feat: 预测性成本智能·宏观敏感性因素联动(IMA 2026.7完整版)

- 内置3宏观因素: 原油价格/美元汇率/CPI通胀率
- 敏感性引擎: 按KPI类别推断弹性(成本类油价0.15/利润类0.12/营收类0.08), 方向+因素涨KPI涨
- 负值KPI(亏损)方向反转修复: 油价涨→净利更亏
- API: GET /predict/kpi-forecast/sensitivity?pct=10 → KPI×因素矩阵(±pct调整后预测)
- 前端: 敏感性幅度选择(±5/10/20%) + 敏感性矩阵表(同向/反向+↑↓调整值)
- 诚实标注: 模型弹性(规则推断,非历史回归), 后续可用宏观历史数据回归校准
- 验证: Chrome实测页面+API矩阵, pytest 46 passed

backend/app/api/predict.py

@@ -1,6 +1,6 @@
 """预测模拟API — 管理会计OS"""
 import logging
-from fastapi import APIRouter, HTTPException, Depends, Request
+from fastapi import APIRouter, HTTPException, Depends, Request, Query
 from app.utils.predict_engine import (
     cvp_analysis, npv, irr,
     sensitivity_analysis, scenario_analysis,
@@ -719,6 +719,7 @@ 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,
 )
 
 
@@ -764,3 +765,56 @@ def api_kpi_forecast_finance(
         "total": len(results),
         "data": results,
     }
+
+
+@router.get("/kpi-forecast/sensitivity")
+def api_kpi_forecast_sensitivity(
+    pct: float = Query(10, description="宏观因素变动幅度% (±)"),
+    periods: int = Query(3),
+    model: str = Query("linear"),
+    entity_id: int = Depends(get_entity_id),
+    db: Session = Depends(get_db),
+):
+    """宏观敏感性因素联动（IMA 2026.7）— 财务KPI × 宏观因素(油价/汇率/CPI)敏感性矩阵
+    输出：每个KPI的预测值 + 各因素 ±pct% 情景下的调整后预测值
+    MVP：弹性系数为规则推断（按KPI类别），诚实标注"模型弹性"非历史回归"""
+    if abs(pct) > 50:
+        raise HTTPException(400, "pct 必须在 ±50 以内")
+    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)
+    matrix = []
+    for r in results:
+        kpi_info = r.get("kpi", {})
+        sens = factor_sensitivity_for_kpi(kpi_info.get("name", ""), kpi_info.get("code", ""))
+        next_val = r.get("next_target")
+        factor_effects = []
+        for s in sens:
+            up_val = adjusted_next_with_factor(next_val, pct, s["direction"], s["elasticity"])
+            down_val = adjusted_next_with_factor(next_val, -pct, s["direction"], s["elasticity"])
+            factor_effects.append({
+                "factor_key": s["factor_key"],
+                "factor_name": s["factor_name"],
+                "factor_unit": s["factor_unit"],
+                "direction": s["direction"],
+                "elasticity": s["elasticity"],
+                "adj_up": up_val,
+                "adj_down": down_val,
+            })
+        matrix.append({
+            "kpi": kpi_info,
+            "category": sens[0]["category"] if sens else "profit",
+            "next_target": next_val,
+            "confidence": r.get("confidence"),
+            "trend": r.get("trend"),
+            "factors": factor_effects,
+        })
+    return {
+        "entity_id": entity_id,
+        "model": model,
+        "periods": periods,
+        "pct": pct,
+        "factors": MACRO_FACTORS,
+        "total": len(matrix),
+        "data": matrix,
+    }

