feat: 2026-04-15~05-02 累积变更基线 — AI 重构 + Runtime Context + DWS 修复
涵盖（每条对应已存的审计记录）： - AI 模块拆分：apps/backend/app/ai/apps -> prompts/（8 个 APP + app2a 派生） audit: 2026-04-20__ai-module-complete.md - admin-web AI 管理套件：AIDashboard / AIOperations / AIRunLogs / AITriggers / TriggerManager audit: 2026-04-21__admin-web-ai-management-suite.md - App2 财务洞察 prompt v3 -> v5.1 + 小程序 AI 接入（chat / board-finance） audit: 2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md - App2 prewarm 全过滤器 + AI 触发器 cron reschedule audit: 2026-04-21__app2-finance-prewarm-all-filters.md migration: 20260420_ai_trigger_jobs_and_app2_prewarm.sql / 20260421_app2_prewarm_cron_reschedule.sql - AppType 联合类型对齐 + adminAiAppTypes.test.ts audit: 2026-04-30__admin_web_ai_app_type_alignment.md - DashScope tokens_used 提取修复 audit: 2026-04-30__backend_dashscope_tokens_used_extraction.md - App3 线索完整详情 prompt audit: 2026-05-01__backend_app3_full_detail_prompt.md - Runtime Context 沙箱（5-1~5-2 主线）： - 后端 schema/service + admin_runtime_context / xcx_runtime_clock 两个 router - admin-web RuntimeContext.tsx + miniprogram runtime-clock.ts - migration: 20260501__runtime_context_sandbox.sql - tools/db/verify_admin_web_sandbox.py + verify_sandbox_end_to_end.py - database/changes: 7 份 sandbox_* 验证报告 - 飞球 DWS 修复：finance_area_daily 区域汇总 + task_engine 调整 + RLS 视图业务日上界（migration 20260502 + scripts/ops/gen_rls_business_date_migration.py） 合规： - .gitignore 启用 tmp/ 排除 - 不入仓：apps/etl/connectors/feiqiu/.env（API_TOKEN secret，本地修改保留） 待验证清单： - docs/audit/changes/2026-05-04__cumulative_baseline_pending_verification.md 每个主题的功能完整性 / 上线验证几乎都未收口，按优先级 P0~P3 逐一处理
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"""应用 2 财务洞察 Prompt 模板。
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"""应用 2 财务洞察 Prompt 拼装。
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构建包含当期和上期收入结构的完整 Prompt，供百炼 API 生成财务洞察。
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cron 每日 10:00 预热触发，对所有筛选组合（时间 × 区域）生成洞察。
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- 数据源：board_service.get_finance_board(time, area, compare=1, site_id)
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- 筛选维度：8 个时间维度 × 9 个区域 = 72 组合
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- 输出字段：insights 数组（seq + title + body）
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- system prompt 在百炼控制台配置
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收入字段映射（严格遵守 items_sum 口径）：
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- table_fee = table_charge_money（台费）
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- assistant_pd = assistant_pd_money（陪打费）
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- assistant_cx = assistant_cx_money（超休费）
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- goods = goods_money（商品收入）
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- recharge = 充值 pay_amount settle_type=5（充值收入）
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禁止使用 consume_money，统一使用：
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items_sum = table_charge_money + goods_money + assistant_pd_money
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+ assistant_cx_money + electricity_money
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Prompt 中 board_data 字段名会自动翻译为中文（KEY_TRANSLATIONS），
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目的：减少 AI 理解英文变量的成本，生成的洞察正文可读性更强。
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"""
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from __future__ import annotations
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import json
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import logging
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from datetime import datetime
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from typing import Any
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from app.services.board_service import get_finance_board, _calc_date_range, _calc_prev_range
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def build_prompt(context: dict) -> list[dict]:
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"""构建 App2 财务洞察 Prompt 消息列表。
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logger = logging.getLogger(__name__)
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Args:
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context: 包含以下字段：
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- site_id: int，门店 ID
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- time_dimension: str，时间维度编码
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- current_data: dict，当期数据
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- previous_data: dict，上期数据
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# App2 时间维度 → board_service 时间枚举
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DIMENSION_MAP: dict[str, str] = {
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"this_month": "month",
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"last_month": "lastMonth",
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"this_week": "week",
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"last_week": "lastWeek",
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"this_quarter": "quarter",
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"last_quarter": "lastQuarter",
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"last_3_months": "last_3m",
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"last_6_months": "last_6m",
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}
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Returns:
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messages 列表（system + user），供 BailianClient.chat_json 调用
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"""
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site_id = context.get("site_id", 0)
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time_dimension = context.get("time_dimension", "")
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current_data = context.get("current_data", {})
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previous_data = context.get("previous_data", {})
