kpi_id)
+    if verify_status:
+        query = query.filter(KPICausality.verify_status == verify_status)
     items = query.order_by(KPICausality.id).all()
 
     result = []
@@ -236,11 +262,51 @@ def list_causalities(
     return {"data": result, "total": len(result)}
 
 
+@router.get("/verify-status")
+def get_verify_status(
+    verify_status: Optional[str] = None,
+    db: Session = Depends(get_db),
+    entity_id: int = Depends(get_entity_id),
+):
+    """验证状态总览 — 按状态统计 + 链列表（2026-08-27 三层验证机制）
+
+    可选 ?verify_status=pending/data_verified/human_verified/disputed 筛选
+    """
+    if verify_status and verify_status not in ALL_STATUSES:
+        raise HTTPException(400, f"verify_status 必须为 {'/'.join(ALL_STATUSES)}")
+
+    query = db.query(KPICausality).filter(KPICausality.entity_id == entity_id)
+    if verify_status:
+        query = query.filter(KPICausality.verify_status == verify_status)
+    items = query.order_by(KPICausality.id).all()
+
+    by_status = {s: 0 for s in ALL_STATUSES}
+    data = []
+    for c in items:
+        by_status[c.verify_status] = by_status.get(c.verify_status, 0) + 1
+        d = _to_dict(c)
+        src = db.query(KPIDefinition).filter(KPIDefinition.id == c.source_kpi_id).first()
+        tgt = db.query(KPIDefinition).filter(KPIDefinition.id == c.target_kpi_id).first()
+        d["source_kpi_code"] = src.kpi_code if src else None
+        d["source_kpi_name"] = src.kpi_name if src else None
+        d["target_kpi_code"] = tgt.kpi_code if tgt else None
+        d["target_kpi_name"] = tgt.kpi_name if tgt else None
+        d["verified_at"] = c.verified_at.isoformat() if c.verified_at else None
+        data.append(d)
+
+    return {
+        "summary": {"total": len(items), "by_status": by_status},
+        "data": data,
+    }
+
+
 @router.get("/{causality_id}")
-def get_causality(causality_id: int, db: Session = Depends(get_db)):
+def get_causality(causality_id: int, db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
     c = db.query(KPICausality).filter(KPICausality.id == causality_id).first()
     if not c:
         raise HTTPException(404, "因果链不存在")
+    if c.entity_id != entity_id:
+        raise HTTPException(404, "因果链不存在")  # 账套隔离
     d = _to_dict(c)
     src = db.query(KPIDefinition).filter(KPIDefinition.id == c.source_kpi_id).first()
     tgt = db.query(KPIDefinition).filter(KPIDefinition.id == c.target_kpi_id).first()
@@ -251,7 +317,7 @@ def get_causality(causality_id: int, db: Session = Depends(get_db)):
 
 @router.post("")
 def create_causality(data: dict, db: Session = Depends(get_db), user=WRITE_ROLES):
-    """创建因果链"""
+    """创建因果链（source_type标记来源, 2026-08-27）"""
     source_id = data.get("source_kpi_id")
     target_id = data.get("target_kpi_id")
     if not source_id or not target_id:
@@ -262,6 +328,8 @@ def create_causality(data: dict, db: Session = Depends(get_db), user=WRITE_ROLES
     tgt = db.query(KPIDefinition).filter(KPIDefinition.id == target_id).first()
     if not src or not tgt:
         raise HTTPException(404, "KPI不存在")
+    if src.entity_id != tgt.entity_id:
+        raise HTTPException(400, "源KPI和目标KPI必须属于同一企业")
 
     existing = db.query(KPICausality).filter(
         KPICausality.source_kpi_id == source_id,
@@ -270,40 +338,91 @@ def create_causality(data: dict, db: Session = Depends(get_db), user=WRITE_ROLES
     if existing:
         raise HTTPException(400, f"因果链已存在: {src.kpi_code}→{tgt.kpi_code}")
 
+    source_type = data.get("source_type", "manual")
+    if source_type not in ALL_SOURCE_TYPES:
+        raise HTTPException(400, f"source_type 必须为 {'/'.join(ALL_SOURCE_TYPES)}")
+
     c = KPICausality(
+        entity_id=src.entity_id,
         source_kpi_id=source_id,
         target_kpi_id=target_id,
         strength=data.get("strength", 0.5),
         lag_months=data.get("lag_months", 1),
         formula=data.get("formula"),
         direction=data.get("direction", "positive"),
+        source_type=source_type,
+        verify_status=STATUS_PENDING,
     )
     db.add(c)
     db.commit()
     db.refresh(c)
     db.add(OperationLog(action="create", target_type="kpi_causality",
-        detail=f"创建因果链: {src.kpi_code}→{tgt.kpi_code}"))
+        detail=f"创建因果链: {src.kpi_code}→{tgt.kpi_code} (source={source_type})"))
     db.commit()
     return _to_dict(c)
 
 
 @router.put("/{causality_id}")
-def update_causality(causality_id: int, data: dict, db: Session = Depends(get_db), user=WRITE_ROLES):
+def update_causality(causality_id: int, data: dict, db: Session = Depends(get_db), user=WRITE_ROLES,
+                     entity_id: int = Depends(get_entity_id)):
     c = db.query(KPICausality).filter(KPICausality.id == causality_id).first()
     if not c:
         raise HTTPException(404, "因果链不存在")
-    for field in ("strength", "lag_months", "formula", "direction"):
+    if c.entity_id != entity_id:
+        raise HTTPException(404, "因果链不存在")  # 账套隔离
+    for field in ("strength", "lag_months", "formula", "direction", "source_type"):
         if field in data:
+            if field == "source_type" and data[field] not in ALL_SOURCE_TYPES:
+                raise HTTPException(400, f"source_type 必须为 {'/'.join(ALL_SOURCE_TYPES)}")
             setattr(c, field, data[field])
+    # 修改链定义后，验证状态回到待检（定义变了旧结论失效）
+    if any(f in data for f in ("strength", "lag_months", "formula", "direction")):
+        c.verify_status = STATUS_PENDING
+        c.verified_at = None
+        c.verified_by = None
     db.commit()
     db.refresh(c)
     return _to_dict(c)
 
