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
This commit is contained in:
Hermes CI Fix
@@ -1,6 +1,6 @@
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"""预测模拟API — 管理会计OS"""
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import logging
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from fastapi import APIRouter, HTTPException, Depends, Request
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from fastapi import APIRouter, HTTPException, Depends, Request, Query
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from app.utils.predict_engine import (
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cvp_analysis, npv, irr,
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sensitivity_analysis, scenario_analysis,
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@@ -719,6 +719,7 @@ def api_growth_quality(request: Request, data: dict):
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# ── KPI趋势预测（预测性成本智能 MVP） ────────────────────────────
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from app.utils.kpi_forecast_engine import ( # noqa: E402
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MODELS, forecast_kpi, forecast_finance_kpis,
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MACRO_FACTORS, factor_sensitivity_for_kpi, adjusted_next_with_factor,
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)
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@@ -764,3 +765,56 @@ def api_kpi_forecast_finance(
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"total": len(results),
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"data": results,
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}
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@router.get("/kpi-forecast/sensitivity")
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def api_kpi_forecast_sensitivity(
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pct: float = Query(10, description="宏观因素变动幅度% (±)"),
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periods: int = Query(3),
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model: str = Query("linear"),
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entity_id: int = Depends(get_entity_id),
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db: Session = Depends(get_db),
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):
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"""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
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输出：每个KPI的预测值 + 各因素 ±pct% 情景下的调整后预测值
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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"""
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if abs(pct) > 50:
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raise HTTPException(400, "pct 必须在 ±50 以内")
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if model not in MODELS:
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raise HTTPException(400, f"不支持的模型: {model}，可选: {'/'.join(MODELS)}")
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results = forecast_finance_kpis(entity_id, db, periods=periods, model=model)
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matrix = []
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for r in results:
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kpi_info = r.get("kpi", {})
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sens = factor_sensitivity_for_kpi(kpi_info.get("name", ""), kpi_info.get("code", ""))
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next_val = r.get("next_target")
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factor_effects = []
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for s in sens:
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up_val = adjusted_next_with_factor(next_val, pct, s["direction"], s["elasticity"])
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down_val = adjusted_next_with_factor(next_val, -pct, s["direction"], s["elasticity"])
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factor_effects.append({
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"factor_key": s["factor_key"],
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"factor_name": s["factor_name"],
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"factor_unit": s["factor_unit"],
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"direction": s["direction"],
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"elasticity": s["elasticity"],
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"adj_up": up_val,
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"adj_down": down_val,
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})
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matrix.append({
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"kpi": kpi_info,
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"category": sens[0]["category"] if sens else "profit",
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"next_target": next_val,
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"confidence": r.get("confidence"),
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"trend": r.get("trend"),
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"factors": factor_effects,
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})
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return {
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"entity_id": entity_id,
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"model": model,
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"periods": periods,
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"pct": pct,
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"factors": MACRO_FACTORS,
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"total": len(matrix),
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"data": matrix,
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}
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@@ -293,3 +293,92 @@ def forecast_finance_kpis(entity_id: int, db: Session,
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score = {"high": 3, "medium": 2, "low": 1}
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results.sort(key=lambda r: (score.get(r["confidence"], 0), r["history_count"]), reverse=True)
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return results
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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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# ════════════════════════════════════════════════════════════
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MACRO_FACTORS = [
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{"key": "oil", "name": "原油价格", "unit": "美元/桶",
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"desc": "油价↑ → 运输/能源成本↑ → 成本类KPI↑、利润类KPI↓"},
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{"key": "usd", "name": "美元汇率", "unit": "USD/CNY",
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"desc": "美元↑ → 进口成本↑(成本类↑)、出口收入↑(营收类↑)"},
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{"key": "cpi", "name": "CPI通胀率", "unit": "%",
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"desc": "CPI↑ → 成本↑、名义营收↑"},
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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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"oil": {"direction": "+", "elasticity": 0.15},
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"usd": {"direction": "+", "elasticity": 0.10},
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"cpi": {"direction": "+", "elasticity": 0.10},
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},
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"revenue": { # 营收类: 通胀涨→名义营收涨
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"oil": {"direction": "-", "elasticity": 0.05},
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"usd": {"direction": "+", "elasticity": 0.08},
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"cpi": {"direction": "+", "elasticity": 0.08},
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},
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"profit": { # 利润类: 宏观涨 → 成本挤压利润
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"oil": {"direction": "-", "elasticity": 0.12},
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"usd": {"direction": "-", "elasticity": 0.08},
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"cpi": {"direction": "-", "elasticity": 0.08},
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},
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"cash": { # 现金流类
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"oil": {"direction": "-", "elasticity": 0.06},
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"usd": {"direction": "-", "elasticity": 0.04},
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"cpi": {"direction": "-", "elasticity": 0.05},
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},
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}
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# 类别关键词匹配（长词优先）
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CATEGORY_KEYWORDS = [
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("profit", ["净利润", "净利", "利润", "毛利", "ROE", "ROI", "EVA", "收益率", "报酬率"]),
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("revenue", ["营收", "收入", "销售额", "销售", "产值", "客单"]),
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("cost", ["费用率", "成本率", "费用", "成本", "费率", "应付", "返利", "渠补", "税"]),
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("cash", ["现金流", "现金", "回款", "FCF", "资金"]),
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]
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def infer_kpi_category(kpi_name: str, kpi_code: str = "") -> str:
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"""按KPI名称/编码推断类别: profit/revenue/cost/cash，兜底 profit（保守）"""
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n = (kpi_name or "") + " " + (kpi_code or "")
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for cat, kws in CATEGORY_KEYWORDS:
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if any(kw in n for kw in kws):
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return cat
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return "profit"
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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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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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for f in MACRO_FACTORS:
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r = rules.get(f["key"], {"direction": "-", "elasticity": 0.05})
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out.append({
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"factor_key": f["key"],
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"factor_name": f["name"],
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"factor_unit": f["unit"],
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"factor_desc": f["desc"],
