709 lines
27 KiB
Python
709 lines
27 KiB
Python
"""现金流预测引擎 — 根据历史KPI数据推算未来30天现金流"""
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import logging
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from datetime import datetime, timedelta
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from typing import Optional, TYPE_CHECKING
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from sqlalchemy.orm import Session
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import math
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import random
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if TYPE_CHECKING:
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from app.models import KPIDefinition
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logger = logging.getLogger("cma.cash_forecast")
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# 默认现金阈值（万元）
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DEFAULT_CASH_WARNING = 20.0 # 黄灯 — 低于20万
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DEFAULT_CASH_CRITICAL = 10.0 # 红灯 — 低于10万
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# 历史KPI编码映射（含候选编码，兼容F_*标准编码）
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KPI_CODES = {
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"operating_cash_flow": "CASH_FLOW_001", # 经营现金流
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"receivables": "AR_001", # 应收账款
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"payables": "AP_001", # 应付账款
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"cash_balance": "CASH_001", # 现金余额
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}
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# 候选编码列表 — 依次尝试，找不到则回退
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KPI_CODE_CANDIDATES = {
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"operating_cash_flow": ["CASH_FLOW_001", "F_OP_CFLOW", "F_REVENUE"],
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"receivables": ["AR_001", "F_AR_DAYS", "C_AR_BALANCE"],
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"payables": ["AP_001", "F_AP_DAYS"],
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"cash_balance": ["CASH_001", "CASH_BALANCE", "F_CASH"],
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}
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def find_kpi(db: Session, entity_id: int, codes: list) -> Optional["KPIDefinition"]:
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"""按候选编码列表查找KPI，返回第一个命中的"""
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from app.models import KPIDefinition
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for code in codes:
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kpi = db.query(KPIDefinition).filter(
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KPIDefinition.kpi_code == code,
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KPIDefinition.entity_id == entity_id,
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).first()
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if kpi:
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return kpi
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return None
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def get_entity_kpi_history(entity_id: int, kpi_code: str, db: Session, limit_months: int = 6) -> list:
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"""获取实体某个KPI的历史值"""
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from app.models import KPIDefinition, KPIValue
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kpi = find_kpi(db, entity_id, [kpi_code])
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if not kpi:
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return []
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values = db.query(KPIValue).filter(
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KPIValue.kpi_id == kpi.id,
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KPIValue.actual_value.isnot(None),
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).order_by(KPIValue.period.desc()).limit(limit_months).all()
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return values
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def calc_trend(values: list) -> float:
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"""计算趋势系数 — 线性回归斜率 / 均值"""
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if len(values) < 2:
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return 0.0
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vals = [v.actual_value for v in values]
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n = len(vals)
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avg_x = (n - 1) / 2.0
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avg_y = sum(vals) / n
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num = sum((i - avg_x) * (vals[i] - avg_y) for i in range(n))
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den = sum((i - avg_x) ** 2 for i in range(n))
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slope = num / den if den != 0 else 0
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return slope / max(abs(avg_y), 1.0) * 100 # 趋势百分比
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def get_current_cash_balance(db: Session, entity_id: int) -> Optional[float]:
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"""获取当前现金余额（万元）— 优先级：KPI实际值 > SystemConfig > None"""
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from app.models import SystemConfig, KPIValue
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kpi = find_kpi(db, entity_id, KPI_CODE_CANDIDATES["cash_balance"])
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if kpi:
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latest_cash = db.query(KPIValue).filter(
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KPIValue.kpi_id == kpi.id,
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KPIValue.actual_value.isnot(None),
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).order_by(KPIValue.period.desc()).first()
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if latest_cash and latest_cash.actual_value is not None:
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return float(latest_cash.actual_value)
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cfg = db.query(SystemConfig).filter(
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SystemConfig.config_key == "cash.current_balance"
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).first()
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if cfg and cfg.config_value:
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try:
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return float(cfg.config_value)
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except Exception:
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pass
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return None
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def set_current_cash_balance(db: Session, value: float) -> float:
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"""设置当前现金余额（写入SystemConfig，供预测引擎使用）"""
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from app.models import SystemConfig
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cfg = db.query(SystemConfig).filter(
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SystemConfig.config_key == "cash.current_balance"
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).first()
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if cfg:
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cfg.config_value = str(value)
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else:
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cfg = SystemConfig(
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config_key="cash.current_balance",
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config_value=str(value),
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description="当前现金余额（万元），资金预测基线",
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)
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db.add(cfg)
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db.commit()
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return value
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def forecast_cash_flow(
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entity_id: int,
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db: Session,
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days: int = 30,
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current_cash: Optional[float] = None,
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) -> dict:
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"""
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预测未来30天现金流
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算法：
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1. 获取历史经营现金流、应收、应付趋势
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2. 推算每日现金流入/流出
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3. 生成每日预测值+置信区间
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"""
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from app.models import KPIDefinition, KPIValue
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# 获取当前现金余额
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if current_cash is None:
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base_cash = get_current_cash_balance(db, entity_id)
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if base_cash is None:
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base_cash = 30.0 # 默认假设30万
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else:
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base_cash = current_cash
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# 获取经营现金流历史
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ocf_history = get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["operating_cash_flow"][0], db) or \
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get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["operating_cash_flow"][1], db) or \
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get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["operating_cash_flow"][2], db)
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ocf_trend = calc_trend(ocf_history)
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# 获取应收历史
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ar_history = get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["receivables"][0], db) or \
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get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["receivables"][1], db)
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ar_trend = calc_trend(ar_history)
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# 获取应付历史
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ap_history = get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["payables"][0], db) or \
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get_entity_kpi_history(entity_id, KPI_CODE_CANDIDATES["payables"][1], db)
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ap_trend = calc_trend(ap_history)
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# 计算日均现金变化
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ocf_avg = sum(v.actual_value for v in ocf_history) / max(len(ocf_history), 1) / 30.0 if ocf_history else 0.5
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# 预测逻辑：趋势影响 + 季节性（月底回款高峰）
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forecast = []
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cash = base_cash
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today = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
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for day_offset in range(1, days + 1):
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d = today + timedelta(days=day_offset)
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day_of_month = d.day
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is_month_end = day_of_month >= 25
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# 每日现金变化 = 经营现金流日均值 × (1 + 趋势调整) + 季节因子
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trend_factor = 1.0 + ocf_trend / 100.0
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daily_change = ocf_avg * trend_factor
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# 月底回款高峰
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if is_month_end:
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daily_change += ocf_avg * 0.3 # 月底多30%回款
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# 周末效应
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if d.weekday() >= 5:
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daily_change *= 0.5 # 周末收支减半
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cash += daily_change
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# 置信区间：随时间增加而扩大
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confidence_band = 1.0 + day_offset * 0.08 # 每过1天，区间扩大8%
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std = max(abs(daily_change) * confidence_band, 0.5)
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lower = cash - std * 0.5
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upper = cash + std * 0.5
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# 预警状态
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if cash < DEFAULT_CASH_CRITICAL:
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status = "red"
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elif cash < DEFAULT_CASH_WARNING:
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status = "yellow"
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else:
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status = "green"
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forecast.append({
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"date": d.strftime("%Y-%m-%d"),
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"day_offset": day_offset,
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"predicted_cash": round(cash, 2),
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"lower_bound": round(max(lower, 0), 2),
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"upper_bound": round(upper, 2),
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"alert_status": status,
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})
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# 整体预警
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min_cash = min(f["predicted_cash"] for f in forecast)
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min_date = next(f["date"] for f in forecast if f["predicted_cash"] == min_cash)
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suggestions = []
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if min_cash < DEFAULT_CASH_CRITICAL:
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suggestions.append({
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"type": "critical",
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"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",
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"actions": [
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"立即催收大额应收账款",
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"暂停非必要支出",
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"准备短期融资安排",
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]
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})
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elif min_cash < DEFAULT_CASH_WARNING:
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suggestions.append({
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"type": "warning",
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"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",
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"actions": [
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"加快应收账款回款",
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"控制采购付款节奏",
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"评估短期现金流压力",
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]
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})
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return {
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"entity_id": entity_id,
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"base_cash": round(base_cash, 2),
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"days": days,
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"forecast": forecast,
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"min_cash": round(min_cash, 2),
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"min_cash_date": min_date,
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"trends": {
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"operating_cash_flow_trend_pct": round(ocf_trend, 2),
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"receivables_trend_pct": round(ar_trend, 2),
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"payables_trend_pct": round(ap_trend, 2),
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},
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"suggestions": suggestions,