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system_content = _build_system_content(
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site_id=site_id,
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time_dimension=time_dimension,
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current_data=current_data,
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previous_data=previous_data,
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)
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user_content = (
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f"请根据以上数据，为门店 {site_id} 生成 {_dimension_label(time_dimension)} 的财务洞察分析。"
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"以 JSON 格式返回，包含 insights 数组，每项含 seq（序号）、title（标题）、body（正文）。"
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)
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return [
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{"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
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{"role": "user", "content": user_content},
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]
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def _build_system_content(
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*,
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site_id: int,
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time_dimension: str,
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current_data: dict,
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previous_data: dict,
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) -> dict:
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"""构建 system prompt JSON 结构。"""
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return {
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"task": (
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"你是台球门店的财务分析 AI 助手。"
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"根据提供的当期和上期经营数据，生成结构化的财务洞察。"
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"分析维度包括：收入结构变化、各收入项占比、环比趋势、异常波动。"
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"输出 JSON 格式：{\"insights\": [{\"seq\": 1, \"title\": \"...\", \"body\": \"...\"}]}"
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),
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"data": {
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"site_id": site_id,
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"time_dimension": time_dimension,
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"time_dimension_label": _dimension_label(time_dimension),
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"current_period": _build_period_data(current_data),
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"previous_period": _build_period_data(previous_data),
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},
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"reference": {
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"field_mapping": {
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"items_sum": (
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"table_charge_money + goods_money + assistant_pd_money"
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" + assistant_cx_money + electricity_money"
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),
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"table_fee": "table_charge_money（台费收入）",
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"assistant_pd": "assistant_pd_money（陪打费）",
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"assistant_cx": "assistant_cx_money（超休费）",
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"goods": "goods_money（商品收入）",
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"recharge": "充值 pay_amount（settle_type=5，充值收入）",
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"electricity": "electricity_money（电费，当前未启用，全为 0）",
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},
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"rules": [
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"统一使用 items_sum 口径计算营收总额",
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"助教费用必须拆分为 assistant_pd_money（陪打）和 assistant_cx_money（超休）",
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"支付渠道恒等式：balance_amount = recharge_card_amount + gift_card_amount",
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"金额单位：元（CNY），保留两位小数",
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],
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},
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}
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def _build_period_data(data: dict) -> dict:
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"""构建单期数据结构，确保字段名遵守 items_sum 口径。"""
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return {
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# 收入结构（items_sum 口径）
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"table_charge_money": data.get("table_charge_money", 0),
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"goods_money": data.get("goods_money", 0),
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"assistant_pd_money": data.get("assistant_pd_money", 0),
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"assistant_cx_money": data.get("assistant_cx_money", 0),
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"electricity_money": data.get("electricity_money", 0),
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# 充值收入
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"recharge_income": data.get("recharge_income", 0),
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# 储值资产
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"balance_pay": data.get("balance_pay", 0),
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"recharge_card_pay": data.get("recharge_card_pay", 0),
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"gift_card_pay": data.get("gift_card_pay", 0),
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# 费用汇总
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"discount_amount": data.get("discount_amount", 0),
