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docs/Python技术人员工作量文档.md
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# 数据资源盘点系统 - Python 技术人员工作量文档
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## 📋 文档说明
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本文档分析数据资源盘点系统中所有涉及大模型技术和需要 Python 开发的功能接口，为 Python 技术人员提供详细的工作量评估和开发任务清单。
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**文档版本**: v1.0
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**更新日期**: 2025-01-XX
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**适用范围**: 数据资源盘点模块
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---
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## 🎯 系统概述
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数据资源盘点系统是一个企业级数据资产管理平台的核心模块，通过 AI 大模型技术实现智能化的数据资产识别、分析和价值挖掘。系统主要包含 4 个核心步骤：
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1. **数据盘点 (InventoryStep)**: 智能识别数据资产、PII 敏感信息、重要数据
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2. **背景调研 (ContextStep)**: 收集企业信息，触发场景挖掘
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3. **价值挖掘 (ValueStep)**: AI 推荐潜在应用场景
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4. **成果交付 (DeliveryStep)**: 生成完整的数据资产盘点工作总结报告
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---
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## 📊 功能模块分析与工作量评估
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### 模块一：数据盘点智能分析服务
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#### 1.1 文档解析服务
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**功能描述**: 解析上传的数据字典文档（Excel/Word），提取表结构信息
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**涉及页面**: `InventoryStep.vue` - 方案一（已有文档导入）
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**Python 开发任务**:
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- **文档解析接口** (`/api/v1/inventory/parse-document`)
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- Excel 文件解析（支持 .xlsx, .xls）
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- Word 文件解析（支持 .doc, .docx）
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- PDF 文档解析（如需要）
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- 表结构信息提取（表名、字段名、字段类型、注释）
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**技术栈**:
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- `openpyxl` / `pandas` (Excel 解析)
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- `python-docx` (Word 解析)
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- `PyPDF2` / `pdfplumber` (PDF 解析，如需要)
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**工作量评估**: **5 人日**
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- 接口开发: 2 人日
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- 文档解析逻辑: 2 人日
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- 单元测试和异常处理: 1 人日
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**输入数据格式**:
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```json
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{
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"file_path": "string", // 上传文件路径
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"file_type": "excel | word | pdf",
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"project_id": "string"
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}
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```
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**输出数据格式**:
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```json
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{
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"success": true,
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"data": {
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"tables": [
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{
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"raw_name": "t_user_base_01",
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"fields": [
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{
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"raw_name": "user_id",
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"type": "varchar(64)",
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"comment": ""
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}
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]
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}
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],
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"total_tables": 10
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}
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}
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```
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---
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#### 1.2 SQL 脚本结果解析服务
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**功能描述**: 解析 IT 执行 SQL 脚本后导出的 Excel/CSV 结果文件
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**涉及页面**: `InventoryStep.vue` - 方案二（IT 脚本提取）
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**Python 开发任务**:
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- **SQL 结果解析接口** (`/api/v1/inventory/parse-sql-result`)
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- Excel/CSV 文件解析
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- 提取表名、字段名、字段类型等信息
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**技术栈**:
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- `pandas` (CSV/Excel 解析)
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**工作量评估**: **2 人日**
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- 接口开发: 1 人日
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- 数据验证和清洗: 1 人日
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**输入数据格式**:
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```json
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{
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"file_path": "string",
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"file_type": "excel | csv",
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"project_id": "string"
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}
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```
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**输出数据格式**: 同 1.1
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---
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#### 1.3 业务表导入解析服务
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**功能描述**: 解析业务人员手动导出的核心业务表（Excel/CSV）
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**涉及页面**: `InventoryStep.vue` - 方案三（业务关键表导入）
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**Python 开发任务**:
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- **业务表解析接口** (`/api/v1/inventory/parse-business-tables`)
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- 批量文件解析
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- 表结构识别
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**技术栈**:
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- `pandas` (批量文件处理)
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**工作量评估**: **3 人日**
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- 批量文件处理: 2 人日
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- 异常处理和进度反馈: 1 人日
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**输入数据格式**:
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```json
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{
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"files": ["file_path1", "file_path2", ...],
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"project_id": "string"
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}
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```
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**输出数据格式**: 同 1.1
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---
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#### 1.4 数据资产智能识别服务 ⭐⭐⭐ **核心功能**
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**功能描述**: 使用大模型识别数据资产的中文名称、业务含义、PII 敏感信息、重要数据特征
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**涉及页面**: `InventoryStep.vue` - AI 盘点处理阶段
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**Python 开发任务**:
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- **AI 智能识别接口** (`/api/v1/inventory/ai-analyze`)
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- 表名和字段名中文命名识别
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- 业务含义描述生成
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- PII（个人信息）识别
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- 重要数据识别（符合《数据安全法》）
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- 置信度评分
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**大模型技术**:
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- **模型选择**: 推荐使用通义千问、文心一言或 GPT-4
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- **提示词工程**: 需要设计专业的提示词模板
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- **少样本学习**: 提供示例数据提升识别准确率
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**工作量评估**: **15 人日**
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- 大模型集成和调用: 3 人日
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- 提示词工程设计和优化: 5 人日
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- PII 识别规则引擎: 3 人日
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- 重要数据识别规则引擎: 2 人日
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- 置信度评分算法: 1 人日
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- 单元测试和调试: 1 人日
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**输入数据格式**:
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```json
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{
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"tables": [
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{
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"raw_name": "t_user_base_01",
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"fields": [
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{
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"raw_name": "user_id",
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"type": "varchar(64)",
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"comment": "用户ID"
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},
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{
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"raw_name": "phone",
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"type": "varchar(11)",
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"comment": "手机号"
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}
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]
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}
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],
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"project_id": "string",
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"industry": "string", // 行业信息
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"context": "string" // 业务背景信息
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}
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```
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**输出数据格式**:
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```json
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{
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"success": true,
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"data": {
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"tables": [
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{
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"raw_name": "t_user_base_01",
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"ai_name": "会员基础信息表",
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"desc": "存储C端注册用户的核心身份信息",
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"fields": [
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{
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"raw_name": "user_id",
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"ai_name": "用户ID",
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"desc": "用户的唯一标识符"
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},
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{
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"raw_name": "phone",
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"ai_name": "手机号",
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"desc": "用户的联系电话",
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"pii": ["手机号"],
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"pii_type": "contact"
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}
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],
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"pii": ["手机号"],
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"important": false,
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"confidence": 98,
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"ai_completed": true
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}
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],
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"processing_time": 5.2
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}
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}
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```
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**提示词模板设计要点**:
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1. **系统提示词**: 定义 AI 角色为"数据资产管理专家"
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2. **用户提示词**: 包含表结构信息、行业背景、业务上下文
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3. **约束条件**:
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- PII 识别必须符合《个人信息保护法》(PIPL)
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- 重要数据识别必须符合《数据安全法》
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- 置信度评分需考虑字段命名规范度、注释完整性等因素
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4. **少样本学习**: 提供 5-10 个典型示例
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---
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### 模块二：场景挖掘智能推荐服务 ⭐⭐
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#### 2.1 潜在场景推荐服务
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**功能描述**: 基于企业背景、数据资产清单和存量场景，使用 AI 推荐潜在的数据应用场景
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**涉及页面**: `ValueStep.vue` - AI 推荐潜在场景清单
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**Python 开发任务**:
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- **场景推荐接口** (`/api/v1/value/scenario-recommendation`)
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- 基于企业行业、数据资产、存量场景生成推荐场景
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- 场景分类（降本增效、营销增长、金融服务、决策支持等）
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- 推荐指数评分（1-5星）
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- 场景依赖分析（依赖哪些数据资产）
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**大模型技术**:
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- **模型选择**: 通义千问 / 文心一言 / GPT-4
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- **提示词工程**: 需要设计场景推荐的提示词模板
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**工作量评估**: **12 人日**
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- 大模型集成和调用: 2 人日
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- 提示词工程设计和优化: 4 人日
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- 场景分类和评分算法: 3 人日
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- 场景依赖关系分析: 2 人日
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- 单元测试和调试: 1 人日
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|
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**输入数据格式**:
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```json
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{
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"project_id": "string",
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"company_info": {
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"industry": ["retail-fresh"],
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"description": "某连锁生鲜零售企业...",
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"data_scale": "100TB",
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"data_sources": ["self-generated"]
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},
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"data_assets": [
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{
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"name": "会员基础信息表",
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"core_tables": ["Dim_Customer"],
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"description": "存储C端注册用户的核心身份信息"
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}
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],
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"existing_scenarios": [
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{
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"name": "月度销售经营报表",
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"description": "统计各区域门店的月度GMV"
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}
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]
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}
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```
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|
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**输出数据格式**:
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```json
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{
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"success": true,
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"data": {
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"recommended_scenarios": [
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{
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"id": 1,
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"name": "精准会员营销",
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"type": "营销增长",
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"recommendation_index": 5,
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"desc": "基于用户画像与历史交易行为，实现千人千面的优惠券发放。",
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"dependencies": ["会员基础信息表", "订单流水记录表"],
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"business_value": "提升复购率 15-20%",
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"implementation_difficulty": "中等"
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}
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],
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"total_count": 10
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}
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}
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```
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**提示词模板设计要点**:
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1. 分析企业行业特点和数据资产价值
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2. 参考存量场景，避免重复推荐
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3. 推荐场景需具有可操作性和商业价值
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4. 推荐指数需综合考虑业务价值、实施难度、数据准备度等因素
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---
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#### 2.2 存量场景优化建议服务
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**功能描述**: 基于存量场景信息和截图，生成优化建议
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|
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**涉及页面**: `ContextStep.vue` - 生成场景挖掘与优化建议按钮
|
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|
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**Python 开发任务**:
|
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- **场景优化建议接口** (`/api/v1/value/scenario-optimization`)
|
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- 分析存量场景的不足
|
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- 提供优化建议和改进方向
|
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- 识别可提升的价值点
|
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|
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**大模型技术**:
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- 支持图片识别（如果上传了场景截图）
|
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- 文本分析和建议生成
|
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|
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**工作量评估**: **8 人日**
|
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- 大模型集成: 2 人日
|
||||
- 图片识别集成（OCR）: 2 人日
|
||||
- 提示词工程: 2 人日
|
||||
- 建议生成算法: 1 人日
|
||||
- 测试: 1 人日
|
||||
|
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**输入数据格式**:
|
||||
```json
|
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{
|
||||
"existing_scenarios": [
|
||||
{
|
||||
"name": "月度销售经营报表",
|
||||
"description": "统计各区域门店的月度GMV，维度单一",
|
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"image_url": "string" // 可选，场景截图
|
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}
|
||||
],
|
||||
"data_assets": [...],
|
||||
"company_info": {...}
|
||||
}
|
||||
```
|
||||
|
||||
**输出数据格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"optimization_suggestions": [
|
||||
{
|
||||
"scenario_name": "月度销售经营报表",
|
||||
"current_status": "维度单一，仅统计GMV",
|
||||
"suggestions": [
|
||||
"增加时间维度分析（同比、环比）",
|
||||
"增加商品类别维度分析",
|
||||
"增加区域对比分析"
|
||||
],
|
||||
"potential_value": "提升决策支持能力 30%"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 模块三：数据资产盘点报告生成服务 ⭐⭐⭐ **核心功能**
|
||||
|
||||
#### 3.1 完整报告生成服务
|
||||
|
||||
**功能描述**: 基于数据盘点结果、背景调研信息和价值挖掘场景，使用大模型生成完整的数据资产盘点工作总结报告
|
||||
|
||||
**涉及页面**: `DeliveryStep.vue` - 成果交付页面
|
||||
|
||||
**Python 开发任务**:
|
||||
- **报告生成接口** (`/api/v1/delivery/generate-report`)
|
||||
- 生成报告四个章节的完整内容
|
||||
- 支持分阶段生成（可选）
|
||||
- 报告内容验证和格式化
|
||||
|
||||
**大模型技术**:
|
||||
- **模型选择**: 推荐使用 GPT-4 或通义千问 Max（支持长文本生成）
|
||||
- **提示词工程**: 需要设计复杂的多章节报告生成提示词
|
||||
- **分阶段生成**: 建议分阶段生成以提高质量和可控性
|
||||
|
||||
**工作量评估**: **20 人日**
|
||||
- 大模型集成和长文本生成: 4 人日
|
||||
- 提示词工程（四个章节）: 8 人日
|
||||
- 报告结构化生成和格式化: 3 人日
|
||||
- 数据验证和合规性检查: 3 人日
|
||||
- 分阶段生成策略实现: 1 人日
|
||||
- 单元测试和调试: 1 人日
|
||||
|
||||
**输入数据格式**:
|
||||
```json
|
||||
{
|
||||
"project_id": "string",
|
||||
"project_info": {
|
||||
"project_name": "数据资产盘点项目",
|
||||
"industry": "retail-fresh",
|
||||
"company_name": "某连锁生鲜零售企业"
|
||||
},
|
||||
"inventory_data": {
|
||||
"total_tables": 14582,
|
||||
"total_fields": 245000,
|
||||
"total_data_volume": "58 PB",
|
||||
"storage_distribution": [
|
||||
{
|
||||
"category": "供应链物流",
|
||||
"volume": "25.4 PB",
|
||||
"storage_type": "HDFS / NoSQL"
|
||||
}
|
||||
],
|
||||
"data_source_structure": {
|
||||
"structured": 35,
|
||||
"semi_structured": 65
|
||||
},
|
||||
"identified_assets": [
|
||||
{
|
||||
"name": "消费者全景画像",
|
||||
"core_tables": ["Dim_Customer", "Fact_Sales"],
|
||||
"description": "客户维度表与销售事实表整合"
|
||||
}
|
||||
]
|
||||
},
|
||||
"context_data": {
|
||||
"enterprise_background": "某连锁生鲜零售企业...",
|
||||
"informatization_status": "使用金蝶云星空作为核心ERP...",
|
||||
"business_flow": "数据流贯穿研发、生产、物流与销售全链路..."
|
||||
},
|
||||
"value_data": {
|
||||
"selected_scenarios": [
|
||||
{
|
||||
"name": "精准会员营销",
|
||||
"description": "基于用户画像实现精准营销"
|
||||
}
|
||||
]
|
||||
},
|
||||
"options": {
|
||||
"language": "zh-CN",
|
||||
"detail_level": "standard"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**输出数据格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"header": {
|
||||
"project_name": "数据资产盘点项目"
|
||||
},
|
||||
"section1": {
|
||||
"enterprise_background": {
|
||||
"description": "某连锁生鲜零售企业..."
|
||||
},
|
||||
"informatization_status": {
|
||||
"overview": "集团采取混合云战略...",
|
||||
"private_cloud": {
|
||||
"title": "私有云部署：保障安全与性能",
|
||||
"description": "承载核心稳态业务系统..."
|
||||
},
|
||||
"public_cloud": {
|
||||
"title": "公有云部署：利用弹性计算能力",
|
||||
"description": "承载面向消费者的电商前台..."
|
||||
}
|
||||
},
|
||||
"business_data_flow": {
|
||||
"overview": "集团的数据流贯穿研发、生产、物流与销售全链路...",
|
||||
"manufacturing": {
|
||||
"title": "制造端",
|
||||
"description": "通过工业物联网（IIoT）网关..."
|
||||
},
|
||||
"logistics": {
|
||||
"title": "物流端",
|
||||
"description": "车载 GPS、手持 PDA..."
|
||||
},
|
||||
"retail": {
|
||||
"title": "零售端",
|
||||
"description": "记录全渠道交易流水..."
|
||||
},
|
||||
"data_aggregation": {
|
||||
"title": "数据汇聚",
|
||||
"description": "上述数据最终汇聚于基于 Greenplum..."
|
||||
}
|
||||
}
|
||||
},
|
||||
"section2": {
|
||||
"summary": {
|
||||
"total_data_volume": "58 PB",
|
||||
"total_data_objects": {
|
||||
"tables": "14,582 张表",
|
||||
"fields": "24.5万+ 字段"
|
||||
}
|
||||
},
|
||||
"storage_distribution": [
|
||||
{
|
||||
"category": "供应链物流",
|
||||
"volume": "25.4 PB",
|
||||
"storage_type": "主要存储于 HDFS / NoSQL",
|
||||
"color": "blue"
|
||||
}
|
||||
],
|
||||
"data_source_structure": {
|
||||
"structured": {
|
||||
"percentage": 35,
|
||||
"description": "主要是数据库表，价值密度高"
|
||||
},
|
||||
"semi_structured": {
|
||||
"percentage": 65,
|
||||
"description": "包含 IoT 日志、文档 (PDF)、影像等，存在巨大的挖掘潜力"
|
||||
}
|
||||
}
|
||||
},
|
||||
"section3": {
|
||||
"overview": {
|
||||
"asset_count": 2,
|
||||
"high_value_assets": ["供应链动态库存模型", "消费者全景画像"],
|
||||
"description": "本次盘点重点识别出 2项具备价值的数据资产..."
|
||||
},
|
||||
"assets": [
|
||||
{
|
||||
"id": "customer360",
|
||||
"title": "消费者全景画像",
|
||||
"subtitle": "Customer 360",
|
||||
"composition": {
|
||||
"description": "核心依赖 Dim_Customer（客户维度表）与 Fact_Sales（销售事实表）...",
|
||||
"core_tables": ["Dim_Customer", "Fact_Sales"]
|
||||
},
|
||||
"application_scenarios": {
|
||||
"description": "旨在构建 OneID 体系，支持计算客户生命周期价值（CLV）..."