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}
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def save_forecast_to_db(entity_id: int, forecast_data: dict, db: Session):
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"""将预测结果保存到数据库"""
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from app.models import CashForecast
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for f in forecast_data["forecast"]:
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forecast_date = datetime.strptime(f["date"], "%Y-%m-%d")
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cf = CashForecast(
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entity_id=entity_id,
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forecast_date=forecast_date,
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predicted_cash=f["predicted_cash"],
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lower_bound=f["lower_bound"],
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upper_bound=f["upper_bound"],
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alert_status=f["alert_status"],
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)
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db.add(cf)
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db.commit()
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def calculate_accuracy(entity_id: int, db: Session) -> list:
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"""计算预测准确率 — 对比上期预测 vs 本期实际"""
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from app.models import CashForecast, KPIDefinition, KPIValue
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# 获取实体最近的预测
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forecasts = db.query(CashForecast).filter(
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CashForecast.entity_id == entity_id,
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).order_by(CashForecast.forecast_date.desc()).limit(90).all()
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# 获取实际的现金余额KPI值
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cash_kpi = db.query(KPIDefinition).filter(
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KPIDefinition.kpi_code == KPI_CODES["cash_balance"],
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KPIDefinition.entity_id == entity_id,
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).first()
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if not cash_kpi or not forecasts:
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return []
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actuals = db.query(KPIValue).filter(
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KPIValue.kpi_id == cash_kpi.id,
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KPIValue.actual_value.isnot(None),
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).order_by(KPIValue.period.desc()).limit(12).all()
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actual_map = {}
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for a in actuals:
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try:
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# period like "2026-07" -> month approx
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actual_map[a.period] = a.actual_value
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except:
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pass
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# 按月汇总预测值和实际值，计算准确率
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from collections import defaultdict
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monthly_forecast = defaultdict(list)
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for f in forecasts:
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month_key = f.forecast_date.strftime("%Y-%m")
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monthly_forecast[month_key].append(f.predicted_cash)
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results = []
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for month, f_vals in sorted(monthly_forecast.items()):
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if month in actual_map:
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f_avg = sum(f_vals) / len(f_vals)
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a_val = actual_map[month]
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mae = abs(f_avg - a_val)
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mape = abs((f_avg - a_val) / max(abs(a_val), 1)) * 100
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results.append({
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"period": month,
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"forecast_value": round(f_avg, 2),
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"actual_value": round(a_val, 2),
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"mae": round(mae, 2),
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"mape": round(mape, 2),
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})
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return results
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def generate_scenario_suggestion(alert_type: str, kpi_name: str, extra: dict = None) -> dict:
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"""根据预警类型生成情景建议"""
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suggestions = {
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"cash_low": {
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"title": "现金流紧张缓解方案",
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"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。",
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"actions": [
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f"催收大额应收账款（预计回款{extra.get('expected_receivables', '待定')}万元）",
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"暂停非紧急采购和资本性支出",
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"与供应商协商延长账期",
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"评估银行短期授信额度",
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],
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"priority": "high",
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},
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"cash_critical": {
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"title": "现金流危机应对方案",
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"description": f"现金余额接近断流，需立即采取紧急措施。",
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"actions": [
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"立即催收所有到期应收账款",
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"暂停所有非必要支出",
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"紧急联系银行安排短期贷款",
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"评估资产变现可能性",
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],
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"priority": "high",
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},
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"cost_high": {
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"title": "成本管控优化方案",
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"description": f"成本率异常偏高，建议进行成本结构分析和优化。",
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"actions": [
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"逐项分析成本构成，识别异常项",
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"与供应商重新谈判采购价格",
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"评估流程优化降本空间",
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"建立费用审批红线上限",
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],
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"priority": "medium",
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},
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"revenue_drop": {
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"title": "收入下滑应对方案",
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"description": f"收入出现下滑趋势，建议分析原因并制定恢复计划。",
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"actions": [
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"分析收入下滑原因（客户流失/价格战/需求变化）",
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"制定客户留存和挽回计划",
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"评估新产品/新市场机会",
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"优化销售激励政策",
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],
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"priority": "high",
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},
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}
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sug = suggestions.get(alert_type, {