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"adjust_amount": data.get("adjust_amount", 0),
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# 平台结算
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"platform_settlement_amount": data.get("platform_settlement_amount", 0),
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"groupbuy_pay_amount": data.get("groupbuy_pay_amount", 0),
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# 汇总
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"order_count": data.get("order_count", 0),
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"member_count": data.get("member_count", 0),
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}
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# 时间维度编码 → 中文标签
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_DIMENSION_LABELS: dict[str, str] = {
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DIMENSION_LABELS: dict[str, str] = {
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"this_month": "本月",
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"last_month": "上月",
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"this_week": "本周",
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"last_week": "上周",
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"last_3_months": "近三个月",
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"this_quarter": "本季度",
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"last_quarter": "上季度",
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"last_6_months": "近六个月",
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"last_3_months": "近三个月（不含本月）",
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"last_6_months": "近六个月（不含本月）",
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}
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# 区域枚举与中文标签（与 miniprogram/board-finance.ts areaOptions 对齐）
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AREA_OPTIONS: tuple[str, ...] = (
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"all", "hall", "hallA", "hallB", "hallC",
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"vip", "snooker", "mahjong", "ktv",
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)
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AREA_LABELS: dict[str, str] = {
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"all": "全部区域",
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"hall": "大厅",
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"hallA": "A区",
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"hallB": "B区",
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"hallC": "C区",
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"vip": "台球包厢",
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"snooker": "斯诺克",
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"mahjong": "麻将房",
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"ktv": "团建房",
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}
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# 业务字段 → 中文名。覆盖 board_service 返回的所有层级字段。
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# 只做键名翻译，不改变值与结构；未命中的键原样保留。
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KEY_TRANSLATIONS: dict[str, str] = {
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# 顶层板块
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"overview": "经营一览",
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"recharge": "预收资产",
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"revenue": "应计收入确认",
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"cashflow": "现金流入",
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"expense": "现金流出",
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"coach_analysis": "助教分析",
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# 经营一览
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"occurrence": "发生额",
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"discount": "总优惠",
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"discount_rate": "优惠率",
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"confirmed_revenue": "成交收入",
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"cash_in": "现金流入",
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"cash_out": "现金流出",
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"cash_balance": "现金结余",
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"balance_rate": "结余率",
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# 预收资产
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"actual_income": "储值卡充值实收",
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"first_charge": "首充",
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"renew_charge": "续费",
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"consumed": "储值卡消耗",
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"card_balance": "储值卡总余额",
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"all_card_balance": "全类别卡余额合计",
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"gift_rows": "赠送卡矩阵",
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"liquor": "酒水卡",
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"table_fee": "台费卡",
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"voucher": "抵用券",
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# 应计收入确认
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"total_occurrence": "发生额合计",
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"discount_total": "优惠合计",
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"confirmed_total": "确认收入合计",
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"structure_rows": "收入结构",
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"price_items": "价目明细",
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"discount_items": "优惠明细",
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"channel_items": "渠道明细",
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"booked": "入账金额",
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"booked_compare": "入账环比",
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# 现金流入/流出
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"consume_items": "消费收款项",
