feat: AI事前预警 — 现金流预测+预警扩展+准确率+情景建议

backend/app/api/alert_rules.py

@@ -25,6 +25,7 @@ class AlertRule(Base):
     id = Column(Integer, primary_key=True, index=True)
     kpi_id = Column(Integer, nullable=False, comment="关联KPI ID")
     rule_type = Column(String(30), nullable=False, comment="static/dynamic/trend_up/trend_down")
+    trigger_on = Column(String(20), default="actual", comment="actual/forecast/both — 实际值/预测值/两者触发")
     enabled = Column(Integer, default=1, comment="1启用 0禁用")
     params = Column(JSON, nullable=True, comment="规则参数")
     # static: {"green": ">=90", "yellow": ">=80", "red": "<80"}
@@ -98,6 +99,7 @@ def create_alert_rule(data: dict, db: Session = Depends(get_db), user=Depends(re
     """创建预警规则"""
     kpi_id = data.get("kpi_id")
     rule_type = data.get("rule_type", "static")
+    trigger_on = data.get("trigger_on", "actual")
     
     kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
     if not kpi:
@@ -108,6 +110,7 @@ def create_alert_rule(data: dict, db: Session = Depends(get_db), user=Depends(re
     rule = AlertRule(
         kpi_id=kpi_id,
         rule_type=rule_type,
+        trigger_on=trigger_on,
         enabled=data.get("enabled", 1),
         params=data.get("params"),
     )
@@ -132,7 +135,7 @@ def update_alert_rule(rule_id: int, data: dict, db: Session = Depends(get_db)):
     if not rule:
         raise HTTPException(404, "预警规则不存在")
     
-    for field in ("rule_type", "enabled", "params"):
+    for field in ("rule_type", "trigger_on", "enabled", "params"):
         if field in data:
             setattr(rule, field, data[field])
     db.commit()
@@ -477,3 +480,139 @@ def _eval_threshold(value: float, threshold_str: str, invert: bool = False) -> b
             return False
     except (ValueError, TypeError):
         return False
+
+
+# ============================================================
+# 预测值检查 + 情景建议
+# ============================================================
+
+def _check_forecast_alerts(db: Session) -> int:
+    """检查未来7天预测值是否超限 — 针对trigger_on='forecast'和'both'的规则"""
+    from app.utils.cash_forecast_engine import forecast_cash_flow, generate_scenario_suggestion
+    from app.models import CashForecast
+
+    rules = db.query(AlertRule).filter(
+        AlertRule.enabled == 1,
+        AlertRule.trigger_on.in_(["forecast", "both"]),
+    ).all()
+
+    if not rules:
+        return 0
+
+    alerts_generated = 0
+    rule_kpi_cache = {}
+
+    for rule in rules:
+        try:
+            kpi = rule_kpi_cache.get(rule.kpi_id)
+            if kpi is None:
+                kpi = db.query(KPIDefinition).filter(KPIDefinition.id == rule.kpi_id).first()
+                if kpi:
+                    rule_kpi_cache[rule.kpi_id] = kpi
+            if not kpi:
+                continue
+
+            entity_id = kpi.entity_id or 1
+            # 获取最新的预测
+            latest_forecasts = db.query(CashForecast).filter(
+                CashForecast.entity_id == entity_id,
+            ).order_by(CashForecast.forecast_date.asc()).limit(7).all()
+
+            if not latest_forecasts:
+                # 没有已有预测，执行一次实时预测
+                from app.utils.cash_forecast_engine import save_forecast_to_db
+                result = forecast_cash_flow(entity_id, db)
+                try:
+                    save_forecast_to_db(entity_id, result, db)
+                except:
+                    pass
+                latest_forecasts = db.query(CashForecast).filter(
