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)

backend/app/api/predict.py

@@ -719,7 +719,8 @@ def api_growth_quality(request: Request, data: dict):
 # ── KPI趋势预测（预测性成本智能 MVP） ────────────────────────────
 from app.utils.kpi_forecast_engine import (  # noqa: E402
     MODELS, forecast_kpi, forecast_finance_kpis,
-    MACRO_FACTORS, factor_sensitivity_for_kpi, adjusted_next_with_factor,
+    MACRO_FACTORS, factor_sensitivity_for_kpi, factor_sensitivity_with_history,
+    adjusted_next_with_factor, save_forecast_logs,
 )
 
 
@@ -758,6 +759,10 @@ def api_kpi_forecast_finance(
     if model not in MODELS:
         raise HTTPException(400, f"不支持的模型: {model}，可选: {'/'.join(MODELS)}")
     results = forecast_finance_kpis(entity_id, db, periods=periods, model=model)
+    try:
+        save_forecast_logs(entity_id, results, db, model=model)  # 升级2a: 预测落库(供偏差告警)
+    except Exception as e:
+        logger.warning(f"预测落库失败(不影响返回): {e}")
     return {
         "entity_id": entity_id,
         "model": model,
@@ -786,7 +791,9 @@ def api_kpi_forecast_sensitivity(
     matrix = []
     for r in results:
         kpi_info = r.get("kpi", {})
-        sens = factor_sensitivity_for_kpi(kpi_info.get("name", ""), kpi_info.get("code", ""))
+        # v2: 有历史数据用变化率弹性校准，无数据回退规则推断
+        sens = factor_sensitivity_with_history(
+            kpi_info.get("name", ""), kpi_info.get("code", ""), r.get("history", []))
         next_val = r.get("next_target")
         factor_effects = []
         for s in sens:
@@ -798,6 +805,9 @@ def api_kpi_forecast_sensitivity(
                 "factor_unit": s["factor_unit"],
                 "direction": s["direction"],
                 "elasticity": s["elasticity"],
+                "elasticity_source": s.get("elasticity_source", "rule"),
+                "matched_periods": s.get("matched_periods"),
+                "rule_direction": s.get("rule_direction"),
                 "adj_up": up_val,
                 "adj_down": down_val,
             })