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"recharge_items": "充值收款项",
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"operation_items": "运营支出",
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"fixed_items": "固定支出",
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"coach_items": "助教支出",
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"platform_items": "平台支出",
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# 助教分析
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"basic": "基础助教",
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"incentive": "激励助教",
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"total_pay": "合计薪酬",
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"total_share": "合计分成",
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"avg_hourly": "平均时薪",
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"level": "级别",
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"pay": "薪酬",
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"share": "分成",
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"hourly": "时薪",
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"rows": "明细",
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# 通用元素
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"label": "名称",
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"amount": "金额",
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"desc": "说明",
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"total": "合计",
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"value": "数值",
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"compare": "环比",
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"id": "编号",
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# 环比后缀（小程序约定）
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"occurrence_compare": "发生额环比",
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"occurrence_down": "发生额是否下降",
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"occurrence_flat": "发生额是否持平",
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"discount_compare": "总优惠环比",
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"discount_down": "总优惠是否下降",
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"discount_flat": "总优惠是否持平",
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"discount_rate_compare": "优惠率环比",
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"discount_rate_down": "优惠率是否下降",
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"discount_rate_flat": "优惠率是否持平",
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"confirmed_revenue_compare": "成交收入环比",
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"confirmed_revenue_down": "成交收入是否下降",
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"confirmed_revenue_flat": "成交收入是否持平",
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"cash_in_compare": "现金流入环比",
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"cash_in_down": "现金流入是否下降",
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"cash_in_flat": "现金流入是否持平",
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"cash_out_compare": "现金流出环比",
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"cash_out_down": "现金流出是否下降",
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"cash_out_flat": "现金流出是否持平",
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"cash_balance_compare": "现金结余环比",
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"cash_balance_down": "现金结余是否下降",
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"cash_balance_flat": "现金结余是否持平",
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"balance_rate_compare": "结余率环比",
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"balance_rate_down": "结余率是否下降",
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"balance_rate_flat": "结余率是否持平",
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"actual_income_compare": "储值卡充值实收环比",
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"actual_income_down": "储值卡充值实收是否下降",
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"first_charge_compare": "首充环比",
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"first_charge_down": "首充是否下降",
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"renew_charge_compare": "续费环比",
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"renew_charge_down": "续费是否下降",
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"consumed_compare": "储值卡消耗环比",
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"consumed_down": "储值卡消耗是否下降",
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"card_balance_compare": "储值卡总余额环比",
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"card_balance_down": "储值卡总余额是否下降",
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"all_card_balance_compare": "全类别卡余额合计环比",
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"all_card_balance_down": "全类别卡余额合计是否下降",
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"total_compare": "合计环比",
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"total_down": "合计是否下降",
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"total_flat": "合计是否持平",
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"total_pay_compare": "合计薪酬环比",
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"total_pay_down": "合计薪酬是否下降",
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"total_share_compare": "合计分成环比",
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"total_share_down": "合计分成是否下降",
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"avg_hourly_compare": "平均时薪环比",
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"avg_hourly_flat": "平均时薪是否持平",
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"pay_compare": "薪酬环比",
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"pay_down": "薪酬是否下降",
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"share_compare": "分成环比",
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"share_down": "分成是否下降",
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"hourly_compare": "时薪环比",
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"hourly_flat": "时薪是否持平",
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# 赠送卡矩阵