backend/app/utils/kpi_forecast_engine.py

@@ -293,3 +293,92 @@ def forecast_finance_kpis(entity_id: int, db: Session,
     score = {"high": 3, "medium": 2, "low": 1}
     results.sort(key=lambda r: (score.get(r["confidence"], 0), r["history_count"]), reverse=True)
     return results
+
+
+# ════════════════════════════════════════════════════════════
+# 宏观敏感性因素联动（IMA 2026.7 Predictive Cost Intelligence 完整版）
+# 内置宏观因素 → 按KPI类型推断弹性系数 → 调整预测值
+# 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"）
+# ════════════════════════════════════════════════════════════
+
+MACRO_FACTORS = [
+    {"key": "oil", "name": "原油价格", "unit": "美元/桶",
+     "desc": "油价↑ → 运输/能源成本↑ → 成本类KPI↑、利润类KPI↓"},
+    {"key": "usd", "name": "美元汇率", "unit": "USD/CNY",
+     "desc": "美元↑ → 进口成本↑(成本类↑)、出口收入↑(营收类↑)"},
+    {"key": "cpi", "name": "CPI通胀率", "unit": "%",
+     "desc": "CPI↑ → 成本↑、名义营收↑"},
+]
+
+# KPI 类别关键词 → 因素方向/弹性 (direction: +因素涨KPI涨, -因素涨KPI跌)
+FACTOR_RULES = {
+    "cost": {  # 成本/费用类: 宏观涨 → 成本涨
+        "oil": {"direction": "+", "elasticity": 0.15},
+        "usd": {"direction": "+", "elasticity": 0.10},
+        "cpi": {"direction": "+", "elasticity": 0.10},
+    },
+    "revenue": {  # 营收类: 通胀涨→名义营收涨
+        "oil": {"direction": "-", "elasticity": 0.05},
+        "usd": {"direction": "+", "elasticity": 0.08},
+        "cpi": {"direction": "+", "elasticity": 0.08},
+    },
+    "profit": {  # 利润类: 宏观涨 → 成本挤压利润
+        "oil": {"direction": "-", "elasticity": 0.12},
+        "usd": {"direction": "-", "elasticity": 0.08},
+        "cpi": {"direction": "-", "elasticity": 0.08},
+    },
+    "cash": {  # 现金流类
+        "oil": {"direction": "-", "elasticity": 0.06},
+        "usd": {"direction": "-", "elasticity": 0.04},
+        "cpi": {"direction": "-", "elasticity": 0.05},
+    },
+}
+
+# 类别关键词匹配（长词优先）
+CATEGORY_KEYWORDS = [
+    ("profit", ["净利润", "净利", "利润", "毛利", "ROE", "ROI", "EVA", "收益率", "报酬率"]),
+    ("revenue", ["营收", "收入", "销售额", "销售", "产值", "客单"]),
+    ("cost", ["费用率", "成本率", "费用", "成本", "费率", "应付", "返利", "渠补", "税"]),
+    ("cash", ["现金流", "现金", "回款", "FCF", "资金"]),
+]
+
+
+def infer_kpi_category(kpi_name: str, kpi_code: str = "") -> str:
+    """按KPI名称/编码推断类别: profit/revenue/cost/cash，兜底 profit（保守）"""
+    n = (kpi_name or "") + " " + (kpi_code or "")
+    for cat, kws in CATEGORY_KEYWORDS:
+        if any(kw in n for kw in kws):
+            return cat
+    return "profit"
+
+
+def factor_sensitivity_for_kpi(kpi_name: str, kpi_code: str = "") -> list:
+    """返回该KPI对3个宏观因素的敏感性（方向+弹性）"""
+    cat = infer_kpi_category(kpi_name, kpi_code)
+    rules = FACTOR_RULES.get(cat, FACTOR_RULES["profit"])
+    out = []
+    for f in MACRO_FACTORS:
+        r = rules.get(f["key"], {"direction": "-", "elasticity": 0.05})
+        out.append({
+            "factor_key": f["key"],
+            "factor_name": f["name"],
+            "factor_unit": f["unit"],
+            "factor_desc": f["desc"],
+            "direction": r["direction"],
+            "elasticity": r["elasticity"],
+            "category": cat,
+        })
+    return out
+
+
+def adjusted_next_with_factor(next_target: Optional[float], pct: float,
+                              direction: str, elasticity: float) -> Optional[float]:
+    """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
+    负值KPI(亏损)方向反转: 方向- 时因素涨 → 更亏(更负)"""
+    if next_target is None:
+        return None
+    factor_change = pct * 0.01  # ±5% → 0.05
+    sign = 1.0 if direction == "+" else -1.0
+    if next_target < 0:
+        sign = -sign  # 负值(亏损): 因素涨 → 更亏
+    return round(next_target * (1 + sign * factor_change * elasticity), 2)

frontend/src/api/index.ts

@@ -223,6 +223,7 @@ export const predictApi = {
   // KPI趋势预测（预测性成本智能）
   kpiForecast: (params?: any) => api.get('/predict/kpi-forecast', { params }),
   kpiForecastFinance: (params?: any) => api.get('/predict/kpi-forecast/finance', { params }),
+  kpiForecastSensitivity: (params?: any) => api.get('/predict/kpi-forecast/sensitivity', { params }),
 }
 