+                    CashForecast.entity_id == entity_id,
+                ).order_by(CashForecast.forecast_date.asc()).limit(7).all()
+
+            if not latest_forecasts:
+                continue
+
+            # 检查预测值是否超限
+            params = rule.params or {}
+            params["kpi"] = kpi
+            for forecast in latest_forecasts:
+                value = forecast.predicted_cash
+                if value is None:
+                    continue
+                alert_level, alert_message = _check_static(value, params, kpi)
+                if alert_level and alert_level != "green":
+                    # 生成情景建议
+                    sug_type = "cash_critical" if alert_level == "red" else "cash_low"
+                    sug = generate_scenario_suggestion(
+                        sug_type, kpi.kpi_name,
+                        {"expected_receivables": 20, "forecast_date": forecast.forecast_date.isoformat()}
+                    )
+                    suggestion_text = f"{sug['title']}：{sug['description']}\\n建议行动：{'；'.join(sug['actions'])}"
+
+                    existing = db.query(KPIAlert).filter(
+                        KPIAlert.kpi_id == rule.kpi_id,
+                        KPIAlert.alert_type == "forecast",
+                        KPIAlert.alert_level == alert_level,
+                        KPIAlert.alert_message == alert_message,
+                        KPIAlert.status == "pending",
+                    ).first()
+                    if not existing:
+                        alert = KPIAlert(
+                            kpi_id=rule.kpi_id,
+                            alert_level=alert_level,
+                            alert_message=alert_message,
+                            alert_type="forecast",
+                            suggestion=suggestion_text,
+                            status="pending",
+                        )
+                        db.add(alert)
+                        alerts_generated += 1
+        except Exception as e:
+            logger.error(f"预测值预警检查失败: rule_id={rule.id}, error={e}")
+            continue
+
+    db.commit()
+    return alerts_generated
+
+
+@router.post("/check-forecast")
+def run_forecast_alert_check(db: Session = Depends(get_db)):
+    """执行预测值预警检查 — 检查未来7天预测值是否超限"""
+    generated = _check_forecast_alerts(db)
+    return {"message": f"预测值预警检查完成: 生成{generated}条", "generated": generated}
+
+
+@router.post("/generate-suggestions")
+def generate_alert_suggestions(db: Session = Depends(get_db)):
+    """为所有未处理的预警生成情景建议"""
+    from app.utils.cash_forecast_engine import generate_scenario_suggestion
+
+    pending = db.query(KPIAlert).filter(
+        KPIAlert.status == "pending",
+        KPIAlert.suggestion.is_(None),
+    ).all()
+
+    updated = 0
+    for alert in pending:
+        try:
+            sug_type = "cash_critical" if alert.alert_level == "red" else "cash_low"
+            if alert.alert_type == "forecast":
+                sug_type = "cash_critical" if alert.alert_level == "red" else "cash_low"
+            else:
+                sug_type = "cash_low"
+
+            kpi = db.query(KPIDefinition).filter(KPIDefinition.id == alert.kpi_id).first()
+            kpi_name = kpi.kpi_name if kpi else "未知KPI"
+
+            sug = generate_scenario_suggestion(sug_type, kpi_name, {
+                "alert_level": alert.alert_level,
+                "alert_message": alert.alert_message,
+            })