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"wine": "酒水",
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"table": "台费",
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"coupon": "抵用券",
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# 元数据
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"down": "是否下降",
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"flat": "是否持平",
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}
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def _dimension_label(dimension: str) -> str:
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"""将时间维度编码转为中文标签。"""
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return _DIMENSION_LABELS.get(dimension, dimension)
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# 裁剪时丢弃的"冗余"字段：_down / _flat 布尔元数据（*_compare 字符串已携带符号）
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_DROP_SUFFIX = ("_down", "_flat")
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# 行级明细字段：展示用，AI 洞察不需要
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_DROP_DETAIL_KEYS = {
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"structure_rows", "price_items", "channel_items", "gift_rows",
|
||||
"discount_items", # 2026-04-22：升顶层"优惠构成"后，明细源从 revenue 里 drop 去重
|
||||
}
|
||||
|
||||
|
||||
def _is_drop_key(k: str) -> bool:
|
||||
if not isinstance(k, str):
|
||||
return False
|
||||
if k in _DROP_DETAIL_KEYS:
|
||||
return True
|
||||
return k.endswith(_DROP_SUFFIX)
|
||||
|
||||
|
||||
def _slim(data: Any) -> Any:
|
||||
"""递归裁剪：drop 明细 + _down/_flat + None 值。"""
|
||||
if isinstance(data, dict):
|
||||
out = {}
|
||||
for k, v in data.items():
|
||||
if _is_drop_key(k):
|
||||
continue
|
||||
slim_v = _slim(v)
|
||||
if slim_v is None:
|
||||
continue
|
||||
out[k] = slim_v
|
||||
return out if out else None
|
||||
if isinstance(data, list):
|
||||
return [_slim(item) for item in data]
|
||||
return data
|
||||
|
||||
|
||||
def _pct(numerator: float, denominator: float) -> float:
|
||||
"""百分比（小数），分母 0 返回 0。保留 4 位便于 AI 读取。"""
|
||||
if not denominator:
|
||||
return 0.0
|
||||
return round(numerator / denominator, 4)
|
||||
|
||||
|
||||
# 日粒度异常检测参数
|
||||
_ANOMALY_MIN_DAYS = 7 # 少于 7 天样本不检测（噪声太大）
|
||||
_ANOMALY_DEVIATION = 0.4 # 偏离"同星期均值" > 40% 标记为异常（2026-04-22 改为同星期基线）
|
||||
_ANOMALY_MAX_ITEMS = 10 # 最多保留 10 条（按 |偏离度| 降序截断，防 prompt 膨胀）
|
||||
_ANOMALY_MIN_SAME_WEEKDAY = 2 # 同星期至少 2 天样本才可作基线；不足时回退到整体均值
|
||||
|
||||
# 星期中文映射（0=Monday）
|
||||
_WEEKDAY_ZH = ("周一", "周二", "周三", "周四", "周五", "周六", "周日")
|
||||
|
||||
# 行业基线常量（综合商业球房）
|
||||
# 2026-04-22：移除各类警戒线/健康区间（各球房定位/地段/业态差异大，不宜一刀切）。
|
||||
# 仅保留"周中客流规律"这类行业普适的时间分布特征。
|
||||
INDUSTRY_BASELINES: dict[str, Any] = {
|
||||
"周中客流规律": "周五至周日旺季 / 周一最淡 / 周二至周四逐步回升",
|
||||
}
|
||||
|
||||
|
||||
def _fetch_daily_series(
|
||||
site_id: int, start_date: str, end_date: str,
|
||||
) -> list[tuple] | None:
|
||||
"""查 [start, end] 日粒度财务流水，一次查完供多个分析函数复用。
|
||||
|
||||
返回字段顺序：(stat_date, gross, cash_in, order_count, member_order_count, confirmed)
|
||||
过滤全 0 停业日；样本不足时返回 None。
|
||||
"""
|
||||
from app.services.fdw_queries import _fdw_context
|
||||
from app.database import get_connection
|
||||
|
||||
try:
|
||||
conn = get_connection()
|
||||
except Exception:
|
||||
logger.debug("日粒度查询连接失败", exc_info=True)
|
||||
return None
|
||||
|
||||
try:
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT stat_date,
|
||||
COALESCE(gross_amount, 0) AS gross,
|
||||
COALESCE(cash_inflow_total, 0) AS cash_in,
|
||||
COALESCE(order_count, 0) AS order_count,
|
||||
COALESCE(member_order_count, 0) AS member_order_count,
|
||||
COALESCE(confirmed_income, 0) AS confirmed
|
||||
FROM app.v_dws_finance_daily_summary
|
||||
WHERE stat_date >= %s::date
|
||||
AND stat_date <= %s::date
|
||||
ORDER BY stat_date
|
||||
""",
|
||||
(start_date, end_date),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
except Exception:
|
||||
logger.debug("日粒度数据查询失败: site_id=%s", site_id, exc_info=True)
|
||||
return None
|
||||
finally:
|
||||
try:
|
||||
conn.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
active = [
|
||||
(r[0], float(r[1]), float(r[2]), int(r[3] or 0), int(r[4] or 0), float(r[5] or 0))
|
||||
for r in rows
|
||||
if float(r[1] or 0) > 0 or float(r[2] or 0) > 0
|
||||
]
|
||||
return active if active else None
|
||||
|
||||
|
||||
_WEEKDAY_MIN_DAYS = 14 # 月初场景：样本 < 14 天时，每个星期最多 1-2 天，"日均"接近单日值，不注入以免 AI 被误导
|
||||
|
||||
|
||||
def _aggregate_by_weekday(series: list[tuple] | None) -> dict | None:
|
||||
"""按星期聚合 7 段日均值（发生额/现金流入/订单数），供 AI 观察周中规律。
|
||||
|
||||
要求至少 14 天样本（保证每个星期至少有 2 天），否则返回 None；
|
||||
防止月初场景下单日值被包装成"日均"迷惑 AI 做周规律判断。
|
||||
"""
|
||||
if not series or len(series) < _WEEKDAY_MIN_DAYS:
|
||||
return None
|
||||
from collections import defaultdict
|
||||
buckets: dict[int, list[tuple]] = defaultdict(list)
|
||||
for row in series:
|
||||
buckets[row[0].weekday()].append(row)
|
||||
out: dict[str, dict] = {}
|
||||
for wd in range(7):
|
||||
rows = buckets.get(wd) or []
|
||||
if not rows:
|
||||
continue
|
||||
n = len(rows)
|
||||
out[_WEEKDAY_ZH[wd]] = {
|
||||
"日均发生额": round(sum(r[1] for r in rows) / n, 2),
|
||||
"日均现金流入": round(sum(r[2] for r in rows) / n, 2),
|
||||
"日均订单数": round(sum(r[3] for r in rows) / n, 1),
|
||||
"营业日数": n,
|
||||
}
|
||||
return out or None
|
||||
|
||||
|
||||
def _build_unit_economics(
|
||||
series: list[tuple] | None,
|
||||
prev_series: list[tuple] | None = None,
|
||||
) -> dict | None:
|
||||
"""单位经济派生：客单价 / 日均订单数 / 会员订单占比 / 散客订单占比。
|
||||
|
||||
口径：全期汇总后再算（避免日均 avg 失真）。
|
||||
客单价取两口径：
|
||||
- 按成交收入（去除优惠的真实收入单价） — 反映真实收入能力
|
||||
- 按发生额（含优惠的账单均值） — 反映顾客端认知的单次消费量级
|
||||
若 prev_series 可用，则附加 _环比 字段避免 AI 推测幻觉。
|
||||
"""
|
||||
if not series:
|
||||
return None
|
||||
total_orders = sum(r[3] for r in series)
|
||||
if total_orders <= 0:
|
||||
return None
|
||||
total_member_orders = sum(r[4] for r in series)
|
||||
total_confirmed = sum(r[5] for r in series)
|
||||
total_gross = sum(r[1] for r in series)
|
||||
days = len(series)
|
||||
|
||||
price_confirmed = total_confirmed / total_orders
|
||||