 
+@router.put("/{causality_id}/verify")
+def verify_causality(causality_id: int, data: dict, db: Session = Depends(get_db), user=WRITE_ROLES,
+                     entity_id: int = Depends(get_entity_id)):
+    """人工确认（战略回顾会核对打标）— 2026-08-27 三层验证机制
+
+    Body: { verify_status: "human_verified"|"disputed", verified_by?: "任富海" }
+    默认打标 human_verified（人工最终确认）。
+    """
+    c = db.query(KPICausality).filter(KPICausality.id == causality_id).first()
+    if not c:
+        raise HTTPException(404, "因果链不存在")
+    if c.entity_id != entity_id:
+        raise HTTPException(404, "因果链不存在")  # 账套隔离
+
+    target_status = data.get("verify_status", "human_verified")
+    if target_status not in ("human_verified", "disputed"):
+        raise HTTPException(400, "verify_status 必须为 human_verified 或 disputed")
+    verified_by = data.get("verified_by") or user.name or user.username
+
+    c.verify_status = target_status
+    c.verified_at = datetime.now()
+    c.verified_by = str(verified_by)[:50]
+    db.add(OperationLog(action="verify", target_type="kpi_causality",
+        detail=f"因果链 #{causality_id} 人工确认: {target_status} (by {verified_by})"))
+    db.commit()
+    db.refresh(c)
+    d = _to_dict(c)
+    d["verified_at"] = c.verified_at.isoformat() if c.verified_at else None
+    return d
+
+
 @router.delete("/{causality_id}")
-def delete_causality(causality_id: int, db: Session = Depends(get_db), user=WRITE_ROLES):
+def delete_causality(causality_id: int, db: Session = Depends(get_db), user=WRITE_ROLES,
+                     entity_id: int = Depends(get_entity_id)):
     c = db.query(KPICausality).filter(KPICausality.id == causality_id).first()
     if c:
+        if c.entity_id != entity_id:
+            raise HTTPException(404, "因果链不存在")  # 账套隔离
         db.delete(c)
         db.commit()
     return {"message": "已删除"}

backend/app/models/__init__.py

@@ -292,12 +292,17 @@ class KPICausality(Base):
     """KPI因果链 — 记录KPI间的因果关系"""
     __tablename__ = "kpi_causality"
     id = Column(Integer, primary_key=True, index=True)
+    entity_id = Column(Integer, default=1, comment="企业ID (多租户隔离 2026-08-27)")
     source_kpi_id = Column(Integer, ForeignKey("kpi_definitions.id"), nullable=False, comment="源KPI（因）")
     target_kpi_id = Column(Integer, ForeignKey("kpi_definitions.id"), nullable=False, comment="目标KPI（果）")
     strength = Column(Float, default=0.5, comment="影响强度 0~1")
     lag_months = Column(Integer, default=1, comment="滞后期（月）")
     formula = Column(String(500), nullable=True, comment="影响公式描述")
     direction = Column(String(10), default="positive", comment="positive/negative 正向/负向影响")
+    source_type = Column(String(20), default="manual", comment="建链来源 AI_suggested/manual/imported (2026-08-27 验证机制)")
+    verify_status = Column(String(20), default="pending", comment="验证状态 pending/data_verified/human_verified/disputed (2026-08-27)")
+    verified_at = Column(DateTime, nullable=True, comment="验证时间")
+    verified_by = Column(String(50), nullable=True, comment="验证人/AI/脚本")
     created_at = Column(DateTime, server_default=func.now())
     updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
 