+            alert.suggestion = f"{sug['title']}：{sug['description']}\\n建议行动：{'；'.join(sug['actions'])}"
+            updated += 1
+        except Exception as e:
+            logger.error(f"生成建议失败: alert_id={alert.id}, error={e}")
+
+    db.commit()
+    return {"message": f"已为{updated}条预警生成情景建议", "updated": updated}

backend/app/api/predict.py

@@ -1,10 +1,16 @@
 """预测模拟API — 管理会计OS"""
 import logging
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, HTTPException, Depends
 from app.utils.predict_engine import (
     cvp_analysis, npv, irr,
     sensitivity_analysis, scenario_analysis,
 )
+from app.utils.cash_forecast_engine import (
+    forecast_cash_flow, save_forecast_to_db,
+    calculate_accuracy, generate_scenario_suggestion,
+)
+from app.database import get_db
+from sqlalchemy.orm import Session
 
 logger = logging.getLogger("cma.predict")
 router = APIRouter(prefix="/api/cma/predict", tags=["预测模拟"])
@@ -154,6 +160,105 @@ def api_cvp_detailed(data: dict):
         raise HTTPException(400, f"CVP详细分析失败: {str(e)}")
 
 
+# ── 现金流预测（AI事前预警） ────────────────────────────────────
+
+
+@router.post("/cash-forecast")
+def api_cash_forecast(data: dict, db: Session = Depends(get_db)):
+    """现金流预测 — 根据历史KPI推算未来30天现金流"""
+    try:
+        entity_id = int(data.get("entity_id", 1))
+        days = int(data.get("days", 30))
+        current_cash = float(data["current_cash"]) if data.get("current_cash") else None
+        result = forecast_cash_flow(entity_id, db, days, current_cash)
+        # 保存到数据库
+        try:
+            save_forecast_to_db(entity_id, result, db)
+        except Exception as e:
+            logger.warning(f"保存预测结果失败: {e}")
+        return result
+    except Exception as e:
+        raise HTTPException(400, f"现金流预测失败: {str(e)}")
+
+
+@router.get("/cash-forecast/history")
+def api_cash_forecast_history(
+    entity_id: int = 1,
+    days: int = 30,
+    db: Session = Depends(get_db),
+):
+    """获取已保存的现金流预测历史"""
+    from app.models import CashForecast
+    forecasts = db.query(CashForecast).filter(
+        CashForecast.entity_id == entity_id,
+    ).order_by(CashForecast.forecast_date.desc()).limit(days).all()
+    return {
+        "data": [{
+            "id": f.id,
+            "forecast_date": f.forecast_date.isoformat(),
+            "predicted_cash": f.predicted_cash,
+            "lower_bound": f.lower_bound,
+            "upper_bound": f.upper_bound,
+            "alert_status": f.alert_status,
+        } for f in forecasts]
+    }
+
+
+@router.get("/accuracy")
+def api_forecast_accuracy(
+    entity_id: int = 1,
+    db: Session = Depends(get_db),
+):
+    """预测准确率报表 — 上期预测 vs 本期实际"""
+    try:
+        results = calculate_accuracy(entity_id, db)
+
+        # 计算整体MAE/MAPE
+        if results:
+            total_mae = sum(r["mae"] for r in results) / len(results)
+            total_mape = sum(r["mape"] for r in results) / len(results)
+        else:
+            total_mae = 0
+            total_mape = 0
+
+        return {
+            "data": results,
+            "summary": {
+                "total_periods": len(results),
+                "avg_mae": round(total_mae, 2),
+                "avg_mape": round(total_mape, 2),
+            },
+        }
+    except Exception as e:
+        raise HTTPException(400, f"获取准确率失败: {str(e)}")
+
+
+@router.get("/scenario-suggestions")
+def api_scenario_suggestions(alert_type: str = None):
+    """获取情景建议模板"""
+    types = ["cash_low", "cash_critical", "cost_high", "revenue_drop"]
