242 lines
9.6 KiB
Python
242 lines
9.6 KiB
Python
"""告警归因引擎 — 管理会计OS (P1-③ 2026-08-28)
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告警从"差多少"到"差在哪+怎么办"：
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- 子KPI维度拆解: 查 kpi_hierarchy 下级KPI各自差异（量差方向）
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- 科目明细拆解: 查 kpi_subject_map → voucher_details 汇总科目发生额（价差方向）
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- 趋势归因: 复用 deviation_engine.check_trend_anomaly 连续3期检测
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- 场景建议: 按 alert_type 联查 scenario_suggestions
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attribution JSON 结构:
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{
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"dimensions": [{"kpi_id":1,"kpi_name":"销售费用","deviation_value":-3.2,"deviation_rate":-18.6,"weight":0.5}],
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"subjects": [{"subject_code":"6601","subject_name":"销售费用","amount_diff":2.1,"share_pct":34.5}],
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"variance_type": "quantity_diff|price_diff|mixed",
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"trend": {"anomaly":true,"type":"continuous_decline","periods":["2026-06","2026-07","2026-08"],"message":"连续3期下滑"}
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}
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"""
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import logging
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from typing import Optional
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from sqlalchemy.orm import Session
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from sqlalchemy import func
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from app.models import KPIHierarchy, KPISubjectMap, VoucherDetail, ScenarioSuggestion, KPIDefinition
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logger = logging.getLogger("cma.alert_attribution")
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# 收入型KPI特征（量差方向: 子KPI量级偏离）
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REVENUE_TYPE_CODES = (
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"SALES_TOTAL", "REVENUE", "F_REVENUE", "SALES_PROFIT_RATE",
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"CUSTOMER_COUNT", "NEW_CUSTOMER", "TURNOVER_RATE",
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)
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def build_dimension_attribution(db: Session, kpi_id: int, period: str) -> list:
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"""子KPI维度拆解 — 查 kpi_hierarchy 下级KPI各自差异（实际vs预算）"""
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from app.models import KPIValue, BudgetPlan
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children = db.query(KPIHierarchy).filter(
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KPIHierarchy.parent_kpi_id == kpi_id
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).all()
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if not children:
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return []
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result = []
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for rel in children:
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child_id = rel.child_kpi_id
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actual = db.query(KPIValue).filter(
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KPIValue.kpi_id == child_id,
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KPIValue.period == period,
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KPIValue.actual_value.isnot(None),
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).order_by(KPIValue.calculated_at.desc()).first()
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budget = db.query(BudgetPlan).filter(
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BudgetPlan.kpi_id == child_id,
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BudgetPlan.period == period,
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BudgetPlan.status == "active",
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).order_by(BudgetPlan.updated_at.desc()).first()
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av = actual.actual_value if actual else None
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bv = budget.budget_value if budget else None
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dev_value = None
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dev_rate = None
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if av is not None and bv is not None and bv != 0:
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dev_value = round(av - bv, 2)
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dev_rate = round(dev_value / bv * 100, 2)
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child_kpi = db.query(KPIDefinition).filter(KPIDefinition.id == child_id).first()
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result.append({
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"kpi_id": child_id,
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"kpi_name": child_kpi.kpi_name if child_kpi else f"KPI-{child_id}",
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"actual_value": av,
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"budget_value": bv,
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"deviation_value": dev_value,
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"deviation_rate": dev_rate,
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"weight": float(rel.weight or 0),
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})
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# 按偏差绝对值降序，最异常的排前面
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result.sort(key=lambda x: -(abs(x["deviation_value"]) if x["deviation_value"] is not None else 0))
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return result
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def _subject_patterns(db: Session, kpi_id: int) -> list:
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"""科目取数口径解析（2026-09-14 P1③④ 激活）:
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优先用已推导的取数映射 kpi_value_sources.filter_rule.subject_codes —— 那是把
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kpi_subject_map(新准则 6xxx) 机械桥接到 voucher_details 实际科目(5xxx/1xxx/2xxx) 的结果；
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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）。
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"""
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from app.models import KPIValueSource, KPISubjectMap
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src = db.query(KPIValueSource).filter(
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KPIValueSource.kpi_id == kpi_id,
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KPIValueSource.source_table == "voucher_details",
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KPIValueSource.status.in_(("active", "draft")),
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).order_by(KPIValueSource.status.asc()).first()
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rule = (src.filter_rule or {}) if src else {}
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labels = {m.subject_code: (m.remark or m.subject_code)
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for m in db.query(KPISubjectMap).filter(KPISubjectMap.kpi_id == kpi_id).all()}
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if rule.get("subject_codes"):
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return [{
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"code": code,
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"label": labels.get(str(code).rstrip("%"), str(code)),
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"weight": float((rule.get("weights") or {}).get(code, 1.0)),
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"measure": rule.get("measure", "credit"),
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"source": "kpi_value_sources",
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} for code in rule["subject_codes"]]
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return [{
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"code": m.subject_code,
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"label": m.remark or m.subject_code,
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"weight": float(m.weight if m.weight is not None else 1.0),
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"measure": "credit",
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"source": "kpi_subject_map",
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} for m in db.query(KPISubjectMap).filter(KPISubjectMap.kpi_id == kpi_id).all()]
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def build_subject_attribution(db: Session, kpi_id: int, period: str) -> list:
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"""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"""
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patterns = _subject_patterns(db, kpi_id)
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if not patterns:
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return []