backend/app/services/causality_verification.py

@@ -0,0 +1,238 @@
+"""因果链验证核心逻辑 — 数据验证（Pearson相关性）+ 状态机 (2026-08-27 P2)
+
+三层验证：
+1. 数据验证（自动化）：kpi_values 历史值 → Pearson 相关系数 + 方向一致性 + 滞后对齐
+2. AI/人工验证：战略回顾会人工打标 human_verified（API PUT /verify）
+3. 状态机流转：
+   pending（初始）→ data_verified / disputed（数据验证 cron）
+   pending/data_verified/disputed → human_verified（人工确认，最终）
+   任何矛盾 → disputed（待检）
+
+判定规则（可解释、可测试）：
+- 对齐后数据点 < MIN_POINTS(4) → pending（数据不足，无法统计验证）
+- |r| >= CORR_THRESHOLD(0.5) 且方向与 direction 一致 → data_verified
+- 否则（点数足够但弱相关/方向矛盾）→ disputed
+- human_verified 为人工最终确认，脚本默认不覆盖（respect_human=True），
+  但若数据矛盾会在报告中给出警示（disputed_note）
+
+脚本入口: scripts/correlation-check.py
+测试入口: tests/test_causality_verification.py
+"""
+from __future__ import annotations
+
+import logging
+import math
+import re
+from typing import Dict, List, Optional, Tuple
+
+logger = logging.getLogger("causality-verification")
+
+# 验证状态
+STATUS_PENDING = "pending"
+STATUS_DATA_VERIFIED = "data_verified"
+STATUS_HUMAN_VERIFIED = "human_verified"
+STATUS_DISPUTED = "disputed"
+ALL_STATUSES = (STATUS_PENDING, STATUS_DATA_VERIFIED, STATUS_HUMAN_VERIFIED, STATUS_DISPUTED)
+
+# 建链来源
+SOURCE_MANUAL = "manual"
+SOURCE_AI = "AI_suggested"
+SOURCE_IMPORTED = "imported"
+ALL_SOURCE_TYPES = (SOURCE_MANUAL, SOURCE_AI, SOURCE_IMPORTED)
+
+# 判定参数
+MIN_POINTS = 4          # 最少对齐数据点（少于则无法统计验证）
+CORR_THRESHOLD = 0.5    # |r| 阈值：达到且方向一致 → 数据证实
+VERIFIER_SCRIPT = "correlation-check"
+
+# period 粒度（对齐时只允许同粒度配对，避免月度/年度量纲混用）
+GRANULARITY_ORDER = ("month", "half", "year")
+
+_PERIOD_RE = {
+    "month": re.compile(r"^(\d{4})-(\d{2})$"),
+    "half": re.compile(r"^(\d{4})-H([12])$"),
+    "year": re.compile(r"^(\d{4})$"),
+}
+
+
+def parse_period(period: str) -> Optional[Tuple[str, int]]:
+    """解析 period 为 (granularity, seq)。
+
+    seq = year*12 + 月序号（0-indexed），可比较/做滞后偏移。
+    - '2026-07' → ('month', 2026*12+6)
+    - '2026-H1' → ('half', 2026*12+5)   (H1≈6月)
+    - '2026-H2' → ('half', 2026*12+11)  (H2≈12月)
+    - '2026'    → ('year', 2026*12+5)   (年中)
+    无法解析 → None
+    """
+    if not period:
+        return None
+    s = str(period).strip()
+    m = _PERIOD_RE["month"].match(s)
+    if m:
+        year, mon = int(m.group(1)), int(m.group(2))
+        if 1 <= mon <= 12:
+            return ("month", year * 12 + (mon - 1))
+    m = _PERIOD_RE["half"].match(s)
+    if m:
+        year, half = int(m.group(1)), int(m.group(2))
+        return ("half", year * 12 + (5 if half == 1 else 11))
+    m = _PERIOD_RE["year"].match(s)
+    if m:
+        return ("year", int(m.group(1)) * 12 + 5)
+    return None
+
+
+def pearson(xs: List[float], ys: List[float]) -> Tuple[Optional[float], int]:
+    """Pearson 相关系数。点数 < 2 返回 (None, n)。"""
+    n = len(xs)
+    if n < 2:
+        return None, n
+    mx = sum(xs) / n
+    my = sum(ys) / n
+    sxy = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
+    sxx = sum((x - mx) ** 2 for x in xs)
+    syy = sum((y - my) ** 2 for y in ys)
+    if sxx <= 0 or syy <= 0:  # 常数列 → 无相关
+        return None, n
+    r = sxy / math.sqrt(sxx * syy)
+    # 数值保护：浮点误差可能略超 [-1,1]
+    return max(-1.0, min(1.0, r)), n
+
+
+def align_series(
+    source_values: List[Tuple[str, float]],
+    target_values: List[Tuple[str, float]],
+    lag_months: int = 0,
+) -> Tuple[Optional[str], List[Tuple[float, float]]]:
+    """按滞后期对齐 source/target 时间序列，返回 (granularity, pairs)。
+
+    语义：source 是因（先发生），target 是果（滞后 lag 月出现）。
+    pair = (source[t], target[t + lag])。
+    只使用同粒度（month/half/year）数据配对，避免量纲混用。
+    按粒度优先级 month > half > year 选取数据点最多的粒度。
+    """
+    parsed_src: Dict[str, Dict[int, float]] = {g: {} for g in GRANULARITY_ORDER}
+    parsed_tgt: Dict[str, Dict[int, float]] = {g: {} for g in GRANULARITY_ORDER}
+    for period, val in source_values:
+        if val is None:
+            continue
+        r = parse_period(period)
+        if r:
+            g, seq = r
+            parsed_src[g][seq] = float(val)
+    for period, val in target_values:
+        if val is None:
+            continue
+        r = parse_period(period)
+        if r:
+            g, seq = r
+            parsed_tgt[g][seq] = float(val)
+
+    best_g, best_pairs = None, []
+    for g in GRANULARITY_ORDER:
+        src_map, tgt_map = parsed_src[g], parsed_tgt[g]
+        pairs = []
+        for seq, sv in sorted(src_map.items()):
+            tv = tgt_map.get(seq + lag_months)
+            if tv is not None:
+                pairs.append((sv, tv))
+        if len(pairs) > len(best_pairs):
+            best_g, best_pairs = g, pairs
+    return best_g, best_pairs
+
+
+def evaluate_chain(
+    source_values: List[Tuple[str, float]],
+    target_values: List[Tuple[str, float]],
+    lag_months: int = 0,
+    direction: str = "positive",
+    min_points: int = MIN_POINTS,
+    corr_threshold: float = CORR_THRESHOLD,
+) -> dict:
+    """对单条因果链做数据验证。
+
+    返回:
+    {
+      granularity, n, r, expected_sign, actual_sign, direction_consistent,
+      status (pending/data_verified/disputed), reason
+    }
+    """
+    granularity, pairs = align_series(source_values, target_values, lag_months)
+    n = len(pairs)
+    r = None
+    if n >= 2:
+        r, _ = pearson([p[0] for p in pairs], [p[1] for p in pairs])
+
+    expected_sign = 1 if direction == "positive" else -1
+    actual_sign = 1 if r is not None and r > 0 else (-1 if r is not None and r < 0 else 0)
+    direction_consistent = r is not None and actual_sign == expected_sign
+
+    if n < min_points or r is None:
+        return {
+            "granularity": granularity, "n": n, "r": r,
+            "expected_sign": expected_sign, "actual_sign": actual_sign,
+            "direction_consistent": direction_consistent,
+            "status": STATUS_PENDING,
+            "reason": f"数据不足（对齐后{n}点，需≥{min_points}点）" if n < min_points else "序列无方差，无法计算相关性",
+        }
+
+    abs_r = abs(r)
+    if abs_r >= corr_threshold and direction_consistent:
+        return {
+            "granularity": granularity, "n": n, "r": r,
+            "expected_sign": expected_sign, "actual_sign": actual_sign,
+            "direction_consistent": True,
+            "status": STATUS_DATA_VERIFIED,
+            "reason": f"|r|={abs_r:.3f}≥{corr_threshold} 且方向一致({direction}) → 数据证实",
+        }
+    if not direction_consistent:
+        return {
+            "granularity": granularity, "n": n, "r": r,
+            "expected_sign": expected_sign, "actual_sign": actual_sign,
+            "direction_consistent": False,
+            "status": STATUS_DISPUTED,
+            "reason": f"方向矛盾: 声明{direction}但实际相关方向{'正' if r > 0 else '负'} (r={r:.3f})",
+        }
+    return {
+        "granularity": granularity, "n": n, "r": r,
+        "expected_sign": expected_sign, "actual_sign": actual_sign,
+        "direction_consistent": True,
+        "status": STATUS_DISPUTED,
+        "reason": f"弱相关: |r|={abs_r:.3f}<{corr_threshold}，数据暂不能证实该因果强度",
+    }
+
+
+def apply_state_machine(
+    current_status: str,
+    eval_status: str,
+    respect_human: bool = True,
+) -> Tuple[str, Optional[str]]:
+    """状态机：根据数据验证结果流转状态。
+
+    规则:
+    - human_verified 是人工最终确认: respect_human=True 时不被脚本覆盖
+      （返回原状态 + disputed_note 警示）
+    - 数据不足(pending) → 保持当前状态（不降级已有结论）
+    - data_verified → 覆盖为非 human_verified 的当前状态
+    - disputed → 覆盖为非 human_verified 的当前状态
+    """
+    if current_status == STATUS_HUMAN_VERIFIED and respect_human:
+        if eval_status == STATUS_DISPUTED:
+            return current_status, "人工已确认但数据复核矛盾，建议重新核对"
+        return current_status, None
+    if eval_status == STATUS_PENDING:
+        return current_status, None
+    return eval_status, None
+
+
+def summarize(results: List[dict]) -> dict:
+    """验证结果汇总统计。"""
+    counter = {s: 0 for s in ALL_STATUSES}
+    for r in results:
+        counter[r.get("status", STATUS_PENDING)] = counter.get(r.get("status", STATUS_PENDING), 0) + 1
+    return {
+        "total": len(results),
+        "by_status": counter,
+        "data_verified_ratio": round(counter[STATUS_DATA_VERIFIED] / len(results), 3) if results else 0,
+    }

