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

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%;