522 lines
23 KiB
Markdown
522 lines
23 KiB
Markdown
# 投后评价指标体系设计方案
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> **版本**: v1.0
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> **日期**: 2026-07-19
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> **状态**: 已定稿
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## 1. 设计目标
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构建一套**动态可配置**的投后企业评价指标体系,根据投资人类型、企业发展阶段、产业赛道、投资策略、基金类型和基金存续期 6 个维度自动调整评价权重和维度组合,同时支持 LP 构成和地域市场 2 个修饰因子。
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### 核心原则
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- **一套框架,多套权重** — 14 维度基础框架不变,权重动态调整
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- **维度可裁剪** — 非 AI 企业自动禁用 AI 相关维度,权重归一化
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- **指标可插拔** — 每个赛道 3-5 个专属原子指标,不影响基础框架
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- **模板可审计** — 每次评分记录使用的模板配置,支持跨期对比和回滚
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- **组合不爆炸** — 50 个预设配置单元运行时动态组合,而非万级模板
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---
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## 2. 现有体系问题
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当前系统使用 14 维度固定权重(`backend/app/services/health_calculator.py`):
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| 问题 | 说明 |
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| 一套权重打天下 | 种子期和 Pre-IPO 企业用同一套权重,评价失真 |
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| AI 维度对非 AI 企业无意义 | 硬件、生物医药企业仍有 AI 商业化评分(默认 40 分) |
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| 无基金类型感知 | PE 基金和天使基金用同一套标准评价同一企业 |
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| 无时间紧迫度 | 基金到期前 1 年仍在按成长期标准评价,错过退出窗口 |
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| 无赛道专属指标 | 生物医药的临床进度、芯片的流片良率无法纳入评分 |
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---
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## 3. 六轴模型
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```
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评价指标体系 = f(
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投资人类型, # 轴1:展示层级
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企业阶段, # 轴2:阶段权重
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产业赛道, # 轴3:维度裁剪 + 专属指标
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投资策略, # 轴4:策略微调
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基金类型, # 轴5:评价哲学
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基金存续期阶段, # 轴6:时间紧迫度
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) + 修饰因子(
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LP 构成, # 附加指标层
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地域/市场, # 基准校准
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)
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```
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### 3.1 轴 1:投资人类型
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不影响权重计算,影响**展示层级和关注入口**。
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| 投资人角色 | 关注层级 | 核心指标 | 展示入口 |
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|---|---|---|---|
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| GP/合伙人 | 组合层面 | IRR、DPI、组合健康度分布、退出时机 | 指挥塔 / 组合再平衡 |
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| 投后负责人 | 运营层面 | 健康度趋势、风险队列、任务推进、月报质量 | 今日行动中心 / 工作台 |
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| 投资经理 | 执行层面 | 今日行动、数据校验、协同匹配、跟进频率 | 今日行动中心 / 企业列表 |
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### 3.2 轴 2:企业发展阶段
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决定阶段适配权重,核心变化是**财务/产品/团队权重的此消彼长**。
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| 阶段 | 财务 | 市场 | 产品 | 团队 | 治理 | AI维度 | 客户成功 | 特征 |
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|---|---|---|---|---|---|---|---|---|
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| 种子/天使 | 10% | 5% | **25%** | **20%** | 5% | 10% | 5% | 产品验证 + 团队潜力 |
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| Pre-A/A轮 | 15% | 15% | **20%** | 15% | 5% | 10% | 10% | PMF 验证 + 增长引擎 |
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| B轮 | **20%** | **20%** | 15% | 10% | 10% | 5% | **15%** | 规模化效率 |