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kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
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entity_id = kpi.entity_id if kpi else None
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result = []
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for p in patterns:
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q = db.query(
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func.coalesce(func.sum(VoucherDetail.credit_amount), 0),
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func.coalesce(func.sum(VoucherDetail.debit_amount), 0),
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func.count(VoucherDetail.id),
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).filter(VoucherDetail.period == period)
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if "%" in p["code"]:
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q = q.filter(VoucherDetail.subject_code.like(p["code"]))
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else:
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q = q.filter(VoucherDetail.subject_code == p["code"])
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if entity_id is not None:
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q = q.filter(VoucherDetail.entity_id == entity_id)
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row = q.first()
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credit_sum = float(row[0] or 0)
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debit_sum = float(row[1] or 0)
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rows = int(row[2] or 0)
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# 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)
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amount = (debit_sum - credit_sum) if p["measure"] == "net" else credit_sum
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amount = round(amount * p["weight"], 2)
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result.append({
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"subject_code": p["code"],
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"subject_name": p["label"],
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"amount_diff": amount,
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"calc_type": p["measure"],
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"weight": p["weight"],
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"rows": rows,
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})
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total = sum(abs(r["amount_diff"]) for r in result) or 0
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for r in result:
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r["share_pct"] = round(abs(r["amount_diff"]) / total * 100, 1) if total else 0
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result.sort(key=lambda x: -abs(x["amount_diff"]))
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return result
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def detect_variance_type(kpi_code: str, dimensions: list, subjects: list) -> str:
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"""量价差判定简化版：
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成本型KPI科目发生额偏离 → price_diff（价差）
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收入型KPI子KPI量级偏离 → quantity_diff（量差）
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两者都有 → mixed
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"""
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has_dimension_dev = any(d.get("deviation_value") is not None and abs(d["deviation_value"]) > 0.01 for d in dimensions)
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has_subject_dev = any(abs(s.get("amount_diff", 0)) > 0.01 for s in subjects)
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is_revenue = any(code in (kpi_code or "").upper() for code in REVENUE_TYPE_CODES)
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if is_revenue:
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# 收入型: 子KPI(量)偏离为主 → quantity_diff
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if has_dimension_dev:
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return "quantity_diff"
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if has_subject_dev:
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return "price_diff"
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return "mixed"
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else:
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# 成本型: 科目发生额(价)偏离为主 → price_diff
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if has_subject_dev:
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return "price_diff"
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if has_dimension_dev:
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return "quantity_diff"
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return "mixed"
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def match_scenario(db: Session, alert_type: Optional[str]) -> Optional[dict]:
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"""按 alert_type 取 scenario_suggestions 建议（四类模板）"""
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if not alert_type:
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return None
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s = db.query(ScenarioSuggestion).filter(
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ScenarioSuggestion.alert_type == alert_type
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).order_by(ScenarioSuggestion.sort_order.asc(), ScenarioSuggestion.id.asc()).first()
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if not s:
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return None
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return {
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"scenario_id": s.id,
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"alert_type": s.alert_type,
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"title": s.title,
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"description": s.description,
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"action_template": s.action_template,
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"priority": s.priority,
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}
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def build_attribution(db: Session, kpi_id: int, period: str, alert_type: Optional[str] = None) -> dict:
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"""组装完整归因JSON（供告警生成/详情接口共用）"""
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from app.utils.deviation_engine import check_trend_anomaly
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kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
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kpi_code = kpi.kpi_code if kpi else ""
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dimensions = build_dimension_attribution(db, kpi_id, period)
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subjects = build_subject_attribution(db, kpi_id, period)
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variance_type = detect_variance_type(kpi_code, dimensions, subjects)
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trend = check_trend_anomaly(db, kpi_id, period, consecutive=3)
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# 默认场景归类（未显式传入时按KPI名称特征推断）
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if not alert_type:
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alert_type = infer_alert_type(kpi_code, kpi.kpi_name if kpi else "")
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attribution = {
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"dimensions": dimensions,
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"subjects": subjects,
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"variance_type": variance_type,
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"trend": trend,
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}
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return attribution, alert_type
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def infer_alert_type(kpi_code: str = "", kpi_name: str = "") -> str:
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"""按KPI特征推断告警场景类型（四类: cash_low/cash_critical/cost_high/revenue_drop）"""
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text = (kpi_code or "").upper() + (kpi_name or "")
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if any(k in text for k in ("CASH", "现金", "货币资金", "资金")):
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return "cash_low"
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if any(k in text for k in ("COST", "费用", "成本", "支出")):
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return "cost_high"
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if any(k in text for k in ("REVENUE", "收入", "销售", "营收")):
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return "revenue_drop"
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return "cost_high"
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