price_gross = total_gross / total_orders
|
||||
member_share = total_member_orders / total_orders
|
||||
daily_orders = total_orders / days
|
||||
|
||||
out: dict[str, Any] = {
|
||||
"总订单数": total_orders,
|
||||
"日均订单数": round(daily_orders, 1),
|
||||
"客单价_按成交收入": round(price_confirmed, 2),
|
||||
"客单价_按发生额": round(price_gross, 2),
|
||||
"会员订单数": total_member_orders,
|
||||
"会员订单占比": round(member_share, 4),
|
||||
"散客订单数": total_orders - total_member_orders,
|
||||
"散客订单占比": round((total_orders - total_member_orders) / total_orders, 4),
|
||||
}
|
||||
|
||||
if prev_series:
|
||||
prev_orders = sum(r[3] for r in prev_series)
|
||||
if prev_orders > 0:
|
||||
prev_days = len(prev_series)
|
||||
prev_confirmed = sum(r[5] for r in prev_series)
|
||||
prev_gross = sum(r[1] for r in prev_series)
|
||||
prev_member = sum(r[4] for r in prev_series)
|
||||
# 月初场景：上期样本 < 5 天时客单价环比噪声极大（单日波动主导），加标注供 AI 降权引用
|
||||
low_sample = prev_days < 5
|
||||
|
||||
def _pct_change(cur: float, prev: float) -> str:
|
||||
if prev <= 0:
|
||||
return "无上期数据"
|
||||
value = f"{(cur - prev) / prev * 100:+.1f}%"
|
||||
return f"{value}（上期仅 {prev_days} 天，样本不足仅供参考）" if low_sample else value
|
||||
|
||||
out["客单价_按成交收入_环比"] = _pct_change(price_confirmed, prev_confirmed / prev_orders)
|
||||
out["客单价_按发生额_环比"] = _pct_change(price_gross, prev_gross / prev_orders)
|
||||
out["日均订单数_环比"] = _pct_change(daily_orders, prev_orders / prev_days)
|
||||
out["会员订单占比_环比"] = _pct_change(member_share, prev_member / prev_orders)
|
||||
return out
|
||||
|
||||
|
||||
def _detect_anomaly_days(
|
||||
site_id: int, start_date: str, end_date: str,
|
||||
series: list[tuple] | None = None,
|
||||
) -> list[dict] | None:
|
||||
"""扫描日粒度财务数据，标记偏离同星期均值 > 40% 的异常日。
|
||||
|
||||
series 可由调用方传入复用，避免重复查 DB。
|
||||
"""
|
||||
if series is None:
|
||||
series = _fetch_daily_series(site_id, start_date, end_date)
|
||||
if not series or len(series) < _ANOMALY_MIN_DAYS:
|
||||
return None
|
||||
active = series
|
||||
|
||||
# 2026-04-22 改进：按"同星期均值"做基线，比"期均"更贴近业态（周一淡/周末旺）
|
||||
# 同星期样本 < _ANOMALY_MIN_SAME_WEEKDAY 天时回退到整体均值
|
||||
from collections import defaultdict
|
||||
|
||||
def _scan(idx: int, label: str) -> list[dict]:
|
||||
vals = [row[idx] for row in active]
|
||||
global_mean = sum(vals) / len(vals)
|
||||
if global_mean <= 0:
|
||||
return []
|
||||
|
||||
# 按 weekday 分组统计均值
|
||||
by_weekday: dict[int, list[float]] = defaultdict(list)
|
||||
for d, *metrics in active:
|
||||
by_weekday[d.weekday()].append(metrics[idx - 1])
|
||||
weekday_mean: dict[int, float] = {
|
||||
wd: (sum(xs) / len(xs)) for wd, xs in by_weekday.items()
|
||||
}
|
||||
|
||||
flagged: list[dict] = []
|
||||
for d, *metrics in active:
|
||||
v = metrics[idx - 1]
|
||||
wd = d.weekday()
|
||||
same_count = len(by_weekday.get(wd, []))
|
||||
# 基线选择：同星期样本 >= 2 用同星期均值，否则用整体均值
|
||||
if same_count >= _ANOMALY_MIN_SAME_WEEKDAY and weekday_mean[wd] > 0:
|
||||
base = weekday_mean[wd]
|
||||
base_label = f"同{_WEEKDAY_ZH[wd]}均值"
|
||||
else:
|
||||
base = global_mean
|
||||
base_label = "期均"
|
||||
|
||||
deviation = (v - base) / base
|
||||
if abs(deviation) >= _ANOMALY_DEVIATION:
|
||||
weekday_zh = _WEEKDAY_ZH[wd]
|
||||
flagged.append({
|
||||
"日期": f"{d} {weekday_zh}",
|
||||
"指标": label,
|
||||
"当日": round(v, 2),
|
||||
"基线": round(base, 2),
|
||||
"基线类型": base_label,
|
||||
"偏离": f"{deviation * 100:+.1f}%",
|
||||
"_abs_dev": abs(deviation),
|
||||
})
|
||||
return flagged
|
||||
|
||||
candidates: list[dict] = _scan(1, "发生额") + _scan(2, "现金流入")
|
||||
if not candidates:
|
||||
return None
|
||||
# 按绝对偏离排序，取 top N，去掉排序用辅助键
|
||||
candidates.sort(key=lambda x: x["_abs_dev"], reverse=True)
|
||||
out = []
|
||||
for c in candidates[:_ANOMALY_MAX_ITEMS]:
|
||||
c.pop("_abs_dev", None)
|
||||
out.append(c)
|
||||
return out
|
||||
|
||||
|
||||
def _fetch_card_balance_opening(site_id: int, start_date: str) -> float | None:
|
||||
"""取 start_date 前一日的储值卡总余额（作为本期期初余额）。
|
||||
|
||||
数据源：etl 库 app.v_dws_finance_recharge_summary（每日快照，total_card_balance 字段）。
|
||||
若前一日无数据（门店刚开业 / 数据缺失），返回 None。
|
||||
"""
|
||||
from app.services.fdw_queries import _fdw_context
|
||||
from app.database import get_connection
|
||||
|
||||
try:
|
||||
conn = get_connection()
|
||||
except Exception:
|
||||
logger.debug("期初余额查询连接失败", exc_info=True)
|
||||
return None
|
||||
|
||||
try:
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT total_card_balance
|
||||
FROM app.v_dws_finance_recharge_summary
|
||||
WHERE stat_date < %s::date
|
||||
ORDER BY stat_date DESC
|
||||
LIMIT 1
|
||||
""",
|
||||
(start_date,),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
except Exception:
|
||||
logger.debug("期初余额查询失败: site_id=%s", site_id, exc_info=True)
|
||||
return None
|
||||
finally:
|
||||
try:
|
||||
conn.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not row or row[0] is None:
|
||||
return None
|
||||
return float(row[0])
|
||||
|
||||
|
||||
def _aggregate_expense(expense: dict | None) -> dict | None:
|
||||
"""从 expense 四类明细聚合出顶层金额，便于 AI 直接看四大块支出占比。"""
|
||||
if not isinstance(expense, dict):
|
||||
return None
|
||||
def _sum(key: str) -> float:
|
||||
items = expense.get(key) or []
|
||||
if not isinstance(items, list):
|
||||
return 0.0
|
||||
return round(sum(float(x.get("amount", 0) or 0) for x in items if isinstance(x, dict)), 2)
|
||||
total = float(expense.get("total", 0) or 0)
|
||||
if total <= 0:
|
||||
return None # 全 0 数据对 AI 无意义，直接丢
|
||||
return {
|
||||
"合计": round(total, 2),
|
||||
"合计环比": expense.get("total_compare") or "持平",
|
||||
"运营支出": _sum("operation_items"),
|
||||
"固定支出": _sum("fixed_items"),
|
||||
"助教支出": _sum("coach_items"),
|
||||
"平台支出": _sum("platform_items"),
|
||||
}
|
||||
|
||||
|
||||
def _build_discount_kpi(revenue: dict | None, overview: dict | None) -> dict | None:
|
||||
"""把优惠拆成顶层 KPI + 派生指标（占比、贡献率）。
|
||||
|
||||
AI 数据挖掘视角：
|
||||
- 按金额排序展示，top1 一眼看出来
|
||||
- 每项带 amount / compare / share（占总优惠比）
|
||||
- 整体带优惠率（discount / occurrence）便于判断利润侵蚀程度
|
||||
"""
|
||||
if not isinstance(revenue, dict):
|
||||
return None
|
||||
items = revenue.get("discount_items") or []