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| C轮+ | **25%** | **20%** | 10% | 5% | **15%** | 5% | **15%** | 退出准备 |
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| Pre-IPO | **25%** | 15% | 10% | 5% | **20%** | 5% | **15%** | 合规 + 估值 |
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### 3.3 轴 3:产业赛道
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决定**维度裁剪 + 专属指标注入**。
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| 赛道 | 启用维度 | 禁用/降权 | 专属指标 |
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|---|---|---|---|
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| AI/SaaS | 全部 14 维度 | — | 模型精度、推理成本、API 调用量、PoC 转化率 |
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| 硬科技/芯片 | 产品技术↑、团队技术↑ | AI商业化↓、AI成本↓ | 专利数、流片进度、良率、研发投入比 |
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| 生物医药 | 产品技术↑、治理↑ | 市场竞争↓(早期无市场) | 临床阶段、管线进度、审批节点、专利布局 |
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| 消费品牌 | 市场竞争↑、客户成功↑ | AI维度↓ | GMV、复购率、品牌指数、渠道覆盖率 |
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| 金融科技 | 治理合规↑↑、数据合规↑ | — | 牌照进度、风控指标、合规事件、坏账率 |
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| 新能源/先进制造 | 产品技术↑、团队技术↑ | AI商业化↓ | 产能利用率、交付周期、供应链稳定性 |
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### 3.4 轴 4:投资策略
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**±5% 微调**,不改变维度选择。
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| 策略 | 评价目标 | 权重调整 |
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|---|---|---|
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| 成长型 | 增长潜力 | 市场+5%、产品+5%、客户成功+5%;财务-5%、治理-5% |
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| 价值型 | 稳健回报 | 财务+5%、治理+5%、客户成功+5%;市场-5%、产品-5% |
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| 投后赋能型 | 协同价值 | 协同+5%、组织+5%、产品+5%;财务-5%、市场-5% |
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| 困境反转型 | 风险控制 | 财务+5%、治理+5%、风险-5%;市场-5%、产品-5% |
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### 3.5 轴 5:基金类型
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**最高优先级**,决定评价哲学。
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| 基金类型 | 持有期 | 回报预期 | 评价哲学 | 权重影响 |
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|---|---|---|---|---|
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| 天使/种子基金 | 7-10年 | 10x+ | 赛道赌注、团队潜力 | 产品↑↑、团队↑↑、市场天花板↑;财务容忍度极高 |
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| 早期VC(A轮) | 5-8年 | 5-10x | PMF 验证、增长引擎 | 产品↑、市场↑、客户成功↑ |
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| 成长期VC(B/C轮) | 3-5年 | 3-5x | 规模化效率、单位经济 | 财务↑↑、市场↑、客户成功↑;关注 LTV/CAC |
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| PE/并购基金 | 3-5年 | 2-3x | 现金流、EBITDA、退出确定性 | 财务↑↑↑、治理↑↑;产品↓、AI维度↓ |
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| 产业基金(CVC) | 长期持有 | 战略协同优先 | 产业链协同、战略价值 | 协同↑↑、市场↑;财务容忍度高 |
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| 母基金(FOF) | 不直接投 | 基金层评价 | 基金 IRR/DPI | 关注组合层面而非单企业 |
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| 困境/特殊机会基金 | 2-3年 | 2-4x | 扭亏为盈、资产处置 | 财务↑↑↑、治理↑;客户成功↓、产品↓ |
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| ESG/影响力基金 | 5-10年 | 社会回报+财务 | ESG 指标、可持续性 | 治理↑↑、数据合规↑;新增 ESG 维度 |
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### 3.6 轴 6:基金存续期阶段
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**时间紧迫度**,影响退出相关指标权重。
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| 存续期阶段 | 时间窗口 | 行为特征 | 权重影响 |
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|---|---|---|---|
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| 投资期 | 前 2 年 | 容忍风险,看重增长潜力 | 产品↑、市场↑、团队↑;财务容忍度高 |
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| 成长期 | 第 3-4 年 | 关注 PMF 和规模化 | 财务↑、客户成功↑;开始关注单位经济 |
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| 退出准备 | 第 5-6 年 | 推动退出,关注估值 | 财务↑↑、治理↑、退出信号↑↑;产品↓ |
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| 清算期 | 最后 1 年 | 紧迫退出 | 退出信号↑↑↑、财务↑↑;一切以退出为导向 |
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---
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## 4. 修饰因子
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### 4.1 LP 构成 — 附加指标层
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不影响 14 维度权重,在评分报告末尾**单独展示**。
