feat(budget): activate P1 auto-collect + attribution subject mapping (yanxue-20260914-budget-p13p14-activation)

backend/app/utils/alert_attribution.py

@@ -78,39 +78,77 @@ def build_dimension_attribution(db: Session, kpi_id: int, period: str) -> list:
     return result
 
 
+def _subject_patterns(db: Session, kpi_id: int) -> list:
+    """科目取数口径解析（2026-09-14 P1③④ 激活）:
+
+    优先用已推导的取数映射 kpi_value_sources.filter_rule.subject_codes —— 那是把
+    kpi_subject_map(新准则 6xxx) 机械桥接到 voucher_details 实际科目(5xxx/1xxx/2xxx) 的结果；
+    无映射时退回 kpi_subject_map 原始科目（新老科目体系不一致时明细多为 0）。
+    """
+    from app.models import KPIValueSource, KPISubjectMap
+
+    src = db.query(KPIValueSource).filter(
+        KPIValueSource.kpi_id == kpi_id,
+        KPIValueSource.source_table == "voucher_details",
+        KPIValueSource.status.in_(("active", "draft")),
+    ).order_by(KPIValueSource.status.asc()).first()
+    rule = (src.filter_rule or {}) if src else {}
+    labels = {m.subject_code: (m.remark or m.subject_code)
+              for m in db.query(KPISubjectMap).filter(KPISubjectMap.kpi_id == kpi_id).all()}
+    if rule.get("subject_codes"):
+        return [{
+            "code": code,
+            "label": labels.get(str(code).rstrip("%"), str(code)),
+            "weight": float((rule.get("weights") or {}).get(code, 1.0)),
+            "measure": rule.get("measure", "credit"),
+            "source": "kpi_value_sources",
+        } for code in rule["subject_codes"]]
+    return [{
+        "code": m.subject_code,
+        "label": m.remark or m.subject_code,
+        "weight": float(m.weight if m.weight is not None else 1.0),
+        "measure": "credit",
+        "source": "kpi_subject_map",
+    } for m in db.query(KPISubjectMap).filter(KPISubjectMap.kpi_id == kpi_id).all()]
+
+
 def build_subject_attribution(db: Session, kpi_id: int, period: str) -> list:
-    """科目明细拆解 — 查 kpi_subject_map → voucher_details 汇总科目发生额"""
-    mappings = db.query(KPISubjectMap).filter(KPISubjectMap.kpi_id == kpi_id).all()
-    if not mappings:
+    """科目明细拆解 — 科目口径取 _subject_patterns（优先修正后的取数映射），按 entity 隔离"""
+    patterns = _subject_patterns(db, kpi_id)
+    if not patterns:
         return []
 
+    kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
+    entity_id = kpi.entity_id if kpi else None
+
     result = []
-    for m in mappings:
+    for p in patterns:
         q = db.query(
-            func.coalesce(func.sum(VoucherDetail.debit_amount), 0),
             func.coalesce(func.sum(VoucherDetail.credit_amount), 0),
-        ).filter(
-            VoucherDetail.subject_code == m.subject_code,
-            VoucherDetail.period == period,
-        )
+            func.coalesce(func.sum(VoucherDetail.debit_amount), 0),
+            func.count(VoucherDetail.id),
+        ).filter(VoucherDetail.period == period)
+        if "%" in p["code"]:
+            q = q.filter(VoucherDetail.subject_code.like(p["code"]))
+        else:
+            q = q.filter(VoucherDetail.subject_code == p["code"])
+        if entity_id is not None:
+            q = q.filter(VoucherDetail.entity_id == entity_id)
         row = q.first()
-        debit_sum = float(row[0] or 0)
-        credit_sum = float(row[1] or 0)
-        # 方向: credit贷方(收入/流入) / debit借方(费用/流出)
-        if m.calc_type == "ratio":
-            amount = credit_sum - debit_sum
-        elif m.calc_type in ("avg", "other"):
-            amount = (credit_sum - debit_sum) / 2
-        else:  # sum
-            amount = credit_sum - debit_sum
-        amount = round(amount * float(m.weight or 1.0), 2)
+        credit_sum = float(row[0] or 0)
+        debit_sum = float(row[1] or 0)
+        rows = int(row[2] or 0)
+        # 口径: 损益类取贷方发生额(月结结转腿=本期发生额) / 余额类取净额(借-贷)
+        amount = (debit_sum - credit_sum) if p["measure"] == "net" else credit_sum
+        amount = round(amount * p["weight"], 2)
 
         result.append({
-            "subject_code": m.subject_code,
-            "subject_name": m.remark or m.subject_code,
+            "subject_code": p["code"],
+            "subject_name": p["label"],
             "amount_diff": amount,
-            "calc_type": m.calc_type,
-            "weight": float(m.weight or 1.0),
+            "calc_type": p["measure"],
+            "weight": p["weight"],
+            "rows": rows,
         })
 
     total = sum(abs(r["amount_diff"]) for r in result) or 0