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"title": "改善建议",
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"description": "根据预警情况制定改善措施。",
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"actions": ["分析预警原因", "制定改善计划", "跟踪执行效果"],
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"priority": "medium",
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})
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if extra:
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sug["extra"] = extra
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return sug
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# ══════════════════════════════════════════════════════════════
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# 资金缺口预测 — 趋势引擎 + 收付款计划叠加 (资金管理智能体)
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# ══════════════════════════════════════════════════════════════
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def get_cash_plans(db: Session, entity_id: int, start_date: datetime, end_date: datetime) -> list:
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"""获取指定日期范围内的待执行收付款计划"""
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from app.models import CashPlan
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return db.query(CashPlan).filter(
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CashPlan.entity_id == entity_id,
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CashPlan.status == "pending",
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CashPlan.plan_date >= start_date,
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CashPlan.plan_date <= end_date,
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).order_by(CashPlan.plan_date.asc()).all()
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def _budget_monthly_ocf(db: Session, entity_id: int) -> Optional[float]:
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"""获取本月经营现金流预算（万元/月），用于校准预测基线"""
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from app.models import BudgetPlan
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kpi = find_kpi(db, entity_id, KPI_CODE_CANDIDATES["operating_cash_flow"])
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if not kpi:
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return None
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month_key = datetime.now().strftime("%Y-%m")
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bp = db.query(BudgetPlan).filter(
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BudgetPlan.kpi_id == kpi.id,
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BudgetPlan.period == month_key,
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BudgetPlan.status == "active",
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).order_by(BudgetPlan.version.desc()).first()
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if bp and bp.budget_value is not None:
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return float(bp.budget_value)
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return None
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def forecast_cash_flow_with_plans(
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entity_id: int,
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db: Session,
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days: int = 30,
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current_cash: Optional[float] = None,
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warning_line: float = DEFAULT_CASH_WARNING,
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critical_line: float = DEFAULT_CASH_CRITICAL,
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include_plans: bool = True,
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) -> dict:
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"""
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资金缺口预测 — 在趋势预测基础上叠加收付款计划：
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1. 趋势引擎生成基线预测
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2. 叠加 cash_plans 的应收(收) / 应付(付)
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3. 识别资金缺口日期（余额 < 警戒线）
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4. 生成缺口前3天预警点
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"""
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from collections import defaultdict
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from app.models import CashPlan
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base = forecast_cash_flow(entity_id, db, days, current_cash)
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base_cash = base["base_cash"]
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# 预算校准：本月经营现金流预算优先作为基线（万元/月）
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budget_ocf = _budget_monthly_ocf(db, entity_id)
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if budget_ocf is not None:
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base["trends"]["budget_monthly_ocf"] = round(budget_ocf, 2)
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# ── 收付款计划加载 ──
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today = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
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end = today + timedelta(days=days)
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plan_map = defaultdict(lambda: {"in": 0.0, "out": 0.0, "items": []})
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plans = []
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if include_plans:
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plans = get_cash_plans(db, entity_id, today, end + timedelta(days=1))
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for p in plans:
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dkey = p.plan_date.strftime("%Y-%m-%d")
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item = {
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"id": p.id,
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"amount": round(p.amount, 2),
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"counterparty": p.counterparty or "",
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"description": p.description or "",
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}
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if p.plan_type == "receive":
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plan_map[dkey]["in"] += p.amount
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plan_map[dkey]["items"].append({"type": "receive", **item})
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else:
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plan_map[dkey]["out"] += p.amount
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plan_map[dkey]["items"].append({"type": "pay", **item})
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# ── 逐日重算余额 ──
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forecast = []
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cash = base_cash
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prev_predicted = base_cash
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for i, f in enumerate(base["forecast"]):
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dkey = f["date"]
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# 趋势日净变化（与上一天预测值的差）
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trend_delta = f["predicted_cash"] - prev_predicted
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prev_predicted = f["predicted_cash"]
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pin = round(plan_map[dkey]["in"], 2)
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pout = round(plan_map[dkey]["out"], 2)
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# 预算校准：有月度预算时用预算日均替代纯趋势增量
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if budget_ocf is not None:
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trend_delta = budget_ocf / 30.0
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if f["day_offset"] % 7 in (5, 6):
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trend_delta *= 0.5 # 周末减半
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if dkey[-2:] >= "25":
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trend_delta *= 1.3 # 月底回款高峰
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cash = round(cash + trend_delta + pin - pout, 2)
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net_flow = round(trend_delta + pin - pout, 2)
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if cash < critical_line:
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status = "red"
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elif cash < warning_line:
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status = "yellow"
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else:
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status = "green"
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forecast.append({
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"date": dkey,
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"day_offset": f["day_offset"],
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"predicted_cash": cash,
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"planned_in": pin,
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"planned_out": pout,
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"trend_delta": round(trend_delta, 2),
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"net_flow": net_flow,
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"lower_bound": round(max(cash - abs(net_flow) * 0.5 - 0.5, 0), 2),
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"upper_bound": round(cash + abs(net_flow) * 0.5 + 0.5, 2),
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"alert_status": status,
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"gap": cash < warning_line,
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"plans": plan_map[dkey]["items"],
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})
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# ── 资金缺口日期 ──
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gap_dates = [
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{"date": f["date"], "predicted_cash": f["predicted_cash"],
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"gap_amount": round(warning_line - f["predicted_cash"], 2),
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"level": "red" if f["predicted_cash"] < critical_line else "yellow"}
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for f in forecast if f["gap"]
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]
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# ── 缺口前3天预警（每个连续缺口区间只预警一次，取区间首日） ──
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pre_alerts = []
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prev_was_gap = False
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for idx, f in enumerate(forecast):
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is_gap = f["gap"]
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gap_run_start = is_gap and not prev_was_gap
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prev_was_gap = is_gap
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if not gap_run_start:
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continue
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# 找到该连续缺口区间的最后一天及区间内最低余额（最严重时点）
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run_end = forecast[idx]
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run_min = forecast[idx]["predicted_cash"]
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run_min_date = forecast[idx]["date"]
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for j in range(idx + 1, len(forecast)):
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if forecast[j]["gap"]:
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run_end = forecast[j]
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if forecast[j]["predicted_cash"] < run_min:
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run_min = forecast[j]["predicted_cash"]
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run_min_date = forecast[j]["date"]
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else:
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break
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for lead in (3, 1): # 缺口前3天(主要)、前1天(紧急)
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pre_idx = idx - lead
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if pre_idx < 0:
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continue
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pf = forecast[pre_idx]
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if pf["alert_status"] == "green" or lead == 3:
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pre_alerts.append({
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"alert_date": pf["date"],
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"alert_offset": pf["day_offset"],
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"gap_date": f["date"],
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"gap_cash": f["predicted_cash"],
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"gap_amount": round(warning_line - f["predicted_cash"], 2),
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"worst_cash": round(run_min, 2),
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"worst_date": run_min_date,
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"lead_days": lead,
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"level": "red" if run_min < critical_line else "yellow",
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})
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break
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# 到期未收款（逾期）
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overdue_receives = db.query(CashPlan).filter(
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CashPlan.entity_id == entity_id,
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CashPlan.plan_type == "receive",
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CashPlan.status == "pending",
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CashPlan.plan_date < today,
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).order_by(CashPlan.plan_date.asc()).all()
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# 未来7天到期
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upcoming_7d = db.query(CashPlan).filter(
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CashPlan.entity_id == entity_id,
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CashPlan.status == "pending",
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CashPlan.plan_date >= today,
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CashPlan.plan_date <= today + timedelta(days=7),
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).order_by(CashPlan.plan_date.asc()).all()
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# 整体结论
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min_cash = min(f["predicted_cash"] for f in forecast) if forecast else base_cash
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min_date = next((f["date"] for f in forecast if f["predicted_cash"] == min_cash), "")
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suggestions = list(base.get("suggestions", []))
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if gap_dates:
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first_gap = gap_dates[0]
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if first_gap["level"] == "red":
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suggestions.insert(0, {
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"type": "critical",
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"message": f"预计{first_gap['date']}现金余额降至{first_gap['predicted_cash']:.1f}万，低于警戒线{warning_line:.0f}万，存在资金断流风险",
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"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"],
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})
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else:
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suggestions.insert(0, {
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"type": "warning",
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"message": f"预计{first_gap['date']}现金余额降至{first_gap['predicted_cash']:.1f}万，低于警戒线{warning_line:.0f}万",
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"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"],
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})
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if overdue_receives:
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total_overdue = sum(p.amount for p in overdue_receives)
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suggestions.append({
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"type": "warning",
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"message": f"有{len(overdue_receives)}笔应收款到期未收，合计{total_overdue:.1f}万",
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"actions": ["逐笔催收到期应收账款", "评估客户信用风险"],
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})
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return {