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| LP 类型 | 附加指标 |
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| 政府引导基金 | 就业人数、税收贡献、产业带动系数、本地化率 |
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| 市场化 LP | IRR、DPI、TVPI |
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| 产业 LP | 产业链协同价值、技术转移数、联合研发项目数 |
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| 保险/银行 LP | 现金流稳定性、合规评级、资产覆盖率 |
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### 4.2 地域/市场 — 基准校准
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不改变权重,改变**评分刻度和及格线**。
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| 市场 | 校准示例 |
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|---|---|
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| 中国大陆 | SaaS 客户留存率及格线 80%(vs 美国 90%);获客成本基准较高 |
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| 美国 | 增长率基准更高;PMF 验证标准更严格 |
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| 东南亚 | 市场分散度修正;支付转化率基准较低 |
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| 欧洲 | 合规权重自动 +5%(GDPR);数据合规及格线更高 |
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---
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## 5. 权重计算引擎
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### 5.1 计算流程
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```
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输入:6 轴参数
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│
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├─ Step 1:基金类型 × 存续期 → 基础权重模板(32 个预设之一)
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│
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├─ Step 2:企业阶段 → 阶段系数调整(5 个预设之一)
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│
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├─ Step 3:产业赛道 → 维度裁剪 + 专属指标注入(6 个预设之一)
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│
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├─ Step 4:投资策略 → ±5% 微调(4 个预设之一)
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│
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├─ Step 5:归一化 — 裁剪后剩余维度权重自动归一化到 100%
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│
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└─ Step 6:修饰因子叠加 — LP 附加指标 + 地域基准校准
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│
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▼
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输出:维度权重字典 + 专属指标列表 + 基准校准参数
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```
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### 5.2 权重合并优先级
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| 优先级 | 轴 | 影响方式 | 影响程度 |
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|---|---|---|---|
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| 1 | 基金类型 | 决定评价哲学 | ★★★★★ |
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| 2 | 基金存续期 | 决定时间紧迫度 | ★★★★★ |
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| 3 | 企业阶段 | 决定阶段适配权重 | ★★★★ |
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| 4 | 产业赛道 | 裁剪维度 + 专属指标 | ★★★★ |
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| 5 | 投资策略 | 微调权重 ±5% | ★★★ |
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| 6 | 投资人类型 | 不影响权重,影响展示入口 | ★★ |
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### 5.3 完整权重示例
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**场景**:早期VC + 退出准备期 + A轮 + AI/SaaS + 成长型策略
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| 维度 | 基础权重 | 基金系数 | 存续期系数 | 阶段系数 | 策略微调 | 最终权重 |
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|---|---|---|---|---|---|---|
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| 财务 | 15% | ×0.7 | ×1.5 | ×1.0 | -5% | **12%** |
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| 经营 | 10% | ×1.0 | ×1.0 | ×1.0 | 0 | **10%** |
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| AI商业化 | 10% | ×1.5 | ×1.0 | ×1.0 | +5% | **18%** |
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| AI成本 | 5% | ×1.0 | ×1.0 | ×1.0 | 0 | **5%** |
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| 组织人才 | 10% | ×1.2 | ×0.8 | ×1.0 | 0 | **10%** |
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| 产品技术 | 10% | ×1.5 | ×0.6 | ×1.2 | +5% | **13%** |
