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/models/__init__.py

@@ -306,6 +306,21 @@ class MpmResult(Base):
     created_at = Column(DateTime, server_default=func.now())
 
 
+class KpiForecastLog(Base):
+    """KPI预测历史 — 预测偏差告警数据源 (2026-08-25 升级2a)"""
+    __tablename__ = "kpi_forecast_log"
+    id = Column(Integer, primary_key=True, index=True)
+    entity_id = Column(Integer, nullable=False, comment="企业ID")
+    kpi_id = Column(Integer, nullable=False, comment="KPI ID")
+    kpi_code = Column(String(50), nullable=False, comment="KPI编码")
+    period = Column(String(20), nullable=False, comment="预测期间")
+    forecast_value = Column(Float, nullable=True, comment="预测值")
+    model = Column(String(30), default="linear", comment="预测模型")
+    confidence = Column(String(10), nullable=True, comment="置信度")
+    trend = Column(String(10), nullable=True, comment="趋势")
+    created_at = Column(DateTime, server_default=func.now())
+
+
 class BotBridgeConfig(Base):
     """Bot桥接鉴权配置"""
     __tablename__ = "bot_bridge_config"