|
||||
},
|
||||
"compliance_risks": {
|
||||
"warnings": [
|
||||
{
|
||||
"type": "个人信息预警",
|
||||
"content": "共识别出 12 项敏感个人信息（SPI），包含生物识别信息（人脸）...",
|
||||
"highlights": ["12 项", "敏感个人信息", "SPI"]
|
||||
},
|
||||
{
|
||||
"type": "去标识化风险",
|
||||
"content": "Dim_Customer 表中的手机号和身份证号部分字段未进行脱敏处理...",
|
||||
"highlights": ["Dim_Customer", "手机号", "身份证号", "脱敏处理"]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"section4": {
|
||||
"compliance_remediation": {
|
||||
"title": "合规整改",
|
||||
"items": [
|
||||
{
|
||||
"order": 1,
|
||||
"category": "跨境传输",
|
||||
"description": "针对涉及重要数据和超过 100 万人个人信息的出境场景，立即申报数据出境安全评估...",
|
||||
"code_references": ["重要数据", "个人信息"]
|
||||
}
|
||||
]
|
||||
},
|
||||
"technical_evolution": {
|
||||
"title": "技术演进",
|
||||
"description": "建议从星型架构向湖仓一体 (Data Lakehouse) 架构演进...",
|
||||
"technologies": ["Iceberg", "Hudi"]
|
||||
},
|
||||
"value_deepening": {
|
||||
"title": "价值深化",
|
||||
"items": [
|
||||
{
|
||||
"description": "建议优先对全链路库存优化场景进行深入加工，利用机器学习算法...",
|
||||
"scenarios": ["全链路库存优化"]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"metadata": {
|
||||
"generation_time": 25.3,
|
||||
"model_used": "gpt-4",
|
||||
"token_count": 8500
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**提示词模板设计要点**:
|
||||
1. **系统提示词**: 定义 AI 角色为"数据资产管理咨询专家"
|
||||
2. **分阶段生成策略**:
|
||||
- 阶段一: 生成章节一和章节二（基于盘点数据）
|
||||
- 阶段二: 生成章节三（基于识别的数据资产，重点合规风险分析）
|
||||
- 阶段三: 生成章节四（基于前面章节的分析结果）
|
||||
3. **约束条件**:
|
||||
- 统计数据必须准确，基于输入数据
|
||||
- 合规风险分析必须符合 PIPL、数据安全法等法规
|
||||
- 专家建议必须具体、可执行
|
||||
- 文本长度和格式要求
|
||||
4. **质量保证**:
|
||||
- 数据验证（百分比总和为100%等）
|
||||
- 合规性验证（风险分析完整性）
|
||||
- 文本质量检查（专业术语使用）
|
||||
|
||||
**参考文档**: 详见 `docs/数据资产盘点报告-大模型接口设计文档.md`
|
||||
|
||||
---
|
||||
|
||||
## 📈 工作量汇总表
|
||||
|
||||
| 模块 | 功能 | 工作量（人日） | 优先级 | 备注 |
|
||||
|------|------|---------------|--------|------|
|
||||
| **模块一：数据盘点智能分析** | | | | |
|
||||
| 1.1 | 文档解析服务 | 5 | 中 | Excel/Word/PDF 解析 |
|
||||
| 1.2 | SQL 结果解析服务 | 2 | 低 | Excel/CSV 解析 |
|
||||
| 1.3 | 业务表导入解析服务 | 3 | 中 | 批量文件处理 |
|
||||
| 1.4 | 数据资产智能识别服务 | 15 | **高** | **核心功能，大模型** |
|
||||
| **小计** | | **25** | | |
|
||||
| **模块二：场景挖掘智能推荐** | | | | |
|
||||
| 2.1 | 潜在场景推荐服务 | 12 | **高** | **大模型** |
|
||||
| 2.2 | 存量场景优化建议服务 | 8 | 中 | 大模型 + OCR |
|
||||
| **小计** | | **20** | | |
|
||||
| **模块三：报告生成服务** | | | | |
|
||||
| 3.1 | 完整报告生成服务 | 20 | **高** | **核心功能，大模型** |
|
||||
| **小计** | | **20** | | |
|
||||
| **总计** | | **65 人日** | | |
|
||||
|
||||
---
|
||||
|
||||
## 🔧 技术架构建议
|
||||
|
||||
### 3.1 大模型选择建议
|
||||
|
||||
| 场景 | 推荐模型 | 理由 |
|
||||
|------|---------|------|
|
||||
| 数据资产识别 | 通义千问 / GPT-4 | 需要准确理解表结构和业务含义 |
|
||||
| 场景推荐 | 通义千问 / 文心一言 | 需要理解业务场景和价值 |
|
||||
| 报告生成 | GPT-4 / 通义千问 Max | 需要生成长文本，质量要求高 |
|
||||
|
||||
### 3.2 技术栈建议
|
||||
|
||||
```python
|
||||
# 核心依赖
|
||||
fastapi>=0.104.0 # Web 框架
|
||||
pydantic>=2.0.0 # 数据验证
|
||||
openai>=1.0.0 # OpenAI API (如果使用 GPT)
|
||||
dashscope>=1.14.0 # 通义千问 API
|
||||
qianfan>=0.1.0 # 文心一言 API
|
||||
|
||||
# 文档处理
|
||||
pandas>=2.0.0 # 数据处理
|
||||
openpyxl>=3.1.0 # Excel 处理
|
||||
python-docx>=1.1.0 # Word 处理
|
||||
pdfplumber>=0.10.0 # PDF 处理（如需要）
|
||||
|
||||
# 图片处理（OCR）
|
||||
paddlepaddle>=2.5.0 # PaddleOCR
|
||||
paddleocr>=2.7.0 # OCR 库（如需要）
|
||||
|
||||
# 工具库
|
||||
python-dotenv>=1.0.0 # 环境变量管理
|
||||
loguru>=0.7.0 # 日志管理
|
||||
redis>=5.0.0 # 缓存（可选）
|
||||
celery>=5.3.0 # 异步任务（可选）
|
||||
```
|
||||
|
||||
### 3.3 接口设计规范
|
||||
|
||||
- **RESTful API 设计**: 遵循 REST 规范
|
||||
- **统一响应格式**: 所有接口返回统一格式
|
||||
- **错误处理**: 完善的错误码和错误信息
|
||||
- **日志记录**: 记录所有 API 调用和异常
|
||||
- **限流和熔断**: 防止大模型 API 调用过多导致成本过高
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项和风险
|
||||
|
||||
### 4.1 大模型 API 调用成本
|
||||
|
||||
- **Token 消耗**: 长文本生成会消耗大量 Token，需要优化提示词
|
||||
- **API 限流**: 注意各大模型 API 的调用频率限制
|
||||
- **成本控制**: 建议实现缓存机制，相同输入复用结果
|
||||
|
||||
### 4.2 数据隐私和安全
|
||||
|
||||
- **敏感信息处理**: 确保输入数据中的敏感信息不会泄露
|
||||
- **数据脱敏**: 在调用大模型 API 前，对敏感数据进行脱敏处理
|
||||
- **API 密钥管理**: 使用安全的密钥管理方案
|
||||
|
||||
### 4.3 质量保证
|
||||
|
||||
- **提示词优化**: 需要反复测试和优化提示词，提升生成质量
|
||||
- **数据验证**: 对 AI 生成的内容进行验证，确保准确性
|
||||
- **人工审核**: 对于重要内容（如合规风险分析），建议加入人工审核环节
|
||||
|
||||
### 4.4 性能优化
|
||||
|
||||
- **异步处理**: 对于耗时的 AI 分析任务，使用异步处理
|
||||
- **流式输出**: 对于长文本生成，考虑实现流式输出，提升用户体验
|
||||
- **缓存策略**: 对相同输入进行缓存，避免重复调用
|
||||
|
||||
---
|
||||
|
||||
## 📅 开发计划建议
|
||||
|
||||
### 阶段一：MVP 版本（4 周）
|
||||
|
||||
**目标**: 实现核心功能，支持基本的数据盘点和报告生成
|
||||
|
||||
**任务清单**:
|
||||
1. ✅ 文档解析服务（1.1, 1.2, 1.3）- 10 人日
|
||||
2. ✅ 数据资产智能识别服务（1.4）- 15 人日
|
||||
3. ✅ 完整报告生成服务（3.1）- 20 人日（简化版）
|
||||
|
||||
**交付物**: 可用的数据盘点和报告生成功能
|
||||
|
||||
### 阶段二：完善版本（3 周）
|
||||
|
||||
**目标**: 完善场景推荐功能，优化 AI 生成质量
|
||||
|
||||
**任务清单**:
|
||||
1. ✅ 潜在场景推荐服务（2.1）- 12 人日
|
||||
2. ✅ 存量场景优化建议服务（2.2）- 8 人日
|
||||
3. ✅ 报告生成质量优化 - 5 人日
|
||||
|
||||
**交付物**: 完整的场景推荐和优化建议功能
|
||||
|
||||
### 阶段三：优化版本（2 周）
|
||||
|
||||
**目标**: 性能优化、质量提升、用户体验优化
|
||||
|
||||
**任务清单**:
|
||||
1. ✅ 提示词优化和 A/B 测试
|
||||
2. ✅ 缓存机制实现
|
||||
3. ✅ 异步处理和流式输出
|
||||
4. ✅ 单元测试和集成测试
|
||||
|
||||
**交付物**: 高性能、高质量的完整系统
|
||||
|
||||
---
|
||||
|
||||
## 📝 接口文档模板
|
||||
|
||||
### 示例：数据资产智能识别接口
|
||||
|
||||
```python
|
||||
from fastapi import FastAPI, UploadFile, File
|
||||
from pydantic import BaseModel
|
||||
from typing import List, Optional
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
class FieldInfo(BaseModel):
|
||||
raw_name: str
|
||||
type: str
|
||||
comment: Optional[str] = ""
|
||||
|
||||
class TableInfo(BaseModel):
|
||||
raw_name: str
|
||||
fields: List[FieldInfo]
|
||||
|
||||
class AnalyzeRequest(BaseModel):
|
||||
tables: List[TableInfo]
|
||||
project_id: str
|
||||
industry: Optional[str] = None
|
||||
context: Optional[str] = None
|
||||
|
||||
class AnalyzeResponse(BaseModel):
|
||||
success: bool
|
||||
data: Optional[dict] = None
|
||||
error: Optional[dict] = None
|
||||
|
||||
@app.post("/api/v1/inventory/ai-analyze", response_model=AnalyzeResponse)
|
||||
async def ai_analyze(request: AnalyzeRequest):
|
||||
"""
|
||||
数据资产智能识别接口
|
||||
|
||||
使用大模型识别数据资产的中文名称、业务含义、PII 敏感信息、重要数据特征
|
||||
"""
|
||||
try:
|
||||
# 1. 构建提示词
|
||||
prompt = build_analyze_prompt(request.tables, request.industry, request.context)
|
||||
|
||||
# 2. 调用大模型 API
|
||||
result = await call_llm_api(prompt)
|
||||
|
||||
# 3. 解析和验证结果
|
||||
analyzed_data = parse_llm_response(result)
|
||||
validated_data = validate_analysis_result(analyzed_data)
|
||||
|
||||
return AnalyzeResponse(success=True, data=validated_data)
|
||||
except Exception as e:
|
||||
return AnalyzeResponse(
|
||||
success=False,
|
||||
error={"code": "ANALYZE_ERROR", "message": str(e)}
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [数据资产盘点报告-大模型接口设计文档](./数据资产盘点报告-大模型接口设计文档.md)
|
||||
- [前端开发规范](./前端开发规范.md)
|
||||
- [Vue 迁移完整方案](./Vue迁移完整方案.md)
|
||||
|
||||
---
|
||||
|
||||
## 📞 联系方式
|
||||
|
||||
如有疑问或需要技术支持，请联系：
|
||||
- **Python 技术负责人**: [待填写]
|
||||
- **大模型技术顾问**: [待填写]
|
||||
- **项目负责人**: [待填写]
|
||||
|
||||
---
|
||||
|
||||
## 📅 更新记录
|
||||
|
||||
| 版本 | 日期 | 更新内容 | 作者 |
|
||||
|------|------|---------|------|
|
||||
| v1.0 | 2025-01-XX | 初始版本创建 | AI Assistant |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 待办事项
|
||||
|
||||
- [ ] 确认大模型 API 选择（通义千问 / 文心一言 / GPT-4）
|
||||
- [ ] 设计详细的提示词模板
|
||||
- [ ] 确认数据格式和接口规范
|
||||
- [ ] 搭建开发环境和测试环境
|
||||
- [ ] 制定详细的开发计划和时间表
|
||||
- [ ] 确定成本预算（大模型 API 调用费用）
|
||||
482
docs/Python接口清单表格.md
Normal file
482
docs/Python接口清单表格.md
Normal file
@ -0,0 +1,482 @@
|
||||
# 数据资源盘点系统 - Python 接口开发清单
|
||||
|
||||
## 📋 接口总览
|
||||
|
||||
本文档以表格形式罗列数据资源盘点系统中所有需要 Python 技术人员开发的接口及其详细说明。
|
||||
|
||||
---
|
||||
|
||||
## 📊 接口清单表格
|
||||
|
||||
| 序号 | 接口路径 | 请求方法 | 功能描述 | 涉及页面 | 是否大模型 | 工作量（人日） | 优先级 | 技术栈 | 备注 |
|
||||
|------|---------|---------|---------|---------|-----------|--------------|--------|--------|------|
|
||||
| **模块一：数据盘点智能分析服务** |
|
||||
| 1.1 | `/api/v1/inventory/parse-document` | POST | 文档解析接口<br/>解析上传的数据字典文档（Excel/Word/PDF），提取表结构信息（表名、字段名、字段类型、注释） | `InventoryStep.vue`<br/>方案一（已有文档导入） | ❌ 否 | 5 | 中 | `openpyxl` / `pandas`<br/>`python-docx`<br/>`pdfplumber` | 支持 Excel/Word/PDF<br/>提取表结构信息 |
|
||||
| 1.2 | `/api/v1/inventory/parse-sql-result` | POST | SQL 结果解析接口<br/>解析 IT 执行 SQL 脚本后导出的 Excel/CSV 结果文件，提取表名、字段名、字段类型等信息 | `InventoryStep.vue`<br/>方案二（IT 脚本提取） | ❌ 否 | 2 | 低 | `pandas` | 支持 Excel/CSV<br/>数据验证和清洗 |
|
||||
| 1.3 | `/api/v1/inventory/parse-business-tables` | POST | 业务表解析接口<br/>解析业务人员手动导出的核心业务表（Excel/CSV），支持批量文件解析和表结构识别 | `InventoryStep.vue`<br/>方案三（业务关键表导入） | ❌ 否 | 3 | 中 | `pandas` | 批量文件处理<br/>异常处理和进度反馈 |
|
||||
| 1.4 | `/api/v1/inventory/ai-analyze` | POST | 数据资产智能识别接口 ⭐⭐⭐<br/>使用大模型识别数据资产的中文名称、业务含义、PII 敏感信息、重要数据特征，并提供置信度评分 | `InventoryStep.vue`<br/>AI 盘点处理阶段 | ✅ **是** | **15** | **高** | `通义千问` / `文心一言` / `GPT-4`<br/>提示词工程<br/>PII 识别规则引擎 | **核心功能**<br/>PII 识别需符合 PIPL<br/>重要数据识别需符合《数据安全法》 |
|
||||
| **模块二：场景挖掘智能推荐服务** |
|
||||
| 2.1 | `/api/v1/value/scenario-recommendation` | POST | 潜在场景推荐接口 ⭐⭐<br/>基于企业背景、数据资产清单和存量场景，使用 AI 推荐潜在的数据应用场景，包括场景分类、推荐指数评分（1-5星）和场景依赖分析 | `ValueStep.vue`<br/>AI 推荐潜在场景清单 | ✅ **是** | **12** | **高** | `通义千问` / `文心一言` / `GPT-4`<br/>提示词工程<br/>场景分类算法 | 场景分类：降本增效、营销增长、金融服务、决策支持等 |
|
||||
| 2.2 | `/api/v1/value/scenario-optimization` | POST | 存量场景优化建议接口<br/>基于存量场景信息和截图，分析场景不足，提供优化建议和改进方向，识别可提升的价值点（支持图片识别 OCR） | `ContextStep.vue`<br/>生成场景挖掘与优化建议按钮 | ✅ **是** | 8 | 中 | `通义千问` / `文心一言` / `GPT-4`<br/>`PaddleOCR` (OCR) | 支持场景截图识别<br/>文本分析和建议生成 |
|
||||
| **模块三：数据资产盘点报告生成服务** |
|
||||
| 3.1 | `/api/v1/delivery/generate-report` | POST | 完整报告生成接口 ⭐⭐⭐<br/>基于数据盘点结果、背景调研信息和价值挖掘场景，使用大模型生成完整的数据资产盘点工作总结报告（四个章节），支持分阶段生成、内容验证和格式化 | `DeliveryStep.vue`<br/>成果交付页面 | ✅ **是** | **20** | **高** | `GPT-4` / `通义千问 Max`<br/>提示词工程（多章节）<br/>数据验证引擎 | **核心功能**<br/>长文本生成<br/>分阶段生成策略<br/>合规性检查 |
|
||||
|
||||
---
|
||||
|
||||
## 📈 接口统计汇总
|
||||
|
||||
| 统计项 | 数量 |
|
||||
|--------|------|
|
||||
| **接口总数** | **7** |
|
||||
| 涉及大模型接口 | 4 |
|
||||
| 非大模型接口 | 3 |
|
||||
| **总工作量（人日）** | **65** |
|
||||
| 大模型接口工作量 | 47 (72%) |
|
||||
| 非大模型接口工作量 | 18 (28%) |
|
||||
|
||||
---
|
||||
|
||||
## 🔍 接口详细说明
|
||||
|
||||
### 模块一：数据盘点智能分析服务
|
||||
|
||||
#### 1.1 文档解析接口
|
||||
|
||||
**接口路径**: `/api/v1/inventory/parse-document`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 解析上传的数据字典文档（Excel/Word/PDF），提取表结构信息
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"file_path": "string", // 上传文件路径
|
||||
"file_type": "excel | word | pdf",
|
||||
"project_id": "string"
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"type": "varchar(64)",
|
||||
"comment": ""
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"total_tables": 10
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### 1.2 SQL 结果解析接口
|
||||
|
||||
**接口路径**: `/api/v1/inventory/parse-sql-result`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 解析 IT 执行 SQL 脚本后导出的 Excel/CSV 结果文件
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"file_path": "string",
|
||||
"file_type": "excel | csv",
|
||||
"project_id": "string"
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**: 同 1.1
|
||||
|
||||
---
|
||||
|
||||
#### 1.3 业务表解析接口
|
||||
|
||||
**接口路径**: `/api/v1/inventory/parse-business-tables`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 解析业务人员手动导出的核心业务表（Excel/CSV），支持批量文件解析
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"files": ["file_path1", "file_path2", ...],
|
||||
"project_id": "string"
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**: 同 1.1
|
||||
|
||||
---
|
||||
|
||||
#### 1.4 数据资产智能识别接口 ⭐⭐⭐
|
||||
|
||||
**接口路径**: `/api/v1/inventory/ai-analyze`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 使用大模型识别数据资产的中文名称、业务含义、PII 敏感信息、重要数据特征
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"type": "varchar(64)",
|
||||
"comment": "用户ID"
|
||||
},
|
||||
{
|
||||
"raw_name": "phone",
|
||||
"type": "varchar(11)",
|
||||
"comment": "手机号"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"project_id": "string",
|
||||
"industry": "string",
|
||||
"context": "string"
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"ai_name": "会员基础信息表",
|
||||
"desc": "存储C端注册用户的核心身份信息",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"ai_name": "用户ID",
|
||||
"desc": "用户的唯一标识符"
|
||||
},
|
||||
{
|
||||
"raw_name": "phone",
|
||||
"ai_name": "手机号",
|
||||
"desc": "用户的联系电话",
|
||||
"pii": ["手机号"],
|
||||
"pii_type": "contact"
|
||||
}
|
||||
],
|
||||
"pii": ["手机号"],
|
||||
"important": false,
|
||||
"confidence": 98,
|
||||
"ai_completed": true
|
||||
}
|
||||
],
|
||||
"processing_time": 5.2
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**关键要求**:
|
||||
- PII 识别必须符合《个人信息保护法》(PIPL)
|
||||
- 重要数据识别必须符合《数据安全法》
|
||||
- 置信度评分需考虑字段命名规范度、注释完整性等因素
|
||||
- 提示词工程需要提供 5-10 个典型示例
|
||||
|
||||
---
|
||||
|
||||
### 模块二：场景挖掘智能推荐服务
|
||||
|
||||
#### 2.1 潜在场景推荐接口 ⭐⭐
|
||||
|
||||
**接口路径**: `/api/v1/value/scenario-recommendation`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 基于企业背景、数据资产清单和存量场景，使用 AI 推荐潜在的数据应用场景
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"project_id": "string",
|
||||
"company_info": {
|
||||
"industry": ["retail-fresh"],
|
||||
"description": "某连锁生鲜零售企业...",
|
||||
"data_scale": "100TB",
|
||||
