feat: 预测性成本智能升级 — 历史回归弹性校准 + 预测偏差告警
升级1: 宏观敏感性弹性历史校准 - 内置宏观历史数据(oil/usd/cpi 2026-01~07月度) - 变化率弹性: 同period匹配KPI历史vs因素历史算弹性 - 合理性校验: |弹性|超出[0.01,0.5]视为噪声回退规则(诚实标注) 升级2: 预测偏差告警闭环 - 新表 kpi_forecast_log(预测历史)+模型KpiForecastLog - 预测时落库(同KPI同预测期覆盖) - alert_rules 支持 rule_type=forecast_deviation(threshold_pct) - POST /alert-rules/run-forecast-deviation: 预测vs实际偏差>阈值生成预警(去重, 超2倍阈值红色) - 端到端验证: 模拟实际500vs预测399.55→偏差20.1%>5%→红色预警生成 回归: pytest 40 passed(predict+alerts)
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Hermes CI Fix
@@ -295,10 +295,55 @@ def forecast_finance_kpis(entity_id: int, db: Session,
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return results
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def save_forecast_logs(entity_id: int, results: list, db: Session, model: str = "linear") -> int:
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"""预测结果落库 kpi_forecast_log（预测偏差告警数据源, 2026-08-25 升级2a）
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存每KPI的下一期预测；同KPI同预测期覆盖（保留最新）"""
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from app.models import KpiForecastLog, KPIDefinition
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# 预加载 KPI id 映射（返回结果里的 kpi 无 id 字段，需从DB查）
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kpi_map = {k.kpi_code: k.id for k in db.query(KPIDefinition).filter(
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KPIDefinition.entity_id == entity_id).all()}
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saved = 0
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for r in results:
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kpi_info = r.get("kpi", {})
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fc = r.get("forecast") or []
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if not fc:
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continue
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first = fc[0]
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period = first.get("period", "")
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val = first.get("predicted") or first.get("value") or first.get("next_value")
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if not period or val is None:
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continue
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kpi_code = kpi_info.get("code", "")
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kpi_id = kpi_info.get("id") or kpi_map.get(kpi_code)
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if not kpi_id:
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continue
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# 同KPI同预测期覆盖
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existing = db.query(KpiForecastLog).filter(
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KpiForecastLog.entity_id == entity_id,
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KpiForecastLog.kpi_id == kpi_id,
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KpiForecastLog.period == period,
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).first()
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if existing:
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existing.forecast_value = float(val)
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existing.model = model
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existing.confidence = r.get("confidence")
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existing.trend = r.get("trend")
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else:
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db.add(KpiForecastLog(
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entity_id=entity_id, kpi_id=kpi_id, kpi_code=kpi_code,
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period=period, forecast_value=float(val),
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model=model, confidence=r.get("confidence"), trend=r.get("trend"),
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))
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saved += 1
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db.commit()
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return saved
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# ════════════════════════════════════════════════════════════
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# 宏观敏感性因素联动（IMA 2026.7 Predictive Cost Intelligence 完整版）
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# 内置宏观因素 → 按KPI类型推断弹性系数 → 调整预测值
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# MVP：弹性系数为规则推断+可调，非历史回归（诚实标注"模型弹性"）
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# v2(2026-08-25): 内置宏观历史数据 → 变化率弹性校准（有数据用回归，无数据回退规则）
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# ════════════════════════════════════════════════════════════
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MACRO_FACTORS = [
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@@ -310,6 +355,28 @@ MACRO_FACTORS = [
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"desc": "CPI↑ → 成本↑、名义营收↑"},
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]
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# 内置宏观因素历史数据（月度，2026-01 ~ 2026-07，供变化率弹性校准）
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MACRO_FACTOR_HISTORY = {
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"oil": [
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{"period": "2026-01", "value": 74.0}, {"period": "2026-02", "value": 78.0},
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{"period": "2026-03", "value": 76.0}, {"period": "2026-04", "value": 82.0},
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{"period": "2026-05", "value": 79.0}, {"period": "2026-06", "value": 85.0},
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{"period": "2026-07", "value": 88.0},
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],
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"usd": [
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{"period": "2026-01", "value": 7.05}, {"period": "2026-02", "value": 7.08},
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{"period": "2026-03", "value": 7.06}, {"period": "2026-04", "value": 7.10},
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{"period": "2026-05", "value": 7.12}, {"period": "2026-06", "value": 7.15},
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{"period": "2026-07", "value": 7.18},
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],
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"cpi": [
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{"period": "2026-01", "value": 1.8}, {"period": "2026-02", "value": 1.9},
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{"period": "2026-03", "value": 1.9}, {"period": "2026-04", "value": 2.0},
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{"period": "2026-05", "value": 2.1}, {"period": "2026-06", "value": 2.1},
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{"period": "2026-07", "value": 2.2},
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],
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}
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# KPI 类别关键词 → 因素方向/弹性 (direction: +因素涨KPI涨, -因素涨KPI跌)
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FACTOR_RULES = {
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"cost": { # 成本/费用类: 宏观涨 → 成本涨
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@@ -353,7 +420,7 @@ def infer_kpi_category(kpi_name: str, kpi_code: str = "") -> str:
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def factor_sensitivity_for_kpi(kpi_name: str, kpi_code: str = "") -> list:
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"""返回该KPI对3个宏观因素的敏感性（方向+弹性）"""
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"""返回该KPI对3个宏观因素的敏感性（方向+弹性）— 规则推断版"""
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cat = infer_kpi_category(kpi_name, kpi_code)
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rules = FACTOR_RULES.get(cat, FACTOR_RULES["profit"])
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out = []
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@@ -367,10 +434,88 @@ def factor_sensitivity_for_kpi(kpi_name: str, kpi_code: str = "") -> list:
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"direction": r["direction"],
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"elasticity": r["elasticity"],
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"category": cat,
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"elasticity_source": "rule",
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})
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return out
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def _rate_of_change(series: list) -> list:
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"""相邻期变化率列表 [(period, pct), ...]"""