|
||||
if not isinstance(items, list) or not items:
|
||||
return None
|
||||
|
||||
total = round(sum(float(x.get("amount", 0) or 0) for x in items if isinstance(x, dict)), 2)
|
||||
breakdown = []
|
||||
for it in items:
|
||||
if not isinstance(it, dict):
|
||||
continue
|
||||
amt = float(it.get("amount", 0) or 0)
|
||||
row: dict[str, Any] = {
|
||||
"名称": it.get("label"),
|
||||
"金额": round(amt, 2),
|
||||
"占总优惠": _pct(amt, total),
|
||||
}
|
||||
if it.get("compare"):
|
||||
row["环比"] = it["compare"]
|
||||
breakdown.append(row)
|
||||
# 按金额从大到小排序 → AI 阅读顺序 = 重要度顺序
|
||||
breakdown.sort(key=lambda r: float(r.get("金额") or 0), reverse=True)
|
||||
|
||||
overview = overview or {}
|
||||
occurrence = float(overview.get("occurrence", 0) or 0)
|
||||
|
||||
kpi: dict[str, Any] = {
|
||||
"总优惠": total,
|
||||
"优惠率": _pct(total, occurrence), # 0.3796 表示 37.96%
|
||||
"占比排序": breakdown,
|
||||
}
|
||||
if breakdown:
|
||||
top = breakdown[0]
|
||||
kpi["最大优惠来源"] = f"{top.get('名称')}（金额 {top.get('金额')} 元，占总优惠 {int(float(top.get('占总优惠', 0))*100)}%）"
|
||||
return kpi
|
||||
|
||||
|
||||
def _build_cashflow_kpi(cashflow: dict | None) -> dict | None:
|
||||
"""消费收款拆三档（纸币/线上/团购）+ 充值到账，给 AI 直接看资金来源结构。"""
|
||||
if not isinstance(cashflow, dict):
|
||||
return None
|
||||
consume = cashflow.get("consume_items") or []
|
||||
recharge = cashflow.get("recharge_items") or []
|
||||
total = float(cashflow.get("total", 0) or 0)
|
||||
if total <= 0:
|
||||
return None
|
||||
|
||||
consume_map = {}
|
||||
for it in consume:
|
||||
if not isinstance(it, dict):
|
||||
continue
|
||||
consume_map[it.get("label")] = {
|
||||
"金额": round(float(it.get("amount", 0) or 0), 2),
|
||||
"环比": it.get("compare") or "持平",
|
||||
}
|
||||
|
||||
recharge_total = round(sum(float(x.get("amount", 0) or 0) for x in recharge if isinstance(x, dict)), 2)
|
||||
consume_total = round(sum(float(v.get("金额", 0) or 0) for v in consume_map.values()), 2)
|
||||
|
||||
return {
|
||||
"合计": round(total, 2),
|
||||
"合计环比": cashflow.get("total_compare") or "持平",
|
||||
"消费收款合计": consume_total,
|
||||
"消费收款占比": _pct(consume_total, total),
|
||||
"充值收款合计": recharge_total,
|
||||
"充值收款占比": _pct(recharge_total, total),
|
||||
"按渠道": consume_map,
|
||||
}
|
||||
|
||||
|
||||
def _build_coach_kpi(coach: dict | None) -> dict | None:
|
||||
"""助教成本压缩：只保留两档的合计薪酬+合计分成+平均时薪+3 级别薪酬分布。"""
|
||||
if not isinstance(coach, dict):
|
||||
return None
|
||||
def _slim_tier(t: dict | None) -> dict | None:
|
||||
if not isinstance(t, dict):
|
||||
return None
|
||||
rows = t.get("rows") or []
|
||||
# 只保留级别-薪酬-时薪 3 字段，作为分布快照
|
||||
tier_dist = [
|
||||
{"级别": r.get("level"), "薪酬": r.get("pay"), "时薪": r.get("hourly")}
|
||||
for r in rows if isinstance(r, dict)
|
||||
]
|
||||
total_pay = float(t.get("total_pay", 0) or 0)
|
||||
if total_pay <= 0:
|
||||
return None
|
||||
return {
|
||||
"合计薪酬": round(total_pay, 2),
|
||||
"合计薪酬环比": t.get("total_pay_compare") or "持平",
|
||||
"合计分成": round(float(t.get("total_share", 0) or 0), 2),
|
||||
"平均时薪": round(float(t.get("avg_hourly", 0) or 0), 2),
|
||||
"各级别分布": tier_dist,
|
||||
}
|
||||
basic = _slim_tier(coach.get("basic"))
|
||||
incentive = _slim_tier(coach.get("incentive"))
|
||||
if not basic and not incentive:
|
||||
return None
|
||||
out: dict[str, Any] = {}
|
||||
if basic:
|
||||
out["基础助教"] = basic
|
||||
if incentive:
|
||||
out["激励助教"] = incentive
|
||||
# 派生：人力成本占收入比（需要收入传进来，这里只给基础值）
|
||||
total_pay = (basic or {}).get("合计薪酬", 0) + (incentive or {}).get("合计薪酬", 0)
|
||||
if total_pay > 0:
|
||||
out["人力薪酬合计"] = round(total_pay, 2)
|
||||
return out
|
||||
|
||||
|
||||
def _build_derived_ratios(overview: dict | None, cashflow_kpi: dict | None,
|
||||
coach_kpi: dict | None, discount_kpi: dict | None) -> dict:
|
||||
"""数据挖掘视角：派生关键比率，让 AI 不用自己算。
|
||||
|
||||
- 储值卡贡献率：充值到账 / 总现金流入
|
||||
- 人力成本占收入比：助教薪酬合计 / 成交收入
|
||||
- 优惠侵蚀率：总优惠 / 发生额
|
||||
- 现金结余率：现金结余 / 现金流入
|
||||
"""
|
||||
ov = overview or {}
|
||||
confirmed = float(ov.get("confirmed_revenue", 0) or 0)
|
||||
occurrence = float(ov.get("occurrence", 0) or 0)
|
||||
cash_in = float(ov.get("cash_in", 0) or 0)
|
||||
cash_balance = float(ov.get("cash_balance", 0) or 0)
|
||||
total_pay = (coach_kpi or {}).get("人力薪酬合计", 0)
|
||||
recharge_in = (cashflow_kpi or {}).get("充值收款合计", 0)
|
||||
discount_total = (discount_kpi or {}).get("总优惠", 0)
|
||||
|
||||
out: dict[str, Any] = {}
|
||||
if confirmed > 0 and total_pay:
|
||||
out["人力成本占成交收入比"] = _pct(total_pay, confirmed)
|
||||
if cash_in > 0 and recharge_in:
|
||||
out["储值卡充值占现金流入比"] = _pct(recharge_in, cash_in)
|
||||
if occurrence > 0 and discount_total:
|
||||
out["优惠侵蚀率"] = _pct(discount_total, occurrence)
|
||||
if cash_in > 0:
|
||||
out["现金结余率"] = _pct(cash_balance, cash_in)
|
||||
return out
|
||||
|
||||
|
||||
# 2026-04-22：异常检测由 AI 侧自行判断，后端只提供客观 KPI（不给规则结论）
|
||||
|
||||
|
||||
def _translate_keys(data: Any) -> Any:
|
||||
"""递归翻译 dict/list 中所有键为中文；值保持不变。
|
||||
|
||||
- dict: 键命中 KEY_TRANSLATIONS 则替换，未命中保留原键
|
||||
- list: 逐项递归
|
||||
- 其他类型（str/int/float/bool/None）原样返回
|
||||
"""
|
||||
if isinstance(data, dict):
|
||||
return {
|
||||
KEY_TRANSLATIONS.get(k, k): _translate_keys(v)
|
||||
for k, v in data.items()
|
||||
}
|
||||
if isinstance(data, list):
|
||||
return [_translate_keys(item) for item in data]
|
||||
return data
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: Any | None = None, # 兼容统一签名，App2 不用
|
||||
) -> str:
|
||||
"""构建 App2 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, time_dimension, area（可选，默认 all）
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串，所有 board 数据字段已翻译为中文。
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
time_dimension = context["time_dimension"]
|
||||
area = context.get("area", "all")
|
||||
|
||||
board_time = DIMENSION_MAP.get(time_dimension)
|
||||
if not board_time:
|
||||
raise ValueError(f"App2 不支持的时间维度: {time_dimension}")
|
||||
|
||||
if area not in AREA_LABELS:
|
||||
raise ValueError(f"App2 不支持的区域: {area}")
|
||||
|
||||
try:
|
||||
board_data = await get_finance_board(
|
||||
time=board_time, area=area, compare=1, site_id=site_id,