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"direction": r["direction"],
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"elasticity": r["elasticity"],
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"category": cat,
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})
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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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负值KPI(亏损)方向反转: 方向- 时因素涨 → 更亏(更负)"""
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if next_target is None:
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return None
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factor_change = pct * 0.01 # ±5% → 0.05
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sign = 1.0 if direction == "+" else -1.0
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if next_target < 0:
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sign = -sign # 负值(亏损): 因素涨 → 更亏
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return round(next_target * (1 + sign * factor_change * elasticity), 2)
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@@ -223,6 +223,7 @@ export const predictApi = {
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// KPI趋势预测（预测性成本智能）
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kpiForecast: (params?: any) => api.get('/predict/kpi-forecast', { params }),
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kpiForecastFinance: (params?: any) => api.get('/predict/kpi-forecast/finance', { params }),
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kpiForecastSensitivity: (params?: any) => api.get('/predict/kpi-forecast/sensitivity', { params }),
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}
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export const deviationPushApi = {
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@@ -15,7 +15,13 @@
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<el-option label="6期" :value="6" />
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<el-option label="12期" :value="12" />
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</el-select>
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<el-button type="primary" :loading="loading" style="margin-left: 16px;" @click="loadData">刷新预测</el-button>
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<span class="ctrl-label" style="margin-left: 16px;">敏感性幅度</span>
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<el-select v-model="sensPct" style="width: 90px" @change="loadSensitivity">
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<el-option label="±5%" :value="5" />
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<el-option label="±10%" :value="10" />
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<el-option label="±20%" :value="20" />
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</el-select>
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<el-button type="primary" :loading="loading" style="margin-left: 16px;" @click="loadAll">刷新预测</el-button>
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</div>
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<div class="ctrl-right">
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<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>
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@@ -92,6 +98,39 @@
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</el-col>
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</el-row>
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<!-- 宏观敏感性联动（IMA 2026.7） -->
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<el-card shadow="never" style="margin-top: 12px;">
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<template #header>
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<div class="card-header">
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<span>🌐 宏观敏感性因素联动</span>
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<el-tag size="small" type="warning">模型弹性（规则推断，非历史回归）</el-tag>
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<span class="empty-hint">因素变动 {{ sensPct }}% 对预测值的影响</span>
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</div>
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</template>
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<el-table :data="sensList" size="small" border stripe max-height="360">
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<el-table-column prop="kpi.name" label="KPI" min-width="110" show-overflow-tooltip />
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<el-table-column label="类别" width="70" align="center">
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<template #default="{ row }">{{ catLabel(row.category) }}</template>
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</el-table-column>
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<el-table-column label="基准预测" width="100" align="right">
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<template #default="{ row }">{{ fmtVal(row.next_target) }}</template>
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</el-table-column>
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<el-table-column v-for="f in sensFactors" :key="f.key" :label="f.name" min-width="150" align="center">
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<template #default="{ row }">
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<template v-if="factorOf(row, f.key)">
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<span :style="{ color: factorOf(row, f.key).direction === '+' ? '#F56C6C' : '#67C23A' }">
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{{ factorOf(row, f.key).direction === '+' ? '同向' : '反向' }}
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</span>
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<span class="sens-val">↑{{ fmtVal(factorOf(row, f.key).adj_up) }} ↓{{ fmtVal(factorOf(row, f.key).adj_down) }}</span>
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</template>
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</template>
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</el-table-column>
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</el-table>
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<div class="note-line" style="margin-top:8px;">
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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。
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</div>
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</el-card>
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<!-- 预测说明 -->
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<el-card shadow="never" style="margin-top: 12px;">
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<div class="note-line">
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@@ -114,6 +153,18 @@ const selected = ref<any>(null)
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const chartRef = ref<HTMLElement | null>(null)
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let chart: any = null
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// ── 宏观敏感性联动 ──
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const sensPct = ref(10)
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const sensList = ref<any[]>([])
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const sensFactors = ref<any[]>([])
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function catLabel(c: string) {
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return { profit: '利润', revenue: '营收', cost: '成本', cash: '现金流' }[c] || c
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}
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function factorOf(row: any, key: string) {
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return (row.factors || []).find((f: any) => f.factor_key === key)
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}
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function lastHistory(row: any) {
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const h = row.history || []
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return h.length ? h[h.length - 1].value : null
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@@ -164,6 +215,20 @@ async function loadData() {
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}
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}
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async function loadSensitivity() {
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try {
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const r: any = await predictApi.kpiForecastSensitivity({ pct: sensPct.value, periods: periods.value, model: model.value })
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sensList.value = r.data || []
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sensFactors.value = r.factors || []
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} catch (e: any) {
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console.error('敏感性加载失败', e)
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}
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}
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async function loadAll() {
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await Promise.all([loadData(), loadSensitivity()])
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}
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function onSelect(row: any) {
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selected.value = row
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renderChart()
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@@ -207,7 +272,7 @@ function resizeChart() {
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}
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onMounted(() => {
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loadData()
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loadAll()
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window.addEventListener('resize', resizeChart)
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})
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onBeforeUnmount(() => {
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@@ -267,6 +332,11 @@ onBeforeUnmount(() => {
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font-size: 12px;
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color: #C0C4CC;
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}
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.sens-val {
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font-size: 12px;
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color: #606266;
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margin-left: 4px;
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}
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.trend-chart {
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height: 520px;
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width: 100%;
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