backend/scripts/correlation-check.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""因果链数据验证脚本 — 每月 cron 自动跑 (2026-08-27 P2)
+
+对 kpi_causality 每条链：
+  取 source/target KPI 的 kpi_values 历史值
+  → Pearson 相关系数 + 方向一致性 + 滞后对齐(lag_months)
+  → 更新 verify_status: data_verified / disputed / pending
+  → 输出验证报告 JSON + 控制台摘要
+
+用法:
+  python3 scripts/correlation-check.py                  # 全部企业，写库
+  python3 scripts/correlation-check.py --entity-id 1    # 指定企业
+  python3 scripts/correlation-check.py --dry-run        # 只算不写库
+
+月度 cron: 0 9 1 * *  cd /root/cma-management/backend && python3 scripts/correlation-check.py
+"""
+import argparse
+import json
+import logging
+import sys
+from datetime import datetime
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from sqlalchemy import text  # noqa: E402
+
+from app.database import get_engine  # noqa: E402
+from app.services.causality_verification import (  # noqa: E402
+    STATUS_DATA_VERIFIED,
+    STATUS_DISPUTED,
+    STATUS_HUMAN_VERIFIED,
+    STATUS_PENDING,
+    VERIFIER_SCRIPT,
+    apply_state_machine,
+    evaluate_chain,
+    summarize,
+)
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+logger = logging.getLogger("correlation-check")
+
+REPORT_DIR = Path(__file__).resolve().parent / "reports"
+
+
+def load_chains(engine, entity_id: int = None) -> list:
+    """加载因果链 + 两端KPI信息。"""
+    q = """
+        SELECT c.id, c.entity_id, c.source_kpi_id, c.target_kpi_id,
+               c.strength, c.lag_months, c.direction, c.source_type, c.verify_status,
+               s.kpi_code AS src_code, s.kpi_name AS src_name,
+               t.kpi_code AS tgt_code, t.kpi_name AS tgt_name
+        FROM kpi_causality c
+        JOIN kpi_definitions s ON s.id = c.source_kpi_id
+        JOIN kpi_definitions t ON t.id = c.target_kpi_id
+    """
+    if entity_id is not None:
+        q += " WHERE c.entity_id = :eid"
+    with engine.connect() as conn:
+        rows = conn.execute(text(q), {"eid": entity_id} if entity_id is not None else {}).mappings().all()
+    return [dict(r) for r in rows]
+
+
+def load_values(engine, kpi_ids: list) -> dict:
+    """加载 KPI 历史值: {kpi_id: [(period, actual_value), ...]}"""
+    if not kpi_ids:
+        return {}
+    ids = list(set(int(i) for i in kpi_ids))
+    q = """
+        SELECT kpi_id, period, actual_value
+        FROM kpi_values
+        WHERE kpi_id IN :ids AND actual_value IS NOT NULL
+        ORDER BY period
+    """
+    with engine.connect() as conn:
+        rows = conn.execute(text(q).bindparams(ids=ids), {"ids": ids}).mappings().all()
+    result = {}
+    for r in rows:
+        result.setdefault(r["kpi_id"], []).append((r["period"], r["actual_value"]))
+    return result
+
+
+def main():
+    ap = argparse.ArgumentParser(description="因果链数据验证")
+    ap.add_argument("--entity-id", type=int, default=None, help="只验证指定企业（默认全部）")
+    ap.add_argument("--dry-run", action="store_true", help="只计算不写库")
+    args = ap.parse_args()
+
+    engine = get_engine()
+    chains = load_chains(engine, args.entity_id)
+    if not chains:
+        logger.info("无因果链，退出")
+        return 0
+
+    kpi_ids = [c["source_kpi_id"] for c in chains] + [c["target_kpi_id"] for c in chains]
+    values = load_values(engine, kpi_ids)
+
+    now = datetime.now()
+    results = []
+    updated = {"data_verified": 0, "disputed": 0, "unchanged": 0}
+    notes = []
+
+    with engine.begin() as conn:
+        for c in chains:
+            src_vals = values.get(c["source_kpi_id"], [])
+            tgt_vals = values.get(c["target_kpi_id"], [])
+            ev = evaluate_chain(
+                src_vals, tgt_vals,
+                lag_months=c["lag_months"] or 0,
+                direction=c["direction"] or "positive",
+            )
+            new_status, note = apply_state_machine(c["verify_status"], ev["status"], respect_human=True)
+
+            if note:
+                notes.append({"causality_id": c["id"], "note": note})
+
+            changed = new_status != c["verify_status"]
+            if changed:
+                updated[new_status if new_status in updated else "unchanged"] = \
+                    updated.get(new_status if new_status in updated else "unchanged", 0) + 1
+            else:
+                updated["unchanged"] += 1
+
+            if not args.dry_run:
+                conn.execute(text(
+                    "UPDATE kpi_causality SET verify_status = :st, verified_at = :va, verified_by = :vb "
+                    "WHERE id = :cid"
+                ), {
+                    "st": new_status, "va": now, "vb": VERIFIER_SCRIPT, "cid": c["id"],
+                })
+
+            results.append({
+                "causality_id": c["id"],
+                "source": f'{c["src_code"]}({c["src_name"]})',
+                "target": f'{c["tgt_code"]}({c["tgt_name"]})',
+                "direction": c["direction"],
+                "lag_months": c["lag_months"],
+                "strength": c["strength"],
+                "granularity": ev["granularity"],
+                "n_points": ev["n"],
+                "r": round(ev["r"], 4) if ev["r"] is not None else None,
+                "direction_consistent": ev["direction_consistent"],
+                "old_status": c["verify_status"],
+                "new_status": new_status,
+                "reason": ev["reason"],
+            })
+
+    summary = summarize([{"status": r["new_status"]} for r in results])
+    report = {
+        "generated_at": now.strftime("%Y-%m-%d %H:%M:%S"),
+        "script": VERIFIER_SCRIPT,
+        "dry_run": args.dry_run,
+        "entity_id": args.entity_id,
+        "summary": summary,
+        "updated": updated,
+        "human_verified_notes": notes,
+        "chains": results,
+    }
+
+    REPORT_DIR.mkdir(exist_ok=True)
+    report_path = REPORT_DIR / f"causality_verification_{now.strftime('%Y%m%d_%H%M%S')}.json"
+    report_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+
+    # 控制台摘要（cron 输出即消息）
+    lines = [
+        f"因果链数据验证{'[dry-run]' if args.dry_run else ''} {now.strftime('%Y-%m-%d %H:%M')}",
+        f"总数: {summary['total']} | 数据证实: {summary['by_status'][STATUS_DATA_VERIFIED]} | "
+        f"存疑: {summary['by_status'][STATUS_DISPUTED]} | 待检(数据不足): {summary['by_status'][STATUS_PENDING]} | "
+        f"人工确认: {summary['by_status'][STATUS_HUMAN_VERIFIED]}",
+        f"本次更新: data_verified={updated['data_verified']} disputed={updated['disputed']} unchanged={updated['unchanged']}",
+    ]
+    verified = [r for r in results if r["new_status"] == STATUS_DATA_VERIFIED]
+    disputed = [r for r in results if r["new_status"] == STATUS_DISPUTED]
+    if verified:
+        lines.append("── 数据证实 ──")
+        for r in verified:
+            lines.append(f"  #{r['causality_id']} {r['source']}→{r['target']} r={r['r']} n={r['n_points']}")
+    if disputed:
+        lines.append("── 数据存疑 ──")
+        for r in disputed:
+            lines.append(f"  #{r['causality_id']} {r['source']}→{r['target']} r={r['r']} n={r['n_points']} ({r['reason']})")
+    if notes:
+        lines.append("── 人工确认链的数据警示 ──")
+        for n in notes:
+            lines.append(f"  #{n['causality_id']}: {n['note']}")
+    lines.append(f"报告: {report_path}")
+    print("\n".join(lines))
+    logger.info("报告已写入 %s", report_path)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