"data_sources": ["self-generated"]
|
||||
},
|
||||
"data_assets": [
|
||||
{
|
||||
"name": "会员基础信息表",
|
||||
"core_tables": ["Dim_Customer"],
|
||||
"description": "存储C端注册用户的核心身份信息"
|
||||
}
|
||||
],
|
||||
"existing_scenarios": [
|
||||
{
|
||||
"name": "月度销售经营报表",
|
||||
"description": "统计各区域门店的月度GMV"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"recommended_scenarios": [
|
||||
{
|
||||
"id": 1,
|
||||
"name": "精准会员营销",
|
||||
"type": "营销增长",
|
||||
"recommendation_index": 5,
|
||||
"desc": "基于用户画像与历史交易行为，实现千人千面的优惠券发放。",
|
||||
"dependencies": ["会员基础信息表", "订单流水记录表"],
|
||||
"business_value": "提升复购率 15-20%",
|
||||
"implementation_difficulty": "中等"
|
||||
}
|
||||
],
|
||||
"total_count": 10
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**关键要求**:
|
||||
- 场景分类：降本增效、营销增长、金融服务、决策支持等
|
||||
- 推荐指数评分（1-5星）
|
||||
- 综合考虑业务价值、实施难度、数据准备度等因素
|
||||
- 避免与存量场景重复推荐
|
||||
|
||||
---
|
||||
|
||||
#### 2.2 存量场景优化建议接口
|
||||
|
||||
**接口路径**: `/api/v1/value/scenario-optimization`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 基于存量场景信息和截图，生成优化建议
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"existing_scenarios": [
|
||||
{
|
||||
"name": "月度销售经营报表",
|
||||
"description": "统计各区域门店的月度GMV，维度单一",
|
||||
"image_url": "string" // 可选，场景截图
|
||||
}
|
||||
],
|
||||
"data_assets": [...],
|
||||
"company_info": {...}
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"optimization_suggestions": [
|
||||
{
|
||||
"scenario_name": "月度销售经营报表",
|
||||
"current_status": "维度单一，仅统计GMV",
|
||||
"suggestions": [
|
||||
"增加时间维度分析（同比、环比）",
|
||||
"增加商品类别维度分析",
|
||||
"增加区域对比分析"
|
||||
],
|
||||
"potential_value": "提升决策支持能力 30%"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**关键要求**:
|
||||
- 支持图片识别（OCR），如果上传了场景截图
|
||||
- 分析存量场景的不足
|
||||
- 提供可操作的优化建议
|
||||
- 识别可提升的价值点
|
||||
|
||||
---
|
||||
|
||||
### 模块三：数据资产盘点报告生成服务
|
||||
|
||||
#### 3.1 完整报告生成接口 ⭐⭐⭐
|
||||
|
||||
**接口路径**: `/api/v1/delivery/generate-report`
|
||||
**请求方法**: `POST`
|
||||
**功能描述**: 基于数据盘点结果、背景调研信息和价值挖掘场景，使用大模型生成完整的数据资产盘点工作总结报告（四个章节）
|
||||
|
||||
**请求参数**:
|
||||
```json
|
||||
{
|
||||
"project_id": "string",
|
||||
"project_info": {
|
||||
"project_name": "数据资产盘点项目",
|
||||
"industry": "retail-fresh",
|
||||
"company_name": "某连锁生鲜零售企业"
|
||||
},
|
||||
"inventory_data": {
|
||||
"total_tables": 14582,
|
||||
"total_fields": 245000,
|
||||
"total_data_volume": "58 PB",
|
||||
"storage_distribution": [...],
|
||||
"data_source_structure": {
|
||||
"structured": 35,
|
||||
"semi_structured": 65
|
||||
},
|
||||
"identified_assets": [...]
|
||||
},
|
||||
"context_data": {
|
||||
"enterprise_background": "...",
|
||||
"informatization_status": "...",
|
||||
"business_flow": "..."
|
||||
},
|
||||
"value_data": {
|
||||
"selected_scenarios": [...]
|
||||
},
|
||||
"options": {
|
||||
"language": "zh-CN",
|
||||
"detail_level": "standard"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**响应格式**:
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"header": {
|
||||
"project_name": "数据资产盘点项目"
|
||||
},
|
||||
"section1": {
|
||||
"enterprise_background": {...},
|
||||
"informatization_status": {...},
|
||||
"business_data_flow": {...}
|
||||
},
|
||||
"section2": {
|
||||
"summary": {...},
|
||||
"storage_distribution": [...],
|
||||
"data_source_structure": {...}
|
||||
},
|
||||
"section3": {
|
||||
"overview": {...},
|
||||
"assets": [...]
|
||||
},
|
||||
"section4": {
|
||||
"compliance_remediation": {...},
|
||||
"technical_evolution": {...},
|
||||
"value_deepening": {...}
|
||||
}
|
||||
},
|
||||
"metadata": {
|
||||
"generation_time": 25.3,
|
||||
"model_used": "gpt-4",
|
||||
"token_count": 8500
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**报告章节说明**:
|
||||
- **章节一**: 企业数字化情况简介（企业背景、信息化建设现状、业务流与数据流）
|
||||
- **章节二**: 数据资源统计（数据总量、存储分布、数据来源结构）
|
||||
- **章节三**: 数据资产情况盘点（资产构成、应用场景、合规风险提示）
|
||||
- **章节四**: 专家建议与下一步计划（合规整改、技术演进、价值深化）
|
||||
|
||||
**关键要求**:
|
||||
- 统计数据必须准确，基于输入数据
|
||||
- 合规风险分析必须符合 PIPL、数据安全法等法规
|
||||
- 专家建议必须具体、可执行
|
||||
- 支持分阶段生成（可选，建议用于生产环境）
|
||||
- 数据验证（百分比总和为100%等）
|
||||
- 合规性验证（风险分析完整性）
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 接口开发注意事项
|
||||
|
||||
### 1. 大模型接口特殊要求
|
||||
|
||||
| 接口 | 特殊要求 |
|
||||
|------|---------|
|
||||
| `/api/v1/inventory/ai-analyze` | 1. PII 识别需符合 PIPL<br/>2. 重要数据识别需符合《数据安全法》<br/>3. 置信度评分算法<br/>4. 提示词工程需要示例学习 |
|
||||
| `/api/v1/value/scenario-recommendation` | 1. 场景分类算法<br/>2. 推荐指数评分算法<br/>3. 场景依赖关系分析<br/>4. 避免重复推荐 |
|
||||
| `/api/v1/value/scenario-optimization` | 1. OCR 图片识别（如需要）<br/>2. 文本分析和建议生成<br/>3. 价值点识别 |
|
||||
| `/api/v1/delivery/generate-report` | 1. 长文本生成能力<br/>2. 分阶段生成策略<br/>3. 数据验证和合规性检查<br/>4. 四个章节的提示词工程 |
|
||||
|
||||
### 2. 性能要求
|
||||
|
||||
- **异步处理**: 大模型接口应支持异步处理，返回任务 ID，前端轮询结果
|
||||
- **流式输出**: 报告生成接口可考虑流式输出，提升用户体验
|
||||
- **缓存机制**: 相同输入建议缓存结果，减少 API 调用成本
|
||||
- **限流熔断**: 防止大模型 API 调用过多导致成本过高
|
||||
|
||||
### 3. 错误处理
|
||||
|
||||
- 所有接口需返回统一的错误格式
|
||||
- 完善的错误码和错误信息
|
||||
- 记录详细的日志，便于问题排查
|
||||
- 大模型 API 调用失败时的降级策略
|
||||
|
||||
### 4. 数据安全
|
||||
|
||||
- **数据脱敏**: 在调用大模型 API 前，对敏感数据进行脱敏处理
|
||||
- **API 密钥管理**: 使用安全的密钥管理方案
|
||||
- **隐私保护**: 确保输入数据中的敏感信息不会泄露
|
||||
|
||||
---
|
||||
|
||||
## 📅 开发优先级建议
|
||||
|
||||
### 第一阶段（MVP 版本）- 4 周
|
||||
|
||||
**优先级顺序**:
|
||||
1. **1.4** 数据资产智能识别接口（核心功能，15 人日）
|
||||
2. **3.1** 完整报告生成接口（核心功能，20 人日，简化版）
|
||||
3. **1.1, 1.2, 1.3** 文档解析接口（10 人日）
|
||||
|
||||
### 第二阶段（完善版本）- 3 周
|
||||
|
||||
**优先级顺序**:
|
||||
1. **2.1** 潜在场景推荐接口（12 人日）
|
||||
2. **2.2** 存量场景优化建议接口（8 人日）
|
||||
3. **3.1** 报告生成质量优化（5 人日）
|
||||
|
||||
### 第三阶段（优化版本）- 2 周
|
||||
|
||||
- 提示词优化和 A/B 测试
|
||||
- 缓存机制实现
|
||||
- 异步处理和流式输出
|
||||
- 单元测试和集成测试
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [Python技术人员工作量文档](./Python技术人员工作量文档.md) - 详细的功能模块分析和工作量评估
|
||||
- [数据资产盘点报告-大模型接口设计文档](./数据资产盘点报告-大模型接口设计文档.md) - 报告生成接口的详细设计
|
||||
- [前端开发规范](./前端开发规范.md) - 前端对接规范
|
||||
|
||||
---
|
||||
|
||||
## 📞 联系方式
|
||||
|
||||
如有接口开发相关问题，请联系：
|
||||
- **Python 技术负责人**: [待填写]
|
||||
- **大模型技术顾问**: [待填写]
|
||||
- **接口对接负责人**: [待填写]
|
||||
|
||||
---
|
||||
|
||||
## 📅 更新记录
|
||||
|
||||
| 版本 | 日期 | 更新内容 | 作者 |
|
||||
|------|------|---------|------|
|
||||
| v1.0 | 2025-01-XX | 初始版本创建，包含 7 个接口清单 | AI Assistant |
|
||||
596
docs/api/01-parse-document.md
Normal file
596
docs/api/01-parse-document.md
Normal file
@ -0,0 +1,596 @@
|
||||
# 接口开发说明 - 文档解析接口
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/inventory/parse-document`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 解析上传的数据字典文档（Excel/Word/PDF），提取表结构信息
|
||||
- **涉及页面**: `InventoryStep.vue` - 方案一（已有文档导入）
|
||||
- **是否涉及大模型**: ❌ 否
|
||||
- **工作量评估**: 5 人日
|
||||
- **优先级**: 中
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口用于解析用户上传的数据字典文档，支持以下格式：
|
||||
- **Excel**: `.xlsx`, `.xls`
|
||||
- **Word**: `.doc`, `.docx`
|
||||
- **PDF**: `.pdf`（可选）
|
||||
|
||||
从文档中提取以下信息：
|
||||
- 表名（英文）
|
||||
- 字段名（英文）
|
||||
- 字段类型
|
||||
- 字段注释/描述（中文）
|
||||
|
||||
---
|
||||
|
||||
## 🔧 技术实现方案
|
||||
|
||||
### 技术栈
|
||||
|
||||
```python
|
||||
# 核心依赖
|
||||
fastapi>=0.104.0 # Web 框架
|
||||
pydantic>=2.0.0 # 数据验证
|
||||
python-multipart>=0.0.6 # 文件上传支持
|
||||
|
||||
# 文档处理
|
||||
pandas>=2.0.0 # 数据处理
|
||||
openpyxl>=3.1.0 # Excel 处理
|
||||
python-docx>=1.1.0 # Word 处理
|
||||
pdfplumber>=0.10.0 # PDF 处理（可选）
|
||||
```
|
||||
|
||||
### 实现思路
|
||||
|
||||
1. **文件上传**: 使用 FastAPI 的 `UploadFile` 接收文件
|
||||
2. **文件类型识别**: 根据文件扩展名或 MIME 类型识别文件格式
|
||||
3. **文档解析**:
|
||||
- Excel: 使用 `pandas` 或 `openpyxl` 读取
|
||||
- Word: 使用 `python-docx` 解析表格和文本
|
||||
- PDF: 使用 `pdfplumber` 提取表格和文本
|
||||
4. **表结构提取**: 识别文档中的表结构信息，提取表名、字段名、类型、注释
|
||||
5. **数据验证**: 验证提取的数据格式是否正确
|
||||
6. **结果返回**: 返回标准化的表结构数据
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
### 请求方式
|
||||
|
||||
**Content-Type**: `multipart/form-data` 或 `application/json`
|
||||
|
||||
### 方式一：文件上传（推荐）
|
||||
|
||||
```http
|
||||
POST /api/v1/inventory/parse-document
|
||||
Content-Type: multipart/form-data
|
||||
|
||||
file: [二进制文件]
|
||||
project_id: string
|
||||
file_type: excel | word | pdf (可选，自动识别)
|
||||
```
|
||||
|
||||
### 方式二：文件路径（如果文件已上传到服务器）
|
||||
|
||||
```json
|
||||
{
|
||||
"file_path": "/path/to/document.xlsx",
|
||||
"file_type": "excel | word | pdf",
|
||||
"project_id": "project_001"
|
||||
}
|
||||
```
|
||||
|
||||
### 请求参数说明
|
||||
|
||||
| 参数名 | 类型 | 必填 | 说明 |
|
||||
|--------|------|------|------|
|
||||
| `file` | File | 是 | 上传的文件（方式一） |
|
||||
| `file_path` | string | 是 | 文件路径（方式二） |
|
||||
| `file_type` | string | 否 | 文件类型：`excel` / `word` / `pdf`，如果不传则根据文件扩展名自动识别 |
|
||||
| `project_id` | string | 是 | 项目ID |
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
### 成功响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"code": 200,
|
||||
"message": "文档解析成功",
|
||||
"data": {
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"display_name": "用户基础信息表",
|
||||
"description": "存储用户基本信息的表",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"display_name": "用户ID",
|
||||
"type": "varchar(64)",
|
||||
"comment": "用户的唯一标识符",
|
||||
"is_primary_key": true,
|
||||
"is_nullable": false,
|
||||
"default_value": null
|
||||
},
|
||||
{
|
||||
"raw_name": "user_name",
|
||||
"display_name": "用户名",
|
||||
"type": "varchar(50)",
|
||||
"comment": "用户登录名",
|
||||
"is_primary_key": false,
|
||||
"is_nullable": true,
|
||||
"default_value": null
|
||||
}
|
||||
],
|
||||
"field_count": 2
|
||||
}
|
||||
],
|
||||
"total_tables": 10,
|
||||
"total_fields": 245,
|
||||
"parse_time": 1.23,
|
||||
"file_info": {
|
||||
"file_name": "数据字典.xlsx",
|
||||
"file_size": 1024000,
|
||||
"file_type": "excel"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 失败响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": false,
|
||||
"code": 400,
|
||||
"message": "文件格式不支持",
|
||||
"error": {
|
||||
"error_code": "UNSUPPORTED_FILE_TYPE",
|
||||
"error_detail": "仅支持 Excel (.xlsx, .xls), Word (.doc, .docx), PDF (.pdf) 格式"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 响应字段说明
|
||||
|
||||
| 字段名 | 类型 | 说明 |
|
||||
|--------|------|------|
|
||||
| `success` | boolean | 请求是否成功 |
|
||||
| `code` | integer | HTTP 状态码 |
|
||||
| `message` | string | 响应消息 |
|
||||
| `data` | object | 响应数据 |
|
||||
| `data.tables` | array | 解析出的表列表 |
|
||||
| `data.tables[].raw_name` | string | 表名（英文/原始名称） |
|
||||
| `data.tables[].display_name` | string | 表显示名称（中文，如果文档中有） |
|
||||
| `data.tables[].description` | string | 表描述 |
|
||||
| `data.tables[].fields` | array | 字段列表 |
|
||||
| `data.tables[].fields[].raw_name` | string | 字段名（英文） |
|
||||
| `data.tables[].fields[].display_name` | string | 字段显示名称（中文） |
|
||||
| `data.tables[].fields[].type` | string | 字段类型 |
|
||||
| `data.tables[].fields[].comment` | string | 字段注释 |
|
||||
| `data.total_tables` | integer | 总表数 |
|
||||
| `data.total_fields` | integer | 总字段数 |
|
||||
| `data.parse_time` | float | 解析耗时（秒） |
|
||||
|
||||
---
|
||||
|
||||
## 💻 代码实现示例
|
||||
|
||||
### FastAPI 实现
|
||||
|
||||
```python
|
||||
from fastapi import FastAPI, UploadFile, File, Form, HTTPException
|
||||
from fastapi.responses import JSONResponse
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional, List
|
||||
import pandas as pd
|
||||
from docx import Document
|
||||
import pdfplumber
|
||||
import os
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
class FieldInfo(BaseModel):
|
||||
raw_name: str
|
||||
display_name: Optional[str] = None
|
||||
type: str
|
||||
comment: Optional[str] = None
|
||||
is_primary_key: bool = False
|
||||
is_nullable: bool = True
|
||||
default_value: Optional[str] = None
|
||||
|
||||
class TableInfo(BaseModel):
|
||||
raw_name: str
|
||||
display_name: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
fields: List[FieldInfo]
|
||||
field_count: int
|
||||
|
||||
class ParseDocumentResponse(BaseModel):
|
||||
success: bool
|
||||
code: int
|
||||
message: str
|
||||
data: Optional[dict] = None
|
||||
error: Optional[dict] = None
|
||||
|
||||
def parse_excel(file_path: str) -> List[TableInfo]:
|
||||
"""解析 Excel 文件"""
|
||||
tables = []
|
||||
try:
|
||||
# 读取 Excel 文件
|
||||
df = pd.read_excel(file_path, sheet_name=None) # 读取所有 sheet
|
||||
|
||||
for sheet_name, df_sheet in df.items():
|
||||
# 识别表结构（根据 Excel 格式约定）
|
||||
# 假设第一列是字段名，第二列是类型，第三列是注释
|
||||
fields = []
|
||||
for _, row in df_sheet.iterrows():
|
||||
if pd.notna(row.iloc[0]): # 字段名不为空
|
||||
field = FieldInfo(
|
||||
raw_name=str(row.iloc[0]).strip(),
|
||||
display_name=str(row.iloc[2]).strip() if len(row) > 2 and pd.notna(row.iloc[2]) else None,
|
||||
type=str(row.iloc[1]).strip() if len(row) > 1 and pd.notna(row.iloc[1]) else "varchar(255)",
|
||||
comment=str(row.iloc[2]).strip() if len(row) > 2 and pd.notna(row.iloc[2]) else None
|
||||
)
|
||||
fields.append(field)
|
||||
|
||||
if fields:
|
||||
table = TableInfo(
|
||||
raw_name=sheet_name,
|
||||
display_name=sheet_name,
|
||||
fields=fields,
|
||||
field_count=len(fields)
|
||||
)
|
||||
tables.append(table)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"Excel 解析失败: {str(e)}")
|
||||
|
||||
return tables
|
||||
|
||||
def parse_word(file_path: str) -> List[TableInfo]:
|
||||