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| 市场竞争 | 10% | ×1.2 | ×1.0 | ×1.0 | +5% | **12%** |
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| 治理合规 | 5% | ×0.5 | ×1.5 | ×0.8 | -5% | **3%** |
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| 融资资本 | 5% | ×1.0 | ×1.3 | ×1.0 | 0 | **7%** |
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| 协同赋能 | 5% | ×1.0 | ×1.0 | ×1.0 | 0 | **5%** |
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| AI模型产品 | 5% | ×1.3 | ×1.0 | ×1.0 | 0 | **7%** |
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| 数据合规 | 5% | ×1.0 | ×1.0 | ×1.0 | 0 | **5%** |
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| 团队技术 | 3% | ×1.0 | ×0.8 | ×1.0 | 0 | **2%** |
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| 客户成功 | 7% | ×1.0 | ×1.2 | ×1.0 | +5% | **9%** |
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归一化后总和 = 100%(自动计算)
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---
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## 6. 数据模型
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### 6.1 评价模板配置表
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```python
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class EvaluationTemplate(Base):
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"""评价指标模板 — 6 轴配置单元。"""
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__tablename__ = "evaluation_templates"
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id: Mapped[str] # UUID
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tenant_id: Mapped[str] # 租户隔离
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name: Mapped[str] # 模板名称
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# 6 轴参数
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fund_type: Mapped[str] # angel/early_vc/growth_vc/pe/cvc/fof/distress/esg
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fund_lifecycle: Mapped[str] # investment/growth/exit_preparation/liquidation
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company_stage: Mapped[str] # seed/a/b/c/pre_ipo
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industry: Mapped[str] # ai/saas/hardware/biotech/consumer/fintech/manufacturing
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strategy: Mapped[str] # growth/value/empowerment/turnaround
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investor_type: Mapped[str] # gp/post_invest_lead/investor(仅影响展示)
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# 权重配置
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weights_json: Mapped[dict] # 14 维度权重(归一化后)
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enabled_dimensions: Mapped[list] # 启用的维度 key 列表
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disabled_dimensions: Mapped[list] # 禁用的维度 key 列表
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# 专属指标
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custom_metrics_json: Mapped[dict] # 赛道专属指标定义
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# 修饰因子
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lp_focus_metrics: Mapped[list | None] # LP 附加指标
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regional_benchmark: Mapped[str | None] # 地域基准标识
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# 元数据
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is_default: Mapped[bool] # 是否为该组合的默认模板
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is_active: Mapped[bool] # 是否启用
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version: Mapped[int] # 版本号
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created_by: Mapped[str]
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created_at: Mapped[datetime]
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updated_at: Mapped[datetime]
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```
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### 6.2 评分记录表(扩展现有 HealthScore)
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```python
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class HealthScore(Base):
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"""健康度评分 — 新增模板关联字段。"""