backend/scripts/migrate_causality_verification.py

@@ -0,0 +1,95 @@
+"""kpi_causality 因果链验证机制迁移脚本 (2026-08-27 P2)
+
+加列:
+- source_type: varchar(20)  建链来源 AI_suggested/manual/imported
+- verify_status: varchar(20) 验证状态 pending/data_verified/human_verified/disputed
+- verified_at: datetime      验证时间
+- verified_by: varchar(50)   验证人/AI/脚本
+- entity_id: int             多租户隔离 (2026-08-27 收官补齐)
+
+幂等: 列已存在则跳过; entity_id 回填只更新 NULL/0 行。
+用法: python scripts/migrate_causality_verification.py
+"""
+import logging
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from sqlalchemy import text
+
+from app.database import get_engine
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+logger = logging.getLogger("migrate-causality-verification")
+
+COLUMNS = [
+    ("source_type", "ALTER TABLE kpi_causality ADD COLUMN source_type VARCHAR(20) NOT NULL DEFAULT 'manual' COMMENT '建链来源 AI_suggested/manual/imported'"),
+    ("verify_status", "ALTER TABLE kpi_causality ADD COLUMN verify_status VARCHAR(20) NOT NULL DEFAULT 'pending' COMMENT '验证状态 pending/data_verified/human_verified/disputed'"),
+    ("verified_at", "ALTER TABLE kpi_causality ADD COLUMN verified_at DATETIME NULL COMMENT '验证时间'"),
+    ("verified_by", "ALTER TABLE kpi_causality ADD COLUMN verified_by VARCHAR(50) NULL COMMENT '验证人/AI/脚本'"),
+    ("entity_id", "ALTER TABLE kpi_causality ADD COLUMN entity_id INT NOT NULL DEFAULT 1 COMMENT '企业ID (多租户隔离 2026-08-27)'"),
+]
+
+
+def run():
+    engine = get_engine()
+    with engine.connect() as conn:
+        # 1. 检查表是否存在
+        exists = conn.execute(text(
+            "SELECT COUNT(*) FROM information_schema.tables WHERE table_schema = DATABASE() AND table_name = 'kpi_causality'"
+        )).scalar()
+        if not exists:
+            logger.error("kpi_causality 表不存在，跳过")
+            return 1
+
+        # 2. 现有列
+        existing = {r[0] for r in conn.execute(text(
+            "SELECT column_name FROM information_schema.columns WHERE table_schema = DATABASE() AND table_name = 'kpi_causality'"
+        ))}
+        logger.info("现有列: %s", sorted(existing))
+
+        # 3. 加列（幂等）
+        for col, ddl in COLUMNS:
+            if col in existing:
+                logger.info("列 %s 已存在，跳过", col)
+            else:
+                conn.execute(text(ddl))
+                logger.info("已添加列 %s", col)
+
+        # 4. 回填 entity_id（无条件从 source KPI 对齐，纠正默认值偏差）
+        #    仅当来源KPI存在才回填；无来源KPI的孤儿链保持原值
+        conn.execute(text(
+            "UPDATE kpi_causality c JOIN kpi_definitions k ON k.id = c.source_kpi_id "
+            "SET c.entity_id = k.entity_id"
+        ))
+        orphan = conn.execute(text(
+            "SELECT COUNT(*) FROM kpi_causality c LEFT JOIN kpi_definitions k ON k.id = c.source_kpi_id "
+            "WHERE k.id IS NULL"
+        )).scalar()
+        if orphan:
+            logger.warning("%d 条因果链无来源KPI（孤儿链）", orphan)
+        else:
+            logger.info("entity_id 已全部按来源KPI回填")
+
+        # 5. 验证
+        cols = {r[0] for r in conn.execute(text(
+            "SELECT column_name FROM information_schema.columns WHERE table_schema = DATABASE() AND table_name = 'kpi_causality'"
+        ))}
+        missing = {c for c, _ in COLUMNS} - cols
+        if missing:
+            logger.error("仍有缺失列: %s", missing)
+            return 1
+        row = conn.execute(text(
+            "SELECT COUNT(*) FROM kpi_causality WHERE entity_id IS NULL OR entity_id = 0"
+        )).scalar()
+        if row:
+            logger.error("仍有 %d 行 entity_id 为空", row)
+            return 1
+        total = conn.execute(text("SELECT COUNT(*) FROM kpi_causality")).scalar()
+        logger.info("迁移完成: kpi_causality %d 条, 新列: source_type/verify_status/verified_at/verified_by/entity_id", total)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(run())