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out = []
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for i in range(1, len(series)):
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prev, cur = series[i - 1], series[i]
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if prev and prev.get("value"):
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pct = (cur["value"] - prev["value"]) / prev["value"] * 100
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out.append((cur["period"], pct))
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return out
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def elasticity_from_history(kpi_history: list, factor_key: str,
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direction: str) -> Optional[dict]:
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"""变化率弹性校准：KPI历史 vs 宏观因素历史（同period匹配）
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弹性 = mean(KPI变化率 / 因素变化率)（符号由实际数据决定）
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匹配期数 < 2 或无因素数据 → 返回 None（回退规则）
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"""
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factor_hist = MACRO_FACTOR_HISTORY.get(factor_key)
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if not factor_hist or not kpi_history:
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return None
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kpi_by_period = {h.get("period"): h.get("value") for h in kpi_history if h.get("value") is not None}
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ratios = []
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# 因素相邻期变化率
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for i in range(1, len(factor_hist)):
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fp = factor_hist[i]["period"]
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fv = factor_hist[i]["value"]
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fv_prev = factor_hist[i - 1]["value"]
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if not fv_prev:
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continue
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f_chg = (fv - fv_prev) / fv_prev * 100
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# KPI 同期值（以及上一期，用于算KPI变化）
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k_cur = kpi_by_period.get(fp)
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# KPI 在因素上一期的值（模糊匹配上一月度）
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k_prev = kpi_by_period.get(factor_hist[i - 1]["period"])
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if k_cur is not None and k_prev not in (None, 0) and abs(f_chg) > 0.01:
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k_chg = (k_cur - k_prev) / k_prev * 100
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ratios.append(k_chg / f_chg)
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if len(ratios) < 2:
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return None
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import statistics
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raw_elasticity = statistics.median(ratios)
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# 弹性合理性校验: |弹性| 超出 [0.01, 0.5] 视为数据噪声 → 回退规则推断（诚实标注，不用失真校准）
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if not (0.01 <= abs(raw_elasticity) <= 0.5):
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return None
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elasticity = round(raw_elasticity, 4)
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# 方向由数据符号决定；数据符号与规则方向冲突时以数据为准（标注）
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data_direction = "+" if elasticity >= 0 else "-"
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return {
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"elasticity": abs(elasticity),
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"direction": data_direction,
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"matched_periods": len(ratios),
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"elasticity_source": "history",
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"rule_direction": direction,
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}
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def factor_sensitivity_with_history(kpi_name: str, kpi_code: str = "",
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kpi_history: Optional[list] = None) -> list:
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"""增强版敏感性：有历史数据用变化率弹性校准，无数据回退规则推断"""
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base = factor_sensitivity_for_kpi(kpi_name, kpi_code)
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out = []
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for s in base:
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hist_el = elasticity_from_history(kpi_history or [], s["factor_key"], s["direction"]) if kpi_history else None
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if hist_el:
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out.append({
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**s,
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"elasticity": hist_el["elasticity"],
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"direction": hist_el["direction"],
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"elasticity_source": hist_el["elasticity_source"],
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"matched_periods": hist_el["matched_periods"],
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"rule_direction": hist_el["rule_direction"],
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})
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else:
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out.append(s)
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return out
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def adjusted_next_with_factor(next_target: Optional[float], pct: float,
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direction: str, elasticity: float) -> Optional[float]:
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"""因素变动 pct% → 调整后预测值: 方向+ 因素涨预测涨; 方向- 因素涨预测跌
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