|
||||
)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"App2 财务看板查询失败: site_id=%s dimension=%s area=%s",
|
||||
site_id, time_dimension, area, exc_info=True,
|
||||
)
|
||||
board_data = {}
|
||||
|
||||
# 2026-04-22 数据挖掘视角 prompt 结构化：
|
||||
# - 优惠/现金流/助教/支出 四大领域分别派生 KPI（带占比/排序/派生指标）
|
||||
# - 异常检测：规则法标注 AI 必看异常点
|
||||
# - 派生比率：人力成本占比/优惠侵蚀率/储值卡贡献率 等不用 AI 再算
|
||||
# - 原始财务数据经 _slim 裁剪后作为"原始指标"补充，避免 AI 失去追溯能力
|
||||
overview = board_data.get("overview") if isinstance(board_data, dict) else None
|
||||
revenue = board_data.get("revenue") if isinstance(board_data, dict) else None
|
||||
cashflow = board_data.get("cashflow") if isinstance(board_data, dict) else None
|
||||
expense = board_data.get("expense") if isinstance(board_data, dict) else None
|
||||
coach = board_data.get("coach_analysis") if isinstance(board_data, dict) else None
|
||||
|
||||
discount_kpi = _build_discount_kpi(revenue, overview)
|
||||
cashflow_kpi = _build_cashflow_kpi(cashflow)
|
||||
expense_kpi = _aggregate_expense(expense)
|
||||
coach_kpi = _build_coach_kpi(coach)
|
||||
ratios = _build_derived_ratios(overview, cashflow_kpi, coach_kpi, discount_kpi)
|
||||
|
||||
# 原始数据：slim 后再翻译，供 AI 追溯细节
|
||||
slim_data = _slim(board_data) or {}
|
||||
raw_cn = _translate_keys(slim_data)
|
||||
|
||||
# 对比口径说明：当期/对比期均为"同天数对齐"，避免 AI 把环比误读为"当期部分 vs 上期整月"
|
||||
compare_caliber: dict[str, Any] | None = None
|
||||
try:
|
||||
from app.services.runtime_context import get_runtime_context
|
||||
|
||||
runtime_ctx = get_runtime_context(site_id)
|
||||
cur_start, cur_end = _calc_date_range(board_time, ref_date=runtime_ctx.business_date)
|
||||
prev_start, prev_end = _calc_prev_range(board_time, cur_start, cur_end)
|
||||
cur_days = (cur_end - cur_start).days + 1
|
||||
prev_days = (prev_end - prev_start).days + 1
|
||||
compare_caliber = {
|
||||
"当期范围": f"{cur_start} ~ {cur_end}（{cur_days} 天）",
|
||||
"对比期范围": f"{prev_start} ~ {prev_end}（{prev_days} 天）",
|
||||
"对齐方式": "上期同天数对齐（非整月/整周对比）",
|
||||
"说明": "所有 _环比 / _compare 字段均按上表口径计算；月中调用时对比期会自动截断到与当期相同天数",
|
||||
}
|
||||
except Exception:
|
||||
logger.debug("对比口径字段生成失败（不影响主流程）", exc_info=True)
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"当前时间": get_runtime_context(site_id).business_now.strftime("%Y-%m-%d %H:%M"),
|
||||
"门店编号": site_id,
|
||||
"时间维度": DIMENSION_LABELS.get(time_dimension, time_dimension),
|
||||
"区域": AREA_LABELS.get(area, area),
|
||||
# 0. 对比口径：让 AI 正确解读环比字段
|
||||
**({"对比口径": compare_caliber} if compare_caliber else {}),
|
||||
# 1. 核心 KPI：AI 洞察首要依据
|
||||
"核心KPI": {
|
||||
"发生额": float(overview.get("occurrence", 0)) if overview else 0,
|
||||
"发生额环比": (overview or {}).get("occurrence_compare") or "持平",
|
||||
"成交收入": float(overview.get("confirmed_revenue", 0)) if overview else 0,
|
||||
"成交收入环比": (overview or {}).get("confirmed_revenue_compare") or "持平",
|
||||
"现金流入": (overview or {}).get("cash_in"),
|
||||
"现金流入环比": (overview or {}).get("cash_in_compare") or "持平",
|
||||
"现金结余": (overview or {}).get("cash_balance"),
|
||||
"现金结余环比": (overview or {}).get("cash_balance_compare") or "持平",
|
||||
},
|
||||
# 2. 派生比率：不用 AI 再算
|
||||
"派生比率": ratios,
|
||||
}
|
||||
# 3. 优惠构成（带排序/占比/环比/最大来源提示）
|
||||
if discount_kpi:
|
||||
payload["优惠构成"] = discount_kpi
|
||||
# 4. 现金流入来源分布
|
||||
if cashflow_kpi:
|
||||
payload["现金流入来源"] = cashflow_kpi
|
||||
# 5. 支出概况（聚合到四大类，total=0 则不给 AI）
|
||||
if expense_kpi:
|
||||
payload["支出概况"] = expense_kpi
|
||||
# 6. 助教成本画像
|
||||
if coach_kpi:
|
||||
payload["助教成本"] = coach_kpi
|
||||
# 7. 储值卡余额变化：期初 + 期末 + 充值 + 消耗 + 其他调整（揭示"充值-消耗≠余额变化"的差异）
|
||||
# 避免 AI 在只看当期充值/消耗时对"余额为何涨"的矛盾自圆其说
|
||||
if area == "all" and isinstance(recharge := board_data.get("recharge"), dict):
|
||||
try:
|
||||
start_date_obj, _end = _calc_date_range(board_time)
|
||||
opening = _fetch_card_balance_opening(site_id, str(start_date_obj))
|
||||
closing = float(recharge.get("card_balance") or 0)
|
||||
period_recharge = float(recharge.get("actual_income") or 0)
|
||||
period_consume = float(recharge.get("consumed") or 0)
|
||||
if opening is not None and (opening > 0 or closing > 0):
|
||||
diff = closing - opening
|
||||
other_adj = round(diff - (period_recharge - period_consume), 2)
|
||||
payload["储值卡余额变化"] = {
|
||||
"期初余额": round(opening, 2),
|
||||
"期末余额": round(closing, 2),
|
||||
"余额变化": round(diff, 2),
|
||||
"本期充值": round(period_recharge, 2),
|
||||
"本期消耗": round(period_consume, 2),
|
||||
"其他调整": other_adj, # 含过期/赠送/退款/手动调整，非 0 时 AI 需要关注
|
||||
}
|
||||
except Exception:
|
||||
logger.debug("储值卡余额变化注入失败", exc_info=True)
|
||||
# 8. 日粒度派生（仅 area=all，样本 ≥ 7 天）：一次 DB 查询，三段派生
|
||||
# - 单位经济：客单价/订单数/会员占比（含环比，避免 AI 对客单走势推测幻觉）
|
||||
# - 按星期聚合：供 E 板块做周中规律宏观洞察
|
||||
# - 日粒度异常：同星期均值基线下的极端偏离
|
||||
if area == "all":
|
||||
try:
|
||||
start_date, end_date = _calc_date_range(board_time)
|
||||
series = _fetch_daily_series(site_id, str(start_date), str(end_date))
|
||||
# 上期序列（用于客单价环比）
|
||||
prev_series: list[tuple] | None = None
|
||||
try:
|
||||
prev_start, prev_end = _calc_prev_range(board_time, start_date, end_date)
|
||||
prev_series = _fetch_daily_series(site_id, str(prev_start), str(prev_end))
|
||||
except Exception:
|
||||
logger.debug("上期 series 查询失败，客单价环比字段将省略", exc_info=True)
|
||||
|
||||
if series:
|
||||
unit_econ = _build_unit_economics(series, prev_series=prev_series)
|
||||
if unit_econ:
|
||||
payload["单位经济"] = unit_econ
|
||||
by_weekday = _aggregate_by_weekday(series)
|
||||
if by_weekday:
|
||||
payload["按星期聚合"] = by_weekday
|
||||
anomalies = _detect_anomaly_days(
|
||||
site_id, str(start_date), str(end_date), series=series,
|
||||
)
|
||||
if anomalies:
|
||||
payload["日粒度异常"] = anomalies
|
||||
except Exception:
|
||||
logger.debug("日粒度派生字段注入失败（不影响主流程）", exc_info=True)
|
||||
# 9. 行业基线：AI 判断是否超警戒线的参照
|
||||
payload["行业基线"] = INDUSTRY_BASELINES
|
||||
# 10. 原始财务数据：供 AI 追溯（大部分 prompt 长度来自这里，已 slim）
|
||||
payload["原始指标"] = raw_cn
|
||||
|
||||
if not board_data:
|
||||
payload["数据缺失提示"] = "财务看板数据获取失败，请基于已有缓存或常识分析"
|
||||
|
||||
return json.dumps(payload, ensure_ascii=False, default=str)
|
||||
|
||||
Reference in New Issue
Block a user