backend/tests/test_causality_verification.py

@@ -0,0 +1,363 @@
+"""因果链验证机制测试 — 数据验证核心 + 状态机 + API (2026-08-27 P2)
+
+覆盖:
+1. 服务层: parse_period / pearson / align_series(滞后) / evaluate_chain / apply_state_machine
+2. API: create(source_type) / verify-status / verify(人工确认) / entity隔离 / 权限
+"""
+import hashlib
+
+import pytest
+from fastapi.testclient import TestClient
+from sqlalchemy.orm import Session
+
+from app.models import KPIDefinition, KPICausality, KPIValue, Entity, User
+from app.services.causality_verification import (
+    STATUS_DATA_VERIFIED,
+    STATUS_DISPUTED,
+    STATUS_HUMAN_VERIFIED,
+    STATUS_PENDING,
+    align_series,
+    apply_state_machine,
+    evaluate_chain,
+    parse_period,
+    pearson,
+    summarize,
+)
+from tests.conftest import create_test_user, get_token_for_user, auth_header
+
+BASE = "/api/cma/kpi-causality"
+
+
+def _seed_kpi(db: Session, code: str, name: str = None, dimension: str = "finance",
+              entity_id: int = 1) -> KPIDefinition:
+    kpi = KPIDefinition(
+        kpi_code=code, kpi_name=name or code, dimension=dimension,
+        entity_id=entity_id, status="active", target_value=100.0,
+    )
+    db.add(kpi)
+    db.commit()
+    db.refresh(kpi)
+    return kpi
+
+
+def _seed_chain(db: Session, source_type: str = "AI_suggested"):
+    src = _seed_kpi(db, "BH_REVENUE", "营业收入")
+    tgt = _seed_kpi(db, "BH_NET_PROFIT", "净利润")
+    c = KPICausality(entity_id=src.entity_id, source_kpi_id=src.id, target_kpi_id=tgt.id,
+                     strength=0.5, lag_months=0, direction="positive",
+                     source_type=source_type, verify_status=STATUS_PENDING)
+    db.add(c)
+    db.commit()
+    db.refresh(c)
+    return src, tgt, c
+
+
+class TestParsePeriod:
+    def test_month(self):
+        assert parse_period("2026-07") == ("month", 2026 * 12 + 6)
+
+    def test_half(self):
+        assert parse_period("2026-H1") == ("half", 2026 * 12 + 5)
+        assert parse_period("2026-H2") == ("half", 2026 * 12 + 11)
+
+    def test_year(self):
+        assert parse_period("2026") == ("year", 2026 * 12 + 5)
+
+    def test_invalid(self):
+        assert parse_period("abc") is None
+        assert parse_period("") is None
+        assert parse_period(None) is None
+
+
+class TestPearson:
+    def test_perfect_positive(self):
+        r, n = pearson([1, 2, 3, 4], [2, 4, 6, 8])
+        assert n == 4
+        assert abs(r - 1.0) < 1e-9
+
+    def test_perfect_negative(self):
+        r, n = pearson([1, 2, 3, 4], [8, 6, 4, 2])
+        assert abs(r + 1.0) < 1e-9
+
+    def test_known_value(self):
+        # 与 numpy 核对过的样例 (F_REVENUE / F_NET_PROFIT 7点)
+        xs = [180.87, 132.33, 120.15, 60.5, 90.09, 129.32, 81.08]
+        ys = [94.31, -85.06, -21.66, -3.74, -21.91, -45.04, -17.63]
+        r, n = pearson(xs, ys)
+        assert n == 7
+        assert abs(r - 0.394012) < 1e-4
+
+    def test_insufficient(self):
+        r, n = pearson([1], [2])
+        assert r is None and n == 1
+
+    def test_constant_series(self):
+        r, n = pearson([3, 3, 3], [1, 2, 3])
+        assert r is None and n == 3
+
+
+class TestAlignSeries:
+    def test_no_lag(self):
+        src = [("2026-01", 1), ("2026-02", 2), ("2026-03", 3)]
+        tgt = [("2026-01", 10), ("2026-02", 20), ("2026-03", 30)]
+        g, pairs = align_series(src, tgt, lag_months=0)
+        assert g == "month"
+        assert pairs == [(1, 10), (2, 20), (3, 30)]
+
+    def test_lag_alignment(self):
+        """source t 与 target t+lag 配对"""
+        src = [("2026-01", 1), ("2026-02", 2), ("2026-03", 3)]
+        tgt = [("2026-02", 10), ("2026-03", 20), ("2026-04", 30)]
+        g, pairs = align_series(src, tgt, lag_months=1)
+        assert pairs == [(1, 10), (2, 20), (3, 30)]
+
+    def test_granularity_filter(self):
+        """月度/半年度混用时只取同粒度（优先月）"""
+        src = [("2026-01", 1), ("2026-02", 2), ("2026-H1", 3)]
+        tgt = [("2026-01", 10), ("2026-02", 20), ("2026-H1", 30)]
+        g, pairs = align_series(src, tgt, lag_months=0)
+        assert g == "month"
+        assert pairs == [(1, 10), (2, 20)]
+
+
+class TestEvaluateChain:
+    def test_data_verified_positive(self):
+        src = [(f"2026-{m:02d}", m) for m in range(1, 9)]
+        tgt = [(f"2026-{m:02d}", m * 2) for m in range(1, 9)]
+        ev = evaluate_chain(src, tgt, lag_months=0, direction="positive")
+        assert ev["status"] == STATUS_DATA_VERIFIED
+        assert ev["direction_consistent"] is True
+        assert ev["n"] == 8
+
+    def test_data_verified_negative(self):
+        src = [(f"2026-{m:02d}", m) for m in range(1, 9)]
+        tgt = [(f"2026-{m:02d}", -m * 2) for m in range(1, 9)]
+        ev = evaluate_chain(src, tgt, lag_months=0, direction="negative")
+        assert ev["status"] == STATUS_DATA_VERIFIED
+
+    def test_direction_conflict(self):
+        """声明 positive 但实际负相关 → disputed"""
+        src = [(f"2026-{m:02d}", m) for m in range(1, 9)]
+        tgt = [(f"2026-{m:02d}", -m) for m in range(1, 9)]
+        ev = evaluate_chain(src, tgt, lag_months=0, direction="positive")
+        assert ev["status"] == STATUS_DISPUTED
+        assert "方向矛盾" in ev["reason"]
+
+    def test_weak_correlation(self):
+        """弱相关（方向一致但|r|<阈值）→ disputed"""
+        # numpy seed=1: x=[1..8], y=x+N(0,6) → r≈0.119 (弱正相关)
+        src = [(f"2026-{m:02d}", m) for m in range(1, 9)]
+        tgt = [(f"2026-{m:02d}", y) for m, y in enumerate(
+            [10.75, -1.67, -0.17, -2.44, 10.19, -7.81, 17.47, 3.43], start=1)]
+        ev = evaluate_chain(src, tgt, lag_months=0, direction="positive")
+        assert ev["status"] == STATUS_DISPUTED
+        assert "弱相关" in ev["reason"]
+
+    def test_insufficient_points(self):
+        """数据点不足 → pending"""
+        src = [("2026-01", 1), ("2026-02", 2)]
+        tgt = [("2026-01", 10), ("2026-02", 20)]
+        ev = evaluate_chain(src, tgt, lag_months=0, direction="positive")
+        assert ev["status"] == STATUS_PENDING
+
+    def test_no_shared_periods(self):
+        src = [("2026-01", 1)]
+        tgt = [("2026-02", 10)]
+        ev = evaluate_chain(src, tgt, lag_months=0, direction="positive")
+        assert ev["status"] == STATUS_PENDING
+
+
+class TestStateMachine:
+    def test_pending_to_verified(self):
+        st, note = apply_state_machine(STATUS_PENDING, STATUS_DATA_VERIFIED)
+        assert st == STATUS_DATA_VERIFIED and note is None
+
+    def test_pending_to_disputed(self):
+        st, _ = apply_state_machine(STATUS_PENDING, STATUS_DISPUTED)
+        assert st == STATUS_DISPUTED
+
+    def test_human_verified_not_overridden(self):
+        st, note = apply_state_machine(STATUS_HUMAN_VERIFIED, STATUS_DISPUTED)
+        assert st == STATUS_HUMAN_VERIFIED
+        assert note is not None  # 数据矛盾警示
+
+    def test_human_verified_positive_note_none(self):
+        st, note = apply_state_machine(STATUS_HUMAN_VERIFIED, STATUS_DATA_VERIFIED)
+        assert st == STATUS_HUMAN_VERIFIED and note is None
+
+    def test_insufficient_keeps_status(self):
+        st, _ = apply_state_machine(STATUS_PENDING, STATUS_PENDING)
+        assert st == STATUS_PENDING