+    results = []
+    for at in types:
+        if alert_type and at != alert_type:
+            continue
+        sug = generate_scenario_suggestion(at, "")
+        results.append({"alert_type": at, **sug})
+    return {"data": results}
+
+
+@router.post("/scenario-suggestion/generate")
+def api_generate_suggestion(data: dict):
+    """根据预警信息动态生成情景建议"""
+    try:
+        alert_type = data.get("alert_type", "cash_low")
+        kpi_name = data.get("kpi_name", "未知KPI")
+        extra = data.get("extra", {})
+        sug = generate_scenario_suggestion(alert_type, kpi_name, extra)
+        return sug
+    except Exception as e:
+        raise HTTPException(400, f"生成建议失败: {str(e)}")
+
+
 # ── 实物期权计算器 ─────────────────────────────────────────────
 import math
 

backend/app/models/__init__.py

@@ -112,6 +112,9 @@ class KPIAlert(Base):
     kpi_value_id = Column(Integer, ForeignKey("kpi_values.id"), nullable=True)
     alert_level = Column(String(20), default="yellow", comment="green/yellow/red")
     alert_message = Column(String(500), nullable=False)
+    alert_type = Column(String(30), default="actual", comment="actual/forecast — 实际值超限/预测值超限")
+    suggestion = Column(Text, nullable=True, comment="情景建议")
+    action_plan_linked_id = Column(Integer, nullable=True, comment="关联的改善计划ID")
     status = Column(String(20), default="pending", comment="pending/processing/resolved")
     assignee = Column(String(100), nullable=True, comment="处理人")
     resolution = Column(Text, nullable=True, comment="处理结果")
@@ -362,3 +365,42 @@ class OKRTemplate(Base):
     is_active = Column(Integer, default=1)
     created_at = Column(DateTime, server_default=func.now())
     updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+
+
+class CashForecast(Base):
+    """现金流预测 — 每日未来30天预测"""
+    __tablename__ = "cash_forecasts"
+    id = Column(Integer, primary_key=True, index=True)
+    entity_id = Column(Integer, ForeignKey("entities.id"), nullable=False, comment="企业ID")
+    forecast_date = Column(DateTime, nullable=False, comment="预测日期（每天一条）")
+    predicted_cash = Column(Float, nullable=True, comment="预测现金余额")
+    lower_bound = Column(Float, nullable=True, comment="置信区间下界")
+    upper_bound = Column(Float, nullable=True, comment="置信区间上界")
+    alert_status = Column(String(20), default="green", comment="green/yellow/red")
+    created_at = Column(DateTime, server_default=func.now())
+
+
+class ForecastAccuracy(Base):
+    """预测准确率 — 上期预测 vs 本期实际"""
+    __tablename__ = "forecast_accuracy"
+    id = Column(Integer, primary_key=True, index=True)
+    entity_id = Column(Integer, ForeignKey("entities.id"), nullable=False, comment="企业ID")
+    period = Column(String(20), nullable=False, comment="期间 2026-07")
+    forecast_value = Column(Float, nullable=True, comment="预测值")
+    actual_value = Column(Float, nullable=True, comment="实际值")
+    mae = Column(Float, nullable=True, comment="绝对误差")
+    mape = Column(Float, nullable=True, comment="百分比误差")
+    created_at = Column(DateTime, server_default=func.now())
+
+
+class ScenarioSuggestion(Base):
+    """情景建议模板 — 根据不同预警类型自动生成建议"""
+    __tablename__ = "scenario_suggestions"
+    id = Column(Integer, primary_key=True, index=True)
+    alert_type = Column(String(30), nullable=False, comment="预警类型: cash_low/cash_critical/cost_high/revenue_drop")
+    title = Column(String(200), nullable=False, comment="建议标题")
+    description = Column(Text, nullable=True, comment="详细建议")