"""解析 Word 文件"""
|
||||
tables = []
|
||||
try:
|
||||
doc = Document(file_path)
|
||||
|
||||
# 遍历文档中的表格
|
||||
for table_idx, table in enumerate(doc.tables):
|
||||
fields = []
|
||||
# 假设第一行是表头，后续行是字段信息
|
||||
# 约定：第一列字段名，第二列类型，第三列注释
|
||||
for row in table.rows[1:]: # 跳过表头
|
||||
if len(row.cells) >= 3:
|
||||
field_name = row.cells[0].text.strip()
|
||||
if field_name: # 字段名不为空
|
||||
field = FieldInfo(
|
||||
raw_name=field_name,
|
||||
display_name=row.cells[2].text.strip() if len(row.cells) > 2 and row.cells[2].text.strip() else None,
|
||||
type=row.cells[1].text.strip() if len(row.cells) > 1 and row.cells[1].text.strip() else "varchar(255)",
|
||||
comment=row.cells[2].text.strip() if len(row.cells) > 2 and row.cells[2].text.strip() else None
|
||||
)
|
||||
fields.append(field)
|
||||
|
||||
if fields:
|
||||
table_info = TableInfo(
|
||||
raw_name=f"table_{table_idx + 1}",
|
||||
display_name=f"表{table_idx + 1}",
|
||||
fields=fields,
|
||||
field_count=len(fields)
|
||||
)
|
||||
tables.append(table_info)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"Word 解析失败: {str(e)}")
|
||||
|
||||
return tables
|
||||
|
||||
def parse_pdf(file_path: str) -> List[TableInfo]:
|
||||
"""解析 PDF 文件"""
|
||||
tables = []
|
||||
try:
|
||||
with pdfplumber.open(file_path) as pdf:
|
||||
for page_idx, page in enumerate(pdf.pages):
|
||||
# 提取表格
|
||||
page_tables = page.extract_tables()
|
||||
for table_idx, table in enumerate(page_tables):
|
||||
if table and len(table) > 1:
|
||||
fields = []
|
||||
# 假设第一行是表头，后续行是字段信息
|
||||
for row in table[1:]:
|
||||
if len(row) >= 3 and row[0]:
|
||||
field = FieldInfo(
|
||||
raw_name=str(row[0]).strip(),
|
||||
display_name=str(row[2]).strip() if len(row) > 2 and row[2] else None,
|
||||
type=str(row[1]).strip() if len(row) > 1 and row[1] else "varchar(255)",
|
||||
comment=str(row[2]).strip() if len(row) > 2 and row[2] else None
|
||||
)
|
||||
fields.append(field)
|
||||
|
||||
if fields:
|
||||
table_info = TableInfo(
|
||||
raw_name=f"table_{page_idx + 1}_{table_idx + 1}",
|
||||
display_name=f"表{page_idx + 1}-{table_idx + 1}",
|
||||
fields=fields,
|
||||
field_count=len(fields)
|
||||
)
|
||||
tables.append(table_info)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"PDF 解析失败: {str(e)}")
|
||||
|
||||
return tables
|
||||
|
||||
def detect_file_type(file_name: str) -> str:
|
||||
"""根据文件扩展名检测文件类型"""
|
||||
ext = Path(file_name).suffix.lower()
|
||||
if ext in ['.xlsx', '.xls']:
|
||||
return 'excel'
|
||||
elif ext in ['.docx', '.doc']:
|
||||
return 'word'
|
||||
elif ext == '.pdf':
|
||||
return 'pdf'
|
||||
else:
|
||||
raise ValueError(f"不支持的文件类型: {ext}")
|
||||
|
||||
@app.post("/api/v1/inventory/parse-document", response_model=ParseDocumentResponse)
|
||||
async def parse_document(
|
||||
file: Optional[UploadFile] = File(None),
|
||||
file_path: Optional[str] = Form(None),
|
||||
file_type: Optional[str] = Form(None),
|
||||
project_id: str = Form(...)
|
||||
):
|
||||
"""
|
||||
文档解析接口
|
||||
|
||||
支持解析 Excel、Word、PDF 格式的数据字典文档，提取表结构信息
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 验证参数
|
||||
if not file and not file_path:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="必须提供文件或文件路径"
|
||||
)
|
||||
|
||||
# 处理文件上传
|
||||
if file:
|
||||
# 保存上传的文件到临时目录
|
||||
upload_dir = Path("/tmp/uploads")
|
||||
upload_dir.mkdir(exist_ok=True)
|
||||
file_path = str(upload_dir / file.filename)
|
||||
|
||||
with open(file_path, "wb") as f:
|
||||
content = await file.read()
|
||||
f.write(content)
|
||||
|
||||
file_name = file.filename
|
||||
file_size = len(content)
|
||||
|
||||
# 自动检测文件类型
|
||||
if not file_type:
|
||||
file_type = detect_file_type(file_name)
|
||||
else:
|
||||
# 使用提供的文件路径
|
||||
if not os.path.exists(file_path):
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail=f"文件不存在: {file_path}"
|
||||
)
|
||||
file_name = Path(file_path).name
|
||||
file_size = os.path.getsize(file_path)
|
||||
|
||||
# 自动检测文件类型
|
||||
if not file_type:
|
||||
file_type = detect_file_type(file_name)
|
||||
|
||||
# 根据文件类型选择解析方法
|
||||
if file_type == 'excel':
|
||||
tables = parse_excel(file_path)
|
||||
elif file_type == 'word':
|
||||
tables = parse_word(file_path)
|
||||
elif file_type == 'pdf':
|
||||
tables = parse_pdf(file_path)
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"不支持的文件类型: {file_type}"
|
||||
)
|
||||
|
||||
# 计算统计信息
|
||||
total_fields = sum(table.field_count for table in tables)
|
||||
parse_time = time.time() - start_time
|
||||
|
||||
# 构建响应数据
|
||||
response_data = {
|
||||
"tables": [table.dict() for table in tables],
|
||||
"total_tables": len(tables),
|
||||
"total_fields": total_fields,
|
||||
"parse_time": round(parse_time, 2),
|
||||
"file_info": {
|
||||
"file_name": file_name,
|
||||
"file_size": file_size,
|
||||
"file_type": file_type
|
||||
}
|
||||
}
|
||||
|
||||
return ParseDocumentResponse(
|
||||
success=True,
|
||||
code=200,
|
||||
message="文档解析成功",
|
||||
data=response_data
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
return ParseDocumentResponse(
|
||||
success=False,
|
||||
code=500,
|
||||
message="文档解析失败",
|
||||
error={
|
||||
"error_code": "PARSE_ERROR",
|
||||
"error_detail": str(e)
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧪 测试用例
|
||||
|
||||
### 单元测试示例
|
||||
|
||||
```python
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
from pathlib import Path
|
||||
import tempfile
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
def test_parse_excel_document():
|
||||
"""测试解析 Excel 文档"""
|
||||
# 创建测试 Excel 文件
|
||||
test_data = {
|
||||
'字段名': ['user_id', 'user_name', 'email'],
|
||||
'类型': ['varchar(64)', 'varchar(50)', 'varchar(100)'],
|
||||
'注释': ['用户ID', '用户名', '邮箱']
|
||||
}
|
||||
df = pd.DataFrame(test_data)
|
||||
|
||||
with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as tmp:
|
||||
df.to_excel(tmp.name, index=False)
|
||||
|
||||
with open(tmp.name, 'rb') as f:
|
||||
response = client.post(
|
||||
"/api/v1/inventory/parse-document",
|
||||
files={"file": ("test.xlsx", f, "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")},
|
||||
data={"project_id": "test_project"}
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["success"] is True
|
||||
assert len(data["data"]["tables"]) > 0
|
||||
assert data["data"]["total_tables"] > 0
|
||||
|
||||
def test_unsupported_file_type():
|
||||
"""测试不支持的文件类型"""
|
||||
with tempfile.NamedTemporaryFile(suffix='.txt', delete=False) as tmp:
|
||||
tmp.write(b"test content")
|
||||
tmp.flush()
|
||||
|
||||
with open(tmp.name, 'rb') as f:
|
||||
response = client.post(
|
||||
"/api/v1/inventory/parse-document",
|
||||
files={"file": ("test.txt", f, "text/plain")},
|
||||
data={"project_id": "test_project"}
|
||||
)
|
||||
|
||||
assert response.status_code == 400
|
||||
data = response.json()
|
||||
assert data["success"] is False
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
### 1. 文件大小限制
|
||||
|
||||
- **Excel**: 建议限制为 50MB
|
||||
- **Word**: 建议限制为 50MB
|
||||
- **PDF**: 建议限制为 50MB
|
||||
|
||||
在 FastAPI 中设置：
|
||||
|
||||
```python
|
||||
from fastapi import File, UploadFile
|
||||
from fastapi.exceptions import RequestEntityTooLarge
|
||||
|
||||
@app.exception_handler(RequestEntityTooLarge)
|
||||
async def handle_upload_limit(exc):
|
||||
return JSONResponse(
|
||||
status_code=413,
|
||||
content={
|
||||
"success": False,
|
||||
"message": "文件大小超过限制（最大 50MB）"
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### 2. 文件格式约定
|
||||
|
||||
由于不同用户的数据字典文档格式可能不同，建议：
|
||||
- **Excel**: 约定格式为第一列字段名，第二列类型，第三列注释
|
||||
- **Word**: 约定使用表格格式，第一行表头，后续行字段信息
|
||||
- **PDF**: 约定使用表格格式
|
||||
|
||||
如果格式不统一，需要增加更智能的识别逻辑。
|
||||
|
||||
### 3. 错误处理
|
||||
|
||||
- 文件读取失败：返回 400 错误
|
||||
- 文件格式错误：返回 400 错误，提示正确的格式
|
||||
- 解析失败：返回 500 错误，记录详细错误日志
|
||||
- 文件过大：返回 413 错误
|
||||
|
||||
### 4. 性能优化
|
||||
|
||||
- 对于大文件，考虑使用异步处理
|
||||
- 使用临时文件，处理完成后删除
|
||||
- 考虑添加缓存机制（相同文件解析结果缓存）
|
||||
|
||||
### 5. 安全性
|
||||
|
||||
- 文件上传路径验证，防止路径遍历攻击
|
||||
- 文件类型验证，防止恶意文件上传
|
||||
- 文件大小限制，防止 DoS 攻击
|
||||
- 临时文件及时清理
|
||||
|
||||
---
|
||||
|
||||
## 📝 开发检查清单
|
||||
|
||||
- [ ] 支持 Excel (.xlsx, .xls) 格式解析
|
||||
- [ ] 支持 Word (.doc, .docx) 格式解析
|
||||
- [ ] 支持 PDF (.pdf) 格式解析（可选）
|
||||
- [ ] 文件类型自动识别
|
||||
- [ ] 文件大小限制（50MB）
|
||||
- [ ] 错误处理和异常捕获
|
||||
- [ ] 单元测试覆盖
|
||||
- [ ] 日志记录
|
||||
- [ ] 临时文件清理
|
||||
- [ ] API 文档生成（Swagger）
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md)
|
||||
- [FastAPI 官方文档](https://fastapi.tiangolo.com/)
|
||||
- [pandas 文档](https://pandas.pydata.org/docs/)
|
||||
- [python-docx 文档](https://python-docx.readthedocs.io/)
|
||||
|
||||
---
|
||||
|
||||
## 📞 联系方式
|
||||
|
||||
如有开发问题，请联系：
|
||||
- **接口负责人**: [待填写]
|
||||
- **技术顾问**: [待填写]
|
||||
595
docs/api/02-parse-sql-result.md
Normal file
595
docs/api/02-parse-sql-result.md
Normal file
@ -0,0 +1,595 @@
|
||||
# 接口开发说明 - SQL 结果解析接口
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/inventory/parse-sql-result`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 解析 IT 执行 SQL 脚本后导出的 Excel/CSV 结果文件，提取表名、字段名、字段类型等信息
|
||||
- **涉及页面**: `InventoryStep.vue` - 方案二（IT 脚本提取）
|
||||
- **是否涉及大模型**: ❌ 否
|
||||
- **工作量评估**: 2 人日
|
||||
- **优先级**: 低
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口用于解析 IT 部门执行标准 SQL 脚本后导出的结果文件。SQL 脚本通常查询 `information_schema.COLUMNS` 表，导出的结果文件包含以下列：
|
||||
- 表英文名 (TABLE_NAME)
|
||||
- 表中文名/描述 (TABLE_COMMENT)
|
||||
- 字段英文名 (COLUMN_NAME)
|
||||
- 字段中文名 (COLUMN_COMMENT)
|
||||
- 字段类型 (COLUMN_TYPE)
|
||||
|
||||
支持的文件格式：
|
||||
- **Excel**: `.xlsx`, `.xls`
|
||||
- **CSV**: `.csv`
|
||||
|
||||
---
|
||||
|
||||
## 🔧 技术实现方案
|
||||
|
||||
### 技术栈
|
||||
|
||||
```python
|
||||
# 核心依赖
|
||||
fastapi>=0.104.0 # Web 框架
|
||||
pydantic>=2.0.0 # 数据验证
|
||||
|
||||
# 数据处理
|
||||
pandas>=2.0.0 # CSV/Excel 解析
|
||||
openpyxl>=3.1.0 # Excel 处理（如果使用 openpyxl）
|
||||
```
|
||||
|
||||
### 实现思路
|
||||
|
||||
1. **文件上传/路径**: 接收 Excel 或 CSV 文件
|
||||
2. **文件解析**: 使用 `pandas` 读取文件
|
||||
3. **数据清洗**: 清理空行、空值，标准化数据格式
|
||||
4. **表结构提取**: 根据列名提取表名、字段名、类型等信息
|
||||
5. **数据验证**: 验证数据完整性和格式正确性
|
||||
6. **结果返回**: 返回标准化的表结构数据
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
### 请求方式
|
||||
|
||||
**Content-Type**: `multipart/form-data` 或 `application/json`
|
||||
|
||||
### 请求参数
|
||||
|
||||
```http
|
||||
POST /api/v1/inventory/parse-sql-result
|
||||
Content-Type: multipart/form-data
|
||||
|
||||
file: [二进制文件]
|
||||
project_id: string
|
||||
file_type: excel | csv (可选，自动识别)
|
||||
```
|
||||
|
||||
或
|
||||
|
||||
```json
|
||||
{
|
||||
"file_path": "/path/to/result.xlsx",
|
||||
"file_type": "excel | csv",
|
||||
"project_id": "project_001"
|
||||
}
|
||||
```
|
||||
|
||||
### 请求参数说明
|
||||
|
||||
| 参数名 | 类型 | 必填 | 说明 |
|
||||
|--------|------|------|------|
|
||||
| `file` | File | 是 | 上传的文件（方式一） |
|
||||
| `file_path` | string | 是 | 文件路径（方式二） |
|
||||
| `file_type` | string | 否 | 文件类型：`excel` / `csv`，如果不传则根据文件扩展名自动识别 |
|
||||
| `project_id` | string | 是 | 项目ID |
|
||||
|
||||
### 标准 SQL 脚本示例
|
||||
|
||||
IT 部门需要执行的 SQL 脚本：
|
||||
|
||||
```sql
|
||||
SELECT
|
||||
TABLE_NAME AS '表英文名',
|
||||
TABLE_COMMENT AS '表中文名/描述',
|
||||
COLUMN_NAME AS '字段英文名',
|
||||
COLUMN_COMMENT AS '字段中文名',
|
||||
COLUMN_TYPE AS '字段类型'
|
||||
FROM information_schema.COLUMNS
|
||||
WHERE TABLE_SCHEMA = '您的数据库名';
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
### 成功响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"code": 200,
|
||||
"message": "SQL 结果解析成功",
|
||||
"data": {
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"display_name": "用户基础信息表",
|
||||
"description": "存储用户基本信息的表",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"display_name": "用户ID",
|
||||
"type": "varchar(64)",
|
||||
"comment": "用户的唯一标识符"
|
||||
}
|
||||
],
|
||||
"field_count": 10
|
||||
}
|
||||
],
|
||||
"total_tables": 5,
|
||||
"total_fields": 245,
|
||||
"parse_time": 0.45,
|
||||
"file_info": {
|
||||
"file_name": "schema_export.xlsx",
|
||||
"file_size": 512000,
|
||||
"file_type": "excel"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 失败响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": false,
|
||||
"code": 400,
|
||||
"message": "文件格式错误或缺少必要列",
|
||||
"error": {
|
||||
"error_code": "INVALID_FILE_FORMAT",
|
||||
"error_detail": "文件缺少必要列：表英文名、字段英文名、字段类型"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💻 代码实现示例
|
||||
|
||||
### FastAPI 实现
|
||||
|
||||
```python
|
||||
from fastapi import FastAPI, UploadFile, File, Form, HTTPException
|
||||
from fastapi.responses import JSONResponse
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional, List, Dict
|
||||
import pandas as pd
|
||||
import os
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
class FieldInfo(BaseModel):
|
||||
raw_name: str
|
||||
display_name: Optional[str] = None
|
||||
type: str
|
||||
comment: Optional[str] = None
|
||||
|
||||
class TableInfo(BaseModel):
|
||||
raw_name: str
|
||||
display_name: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
fields: List[FieldInfo]
|
||||
field_count: int
|
||||
|
||||
def parse_sql_result_excel(file_path: str) -> List[TableInfo]:
|
||||
"""解析 Excel 格式的 SQL 结果"""
|
||||
try:
|
||||
# 读取 Excel 文件
|
||||
df = pd.read_excel(file_path)
|
||||
|
||||
# 标准化列名（支持多种可能的列名）
|
||||
column_mapping = {
|
||||
'表英文名': 'table_name',
|
||||
'TABLE_NAME': 'table_name',
|
||||
'table_name': 'table_name',
|
||||
'表中文名/描述': 'table_comment',
|
||||
'TABLE_COMMENT': 'table_comment',
|
||||
'table_comment': 'table_comment',
|
||||
'字段英文名': 'column_name',
|
||||
'COLUMN_NAME': 'column_name',
|
||||
'column_name': 'column_name',
|
||||
'字段中文名': 'column_comment',
|
||||
'COLUMN_COMMENT': 'column_comment',
|
||||
'column_comment': 'column_comment',
|
||||
'字段类型': 'column_type',
|
||||
'COLUMN_TYPE': 'column_type',
|
||||
'column_type': 'column_type'
|
||||
}
|
||||
|
||||
# 重命名列
|
||||
df.columns = df.columns.str.strip()
|
||||
df = df.rename(columns=column_mapping)
|
||||
|
||||
# 验证必要列是否存在
|
||||
required_columns = ['table_name', 'column_name', 'column_type']
|
||||
missing_columns = [col for col in required_columns if col not in df.columns]
|
||||
if missing_columns:
|
||||
raise ValueError(f"缺少必要列: {', '.join(missing_columns)}")
|
||||
|
||||
# 清理数据（去除空值）
|
||||
df = df.dropna(subset=['table_name', 'column_name'])
|
||||
|
||||
# 按表名分组
|
||||
tables_dict: Dict[str, List[FieldInfo]] = {}
|
||||
for _, row in df.iterrows():
|
||||
table_name = str(row['table_name']).strip()
|
||||
column_name = str(row['column_name']).strip()
|
||||
|
||||
if not table_name or not column_name:
|
||||
continue
|
||||
|
||||
# 获取字段信息
|
||||
field = FieldInfo(
|
||||
raw_name=column_name,
|
||||
display_name=str(row.get('column_comment', '')).strip() if pd.notna(row.get('column_comment')) else None,
|
||||
type=str(row.get('column_type', 'varchar(255)')).strip() if pd.notna(row.get('column_type')) else 'varchar(255)',
|
||||
comment=str(row.get('column_comment', '')).strip() if pd.notna(row.get('column_comment')) else None
|
||||
)
|
||||
|
||||
# 按表分组
|
||||
if table_name not in tables_dict:
|
||||
tables_dict[table_name] = []
|
||||
tables_dict[table_name].append(field)
|
||||
|
||||
# 构建表信息
|
||||
tables = []
|
||||
for table_name, fields in tables_dict.items():
|
||||
# 获取表的描述信息（取第一个字段的表描述，或使用表名）
|
||||
table_comment = None
|
||||
if 'table_comment' in df.columns:
|
||||
table_comment_row = df[df['table_name'] == table_name].iloc[0]
|
||||
if pd.notna(table_comment_row.get('table_comment')):
|
||||
table_comment = str(table_comment_row['table_comment']).strip()
|
||||
|
||||
table = TableInfo(
|
||||
raw_name=table_name,
|
||||
display_name=table_comment if table_comment else table_name,
|
||||
description=table_comment,
|
||||
fields=fields,
|
||||
field_count=len(fields)
|
||||
)
|
||||
tables.append(table)
|
||||
|
||||
return tables
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"Excel 解析失败: {str(e)}")
|
||||
|
||||
def parse_sql_result_csv(file_path: str) -> List[TableInfo]:
|
||||
"""解析 CSV 格式的 SQL 结果"""
|
||||
try:
|
||||
# 读取 CSV 文件（尝试不同的编码）
|
||||
encodings = ['utf-8', 'gbk', 'gb2312', 'latin-1']
|
||||
df = None
|
||||
|
||||
for encoding in encodings:
|
||||
try:
|
||||
df = pd.read_csv(file_path, encoding=encoding)
|
||||
break
|
||||
except UnicodeDecodeError:
|
||||
continue
|
||||
|
||||
if df is None:
|
||||
raise ValueError("无法解析 CSV 文件，请检查文件编码")
|
||||
|
||||
# 后续处理与 Excel 相同
|
||||
return parse_sql_result_excel_dataframe(df)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"CSV 解析失败: {str(e)}")
|
||||
|
||||
def parse_sql_result_excel_dataframe(df: pd.DataFrame) -> List[TableInfo]:
|
||||
"""从 DataFrame 解析 SQL 结果（共用逻辑）"""
|
||||
# 标准化列名
|
||||
column_mapping = {
|
||||
'表英文名': 'table_name',
|
||||
'TABLE_NAME': 'table_name',
|
||||
'table_name': 'table_name',
|
||||
'表中文名/描述': 'table_comment',
|
||||
'TABLE_COMMENT': 'table_comment',
|
||||
'table_comment': 'table_comment',
|
||||
'字段英文名': 'column_name',
|
||||
'COLUMN_NAME': 'column_name',
|
||||
'column_name': 'column_name',
|
||||
'字段中文名': 'column_comment',
|
||||
'COLUMN_COMMENT': 'column_comment',
|
||||
'column_comment': 'column_comment',
|
||||
'字段类型': 'column_type',
|
||||
'COLUMN_TYPE': 'column_type',
|
||||
'column_type': 'column_type'
|
||||
}
|
||||
|
||||
df.columns = df.columns.str.strip()
|
||||
df = df.rename(columns=column_mapping)
|
||||
|
||||
# 验证必要列
|
||||
required_columns = ['table_name', 'column_name', 'column_type']
|
||||
missing_columns = [col for col in required_columns if col not in df.columns]
|
||||
if missing_columns:
|
||||
raise ValueError(f"缺少必要列: {', '.join(missing_columns)}")
|
||||
|
||||
# 清理数据
|
||||
df = df.dropna(subset=['table_name', 'column_name'])
|
||||
|
||||
# 按表分组
|
||||
tables_dict = {}
|
||||
for _, row in df.iterrows():
|
||||
table_name = str(row['table_name']).strip()
|
||||
column_name = str(row['column_name']).strip()
|
||||
|
||||
if not table_name or not column_name:
|
||||
continue
|
||||
|
||||
field = FieldInfo(
|
||||
raw_name=column_name,
|
||||
display_name=str(row.get('column_comment', '')).strip() if pd.notna(row.get('column_comment')) else None,
|
||||
type=str(row.get('column_type', 'varchar(255)')).strip() if pd.notna(row.get('column_type')) else 'varchar(255)',
|
||||
comment=str(row.get('column_comment', '')).strip() if pd.notna(row.get('column_comment')) else None
|
||||
)
|
||||
|
||||
if table_name not in tables_dict:
|
||||
tables_dict[table_name] = []
|
||||
tables_dict[table_name].append(field)
|
||||
|
||||
# 构建表信息
|
||||
tables = []
|
||||
for table_name, fields in tables_dict.items():
|
||||
table_comment = None
|
||||
if 'table_comment' in df.columns:
|
||||
table_comment_row = df[df['table_name'] == table_name].iloc[0]
|
||||
if pd.notna(table_comment_row.get('table_comment')):
|
||||
table_comment = str(table_comment_row['table_comment']).strip()
|
||||
|
||||
table = TableInfo(
|
||||
raw_name=table_name,
|
||||
display_name=table_comment if table_comment else table_name,
|
||||
description=table_comment,
|
||||
fields=fields,
|
||||
field_count=len(fields)
|
||||
)
|
||||
tables.append(table)
|
||||
|
||||
return tables
|
||||
|
||||
@app.post("/api/v1/inventory/parse-sql-result")
|
||||
async def parse_sql_result(
|
||||
file: Optional[UploadFile] = File(None),
|
||||
file_path: Optional[str] = Form(None),
|
||||
file_type: Optional[str] = Form(None),
|
||||
project_id: str = Form(...)
|
||||
):
|
||||
"""
|
||||
SQL 结果解析接口
|
||||
|
||||
解析 IT 执行 SQL 脚本后导出的 Excel/CSV 结果文件
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 验证参数
|
||||
if not file and not file_path:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="必须提供文件或文件路径"
|
||||
)
|
||||
|
||||
# 处理文件上传
|
||||
if file:
|
||||
upload_dir = Path("/tmp/uploads")
|
||||
upload_dir.mkdir(exist_ok=True)
|
||||
file_path = str(upload_dir / file.filename)
|
||||
|
||||
with open(file_path, "wb") as f:
|
||||
content = await file.read()
|
||||
f.write(content)
|
||||
|
||||
file_name = file.filename
|
||||
file_size = len(content)
|
||||
|
||||
if not file_type:
|
||||
ext = Path(file_name).suffix.lower()
|
||||
if ext in ['.xlsx', '.xls']:
|
||||
file_type = 'excel'
|
||||
elif ext == '.csv':
|
||||
file_type = 'csv'
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"不支持的文件类型: {ext}"
|
||||
)
|
||||
else:
|
||||
if not os.path.exists(file_path):
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail=f"文件不存在: {file_path}"
|
||||
)
|
||||
file_name = Path(file_path).name
|
||||
file_size = os.path.getsize(file_path)
|
||||
|
||||
if not file_type:
|
||||
ext = Path(file_name).suffix.lower()
|
||||
if ext in ['.xlsx', '.xls']:
|
||||
file_type = 'excel'
|
||||
elif ext == '.csv':
|
||||
file_type = 'csv'
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"不支持的文件类型: {ext}"
|
||||
)
|
||||
|
||||
# 根据文件类型解析
|
||||
if file_type == 'excel':
|
||||
tables = parse_sql_result_excel(file_path)
|
||||
elif file_type == 'csv':
|
||||
tables = parse_sql_result_csv(file_path)
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"不支持的文件类型: {file_type}"
|
||||
)
|
||||
|
||||
# 计算统计信息
|
||||
total_fields = sum(table.field_count for table in tables)
|
||||
parse_time = time.time() - start_time
|
||||
|
||||
# 构建响应
|
||||
response_data = {
|
||||
"tables": [table.dict() for table in tables],
|
||||
"total_tables": len(tables),
|
||||
"total_fields": total_fields,
|
||||
"parse_time": round(parse_time, 2),
|
||||
"file_info": {
|
||||
"file_name": file_name,
|
||||
"file_size": file_size,
|
||||
"file_type": file_type
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"code": 200,
|
||||
"message": "SQL 结果解析成功",
|
||||
"data": response_data
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
return JSONResponse(
|
||||
status_code=500,
|
||||
content={
|
||||
"success": False,
|
||||
"code": 500,
|
||||
"message": "SQL 结果解析失败",
|
||||
"error": {
|
||||
"error_code": "PARSE_ERROR",
|
||||
"error_detail": str(e)
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧪 测试用例
|
||||
|
||||
### 单元测试示例
|
||||
|
||||
```python
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
import pandas as pd
|
||||
import tempfile
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
def test_parse_sql_result_excel():
|
||||
"""测试解析 Excel 格式的 SQL 结果"""
|
||||
# 创建测试数据
|
||||
test_data = {
|
||||
'表英文名': ['t_user', 't_user', 't_order'],
|
||||
'表中文名/描述': ['用户表', '用户表', '订单表'],
|
||||
'字段英文名': ['user_id', 'user_name', 'order_id'],
|
||||
'字段中文名': ['用户ID', '用户名', '订单ID'],
|
||||
'字段类型': ['varchar(64)', 'varchar(50)', 'bigint']
|
||||
}
|
||||
df = pd.DataFrame(test_data)
|
||||
|
||||
with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as tmp:
|
||||
df.to_excel(tmp.name, index=False)
|
||||
|
||||
with open(tmp.name, 'rb') as f:
|
||||
response = client.post(
|
||||
"/api/v1/inventory/parse-sql-result",
|
||||
files={"file": ("test.xlsx", f, "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")},
|
||||
data={"project_id": "test_project"}
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["success"] is True
|
||||
assert data["data"]["total_tables"] == 2 # t_user 和 t_order
|
||||
assert data["data"]["total_fields"] == 3
|
||||
|
||||
def test_invalid_file_format():
|
||||
"""测试无效文件格式"""
|
||||
response = client.post(
|
||||
"/api/v1/inventory/parse-sql-result",
|
||||
files={"file": ("test.txt", b"invalid content", "text/plain")},
|
||||
data={"project_id": "test_project"}
|
||||
)
|
||||
|
||||
assert response.status_code == 400
|
||||
data = response.json()
|
||||
assert data["success"] is False
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
### 1. 列名映射
|
||||
|
||||
由于不同数据库导出的列名可能不同，需要支持多种列名映射：
|
||||
- `表英文名` / `TABLE_NAME` / `table_name`
|
||||
- `字段英文名` / `COLUMN_NAME` / `column_name`
|
||||
- `字段类型` / `COLUMN_TYPE` / `column_type`
|
||||
|
||||
### 2. CSV 编码问题
|
||||
|
||||
CSV 文件可能存在编码问题（GBK、UTF-8 等），需要尝试多种编码：
|
||||
- UTF-8（优先）
|
||||
- GBK
|
||||
- GB2312
|
||||
- Latin-1
|
||||
|
||||
### 3. 数据清洗
|
||||
|
||||
- 去除空行和空值
|
||||
- 标准化表名和字段名（去除前后空格）
|
||||
- 处理特殊字符
|
||||
|
||||
### 4. 错误处理
|
||||
|
||||
- 文件格式验证
|
||||
- 必要列验证
|
||||
- 数据完整性验证
|
||||
- 异常捕获和日志记录
|
||||
|
||||
---
|
||||
|
||||
## 📝 开发检查清单
|
||||
|
||||
- [ ] 支持 Excel (.xlsx, .xls) 格式解析
|
||||
- [ ] 支持 CSV (.csv) 格式解析
|
||||
- [ ] 支持多种列名映射
|
||||
- [ ] CSV 编码自动检测
|
||||
- [ ] 数据清洗和验证
|
||||
- [ ] 错误处理和异常捕获
|
||||
- [ ] 单元测试覆盖
|
||||
- [ ] 日志记录
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md)
|
||||
547
docs/api/03-parse-business-tables.md
Normal file
547
docs/api/03-parse-business-tables.md
Normal file
@ -0,0 +1,547 @@
|
||||
# 接口开发说明 - 业务表解析接口
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/inventory/parse-business-tables`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 解析业务人员手动导出的核心业务表（Excel/CSV），支持批量文件解析和表结构识别
|
||||
- **涉及页面**: `InventoryStep.vue` - 方案三（业务关键表导入）
|
||||
- **是否涉及大模型**: ❌ 否
|
||||
- **工作量评估**: 3 人日
|
||||
- **优先级**: 中
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口用于解析业务人员手动导出的核心业务表文件，支持：
|
||||
- **批量文件上传**: 一次可上传多个文件
|
||||
- **格式支持**: Excel (.xlsx, .xls)、CSV (.csv)
|
||||
- **表结构识别**: 自动识别 Excel 中的表结构（通过 Sheet 名称或文件名）
|
||||
- **进度反馈**: 支持批量处理时的进度反馈
|
||||
|
||||
适用场景：
|
||||
- SaaS 系统（如 Salesforce、金蝶、有赞）无法直接连接数据库
|
||||
- 业务人员手动导出核心业务表
|
||||
- 需要批量处理多个文件
|
||||
|
||||
---
|
||||
|
||||
## 🔧 技术实现方案
|
||||
|
||||
### 技术栈
|
||||
|
||||
```python
|
||||
# 核心依赖
|
||||
fastapi>=0.104.0 # Web 框架
|
||||
pydantic>=2.0.0 # 数据验证
|
||||
celery>=5.3.0 # 异步任务（可选）
|
||||
|
||||
# 数据处理
|
||||
pandas>=2.0.0 # 批量文件处理
|
||||
openpyxl>=3.1.0 # Excel 处理
|
||||
```
|
||||
|
||||
### 实现思路
|
||||
|
||||
1. **批量文件上传**: 接收多个文件
|
||||
2. **文件解析**: 使用 `pandas` 批量读取文件
|
||||
3. **表结构识别**: 根据文件名或 Sheet 名称识别表名
|
||||
4. **字段识别**: 从 Excel/CSV 的表头识别字段名和类型
|
||||
5. **进度反馈**: 使用异步任务或进度回调
|
||||
6. **结果汇总**: 汇总所有文件的解析结果
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
### 请求方式
|
||||
|
||||
**Content-Type**: `multipart/form-data`
|
||||
|
||||
### 请求参数
|
||||
|
||||
```http
|
||||
POST /api/v1/inventory/parse-business-tables
|
||||
Content-Type: multipart/form-data
|
||||
|
||||
files: [文件1, 文件2, ...] # 多个文件
|
||||
project_id: string
|
||||
```
|
||||
|
||||
或
|
||||
|
||||
```json
|
||||
{
|
||||
"file_paths": ["/path/to/file1.xlsx", "/path/to/file2.csv", ...],
|
||||
"project_id": "project_001"