+
+
+class TestSummarize:
+    def test_counts(self):
+        s = summarize([{"status": STATUS_DATA_VERIFIED}, {"status": STATUS_DISPUTED},
+                       {"status": STATUS_PENDING}, {"status": STATUS_HUMAN_VERIFIED}])
+        assert s["total"] == 4
+        assert s["by_status"][STATUS_DATA_VERIFIED] == 1
+        assert s["by_status"][STATUS_DISPUTED] == 1
+        assert s["by_status"][STATUS_PENDING] == 1
+        assert s["by_status"][STATUS_HUMAN_VERIFIED] == 1
+
+
+class TestCausalityVerificationAPI:
+    def test_create_with_source_type(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src = _seed_kpi(db, "BH_REVENUE", "营业收入")
+        tgt = _seed_kpi(db, "BH_NET_PROFIT", "净利润")
+
+        resp = client.post(BASE, headers=auth_header(token), json={
+            "source_kpi_id": src.id, "target_kpi_id": tgt.id,
+            "source_type": "AI_suggested",
+        })
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["source_type"] == "AI_suggested"
+        assert body["verify_status"] == STATUS_PENDING
+
+    def test_create_invalid_source_type(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src = _seed_kpi(db, "BH_REVENUE", "营业收入")
+        tgt = _seed_kpi(db, "BH_NET_PROFIT", "净利润")
+        resp = client.post(BASE, headers=auth_header(token), json={
+            "source_kpi_id": src.id, "target_kpi_id": tgt.id, "source_type": "unknown",
+        })
+        assert resp.status_code == 400
+
+    def test_update_resets_verification(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+        # 先人工确认
+        resp = client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token),
+                          json={"verify_status": "human_verified", "verified_by": "任富海"})
+        assert resp.json()["verify_status"] == STATUS_HUMAN_VERIFIED
+        # 修改链定义 → 状态回到 pending
+        resp2 = client.put(f"{BASE}/{c.id}", headers=auth_header(token), json={"strength": 0.9})
+        assert resp2.json()["verify_status"] == STATUS_PENDING
+        assert resp2.json()["verified_by"] is None
+
+    def test_verify_status_endpoint(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+
+        resp = client.get(f"{BASE}/verify-status", headers=auth_header(token))
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["summary"]["total"] == 1
+        assert body["summary"]["by_status"][STATUS_PENDING] == 1
+        assert body["data"][0]["id"] == c.id
+        assert body["data"][0]["verify_status"] == STATUS_PENDING
+
+    def test_verify_status_filter(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+        client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token),
+                   json={"verify_status": "human_verified"})
+
+        resp = client.get(f"{BASE}/verify-status?verify_status=human_verified",
+                          headers=auth_header(token))
+        assert resp.json()["summary"]["total"] == 1
+        resp2 = client.get(f"{BASE}/verify-status?verify_status=pending", headers=auth_header(token))
+        assert resp2.json()["summary"]["total"] == 0
+
+    def test_human_verify(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+
+        resp = client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token),
+                          json={"verify_status": "human_verified", "verified_by": "任富海"})
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["verify_status"] == STATUS_HUMAN_VERIFIED
+        assert body["verified_by"] == "任富海"
+        assert body["verified_at"] is not None
+
+    def test_human_verify_default(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+        resp = client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token), json={})
+        assert resp.json()["verify_status"] == STATUS_HUMAN_VERIFIED
+        assert resp.json()["verified_by"] is not None  # 默认取用户名
+
+    def test_verify_disputed(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+        resp = client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token),
+                          json={"verify_status": "disputed"})
+        assert resp.json()["verify_status"] == STATUS_DISPUTED
+
+    def test_verify_invalid_status(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+        resp = client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token),
+                          json={"verify_status": "bogus"})
+        assert resp.status_code == 400
+
+    def test_verify_not_found(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        resp = client.put(f"{BASE}/99999/verify", headers=auth_header(token),
+                          json={"verify_status": "human_verified"})
+        assert resp.status_code == 404
+
+    def test_business_cannot_verify(self, client: TestClient, db: Session):
+        """business 角色无写权限 → 403"""
+        business = User(
+            username="business_verify", password_hash=hashlib.sha256("pass123".encode()).hexdigest(),
+            name="业务员", role="business",
+        )
+        db.add(business)
+        db.commit()
+        token = get_token_for_user(client, username="business_verify", password="pass123")
+        src, tgt, c = _seed_chain(db)
+        resp = client.put(f"{BASE}/{c.id}/verify", headers=auth_header(token),
+                          json={"verify_status": "human_verified"})
+        assert resp.status_code == 403
+
+    def test_entity_isolation(self, client: TestClient, db: Session):
+        """企业B看不到企业A的链，也不能verify企业A的链"""
+        create_test_user(db)
+        token = get_token_for_user(client)  # entity_id=1
+        _seed_entity2(db)
+        src2 = _seed_kpi(db, "BH2_REVENUE", "博海收入", entity_id=2)
+        tgt2 = _seed_kpi(db, "BH2_PROFIT", "博海利润", entity_id=2)
+        c2 = KPICausality(entity_id=2, source_kpi_id=src2.id, target_kpi_id=tgt2.id,
+                          strength=0.5, lag_months=0, direction="positive",
+                          source_type="manual", verify_status=STATUS_PENDING)
+        db.add(c2)
+        db.commit()
+
+        # entity1 的 verify-status 看不到 entity2 的链
+        resp = client.get(f"{BASE}/verify-status", headers=auth_header(token))
+        assert resp.json()["summary"]["total"] == 0
+        # entity1 的 token verify entity2 的链 → 404
+        resp2 = client.put(f"{BASE}/{c2.id}/verify", headers=auth_header(token),
+                           json={"verify_status": "human_verified"})
+        assert resp2.status_code == 404
+
+    def test_network_includes_verify_status(self, client: TestClient, db: Session):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        src, tgt, c = _seed_chain(db)
+        resp = client.get(f"{BASE}/kpi/{src.id}/network", headers=auth_header(token))
+        assert resp.status_code == 200
+        downstream = resp.json()["downstream"]
+        assert downstream[0]["verify_status"] == STATUS_PENDING
+        assert downstream[0]["source_type"] == "AI_suggested"
+
+
+def _seed_entity2(db: Session) -> None:
+    ent = db.query(Entity).filter(Entity.id == 2).first()
+    if not ent:
+        db.add(Entity(id=2, name="博海网络科技", short_name="博海", status="active"))
+        db.commit()