+    action_template = Column(Text, nullable=True, comment="改善行动模板")
+    priority = Column(String(20), default="medium", comment="high/medium/low")
+    sort_order = Column(Integer, default=0, comment="排序")
+    created_at = Column(DateTime, server_default=func.now())

backend/app/utils/cash_forecast_engine.py

@@ -0,0 +1,322 @@
+"""现金流预测引擎 — 根据历史KPI数据推算未来30天现金流"""
+import logging
+from datetime import datetime, timedelta
+from typing import Optional
+from sqlalchemy.orm import Session
+import math
+import random
+
+logger = logging.getLogger("cma.cash_forecast")
+
+# 默认现金阈值（万元）
+DEFAULT_CASH_WARNING = 20.0   # 黄灯 — 低于20万
+DEFAULT_CASH_CRITICAL = 10.0  # 红灯 — 低于10万
+
+# 历史KPI编码映射
+KPI_CODES = {
+    "operating_cash_flow": "CASH_FLOW_001",   # 经营现金流
+    "receivables": "AR_001",                    # 应收账款
+    "payables": "AP_001",                      # 应付账款
+    "cash_balance": "CASH_001",                # 现金余额
+}
+
+
+def get_entity_kpi_history(entity_id: int, kpi_code: str, db: Session, limit_months: int = 6) -> list:
+    """获取实体某个KPI的历史值"""
+    from app.models import KPIDefinition, KPIValue
+    kpi = db.query(KPIDefinition).filter(
+        KPIDefinition.kpi_code == kpi_code,
+        KPIDefinition.entity_id == entity_id,
+    ).first()
+    if not kpi:
+        return []
+    values = db.query(KPIValue).filter(
+        KPIValue.kpi_id == kpi.id,
+        KPIValue.actual_value.isnot(None),
+    ).order_by(KPIValue.period.desc()).limit(limit_months).all()
+    return values
+
+
+def calc_trend(values: list) -> float:
+    """计算趋势系数 — 线性回归斜率 / 均值"""
+    if len(values) < 2:
+        return 0.0
+    vals = [v.actual_value for v in values]
+    n = len(vals)
+    avg_x = (n - 1) / 2.0
+    avg_y = sum(vals) / n
+    num = sum((i - avg_x) * (vals[i] - avg_y) for i in range(n))
+    den = sum((i - avg_x) ** 2 for i in range(n))
+    slope = num / den if den != 0 else 0
+    return slope / max(abs(avg_y), 1.0) * 100  # 趋势百分比
+
+
+def forecast_cash_flow(
+    entity_id: int,
+    db: Session,
+    days: int = 30,
+    current_cash: Optional[float] = None,
+) -> dict:
+    """
+    预测未来30天现金流
+    
+    算法：
+    1. 获取历史经营现金流、应收、应付趋势
+    2. 推算每日现金流入/流出
+    3. 生成每日预测值+置信区间
+    """
+    from app.models import KPIDefinition, KPIValue
+
+    # 获取当前现金余额
+    if current_cash is None:
+        cash_kpi = db.query(KPIDefinition).filter(
+            KPIDefinition.kpi_code == KPI_CODES["cash_balance"],
+            KPIDefinition.entity_id == entity_id,
+        ).first()
+        if cash_kpi:
+            latest_cash = db.query(KPIValue).filter(
+                KPIValue.kpi_id == cash_kpi.id,
+                KPIValue.actual_value.isnot(None),
+            ).order_by(KPIValue.period.desc()).first()
+            base_cash = latest_cash.actual_value if latest_cash else 30.0
+        else:
+            base_cash = 30.0  # 默认假设30万
+    else:
+        base_cash = current_cash
+
+    # 获取经营现金流历史
+    ocf_history = get_entity_kpi_history(entity_id, KPI_CODES["operating_cash_flow"], db)
+    ocf_trend = calc_trend(ocf_history)
+
+    # 获取应收历史
+    ar_history = get_entity_kpi_history(entity_id, KPI_CODES["receivables"], db)
+    ar_trend = calc_trend(ar_history)
+
+    # 获取应付历史
+    ap_history = get_entity_kpi_history(entity_id, KPI_CODES["payables"], db)
+    ap_trend = calc_trend(ap_history)
+
+    # 计算日均现金变化
+    ocf_avg = sum(v.actual_value for v in ocf_history) / max(len(ocf_history), 1) / 30.0 if ocf_history else 0.5
+