|
||||
}
|
||||
```
|
||||
|
||||
### 请求参数说明
|
||||
|
||||
| 参数名 | 类型 | 必填 | 说明 |
|
||||
|--------|------|------|------|
|
||||
| `files` | File[] | 是 | 上传的文件列表（方式一，支持多个） |
|
||||
| `file_paths` | string[] | 是 | 文件路径列表（方式二） |
|
||||
| `project_id` | string | 是 | 项目ID |
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
### 成功响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"code": 200,
|
||||
"message": "业务表解析成功",
|
||||
"data": {
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "orders",
|
||||
"display_name": "订单流水明细表",
|
||||
"description": "从文件 orders.xlsx 解析",
|
||||
"source_file": "orders.xlsx",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "order_id",
|
||||
"display_name": "订单ID",
|
||||
"type": "string",
|
||||
"comment": null,
|
||||
"inferred_type": "varchar(64)"
|
||||
}
|
||||
],
|
||||
"field_count": 10,
|
||||
"row_count": 10000
|
||||
}
|
||||
],
|
||||
"total_tables": 5,
|
||||
"total_fields": 150,
|
||||
"total_files": 5,
|
||||
"success_files": 5,
|
||||
"failed_files": [],
|
||||
"parse_time": 3.45,
|
||||
"file_info": {
|
||||
"processed_files": [
|
||||
{
|
||||
"file_name": "orders.xlsx",
|
||||
"file_size": 1024000,
|
||||
"tables_extracted": 1,
|
||||
"status": "success"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 异步任务响应（如果使用异步处理）
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"code": 202,
|
||||
"message": "任务已提交，正在处理中",
|
||||
"data": {
|
||||
"task_id": "task_123456",
|
||||
"total_files": 5,
|
||||
"status": "processing",
|
||||
"estimated_time": 30
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💻 代码实现示例
|
||||
|
||||
### FastAPI 实现（同步版本）
|
||||
|
||||
```python
|
||||
from fastapi import FastAPI, UploadFile, File, Form, HTTPException
|
||||
from fastapi.responses import JSONResponse
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional, List, Dict
|
||||
import pandas as pd
|
||||
import os
|
||||
from pathlib import Path
|
||||
import time
|
||||
from collections import defaultdict
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
class FieldInfo(BaseModel):
|
||||
raw_name: str
|
||||
display_name: Optional[str] = None
|
||||
type: str
|
||||
comment: Optional[str] = None
|
||||
inferred_type: Optional[str] = None
|
||||
|
||||
class TableInfo(BaseModel):
|
||||
raw_name: str
|
||||
display_name: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
source_file: str
|
||||
fields: List[FieldInfo]
|
||||
field_count: int
|
||||
row_count: Optional[int] = None
|
||||
|
||||
def infer_field_type(pd_type: str) -> str:
|
||||
"""根据 pandas 类型推断数据库字段类型"""
|
||||
type_mapping = {
|
||||
'object': 'varchar(255)',
|
||||
'int64': 'bigint',
|
||||
'int32': 'int',
|
||||
'float64': 'double',
|
||||
'float32': 'float',
|
||||
'bool': 'tinyint',
|
||||
'datetime64[ns]': 'datetime',
|
||||
'date': 'date'
|
||||
}
|
||||
return type_mapping.get(str(pd_type), 'varchar(255)')
|
||||
|
||||
def parse_excel_file(file_path: str, file_name: str) -> List[TableInfo]:
|
||||
"""解析单个 Excel 文件"""
|
||||
tables = []
|
||||
|
||||
try:
|
||||
# 读取所有 Sheet
|
||||
excel_file = pd.ExcelFile(file_path)
|
||||
|
||||
for sheet_name in excel_file.sheet_names:
|
||||
df = pd.read_excel(file_path, sheet_name=sheet_name)
|
||||
|
||||
# 跳过空 Sheet
|
||||
if df.empty:
|
||||
continue
|
||||
|
||||
# 识别字段
|
||||
fields = []
|
||||
for col in df.columns:
|
||||
# 推断字段类型
|
||||
col_type = str(df[col].dtype)
|
||||
inferred_type = infer_field_type(col_type)
|
||||
|
||||
field = FieldInfo(
|
||||
raw_name=str(col).strip(),
|
||||
display_name=str(col).strip(),
|
||||
type=inferred_type,
|
||||
comment=None,
|
||||
inferred_type=inferred_type
|
||||
)
|
||||
fields.append(field)
|
||||
|
||||
if fields:
|
||||
# 使用 Sheet 名称或文件名作为表名
|
||||
table_name = sheet_name.lower().replace(' ', '_').replace('-', '_')
|
||||
if not table_name:
|
||||
table_name = Path(file_name).stem.lower().replace(' ', '_').replace('-', '_')
|
||||
|
||||
table = TableInfo(
|
||||
raw_name=table_name,
|
||||
display_name=sheet_name,
|
||||
description=f"从文件 {file_name} 的 Sheet '{sheet_name}' 解析",
|
||||
source_file=file_name,
|
||||
fields=fields,
|
||||
field_count=len(fields),
|
||||
row_count=len(df)
|
||||
)
|
||||
tables.append(table)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"解析文件 {file_name} 失败: {str(e)}")
|
||||
|
||||
return tables
|
||||
|
||||
def parse_csv_file(file_path: str, file_name: str) -> List[TableInfo]:
|
||||
"""解析单个 CSV 文件"""
|
||||
tables = []
|
||||
|
||||
try:
|
||||
# 尝试多种编码
|
||||
encodings = ['utf-8', 'gbk', 'gb2312', 'latin-1']
|
||||
df = None
|
||||
|
||||
for encoding in encodings:
|
||||
try:
|
||||
df = pd.read_csv(file_path, encoding=encoding)
|
||||
break
|
||||
except UnicodeDecodeError:
|
||||
continue
|
||||
|
||||
if df is None:
|
||||
raise ValueError("无法解析 CSV 文件，请检查文件编码")
|
||||
|
||||
if df.empty:
|
||||
return tables
|
||||
|
||||
# 识别字段
|
||||
fields = []
|
||||
for col in df.columns:
|
||||
col_type = str(df[col].dtype)
|
||||
inferred_type = infer_field_type(col_type)
|
||||
|
||||
field = FieldInfo(
|
||||
raw_name=str(col).strip(),
|
||||
display_name=str(col).strip(),
|
||||
type=inferred_type,
|
||||
comment=None,
|
||||
inferred_type=inferred_type
|
||||
)
|
||||
fields.append(field)
|
||||
|
||||
if fields:
|
||||
# 使用文件名作为表名
|
||||
table_name = Path(file_name).stem.lower().replace(' ', '_').replace('-', '_')
|
||||
|
||||
table = TableInfo(
|
||||
raw_name=table_name,
|
||||
display_name=Path(file_name).stem,
|
||||
description=f"从文件 {file_name} 解析",
|
||||
source_file=file_name,
|
||||
fields=fields,
|
||||
field_count=len(fields),
|
||||
row_count=len(df)
|
||||
)
|
||||
tables.append(table)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"解析文件 {file_name} 失败: {str(e)}")
|
||||
|
||||
return tables
|
||||
|
||||
@app.post("/api/v1/inventory/parse-business-tables")
|
||||
async def parse_business_tables(
|
||||
files: List[UploadFile] = File(...),
|
||||
project_id: str = Form(...)
|
||||
):
|
||||
"""
|
||||
业务表解析接口
|
||||
|
||||
批量解析业务人员导出的核心业务表文件
|
||||
"""
|
||||
start_time = time.time()
|
||||
upload_dir = Path("/tmp/uploads")
|
||||
upload_dir.mkdir(exist_ok=True)
|
||||
|
||||
all_tables = []
|
||||
processed_files = []
|
||||
failed_files = []
|
||||
|
||||
try:
|
||||
# 处理每个文件
|
||||
for file in files:
|
||||
file_name = file.filename
|
||||
file_path = str(upload_dir / file_name)
|
||||
|
||||
try:
|
||||
# 保存文件
|
||||
with open(file_path, "wb") as f:
|
||||
content = await file.read()
|
||||
f.write(content)
|
||||
|
||||
file_size = len(content)
|
||||
|
||||
# 根据文件扩展名选择解析方法
|
||||
ext = Path(file_name).suffix.lower()
|
||||
if ext in ['.xlsx', '.xls']:
|
||||
tables = parse_excel_file(file_path, file_name)
|
||||
elif ext == '.csv':
|
||||
tables = parse_csv_file(file_path, file_name)
|
||||
else:
|
||||
failed_files.append({
|
||||
"file_name": file_name,
|
||||
"error": f"不支持的文件类型: {ext}"
|
||||
})
|
||||
continue
|
||||
|
||||
all_tables.extend(tables)
|
||||
processed_files.append({
|
||||
"file_name": file_name,
|
||||
"file_size": file_size,
|
||||
"tables_extracted": len(tables),
|
||||
"status": "success"
|
||||
})
|
||||
|
||||
# 清理临时文件
|
||||
os.remove(file_path)
|
||||
|
||||
except Exception as e:
|
||||
failed_files.append({
|
||||
"file_name": file_name,
|
||||
"error": str(e)
|
||||
})
|
||||
# 清理临时文件
|
||||
if os.path.exists(file_path):
|
||||
os.remove(file_path)
|
||||
|
||||
# 计算统计信息
|
||||
total_fields = sum(table.field_count for table in all_tables)
|
||||
parse_time = time.time() - start_time
|
||||
|
||||
# 构建响应
|
||||
response_data = {
|
||||
"tables": [table.dict() for table in all_tables],
|
||||
"total_tables": len(all_tables),
|
||||
"total_fields": total_fields,
|
||||
"total_files": len(files),
|
||||
"success_files": len(processed_files),
|
||||
"failed_files": failed_files,
|
||||
"parse_time": round(parse_time, 2),
|
||||
"file_info": {
|
||||
"processed_files": processed_files
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"code": 200,
|
||||
"message": f"成功解析 {len(processed_files)} 个文件，提取 {len(all_tables)} 个表",
|
||||
"data": response_data
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return JSONResponse(
|
||||
status_code=500,
|
||||
content={
|
||||
"success": False,
|
||||
"code": 500,
|
||||
"message": "业务表解析失败",
|
||||
"error": {
|
||||
"error_code": "PARSE_ERROR",
|
||||
"error_detail": str(e)
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### 异步版本（使用 Celery，可选）
|
||||
|
||||
```python
|
||||
from celery import Celery
|
||||
|
||||
celery_app = Celery('tasks', broker='redis://localhost:6379')
|
||||
|
||||
@celery_app.task
|
||||
def parse_business_tables_async(file_paths: List[str], project_id: str):
|
||||
"""异步解析业务表"""
|
||||
# 解析逻辑同上
|
||||
pass
|
||||
|
||||
@app.post("/api/v1/inventory/parse-business-tables-async")
|
||||
async def parse_business_tables_async_endpoint(
|
||||
files: List[UploadFile] = File(...),
|
||||
project_id: str = Form(...)
|
||||
):
|
||||
"""异步业务表解析接口"""
|
||||
# 保存文件
|
||||
file_paths = []
|
||||
for file in files:
|
||||
file_path = f"/tmp/uploads/{file.filename}"
|
||||
with open(file_path, "wb") as f:
|
||||
content = await file.read()
|
||||
f.write(content)
|
||||
file_paths.append(file_path)
|
||||
|
||||
# 提交异步任务
|
||||
task = parse_business_tables_async.delay(file_paths, project_id)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"code": 202,
|
||||
"message": "任务已提交，正在处理中",
|
||||
"data": {
|
||||
"task_id": task.id,
|
||||
"total_files": len(files),
|
||||
"status": "processing",
|
||||
"estimated_time": len(files) * 10 # 估算时间（秒）
|
||||
}
|
||||
}
|
||||
|
||||
@app.get("/api/v1/inventory/parse-business-tables-status/{task_id}")
|
||||
async def get_parse_status(task_id: str):
|
||||
"""查询解析任务状态"""
|
||||
task = celery_app.AsyncResult(task_id)
|
||||
|
||||
if task.ready():
|
||||
return {
|
||||
"success": True,
|
||||
"code": 200,
|
||||
"data": {
|
||||
"task_id": task_id,
|
||||
"status": "completed",
|
||||
"result": task.result
|
||||
}
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"success": True,
|
||||
"code": 200,
|
||||
"data": {
|
||||
"task_id": task_id,
|
||||
"status": "processing",
|
||||
"progress": task.info.get('progress', 0) if task.info else 0
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
### 1. 批量处理性能
|
||||
|
||||
- 对于大量文件，建议使用异步处理
|
||||
- 设置合理的文件大小限制
|
||||
- 考虑并行处理以提高性能
|
||||
|
||||
### 2. 表名识别
|
||||
|
||||
由于是业务人员手动导出，表名识别可能不准确：
|
||||
- 优先使用 Excel Sheet 名称
|
||||
- 其次使用文件名
|
||||
- 提供手动修正功能（可选）
|
||||
|
||||
### 3. 字段类型推断
|
||||
|
||||
- 基于 pandas 类型推断，可能不够准确
|
||||
- 后续可通过 AI 识别接口进一步优化
|
||||
- 记录推断类型，便于后续验证
|
||||
|
||||
### 4. 错误处理
|
||||
|
||||
- 单个文件失败不应影响其他文件处理
|
||||
- 记录详细的错误信息
|
||||
- 提供失败文件列表
|
||||
|
||||
### 5. 资源管理
|
||||
|
||||
- 及时清理临时文件
|
||||
- 控制并发文件数量
|
||||
- 限制单个文件大小
|
||||
|
||||
---
|
||||
|
||||
## 📝 开发检查清单
|
||||
|
||||
- [ ] 支持批量文件上传
|
||||
- [ ] 支持 Excel (.xlsx, .xls) 格式
|
||||
- [ ] 支持 CSV (.csv) 格式
|
||||
- [ ] Excel 多 Sheet 支持
|
||||
- [ ] CSV 编码自动检测
|
||||
- [ ] 字段类型推断
|
||||
- [ ] 进度反馈（异步版本）
|
||||
- [ ] 错误处理（单个文件失败不影响其他）
|
||||
- [ ] 临时文件清理
|
||||
- [ ] 单元测试覆盖
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [接口 1.1 - 文档解析接口](./01-parse-document.md)
|
||||
- [接口 1.2 - SQL 结果解析接口](./02-parse-sql-result.md)
|
||||
- [接口 1.4 - 数据资产智能识别接口](./04-ai-analyze.md) - 可进一步优化识别结果
|
||||
751
docs/api/04-ai-analyze.md
Normal file
751
docs/api/04-ai-analyze.md
Normal file
@ -0,0 +1,751 @@
|
||||
# 接口开发说明 - 数据资产智能识别接口 ⭐⭐⭐
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/inventory/ai-analyze`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 使用大模型识别数据资产的中文名称、业务含义、PII 敏感信息、重要数据特征，并提供置信度评分
|
||||
- **涉及页面**: `InventoryStep.vue` - AI 盘点处理阶段
|
||||
- **是否涉及大模型**: ✅ **是**（核心功能）
|
||||
- **工作量评估**: **15 人日**
|
||||
- **优先级**: **高**
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口是数据资产盘点系统的核心功能，使用大模型技术智能识别和标注数据资产，具体功能包括：
|
||||
|
||||
1. **表名和字段名中文命名识别**
|
||||
- 将英文表名/字段名转换为中文名称
|
||||
- 识别业务含义
|
||||
|
||||
2. **业务含义描述生成**
|
||||
- 自动生成表的中文描述
|
||||
- 自动生成字段的中文描述
|
||||
|
||||
3. **PII（个人信息）识别**
|
||||
- 识别敏感个人信息（SPI）
|
||||
- 符合《个人信息保护法》(PIPL) 要求
|
||||
- 识别类型：手机号、身份证、姓名、邮箱、地址等
|
||||
|
||||
4. **重要数据识别**
|
||||
- 识别《数据安全法》定义的重要数据
|
||||
- 涉及国家安全、公共利益的数据
|
||||
|
||||
5. **置信度评分**
|
||||
- 评估识别结果的可靠性（0-100%）
|
||||
- 考虑字段命名规范度、注释完整性等因素
|
||||
|
||||
---
|
||||
|
||||
## 🔧 技术实现方案
|
||||
|
||||
### 技术栈
|
||||
|
||||
```python
|
||||
# 核心依赖
|
||||
fastapi>=0.104.0 # Web 框架
|
||||
pydantic>=2.0.0 # 数据验证
|
||||
httpx>=0.24.0 # HTTP 客户端（用于调用大模型 API）
|
||||
|
||||
# 大模型 SDK
|
||||
openai>=1.0.0 # OpenAI API (如果使用 GPT-4)
|
||||
dashscope>=1.14.0 # 通义千问 API
|
||||
qianfan>=0.1.0 # 文心一言 API
|
||||
|
||||
# 工具库
|
||||
python-dotenv>=1.0.0 # 环境变量管理
|
||||
loguru>=0.7.0 # 日志管理
|
||||
redis>=5.0.0 # 缓存（可选）
|
||||
```
|
||||
|
||||
### 大模型选择建议
|
||||
|
||||
| 场景 | 推荐模型 | 理由 |
|
||||
|------|---------|------|
|
||||
| 数据资产识别 | 通义千问 / GPT-4 | 需要准确理解表结构和业务含义 |
|
||||
|
||||
### 实现思路
|
||||
|
||||
1. **输入数据准备**: 整理表结构信息、行业背景、业务上下文
|
||||
2. **提示词构建**: 根据输入数据构建专业的提示词
|
||||
3. **大模型调用**: 调用大模型 API 进行识别
|
||||
4. **结果解析**: 解析大模型返回的 JSON 结果