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# ... 现有 14 维度字段保留
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# 新增字段
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template_id: Mapped[str | None] # 使用的评价模板 ID
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fund_type: Mapped[str | None] # 冗余存储,便于查询
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fund_lifecycle: Mapped[str | None]
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company_stage: Mapped[str | None]
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industry: Mapped[str | None]
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strategy: Mapped[str | None]
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custom_metrics_result: Mapped[dict | None] # 专属指标评分结果
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lp_focus_result: Mapped[dict | None] # LP 附加指标结果
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```
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### 6.3 基金信息表(新增)
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```python
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class Fund(Base):
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"""基金信息 — 管理基金类型和存续期。"""
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__tablename__ = "funds"
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id: Mapped[str]
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tenant_id: Mapped[str]
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name: Mapped[str] # 基金名称
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fund_type: Mapped[str] # angel/early_vc/growth_vc/pe/cvc/fof/distress/esg
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strategy: Mapped[str] # growth/value/empowerment/turnaround
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# 存续期信息
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established_date: Mapped[date] # 基金成立日
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total_lifespan_months: Mapped[int] # 总存续期(月)
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investment_period_months: Mapped[int] # 投资期(月)
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current_lifecycle: Mapped[str] # 当前阶段(自动计算)
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# LP 构成
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lp_composition_json: Mapped[dict | None] # {government: 30%, market: 50%, corporate: 20%}
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# 地域
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primary_market: Mapped[str | None] # china_mainland/us/sea/europe
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# 状态
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is_active: Mapped[bool]
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```
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### 6.4 企业-基金关联表(新增)
|
||
|
||
```python
|
||
class CompanyFundLink(Base):
|
||
"""企业-基金关联 — 一个企业可能被多支基金投资。"""
|
||
__tablename__ = "company_fund_links"
|
||
|
||
id: Mapped[str]
|
||
company_id: Mapped[str]
|
||
fund_id: Mapped[str]
|
||
investment_date: Mapped[date] # 投资日期
|
||
investment_stage: Mapped[str] # 投资时企业阶段
|
||
round: Mapped[str] # 轮次
|
||
amount: Mapped[float] # 投资金额
|
||
ownership_pct: Mapped[float] # 持股比例
|
||
is_current: Mapped[bool] # 当前是否持有
|
||
```
|
||
|
||
---
|
||
|
||
## 7. 预设配置
|
||
|
||
### 7.1 基金类型 × 存续期基础权重(32 个预设)
|
||
|
||
```python
|
||
FUND_LIFECYCLE_WEIGHTS = {
|
||
# (fund_type, lifecycle): {dimension: weight_multiplier}
|
||
("angel", "investment"): {"financial": 0.5, "product_tech": 1.8, "org_talent": 1.5, "market": 1.2},
|
||
("angel", "growth"): {"financial": 0.7, "product_tech": 1.5, "org_talent": 1.3, "market": 1.2},
|
||
("angel", "exit_preparation"): {"financial": 1.0, "product_tech": 1.2, "org_talent": 1.0, "market": 1.0, "financing": 1.5},
|
||
("angel", "liquidation"): {"financial": 1.5, "financing": 2.0, "product_tech": 0.8},
|
||
|
||
("early_vc", "investment"): {"financial": 0.7, "product_tech": 1.5, "market": 1.2, "customer_success": 0.8},
|
||
("early_vc", "growth"): {"financial": 0.9, "product_tech": 1.3, "market": 1.2, "customer_success": 1.0},