 export const deviationPushApi = {

frontend/src/views/CostIntelligence.vue

@@ -15,7 +15,13 @@
             <el-option label="6期" :value="6" />
             <el-option label="12期" :value="12" />
           </el-select>
-          <el-button type="primary" :loading="loading" style="margin-left: 16px;" @click="loadData">刷新预测</el-button>
+          <span class="ctrl-label" style="margin-left: 16px;">敏感性幅度</span>
+          <el-select v-model="sensPct" style="width: 90px" @change="loadSensitivity">
+            <el-option label="±5%" :value="5" />
+            <el-option label="±10%" :value="10" />
+            <el-option label="±20%" :value="20" />
+          </el-select>
+          <el-button type="primary" :loading="loading" style="margin-left: 16px;" @click="loadAll">刷新预测</el-button>
         </div>
         <div class="ctrl-right">
           <span class="mapping-hint">基于 kpi_values 历史数据 · 线性回归/移动平均 · 置信度诚实标注</span>
@@ -92,6 +98,39 @@
       </el-col>
     </el-row>
 
+    <!-- 宏观敏感性联动（IMA 2026.7） -->
+    <el-card shadow="never" style="margin-top: 12px;">
+      <template #header>
+        <div class="card-header">
+          <span>🌐 宏观敏感性因素联动</span>
+          <el-tag size="small" type="warning">模型弹性（规则推断，非历史回归）</el-tag>
+          <span class="empty-hint">因素变动 {{ sensPct }}% 对预测值的影响</span>
+        </div>
+      </template>
+      <el-table :data="sensList" size="small" border stripe max-height="360">
+        <el-table-column prop="kpi.name" label="KPI" min-width="110" show-overflow-tooltip />
+        <el-table-column label="类别" width="70" align="center">
+          <template #default="{ row }">{{ catLabel(row.category) }}</template>
+        </el-table-column>
+        <el-table-column label="基准预测" width="100" align="right">
+          <template #default="{ row }">{{ fmtVal(row.next_target) }}</template>
+        </el-table-column>
+        <el-table-column v-for="f in sensFactors" :key="f.key" :label="f.name" min-width="150" align="center">
+          <template #default="{ row }">
+            <template v-if="factorOf(row, f.key)">
+              <span :style="{ color: factorOf(row, f.key).direction === '+' ? '#F56C6C' : '#67C23A' }">
+                {{ factorOf(row, f.key).direction === '+' ? '同向' : '反向' }}
+              </span>
+              <span class="sens-val">↑{{ fmtVal(factorOf(row, f.key).adj_up) }} ↓{{ fmtVal(factorOf(row, f.key).adj_down) }}</span>
+            </template>
+          </template>
+        </el-table-column>
+      </el-table>
+      <div class="note-line" style="margin-top:8px;">
+        弹性系数按KPI类别推断（成本类对油价最敏感0.15、利润类0.12、营收类0.08），MVP规则模型，后续可用宏观历史数据回归校准。
+      </div>
+    </el-card>
+
     <!-- 预测说明 -->
     <el-card shadow="never" style="margin-top: 12px;">
       <div class="note-line">
@@ -114,6 +153,18 @@ const selected = ref<any>(null)
 const chartRef = ref<HTMLElement | null>(null)
 let chart: any = null
 
+// ── 宏观敏感性联动 ──
+const sensPct = ref(10)
+const sensList = ref<any[]>([])
+const sensFactors = ref<any[]>([])
+
+function catLabel(c: string) {
+  return { profit: '利润', revenue: '营收', cost: '成本', cash: '现金流' }[c] || c
+}
+function factorOf(row: any, key: string) {
+  return (row.factors || []).find((f: any) => f.factor_key === key)
+}
+
 function lastHistory(row: any) {
   const h = row.history || []
   return h.length ? h[h.length - 1].value : null
@@ -164,6 +215,20 @@ async function loadData() {
   }
 }
 
+async function loadSensitivity() {
+  try {
+    const r: any = await predictApi.kpiForecastSensitivity({ pct: sensPct.value, periods: periods.value, model: model.value })
+    sensList.value = r.data || []
+    sensFactors.value = r.factors || []
+  } catch (e: any) {
+    console.error('敏感性加载失败', e)
+  }
+}
+
+async function loadAll() {
+  await Promise.all([loadData(), loadSensitivity()])
+}
+
 function onSelect(row: any) {
   selected.value = row
   renderChart()
@@ -207,7 +272,7 @@ function resizeChart() {
 }
 
 onMounted(() => {
-  loadData()
+  loadAll()
   window.addEventListener('resize', resizeChart)
 })
 onBeforeUnmount(() => {
@@ -267,6 +332,11 @@ onBeforeUnmount(() => {
   font-size: 12px;
   color: #C0C4CC;
 }
+.sens-val {
+  font-size: 12px;
+  color: #606266;
+  margin-left: 4px;
+}
 .trend-chart {
   height: 520px;
   width: 100%;