|
||||
5. **规则引擎验证**: 使用规则引擎验证和补充识别结果
|
||||
6. **置信度评分**: 计算识别结果的置信度
|
||||
7. **结果验证**: 验证数据格式和逻辑正确性
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
### 请求方式
|
||||
|
||||
**Content-Type**: `application/json`
|
||||
|
||||
### 请求参数
|
||||
|
||||
```json
|
||||
{
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"type": "varchar(64)",
|
||||
"comment": "用户ID"
|
||||
},
|
||||
{
|
||||
"raw_name": "phone",
|
||||
"type": "varchar(11)",
|
||||
"comment": "手机号"
|
||||
},
|
||||
{
|
||||
"raw_name": "id_card",
|
||||
"type": "varchar(18)",
|
||||
"comment": "身份证号"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"project_id": "project_001",
|
||||
"industry": "retail-fresh",
|
||||
"context": "某连锁生鲜零售企业，主营水果、蔬菜等生鲜产品",
|
||||
"options": {
|
||||
"model": "qwen-max",
|
||||
"temperature": 0.3,
|
||||
"enable_pii_detection": true,
|
||||
"enable_important_data_detection": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 请求参数说明
|
||||
|
||||
| 参数名 | 类型 | 必填 | 说明 |
|
||||
|--------|------|------|------|
|
||||
| `tables` | array | 是 | 表列表，每个表包含表名和字段列表 |
|
||||
| `tables[].raw_name` | string | 是 | 表名（英文/原始名称） |
|
||||
| `tables[].fields` | array | 是 | 字段列表 |
|
||||
| `tables[].fields[].raw_name` | string | 是 | 字段名（英文） |
|
||||
| `tables[].fields[].type` | string | 是 | 字段类型 |
|
||||
| `tables[].fields[].comment` | string | 否 | 字段注释（如果有） |
|
||||
| `project_id` | string | 是 | 项目ID |
|
||||
| `industry` | string | 否 | 行业信息（如：retail-fresh） |
|
||||
| `context` | string | 否 | 业务背景信息 |
|
||||
| `options` | object | 否 | 可选配置 |
|
||||
| `options.model` | string | 否 | 大模型选择（qwen-max/gpt-4/ernie-bot） |
|
||||
| `options.temperature` | float | 否 | 温度参数（0.0-1.0），默认 0.3 |
|
||||
| `options.enable_pii_detection` | boolean | 否 | 是否启用 PII 识别，默认 true |
|
||||
| `options.enable_important_data_detection` | boolean | 否 | 是否启用重要数据识别，默认 true |
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
### 成功响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"code": 200,
|
||||
"message": "数据资产识别成功",
|
||||
"data": {
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "t_user_base_01",
|
||||
"ai_name": "会员基础信息表",
|
||||
"desc": "存储C端注册用户的核心身份信息",
|
||||
"confidence": 98,
|
||||
"ai_completed": true,
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "user_id",
|
||||
"ai_name": "用户ID",
|
||||
"desc": "用户的唯一标识符",
|
||||
"type": "varchar(64)",
|
||||
"pii": [],
|
||||
"pii_type": null,
|
||||
"is_important_data": false,
|
||||
"confidence": 95
|
||||
},
|
||||
{
|
||||
"raw_name": "phone",
|
||||
"ai_name": "手机号",
|
||||
"desc": "用户的联系电话",
|
||||
"type": "varchar(11)",
|
||||
"pii": ["手机号"],
|
||||
"pii_type": "contact",
|
||||
"is_important_data": false,
|
||||
"confidence": 98
|
||||
},
|
||||
{
|
||||
"raw_name": "id_card",
|
||||
"ai_name": "身份证号",
|
||||
"desc": "用户的身份证号码",
|
||||
"type": "varchar(18)",
|
||||
"pii": ["身份证号"],
|
||||
"pii_type": "identity",
|
||||
"is_important_data": false,
|
||||
"confidence": 99
|
||||
}
|
||||
],
|
||||
"pii": ["手机号", "身份证号"],
|
||||
"important": false,
|
||||
"important_data_types": []
|
||||
}
|
||||
],
|
||||
"statistics": {
|
||||
"total_tables": 1,
|
||||
"total_fields": 3,
|
||||
"pii_fields_count": 2,
|
||||
"important_data_fields_count": 0,
|
||||
"average_confidence": 97.3
|
||||
},
|
||||
"processing_time": 5.2,
|
||||
"model_used": "qwen-max",
|
||||
"token_usage": {
|
||||
"prompt_tokens": 1200,
|
||||
"completion_tokens": 800,
|
||||
"total_tokens": 2000
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 失败响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": false,
|
||||
"code": 500,
|
||||
"message": "数据资产识别失败",
|
||||
"error": {
|
||||
"error_code": "AI_ANALYZE_ERROR",
|
||||
"error_detail": "大模型 API 调用失败: Rate limit exceeded",
|
||||
"retryable": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💻 代码实现示例
|
||||
|
||||
### 提示词模板设计
|
||||
|
||||
```python
|
||||
SYSTEM_PROMPT = """你是一位专业的数据资产管理专家，擅长识别数据资产的中文名称、业务含义、敏感信息和重要数据特征。
|
||||
|
||||
## 你的专业能力
|
||||
- 深入理解数据资产管理、数据合规（PIPL、数据安全法）等法规要求
|
||||
- 熟悉各种业务场景下的数据资产命名规范
|
||||
- 能够准确识别敏感个人信息（SPI）和重要数据
|
||||
- 具备优秀的文本理解和生成能力
|
||||
|
||||
## 输出要求
|
||||
1. **准确性**: 中文命名必须准确反映业务含义
|
||||
2. **合规性**: PII 识别必须符合《个人信息保护法》(PIPL)
|
||||
3. **完整性**: 重要数据识别必须符合《数据安全法》
|
||||
4. **专业性**: 使用专业术语，符合行业标准
|
||||
5. **结构化**: 严格按照JSON格式输出
|
||||
"""
|
||||
|
||||
USER_PROMPT_TEMPLATE = """请基于以下信息识别数据资产：
|
||||
|
||||
## 行业背景
|
||||
{industry_info}
|
||||
|
||||
## 业务背景
|
||||
{context_info}
|
||||
|
||||
## 表结构信息
|
||||
{tables_info}
|
||||
|
||||
## 识别要求
|
||||
1. 为每个表生成中文名称（ai_name）和业务描述（desc）
|
||||
2. 为每个字段生成中文名称（ai_name）和业务描述（desc）
|
||||
3. 识别敏感个人信息（PII）：
|
||||
- 手机号、身份证号、姓名、邮箱、地址等
|
||||
- 生物识别信息（人脸、指纹等）
|
||||
- 医疗健康信息
|
||||
- 金融账户信息
|
||||
- 行踪轨迹信息
|
||||
4. 识别重要数据（符合《数据安全法》）：
|
||||
- 涉及国家安全的数据
|
||||
- 涉及公共利益的数据
|
||||
- 高精度地理信息（军事禁区周边）
|
||||
- 关键物资流向（稀土、芯片等）
|
||||
5. 计算置信度评分（0-100）：
|
||||
- 字段命名规范度
|
||||
- 注释完整性
|
||||
- 业务含义明确度
|
||||
|
||||
## 输出格式（JSON）
|
||||
{json_schema}
|
||||
"""
|
||||
|
||||
JSON_SCHEMA = """
|
||||
{
|
||||
"tables": [
|
||||
{
|
||||
"raw_name": "string",
|
||||
"ai_name": "string",
|
||||
"desc": "string",
|
||||
"confidence": "integer (0-100)",
|
||||
"fields": [
|
||||
{
|
||||
"raw_name": "string",
|
||||
"ai_name": "string",
|
||||
"desc": "string",
|
||||
"pii": ["string"],
|
||||
"pii_type": "string | null",
|
||||
"is_important_data": "boolean",
|
||||
"confidence": "integer (0-100)"
|
||||
}
|
||||
],
|
||||
"pii": ["string"],
|
||||
"important": "boolean",
|
||||
"important_data_types": ["string"]
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
```
|
||||
|
||||
### FastAPI 实现
|
||||
|
||||
```python
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.responses import JSONResponse
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Optional, List, Dict
|
||||
import json
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
import httpx
|
||||
import time
|
||||
from loguru import logger
|
||||
|
||||
load_dotenv()
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
# 大模型配置
|
||||
MODEL_CONFIG = {
|
||||
"qwen-max": {
|
||||
"api_key": os.getenv("DASHSCOPE_API_KEY"),
|
||||
"base_url": "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation",
|
||||
"model_name": "qwen-max"
|
||||
},
|
||||
"gpt-4": {
|
||||
"api_key": os.getenv("OPENAI_API_KEY"),
|
||||
"base_url": "https://api.openai.com/v1/chat/completions",
|
||||
"model_name": "gpt-4"
|
||||
}
|
||||
}
|
||||
|
||||
class FieldInput(BaseModel):
|
||||
raw_name: str
|
||||
type: str
|
||||
comment: Optional[str] = None
|
||||
|
||||
class TableInput(BaseModel):
|
||||
raw_name: str
|
||||
fields: List[FieldInput]
|
||||
|
||||
class AnalyzeRequest(BaseModel):
|
||||
tables: List[TableInput]
|
||||
project_id: str
|
||||
industry: Optional[str] = None
|
||||
context: Optional[str] = None
|
||||
options: Optional[Dict] = None
|
||||
|
||||
class FieldOutput(BaseModel):
|
||||
raw_name: str
|
||||
ai_name: str
|
||||
desc: str
|
||||
type: str
|
||||
pii: List[str] = []
|
||||
pii_type: Optional[str] = None
|
||||
is_important_data: bool = False
|
||||
confidence: int = Field(ge=0, le=100)
|
||||
|
||||
class TableOutput(BaseModel):
|
||||
raw_name: str
|
||||
ai_name: str
|
||||
desc: str
|
||||
confidence: int = Field(ge=0, le=100)
|
||||
ai_completed: bool = True
|
||||
fields: List[FieldOutput]
|
||||
pii: List[str] = []
|
||||
important: bool = False
|
||||
important_data_types: List[str] = []
|
||||
|
||||
def build_prompt(tables: List[TableInput], industry: str = None, context: str = None) -> str:
|
||||
"""构建提示词"""
|
||||
# 格式化表信息
|
||||
tables_info = []
|
||||
for table in tables:
|
||||
table_info = f"表名: {table.raw_name}\n字段列表:\n"
|
||||
for field in table.fields:
|
||||
field_info = f" - {field.raw_name} ({field.type})"
|
||||
if field.comment:
|
||||
field_info += f" - {field.comment}"
|
||||
table_info += field_info + "\n"
|
||||
tables_info.append(table_info)
|
||||
|
||||
tables_info_str = "\n\n".join(tables_info)
|
||||
|
||||
# 行业信息
|
||||
industry_info = industry if industry else "未指定"
|
||||
|
||||
# 业务背景
|
||||
context_info = context if context else "未提供业务背景信息"
|
||||
|
||||
# 构建用户提示词
|
||||
user_prompt = USER_PROMPT_TEMPLATE.format(
|
||||
industry_info=industry_info,
|
||||
context_info=context_info,
|
||||
tables_info=tables_info_str,
|
||||
json_schema=JSON_SCHEMA
|
||||
)
|
||||
|
||||
return user_prompt
|
||||
|
||||
async def call_llm_api(prompt: str, model: str = "qwen-max", temperature: float = 0.3) -> str:
|
||||
"""调用大模型 API"""
|
||||
config = MODEL_CONFIG.get(model)
|
||||
if not config:
|
||||
raise ValueError(f"不支持的大模型: {model}")
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {config['api_key']}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
if model == "qwen-max":
|
||||
# 通义千问 API
|
||||
payload = {
|
||||
"model": config["model_name"],
|
||||
"input": {
|
||||
"messages": [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
},
|
||||
"parameters": {
|
||||
"temperature": temperature,
|
||||
"result_format": "message"
|
||||
}
|
||||
}
|
||||
elif model == "gpt-4":
|
||||
# OpenAI API
|
||||
payload = {
|
||||
"model": config["model_name"],
|
||||
"messages": [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
"temperature": temperature,
|
||||
"response_format": {"type": "json_object"}
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||||
try:
|
||||
response = await client.post(
|
||||
config["base_url"],
|
||||
headers=headers,
|
||||
json=payload
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
|
||||
# 解析响应（根据不同的 API 格式）
|
||||
if model == "qwen-max":
|
||||
content = result["output"]["choices"][0]["message"]["content"]
|
||||
elif model == "gpt-4":
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
|
||||
return content
|
||||
|
||||
except httpx.HTTPError as e:
|
||||
logger.error(f"大模型 API 调用失败: {str(e)}")
|
||||
raise Exception(f"大模型 API 调用失败: {str(e)}")
|
||||
|
||||
def parse_llm_response(response_text: str) -> Dict:
|
||||
"""解析大模型返回的 JSON 结果"""
|
||||
try:
|
||||
# 提取 JSON 部分（如果返回的是 Markdown 格式）
|
||||
if "```json" in response_text:
|
||||
json_text = response_text.split("```json")[1].split("```")[0].strip()
|
||||
elif "```" in response_text:
|
||||
json_text = response_text.split("```")[1].split("```")[0].strip()
|
||||
else:
|
||||
json_text = response_text.strip()
|
||||
|
||||
# 解析 JSON
|
||||
result = json.loads(json_text)
|
||||
return result
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"JSON 解析失败: {str(e)}")
|
||||
logger.error(f"原始响应: {response_text}")
|
||||
raise Exception(f"大模型返回的 JSON 格式错误: {str(e)}")
|
||||
|
||||
def validate_pii_detection(field: FieldOutput, field_input: FieldInput) -> FieldOutput:
|
||||
"""使用规则引擎验证和补充 PII 识别"""
|
||||
# PII 关键词规则
|
||||
pii_keywords = {
|
||||
"phone": ["手机", "phone", "mobile", "tel", "telephone"],
|
||||
"id_card": ["身份证", "id_card", "idcard", "identity"],
|
||||
"name": ["姓名", "name", "real_name"],
|
||||
"email": ["邮箱", "email", "mail"],
|
||||
"address": ["地址", "address", "addr"]
|
||||
}
|
||||
|
||||
field_name_lower = field.raw_name.lower()
|
||||
|
||||
# 如果 AI 未识别，使用规则引擎识别
|
||||
if not field.pii:
|
||||
for pii_type, keywords in pii_keywords.items():
|
||||
if any(keyword in field_name_lower for keyword in keywords):
|
||||
field.pii = [pii_type]
|
||||
field.pii_type = pii_type
|
||||
break
|
||||
|
||||
return field
|
||||
|
||||
def calculate_confidence(field: FieldInput, field_output: FieldOutput) -> int:
|
||||
"""计算置信度评分"""
|
||||
score = 50 # 基础分
|
||||
|
||||
# 命名规范度（30分）
|
||||
if field.raw_name.islower() and '_' in field.raw_name:
|
||||
score += 15 # 蛇形命名
|
||||
elif field.raw_name.islower() and field.raw_name.isalnum():
|
||||
score += 10 # 小写字母数字
|
||||
|
||||
# 注释完整性（20分）
|
||||
if field.comment:
|
||||
score += 20
|
||||
|
||||
# AI 识别结果（50分）
|
||||
if field_output.ai_name and field_output.ai_name != field.raw_name:
|
||||
score += 25
|
||||
if field_output.desc:
|
||||
score += 25
|
||||
|
||||
return min(score, 100)
|
||||
|
||||
@app.post("/api/v1/inventory/ai-analyze")
|
||||
async def ai_analyze(request: AnalyzeRequest):
|
||||
"""
|
||||
数据资产智能识别接口
|
||||
|
||||
使用大模型识别数据资产的中文名称、业务含义、PII 敏感信息、重要数据特征
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 获取配置
|
||||
model = request.options.get("model", "qwen-max") if request.options else "qwen-max"
|
||||
temperature = request.options.get("temperature", 0.3) if request.options else 0.3
|
||||
enable_pii = request.options.get("enable_pii_detection", True) if request.options else True
|
||||
enable_important = request.options.get("enable_important_data_detection", True) if request.options else True
|
||||
|
||||
# 构建提示词
|
||||
prompt = build_prompt(
|
||||
tables=request.tables,
|
||||
industry=request.industry,
|
||||
context=request.context
|
||||
)
|
||||
|
||||
logger.info(f"调用大模型 {model} 进行数据资产识别")
|
||||
|
||||
# 调用大模型
|
||||
response_text = await call_llm_api(prompt, model=model, temperature=temperature)
|
||||
|
||||
# 解析结果
|
||||
llm_result = parse_llm_response(response_text)
|
||||
|
||||
# 转换为标准格式并验证
|
||||
tables_output = []
|
||||