|
||
("early_vc", "exit_preparation"): {"financial": 1.3, "market": 1.0, "customer_success": 1.2, "financing": 1.5},
|
||
("early_vc", "liquidation"): {"financial": 1.8, "financing": 2.0, "product_tech": 0.6},
|
||
|
||
("growth_vc", "investment"): {"financial": 1.0, "market": 1.3, "customer_success": 1.2},
|
||
("growth_vc", "growth"): {"financial": 1.2, "market": 1.2, "customer_success": 1.3},
|
||
("growth_vc", "exit_preparation"): {"financial": 1.5, "market": 1.0, "customer_success": 1.2, "financing": 1.5},
|
||
("growth_vc", "liquidation"): {"financial": 2.0, "financing": 2.0, "market": 0.8},
|
||
|
||
("pe", "investment"): {"financial": 1.8, "governance": 1.5, "customer_success": 1.3, "product_tech": 0.5, "ai_commercial": 0.3},
|
||
("pe", "growth"): {"financial": 2.0, "governance": 1.5, "customer_success": 1.3, "product_tech": 0.5},
|
||
("pe", "exit_preparation"): {"financial": 2.5, "governance": 1.8, "financing": 1.5},
|
||
("pe", "liquidation"): {"financial": 3.0, "financing": 2.0, "governance": 1.5},
|
||
|
||
("cvc", "investment"): {"synergy": 2.0, "market": 1.3, "product_tech": 1.2, "financial": 0.7},
|
||
("cvc", "growth"): {"synergy": 1.8, "market": 1.2, "product_tech": 1.2, "financial": 0.8},
|
||
("cvc", "exit_preparation"): {"synergy": 1.5, "market": 1.0, "financial": 1.2, "financing": 1.3},
|
||
("cvc", "liquidation"): {"financial": 1.5, "financing": 1.5, "synergy": 1.0},
|
||
|
||
("distress", "investment"): {"financial": 2.5, "governance": 1.5, "product_tech": 0.5, "market": 0.5},
|
||
("distress", "growth"): {"financial": 2.5, "governance": 1.5, "product_tech": 0.5},
|
||
("distress", "exit_preparation"): {"financial": 3.0, "governance": 1.5, "financing": 1.5},
|
||
("distress", "liquidation"): {"financial": 3.0, "financing": 2.0, "governance": 1.5},
|
||
|
||
("esg", "investment"): {"governance": 1.8, "data_compliance": 1.5, "product_tech": 1.2, "financial": 0.8},
|
||
("esg", "growth"): {"governance": 1.5, "data_compliance": 1.3, "customer_success": 1.2},
|
||
("esg", "exit_preparation"): {"governance": 1.8, "financial": 1.3, "financing": 1.3},
|
||
("esg", "liquidation"): {"financial": 1.5, "governance": 1.5, "financing": 1.5},
|
||
|
||
# FOF 不直接评价单企业,使用默认权重
|
||
("fof", "investment"): {},
|
||
("fof", "growth"): {},
|
||
("fof", "exit_preparation"): {},
|
||
("fof", "liquidation"): {},
|
||
}
|
||
```
|
||
|
||
### 7.2 企业阶段系数(5 个预设)
|
||
|
||
```python
|
||
STAGE_MULTIPLIERS = {
|
||
"seed": {"financial": 0.6, "product_tech": 1.8, "org_talent": 1.5, "market": 0.5, "governance": 0.6, "customer_success": 0.5},
|
||
"a": {"financial": 0.8, "product_tech": 1.5, "org_talent": 1.2, "market": 1.0, "governance": 0.7, "customer_success": 0.8},
|
||
"b": {"financial": 1.2, "market": 1.3, "customer_success": 1.3, "product_tech": 1.0, "governance": 1.0},
|
||
"c": {"financial": 1.5, "market": 1.3, "governance": 1.3, "customer_success": 1.3, "product_tech": 0.8},
|
||
"pre_ipo": {"financial": 1.5, "governance": 1.8, "customer_success": 1.3, "market": 1.0, "product_tech": 0.8},
|
||
}
|
||
```
|
||
|
||
### 7.3 产业赛道配置(6 个预设)
|
||
|
||
```python
|
||
INDUSTRY_CONFIGS = {
|
||
"ai": {
|
||
"enabled": ["financial_score", "operational_score", "ai_commercial_score", "ai_cost_score",
|
||
"org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
|
||
"financing_score", "synergy_score", "ai_model_product_score",
|
||
"data_compliance_score", "team_tech_score", "customer_success_score"],
|
||
"disabled": [],
|
||
"custom_metrics": ["model_accuracy", "inference_cost", "api_call_volume", "poc_conversion_rate"],
|
||
},
|
||
"saas": {
|
||
"enabled": ["financial_score", "operational_score", "ai_commercial_score",
|
||
"org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
|
||
"financing_score", "synergy_score", "data_compliance_score",
|
||
"team_tech_score", "customer_success_score"],
|
||
"disabled": ["ai_cost_score", "ai_model_product_score"],
|
||
"custom_metrics": ["arr_growth", "net_revenue_retention", "cac_payback", "rule_of_40"],
|
||
},
|
||
"hardware": {
|
||
"enabled": ["financial_score", "operational_score",