+    # 预测逻辑：趋势影响 + 季节性（月底回款高峰）
+    forecast = []
+    cash = base_cash
+    today = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
+
+    for day_offset in range(1, days + 1):
+        d = today + timedelta(days=day_offset)
+        day_of_month = d.day
+        is_month_end = day_of_month >= 25
+
+        # 每日现金变化 = 经营现金流日均值 × (1 + 趋势调整) + 季节因子
+        trend_factor = 1.0 + ocf_trend / 100.0
+        daily_change = ocf_avg * trend_factor
+
+        # 月底回款高峰
+        if is_month_end:
+            daily_change += ocf_avg * 0.3  # 月底多30%回款
+
+        # 周末效应
+        if d.weekday() >= 5:
+            daily_change *= 0.5  # 周末收支减半
+
+        cash += daily_change
+
+        # 置信区间：随时间增加而扩大
+        confidence_band = 1.0 + day_offset * 0.08  # 每过1天，区间扩大8%
+        std = max(abs(daily_change) * confidence_band, 0.5)
+        lower = cash - std * 0.5
+        upper = cash + std * 0.5
+
+        # 预警状态
+        if cash < DEFAULT_CASH_CRITICAL:
+            status = "red"
+        elif cash < DEFAULT_CASH_WARNING:
+            status = "yellow"
+        else:
+            status = "green"
+
+        forecast.append({
+            "date": d.strftime("%Y-%m-%d"),
+            "day_offset": day_offset,
+            "predicted_cash": round(cash, 2),
+            "lower_bound": round(max(lower, 0), 2),
+            "upper_bound": round(upper, 2),
+            "alert_status": status,
+        })
+
+    # 整体预警
+    min_cash = min(f["predicted_cash"] for f in forecast)
+    min_date = next(f["date"] for f in forecast if f["predicted_cash"] == min_cash)
+
+    suggestions = []
+    if min_cash < DEFAULT_CASH_CRITICAL:
+        suggestions.append({
+            "type": "critical",
+            "message": f"预计{min_date}现金余额降至{min_cash:.1f}万，低于警戒线{DEFAULT_CASH_CRITICAL}万",
+            "actions": [
+                "立即催收大额应收账款",
+                "暂停非必要支出",
+                "准备短期融资安排",
+            ]
+        })
+    elif min_cash < DEFAULT_CASH_WARNING:
+        suggestions.append({
+            "type": "warning",
+            "message": f"预计{min_date}现金余额降至{min_cash:.1f}万，低于关注线{DEFAULT_CASH_WARNING}万",
+            "actions": [
+                "加快应收账款回款",
+                "控制采购付款节奏",
+                "评估短期现金流压力",
+            ]
+        })
+
+    return {
+        "entity_id": entity_id,
+        "base_cash": round(base_cash, 2),
+        "days": days,
+        "forecast": forecast,
+        "min_cash": round(min_cash, 2),
+        "min_cash_date": min_date,
+        "trends": {
+            "operating_cash_flow_trend_pct": round(ocf_trend, 2),
+            "receivables_trend_pct": round(ar_trend, 2),
+            "payables_trend_pct": round(ap_trend, 2),
+        },
+        "suggestions": suggestions,
+    }
+
+
+def save_forecast_to_db(entity_id: int, forecast_data: dict, db: Session):
+    """将预测结果保存到数据库"""
+    from app.models import CashForecast
+    for f in forecast_data["forecast"]:
+        forecast_date = datetime.strptime(f["date"], "%Y-%m-%d")
+        cf = CashForecast(
+            entity_id=entity_id,
+            forecast_date=forecast_date,
+            predicted_cash=f["predicted_cash"],
+            lower_bound=f["lower_bound"],
+            upper_bound=f["upper_bound"],
+            alert_status=f["alert_status"],
+        )
+        db.add(cf)
+    db.commit()
+
+
+def calculate_accuracy(entity_id: int, db: Session) -> list:
+    """计算预测准确率 — 对比上期预测 vs 本期实际"""
+    from app.models import CashForecast, KPIDefinition, KPIValue