backend/tests/test_kpi_causality.py

@@ -348,6 +348,6 @@ class TestPermissions:
         assert resp.status_code == 200
 
     def test_no_token_denied(self, client: TestClient):
-        """无token → 403"""
+        """无token → 401（HTTPBearer 标准行为）"""
         resp = client.get(BASE)
-        assert resp.status_code == 403
+        assert resp.status_code == 401

frontend/src/api/index.ts

@@ -293,6 +293,8 @@ export const kpiCausalityApi = {
   getNetwork: (kpiId: number) => api.get(`/kpi-causality/kpi/${kpiId}/network`),
   getFullNetwork: () => api.get('/kpi-causality/full-network'),
   simulate: (data: any) => api.post('/kpi-causality/simulate', data),
+  getVerifyStatus: (params?: any) => api.get('/kpi-causality/verify-status', { params }),
+  verify: (id: number, data: any) => api.put(`/kpi-causality/${id}/verify`, data),
 }
 
 export const dataQualityApi = {

frontend/src/views/KPIDetail.vue

@@ -216,7 +216,13 @@
                     <el-timeline-item v-for="item in upstream" :key="item.kpi_id"
                       :color="item.direction === 'positive' ? 'var(--bsc-process)' : 'var(--bsc-finance)'"
                       :timestamp="'强度:' + item.strength + ' / 滞后期:' + item.lag_months + '月'">
-                      <div><b>{{ item.kpi_name }}</b> ({{ item.kpi_code }})</div>
+                      <div>
+                        <b>{{ item.kpi_name }}</b> ({{ item.kpi_code }})
+                        <span v-if="item.verify_status"
+                          :style="{ display:'inline-block', marginLeft:'8px', padding:'0 8px', borderRadius:'10px', fontSize:'12px', lineHeight:'20px', color: verifyBadge(item.verify_status).color, background: verifyBadge(item.verify_status).bg }">
+                          {{ verifyBadge(item.verify_status).text }}
+                        </span>
+                      </div>
                       <div style="font-size:12px;color:#999;">
                         {{ item.direction === 'positive' ? '正向驱动' : '负向抑制' }}
                         <span v-if="item.formula"> · {{ item.formula }}</span>
@@ -233,7 +239,13 @@
                     <el-timeline-item v-for="item in downstream" :key="item.kpi_id"
                       :color="item.direction === 'positive' ? 'var(--bsc-process)' : 'var(--bsc-finance)'"
                       :timestamp="'强度:' + item.strength + ' / 滞后期:' + item.lag_months + '月'">
-                      <div><b>{{ item.kpi_name }}</b> ({{ item.kpi_code }})</div>
+                      <div>
+                        <b>{{ item.kpi_name }}</b> ({{ item.kpi_code }})
+                        <span v-if="item.verify_status"
+                          :style="{ display:'inline-block', marginLeft:'8px', padding:'0 8px', borderRadius:'10px', fontSize:'12px', lineHeight:'20px', color: verifyBadge(item.verify_status).color, background: verifyBadge(item.verify_status).bg }">
+                          {{ verifyBadge(item.verify_status).text }}
+                        </span>
+                      </div>
                       <div style="font-size:12px;color:#999;">
                         {{ item.direction === 'positive' ? '正向推动' : '负向抑制' }}
                         <span v-if="item.formula"> · {{ item.formula }}</span>
@@ -408,6 +420,17 @@ function statusLabel(s: string) {
   }
   return map[s] || s || '—'
 }
+
+// 因果链验证状态映射（三层验证机制 2026-08-27）
+function verifyBadge(s: string) {
+  const map: Record<string, { text: string; color: string; bg: string }> = {
+    data_verified: { text: '✅ 数据证实', color: '#67c23a', bg: 'rgba(103,194,58,.12)' },
+    human_verified: { text: '✅ 人工确认', color: '#409eff', bg: 'rgba(64,158,255,.12)' },
+    disputed: { text: '⚠️ 存疑', color: '#e6a23c', bg: 'rgba(230,162,60,.12)' },
+    pending: { text: '⏳ 待检', color: '#909399', bg: 'rgba(144,147,153,.12)' },
+  }
+  return map[s] || { text: s || '—', color: '#909399', bg: 'rgba(144,147,153,.12)' }
+}
 const mapOptions = ref<any[]>([])
 const activeTab = ref('basic')