|
||
"org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
|
||
"financing_score", "synergy_score", "data_compliance_score",
|
||
"team_tech_score", "customer_success_score"],
|
||
"disabled": ["ai_commercial_score", "ai_cost_score", "ai_model_product_score"],
|
||
"custom_metrics": ["patent_count", "tape_out_progress", "yield_rate", "rd_investment_ratio"],
|
||
},
|
||
"biotech": {
|
||
"enabled": ["financial_score", "operational_score",
|
||
"org_talent_score", "product_tech_score", "governance_score",
|
||
"financing_score", "synergy_score", "data_compliance_score",
|
||
"team_tech_score"],
|
||
"disabled": ["ai_commercial_score", "ai_cost_score", "ai_model_product_score", "market_compete_score", "customer_success_score"],
|
||
"custom_metrics": ["clinical_stage", "pipeline_progress", "regulatory_milestone", "patent_landscape"],
|
||
},
|
||
"consumer": {
|
||
"enabled": ["financial_score", "operational_score",
|
||
"org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
|
||
"financing_score", "synergy_score", "team_tech_score", "customer_success_score"],
|
||
"disabled": ["ai_commercial_score", "ai_cost_score", "ai_model_product_score", "data_compliance_score"],
|
||
"custom_metrics": ["gmv", "repurchase_rate", "brand_index", "channel_coverage"],
|
||
},
|
||
"fintech": {
|
||
"enabled": ["financial_score", "operational_score", "ai_commercial_score",
|
||
"org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
|
||
"financing_score", "synergy_score", "ai_model_product_score",
|
||
"data_compliance_score", "team_tech_score", "customer_success_score"],
|
||
"disabled": ["ai_cost_score"],
|
||
"custom_metrics": ["license_progress", "risk_control_score", "compliance_events", "npl_ratio"],
|
||
},
|
||
}
|
||
```
|
||
|
||
### 7.4 投资策略微调(4 个预设)
|
||
|
||
```python
|
||
STRATEGY_ADJUSTMENTS = {
|
||
"growth": {"market": +5, "product_tech": +5, "customer_success": +5, "financial": -5, "governance": -5},
|
||
"value": {"financial": +5, "governance": +5, "customer_success": +5, "market": -5, "product_tech": -5},
|
||
"empowerment": {"synergy": +5, "org_talent": +5, "product_tech": +5, "financial": -5, "market": -5},
|
||
"turnaround": {"financial": +5, "governance": +5, "market": -5, "product_tech": -5},
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 8. 模板组合数量
|
||
|
||
| 配置单元类型 | 预设数 | 说明 |
|
||
|---|---|---|
|
||
| 基金类型 × 存续期 | 32 | 8 × 4 基础权重模板 |
|
||
| 企业阶段 | 5 | 阶段系数 |
|
||
| 产业赛道 | 6 | 维度裁剪 + 专属指标 |
|
||
| 投资策略 | 4 | ±5% 微调 |
|
||
| 投资人类型 | 3 | 展示模板 |
|
||
| **合计** | **50** | 运行时动态组合 |
|
||
|
||
无需 8×4×5×6×4×3 = 11,520 个模板。
|
||
|
||
---
|
||
|
||
## 9. API 设计
|
||
|
||
### 9.1 获取评价模板
|
||
|
||
```
|
||
GET /api/v1/evaluation/templates?fund_type=early_vc&lifecycle=growth&stage=a&industry=ai&strategy=growth
|
||
```
|
||
|
||
### 9.2 计算评分
|
||
|
||
```
|
||
POST /api/v1/evaluation/score
|
||
{
|
||
"company_id": "xxx",
|
||
"template_id": "yyy", // 可选,不传则自动匹配
|
||
"structured_data": { ... } // 月报结构化数据
|
||
}
|
||
```
|
||
|
||
### 9.3 查看评分历史
|
||
|
||
```
|
||
GET /api/v1/evaluation/scores?company_id=xxx&template_id=yyy
|
||
```
|
||
|
||
### 9.4 管理模板
|
||
|
||
```
|
||
POST /api/v1/evaluation/templates # 创建模板
|
||
PUT /api/v1/evaluation/templates/:id # 更新模板
|
||
GET /api/v1/evaluation/templates # 列表
|
||
DELETE /api/v1/evaluation/templates/:id # 删除模板
|
||
```
|
||
|
||
---
|
||
|
||
## 10. 实现计划
|
||
|
||
| 阶段 | 任务 | 优先级 |
|
||
|---|---|---|
|
||
| Phase 1 | 数据模型:Fund / CompanyFundLink / EvaluationTemplate 表 | P0 |
|
||
| Phase 2 | 权重计算引擎:6 轴动态权重合并 + 归一化 | P0 |
|
||
| Phase 3 | 50 个预设配置写入数据库 | P0 |
|
||
| Phase 4 | API:模板管理 + 评分计算 + 历史查询 | P1 |
|
||
| Phase 5 | 前端:模板配置页面 + 评分对比视图 | P1 |
|
||
| Phase 6 | 修饰因子:LP 附加指标 + 地域基准校准 | P2 |
|
||
| Phase 7 | 专属指标采集:各赛道 3-5 个原子指标接入 | P2 |
|
||
|
||
---
|
||
|
||
## 11. 与现有系统的兼容性
|
||
|
||
| 现有功能 | 兼容方案 |
|
||
|---|---|
|
||
| `health_calculator.py` 14 维度计算 | 保留,权重从固定改为动态读取模板 |
|
||
| `HealthScore` 模型 | 新增 `template_id` 等字段,旧数据 `template_id = NULL` |
|
||
| 前端 `HealthRadar` 雷达图 | 保留,`dimensions` 参数从模板动态传入 |
|
||
| 月报 AI 解析流程 | 保留,解析后增加模板匹配 + 动态权重计算步骤 |
|
||
| Dashboard 汇总 | 保留,跨企业汇总时按各自模板计算 |
|