+
+    # 获取实体最近的预测
+    forecasts = db.query(CashForecast).filter(
+        CashForecast.entity_id == entity_id,
+    ).order_by(CashForecast.forecast_date.desc()).limit(90).all()
+
+    # 获取实际的现金余额KPI值
+    cash_kpi = db.query(KPIDefinition).filter(
+        KPIDefinition.kpi_code == KPI_CODES["cash_balance"],
+        KPIDefinition.entity_id == entity_id,
+    ).first()
+
+    if not cash_kpi or not forecasts:
+        return []
+
+    actuals = db.query(KPIValue).filter(
+        KPIValue.kpi_id == cash_kpi.id,
+        KPIValue.actual_value.isnot(None),
+    ).order_by(KPIValue.period.desc()).limit(12).all()
+
+    actual_map = {}
+    for a in actuals:
+        try:
+            # period like "2026-07" -> month approx
+            actual_map[a.period] = a.actual_value
+        except:
+            pass
+
+    # 按月汇总预测值和实际值，计算准确率
+    from collections import defaultdict
+    monthly_forecast = defaultdict(list)
+    for f in forecasts:
+        month_key = f.forecast_date.strftime("%Y-%m")
+        monthly_forecast[month_key].append(f.predicted_cash)
+
+    results = []
+    for month, f_vals in sorted(monthly_forecast.items()):
+        if month in actual_map:
+            f_avg = sum(f_vals) / len(f_vals)
+            a_val = actual_map[month]
+            mae = abs(f_avg - a_val)
+            mape = abs((f_avg - a_val) / max(abs(a_val), 1)) * 100
+            results.append({
+                "period": month,
+                "forecast_value": round(f_avg, 2),
+                "actual_value": round(a_val, 2),
+                "mae": round(mae, 2),
+                "mape": round(mape, 2),
+            })
+
+    return results
+
+
+def generate_scenario_suggestion(alert_type: str, kpi_name: str, extra: dict = None) -> dict:
+    """根据预警类型生成情景建议"""
+    suggestions = {
+        "cash_low": {
+            "title": "现金流紧张缓解方案",
+            "description": f"现金余额低于阈值，建议加快应收账款催收、控制支出、评估短期融资。",
+            "actions": [
+                f"催收大额应收账款（预计回款{extra.get('expected_receivables', '待定')}万元）",
+                "暂停非紧急采购和资本性支出",
+                "与供应商协商延长账期",
+                "评估银行短期授信额度",
+            ],
+            "priority": "high",
+        },
+        "cash_critical": {
+            "title": "现金流危机应对方案",
+            "description": f"现金余额接近断流，需立即采取紧急措施。",
+            "actions": [
+                "立即催收所有到期应收账款",
+                "暂停所有非必要支出",
+                "紧急联系银行安排短期贷款",
+                "评估资产变现可能性",
+            ],
+            "priority": "high",
+        },
+        "cost_high": {
+            "title": "成本管控优化方案",
+            "description": f"成本率异常偏高，建议进行成本结构分析和优化。",
+            "actions": [
+                "逐项分析成本构成，识别异常项",
+                "与供应商重新谈判采购价格",
+                "评估流程优化降本空间",
+                "建立费用审批红线上限",
+            ],
+            "priority": "medium",
+        },
+        "revenue_drop": {
+            "title": "收入下滑应对方案",
+            "description": f"收入出现下滑趋势，建议分析原因并制定恢复计划。",
+            "actions": [
+                "分析收入下滑原因（客户流失/价格战/需求变化）",
+                "制定客户留存和挽回计划",
+                "评估新产品/新市场机会",
+                "优化销售激励政策",
+            ],
+            "priority": "high",
+        },
+    }
+
+    sug = suggestions.get(alert_type, {
+        "title": "改善建议",
+        "description": "根据预警情况制定改善措施。",
+        "actions": ["分析预警原因", "制定改善计划", "跟踪执行效果"],
+        "priority": "medium",
+    })
+
+    if extra:
+        sug["extra"] = extra
+    return sug