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"entity_id": entity_id,
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"base_cash": round(base_cash, 2),
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"days": days,
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"warning_line": warning_line,
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"critical_line": critical_line,
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"budget_monthly_ocf": budget_ocf,
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"forecast": forecast,
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"gap_dates": gap_dates,
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"pre_alerts": pre_alerts,
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"min_cash": round(min_cash, 2),
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"min_cash_date": min_date,
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"trends": base.get("trends", {}),
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"suggestions": suggestions,
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"summary": {
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"total_planned_in": round(sum(p.amount for p in plans if p.plan_type == "receive"), 2),
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"total_planned_out": round(sum(p.amount for p in plans if p.plan_type == "pay"), 2),
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"plan_count": len(plans),
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"overdue_receive_count": len(overdue_receives),
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"overdue_receive_amount": round(sum(p.amount for p in overdue_receives), 2),
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"upcoming_7d_count": len(upcoming_7d),
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},
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}
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def check_cash_alerts(db: Session, entity_id: int = 1) -> dict:
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"""资金预警 — 缺口前3天预警 + 到期未收款提醒，写入预警中心(kpi_alerts)"""
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import json as _json
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from app.models import KPIAlert, CashPlan
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result = forecast_cash_flow_with_plans(entity_id, db, days=30)
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new_alerts = []
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# 兜底KPI：现金KPI → 经营现金流KPI → 该实体任意KPI
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kpi = find_kpi(db, entity_id, KPI_CODE_CANDIDATES["cash_balance"]) or \
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find_kpi(db, entity_id, KPI_CODE_CANDIDATES["operating_cash_flow"]) or \
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db.query(KPIDefinition).filter(KPIDefinition.entity_id == entity_id).first()
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ar_kpi = find_kpi(db, entity_id, KPI_CODE_CANDIDATES["receivables"]) or kpi
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def _exists(msg: str) -> bool:
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return db.query(KPIAlert).filter(
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KPIAlert.alert_message == msg,
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KPIAlert.status.in_(["pending", "processing"]),
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).first() is not None
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# ── 1. 缺口前3天预警 ──
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if kpi:
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for pre in result["pre_alerts"]:
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level = pre["level"]
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msg = (f"【资金缺口预警】预计{pre['gap_date']}现金余额降至{pre['gap_cash']:.1f}万"
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f"（低于警戒线{result['warning_line']:.0f}万，缺口{pre['gap_amount']:.1f}万），"
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f"请于{pre['alert_date']}前（提前{pre['lead_days']}天）安排资金")
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if _exists(msg):
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continue
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alert = KPIAlert(
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kpi_id=kpi.id,
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alert_level=level,
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alert_message=msg[:500],
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alert_type="forecast",
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status="pending",
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suggestion=_json.dumps({
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"actions": ["加快应收账款回款", "控制付款节奏", "评估短期融资"],
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"gap_date": pre["gap_date"],
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"gap_amount": pre["gap_amount"],
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}, ensure_ascii=False),
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)
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db.add(alert)
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new_alerts.append({"type": "gap_forecast", "level": level, "message": msg})
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logger.info(f"资金缺口预警: {msg}")
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# ── 2. 到期未收款提醒 ──
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if ar_kpi:
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today = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
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overdue = db.query(CashPlan).filter(
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CashPlan.entity_id == entity_id,
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CashPlan.plan_type == "receive",
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CashPlan.status == "pending",
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CashPlan.plan_date < today,
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).order_by(CashPlan.plan_date.asc()).all()
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for p in overdue:
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days_late = (today - p.plan_date).days
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msg = (f"【到期未收款】应收款{p.counterparty or '客户'} {p.amount:.1f}万 "
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f"原计划{p.plan_date.strftime('%Y-%m-%d')}到期，已逾期{days_late}天未收回")
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if _exists(msg):
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continue
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alert = KPIAlert(
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kpi_id=ar_kpi.id,
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alert_level="red" if days_late >= 7 else "yellow",
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alert_message=msg[:500],
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alert_type="cash_plan",
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status="pending",
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suggestion=_json.dumps({
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"actions": ["联系客户催收", "评估坏账风险", "调整信用政策"],
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"plan_id": p.id,
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"days_late": days_late,
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}, ensure_ascii=False),
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)
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db.add(alert)
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new_alerts.append({"type": "overdue_receive", "level": alert.alert_level, "message": msg})
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logger.info(f"到期未收款提醒: {msg}")
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db.commit()
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return {
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"entity_id": entity_id,
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"new_alerts": len(new_alerts),
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"alerts": new_alerts,
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"gap_dates": result["gap_dates"],
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"pre_alerts": result["pre_alerts"],
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"summary": result["summary"],
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}
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