total_pii_fields = 0
|
||||
total_important_fields = 0
|
||||
total_confidence = 0
|
||||
|
||||
for table_result, table_input in zip(llm_result.get("tables", []), request.tables):
|
||||
fields_output = []
|
||||
table_pii = []
|
||||
table_important = False
|
||||
|
||||
for field_result, field_input in zip(table_result.get("fields", []), table_input.fields):
|
||||
field_output = FieldOutput(
|
||||
raw_name=field_result.get("raw_name", field_input.raw_name),
|
||||
ai_name=field_result.get("ai_name", field_input.raw_name),
|
||||
desc=field_result.get("desc", ""),
|
||||
type=field_input.type,
|
||||
pii=field_result.get("pii", []),
|
||||
pii_type=field_result.get("pii_type"),
|
||||
is_important_data=field_result.get("is_important_data", False),
|
||||
confidence=field_result.get("confidence", 80)
|
||||
)
|
||||
|
||||
# 规则引擎验证和补充
|
||||
if enable_pii:
|
||||
field_output = validate_pii_detection(field_output, field_input)
|
||||
|
||||
# 重新计算置信度
|
||||
field_output.confidence = calculate_confidence(field_input, field_output)
|
||||
|
||||
# 收集 PII 信息
|
||||
if field_output.pii:
|
||||
table_pii.extend(field_output.pii)
|
||||
total_pii_fields += 1
|
||||
|
||||
# 收集重要数据信息
|
||||
if field_output.is_important_data:
|
||||
table_important = True
|
||||
total_important_fields += 1
|
||||
|
||||
fields_output.append(field_output)
|
||||
total_confidence += field_output.confidence
|
||||
|
||||
table_output = TableOutput(
|
||||
raw_name=table_result.get("raw_name", table_input.raw_name),
|
||||
ai_name=table_result.get("ai_name", table_input.raw_name),
|
||||
desc=table_result.get("desc", ""),
|
||||
confidence=table_result.get("confidence", 80),
|
||||
ai_completed=True,
|
||||
fields=fields_output,
|
||||
pii=list(set(table_pii)), # 去重
|
||||
important=table_important,
|
||||
important_data_types=table_result.get("important_data_types", [])
|
||||
)
|
||||
|
||||
tables_output.append(table_output)
|
||||
|
||||
# 计算统计信息
|
||||
total_fields = sum(len(table.fields) for table in tables_output)
|
||||
avg_confidence = total_confidence / total_fields if total_fields > 0 else 0
|
||||
processing_time = time.time() - start_time
|
||||
|
||||
# 构建响应
|
||||
response_data = {
|
||||
"tables": [table.dict() for table in tables_output],
|
||||
"statistics": {
|
||||
"total_tables": len(tables_output),
|
||||
"total_fields": total_fields,
|
||||
"pii_fields_count": total_pii_fields,
|
||||
"important_data_fields_count": total_important_fields,
|
||||
"average_confidence": round(avg_confidence, 2)
|
||||
},
|
||||
"processing_time": round(processing_time, 2),
|
||||
"model_used": model,
|
||||
"token_usage": {
|
||||
"prompt_tokens": len(prompt) // 4, # 粗略估算
|
||||
"completion_tokens": len(response_text) // 4,
|
||||
"total_tokens": (len(prompt) + len(response_text)) // 4
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"code": 200,
|
||||
"message": "数据资产识别成功",
|
||||
"data": response_data
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"数据资产识别失败: {str(e)}")
|
||||
return JSONResponse(
|
||||
status_code=500,
|
||||
content={
|
||||
"success": False,
|
||||
"code": 500,
|
||||
"message": "数据资产识别失败",
|
||||
"error": {
|
||||
"error_code": "AI_ANALYZE_ERROR",
|
||||
"error_detail": str(e),
|
||||
"retryable": "Rate limit" in str(e) or "timeout" in str(e).lower()
|
||||
}
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
### 1. 提示词工程
|
||||
|
||||
- **系统提示词**: 定义 AI 角色为"数据资产管理专家"
|
||||
- **少样本学习**: 提供 5-10 个典型示例
|
||||
- **约束条件**: 明确 PII 和重要数据的识别标准
|
||||
- **输出格式**: 使用 JSON Schema 确保输出格式正确
|
||||
|
||||
### 2. PII 识别规则
|
||||
|
||||
必须符合《个人信息保护法》(PIPL)，识别以下类型：
|
||||
- **身份信息**: 姓名、身份证号、护照号
|
||||
- **联系信息**: 手机号、邮箱、地址
|
||||
- **生物识别**: 人脸、指纹、声纹
|
||||
- **医疗健康**: 体检报告、疾病信息
|
||||
- **金融账户**: 银行卡号、账户信息
|
||||
- **行踪轨迹**: GPS 位置、行程记录
|
||||
|
||||
### 3. 重要数据识别规则
|
||||
|
||||
必须符合《数据安全法》，识别以下类型：
|
||||
- **国家安全**: 军事信息、国家秘密
|
||||
- **公共利益**: 关键基础设施信息
|
||||
- **高精度地理**: 军事禁区周边位置
|
||||
- **关键物资**: 稀土、芯片等关键物资流向
|
||||
|
||||
### 4. 错误处理和重试
|
||||
|
||||
- **API 限流**: 实现指数退避重试策略
|
||||
- **超时处理**: 设置合理的超时时间（60秒）
|
||||
- **降级策略**: API 失败时使用规则引擎作为降级方案
|
||||
- **日志记录**: 详细记录每次 API 调用的请求和响应
|
||||
|
||||
### 5. 性能优化
|
||||
|
||||
- **批量处理**: 对于大量表，考虑批量调用 API
|
||||
- **缓存机制**: 相同输入缓存结果，减少 API 调用
|
||||
- **异步处理**: 对于大量数据，考虑异步处理
|
||||
|
||||
### 6. 成本控制
|
||||
|
||||
- **Token 优化**: 优化提示词，减少 Token 消耗
|
||||
- **模型选择**: 根据需求选择合适的模型（平衡成本和质量）
|
||||
- **缓存策略**: 对相同输入进行缓存
|
||||
|
||||
---
|
||||
|
||||
## 📝 开发检查清单
|
||||
|
||||
- [ ] 大模型 API 集成（通义千问/GPT-4）
|
||||
- [ ] 提示词工程设计和优化
|
||||
- [ ] PII 识别规则引擎
|
||||
- [ ] 重要数据识别规则引擎
|
||||
- [ ] 置信度评分算法
|
||||
- [ ] JSON 解析和验证
|
||||
- [ ] 错误处理和重试机制
|
||||
- [ ] 缓存机制（可选）
|
||||
- [ ] 日志记录
|
||||
- [ ] 单元测试覆盖
|
||||
- [ ] 性能测试
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md)
|
||||
- [数据资产盘点报告-大模型接口设计文档](../数据资产盘点报告-大模型接口设计文档.md)
|
||||
- [通义千问 API 文档](https://help.aliyun.com/zh/model-studio/)
|
||||
- [OpenAI API 文档](https://platform.openai.com/docs)
|
||||
145
docs/api/05-scenario-recommendation.md
Normal file
145
docs/api/05-scenario-recommendation.md
Normal file
@ -0,0 +1,145 @@
|
||||
# 接口开发说明 - 潜在场景推荐接口 ⭐⭐
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/value/scenario-recommendation`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 基于企业背景、数据资产清单和存量场景，使用 AI 推荐潜在的数据应用场景
|
||||
- **涉及页面**: `ValueStep.vue` - AI 推荐潜在场景清单
|
||||
- **是否涉及大模型**: ✅ **是**
|
||||
- **工作量评估**: **12 人日**
|
||||
- **优先级**: **高**
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口使用大模型技术，基于企业背景、数据资产清单和存量场景，智能推荐潜在的数据应用场景，包括：
|
||||
|
||||
1. **场景分类**: 降本增效、营销增长、金融服务、决策支持等
|
||||
2. **推荐指数评分**: 1-5星评分
|
||||
3. **场景依赖分析**: 分析场景依赖哪些数据资产
|
||||
4. **商业价值评估**: 评估场景的商业价值和实施难度
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
### 请求参数
|
||||
|
||||
```json
|
||||
{
|
||||
"project_id": "project_001",
|
||||
"company_info": {
|
||||
"industry": ["retail-fresh"],
|
||||
"description": "某连锁生鲜零售企业，主营水果、蔬菜等生鲜产品，拥有线下门店500家",
|
||||
"data_scale": "100TB",
|
||||
"data_sources": ["self-generated"]
|
||||
},
|
||||
"data_assets": [
|
||||
{
|
||||
"name": "会员基础信息表",
|
||||
"core_tables": ["Dim_Customer"],
|
||||
"description": "存储C端注册用户的核心身份信息"
|
||||
},
|
||||
{
|
||||
"name": "订单流水记录表",
|
||||
"core_tables": ["Fact_Sales"],
|
||||
"description": "全渠道销售交易明细"
|
||||
}
|
||||
],
|
||||
"existing_scenarios": [
|
||||
{
|
||||
"name": "月度销售经营报表",
|
||||
"description": "统计各区域门店的月度GMV，维度单一"
|
||||
}
|
||||
],
|
||||
"options": {
|
||||
"model": "qwen-max",
|
||||
"recommendation_count": 10,
|
||||
"exclude_types": []
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
### 成功响应
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"code": 200,
|
||||
"message": "场景推荐成功",
|
||||
"data": {
|
||||
"recommended_scenarios": [
|
||||
{
|
||||
"id": 1,
|
||||
"name": "精准会员营销",
|
||||
"type": "营销增长",
|
||||
"recommendation_index": 5,
|
||||
"desc": "基于用户画像与历史交易行为，实现千人千面的优惠券发放。",
|
||||
"dependencies": ["会员基础信息表", "订单流水记录表"],
|
||||
"business_value": "提升复购率 15-20%",
|
||||
"implementation_difficulty": "中等",
|
||||
"estimated_roi": "高",
|
||||
"technical_requirements": ["用户画像引擎", "推荐算法"],
|
||||
"data_requirements": ["会员基础信息", "交易历史", "行为数据"]
|
||||
}
|
||||
],
|
||||
"total_count": 10,
|
||||
"generation_time": 8.5,
|
||||
"model_used": "qwen-max"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💻 提示词模板
|
||||
|
||||
```python
|
||||
SCENARIO_RECOMMENDATION_PROMPT = """基于以下企业信息，推荐潜在的数据应用场景：
|
||||
|
||||
## 企业信息
|
||||
行业: {industry}
|
||||
企业描述: {company_description}
|
||||
数据规模: {data_scale}
|
||||
数据来源: {data_sources}
|
||||
|
||||
## 可用数据资产
|
||||
{data_assets_info}
|
||||
|
||||
## 存量场景（避免重复推荐）
|
||||
{existing_scenarios_info}
|
||||
|
||||
## 推荐要求
|
||||
1. 推荐 {count} 个潜在数据应用场景
|
||||
2. 场景分类：降本增效、营销增长、金融服务、决策支持、风险控制等
|
||||
3. 推荐指数评分：1-5星（综合考虑业务价值、实施难度、数据准备度）
|
||||
4. 分析场景依赖的数据资产
|
||||
5. 评估商业价值和实施难度
|
||||
6. 避免与存量场景重复
|
||||
|
||||
## 输出格式（JSON）
|
||||
{json_schema}
|
||||
"""
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
1. **场景分类**: 需要明确定义场景分类标准
|
||||
2. **推荐指数算法**: 综合考虑业务价值、实施难度、数据准备度
|
||||
3. **依赖分析**: 准确识别场景依赖的数据资产
|
||||
4. **避免重复**: 与存量场景对比，避免重复推荐
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md)
|
||||
86
docs/api/06-scenario-optimization.md
Normal file
86
docs/api/06-scenario-optimization.md
Normal file
@ -0,0 +1,86 @@
|
||||
# 接口开发说明 - 存量场景优化建议接口
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/value/scenario-optimization`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 基于存量场景信息和截图，分析场景不足，提供优化建议和改进方向
|
||||
- **涉及页面**: `ContextStep.vue` - 生成场景挖掘与优化建议按钮
|
||||
- **是否涉及大模型**: ✅ **是**
|
||||
- **工作量评估**: 8 人日
|
||||
- **优先级**: 中
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口使用大模型技术分析存量场景的不足，并提供优化建议，支持：
|
||||
1. **图片识别（OCR）**: 如果上传了场景截图，使用 OCR 识别内容
|
||||
2. **场景分析**: 分析现有场景的功能和不足
|
||||
3. **优化建议**: 提供具体的优化建议和改进方向
|
||||
4. **价值提升**: 识别可提升的价值点
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
```json
|
||||
{
|
||||
"existing_scenarios": [
|
||||
{
|
||||
"name": "月度销售经营报表",
|
||||
"description": "统计各区域门店的月度GMV，维度单一",
|
||||
"image_url": "https://example.com/screenshot.png" // 可选
|
||||
}
|
||||
],
|
||||
"data_assets": [...],
|
||||
"company_info": {...}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"data": {
|
||||
"optimization_suggestions": [
|
||||
{
|
||||
"scenario_name": "月度销售经营报表",
|
||||
"current_status": "维度单一，仅统计GMV",
|
||||
"suggestions": [
|
||||
"增加时间维度分析（同比、环比）",
|
||||
"增加商品类别维度分析",
|
||||
"增加区域对比分析"
|
||||
],
|
||||
"potential_value": "提升决策支持能力 30%"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 💻 技术实现要点
|
||||
|
||||
1. **OCR 集成**: 使用 PaddleOCR 识别场景截图
|
||||
2. **大模型分析**: 调用大模型分析场景不足
|
||||
3. **建议生成**: 基于分析结果生成优化建议
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
1. **图片处理**: 支持常见图片格式（PNG、JPG、JPEG）
|
||||
2. **OCR 准确性**: 需要处理 OCR 识别错误的情况
|
||||
3. **建议可操作性**: 优化建议必须具体、可执行
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md)
|
||||
150
docs/api/07-generate-report.md
Normal file
150
docs/api/07-generate-report.md
Normal file
@ -0,0 +1,150 @@
|
||||
# 接口开发说明 - 完整报告生成接口 ⭐⭐⭐
|
||||
|
||||
## 📋 接口基本信息
|
||||
|
||||
- **接口路径**: `/api/v1/delivery/generate-report`
|
||||
- **请求方法**: `POST`
|
||||
- **接口功能**: 基于数据盘点结果、背景调研信息和价值挖掘场景，使用大模型生成完整的数据资产盘点工作总结报告（四个章节）
|
||||
- **涉及页面**: `DeliveryStep.vue` - 成果交付页面
|
||||
- **是否涉及大模型**: ✅ **是**（核心功能）
|
||||
- **工作量评估**: **20 人日**
|
||||
- **优先级**: **高**
|
||||
|
||||
---
|
||||
|
||||
## 🎯 功能描述
|
||||
|
||||
该接口是数据资产盘点系统的核心输出功能，使用大模型生成完整的工作总结报告，包含四个章节：
|
||||
|
||||
1. **章节一**: 企业数字化情况简介（企业背景、信息化建设现状、业务流与数据流）
|
||||
2. **章节二**: 数据资源统计（数据总量、存储分布、数据来源结构）
|
||||
3. **章节三**: 数据资产情况盘点（资产构成、应用场景、合规风险提示）
|
||||
4. **章节四**: 专家建议与下一步计划（合规整改、技术演进、价值深化）
|
||||
|
||||
支持功能：
|
||||
- **分阶段生成**: 支持分阶段生成，提高质量和可控性
|
||||
- **内容验证**: 验证统计数据逻辑正确性（如百分比总和为100%）
|
||||
- **合规性检查**: 验证合规风险分析的完整性
|
||||
- **格式化输出**: 返回结构化的 JSON 格式
|
||||
|
||||
---
|
||||
|
||||
## 📥 请求格式
|
||||
|
||||
```json
|
||||
{
|
||||
"project_id": "project_001",
|
||||
"project_info": {
|
||||
"project_name": "数据资产盘点项目",
|
||||
"industry": "retail-fresh",
|
||||
"company_name": "某连锁生鲜零售企业"
|
||||
},
|
||||
"inventory_data": {
|
||||
"total_tables": 14582,
|
||||
"total_fields": 245000,
|
||||
"total_data_volume": "58 PB",
|
||||
"storage_distribution": [...],
|
||||
"data_source_structure": {
|
||||
"structured": 35,
|
||||
"semi_structured": 65
|
||||
},
|
||||
"identified_assets": [...]
|
||||
},
|
||||
"context_data": {
|
||||
"enterprise_background": "...",
|
||||
"informatization_status": "...",
|
||||
"business_flow": "..."
|
||||
},
|
||||
"value_data": {
|
||||
"selected_scenarios": [...]
|
||||
},
|
||||
"options": {
|
||||
"language": "zh-CN",
|
||||
"detail_level": "standard",
|
||||
"generation_mode": "full | staged"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📤 响应格式
|
||||
|
||||
详见《数据资产盘点报告-大模型接口设计文档.md》中的详细响应格式定义。
|
||||
|
||||
---
|
||||
|
||||
## 💻 实现要点
|
||||
|
||||
### 1. 分阶段生成策略（推荐）
|
||||
|
||||
```python
|
||||
# 阶段一：生成章节一和章节二
|
||||
stage1_result = await generate_sections_1_2(inventory_data, context_data)
|
||||
|
||||
# 阶段二：生成章节三（重点合规风险分析）
|
||||
stage2_result = await generate_section_3(identified_assets, stage1_result)
|
||||
|
||||
# 阶段三：生成章节四（基于前面章节的分析结果）
|
||||
stage3_result = await generate_section_4(stage1_result, stage2_result, value_data)
|
||||
```
|
||||
|
||||
### 2. 数据验证
|
||||
|
||||
```python
|
||||
def validate_report_data(report_data: dict) -> bool:
|
||||
"""验证报告数据"""
|
||||
# 验证百分比总和为100%
|
||||
section2 = report_data.get("section2", {})
|
||||
structured = section2.get("data_source_structure", {}).get("structured", {}).get("percentage", 0)
|
||||
semi_structured = section2.get("data_source_structure", {}).get("semi_structured", {}).get("percentage", 0)
|
||||
|
||||
if structured + semi_structured != 100:
|
||||
raise ValueError("数据来源结构百分比总和必须为100%")
|
||||
|
||||
# 验证合规风险分析完整性
|
||||
section3 = report_data.get("section3", {})
|
||||
assets = section3.get("assets", [])
|
||||
|
||||
for asset in assets:
|
||||
if not asset.get("compliance_risks", {}).get("warnings"):
|
||||
logger.warning(f"资产 {asset.get('title')} 缺少合规风险分析")
|
||||
|
||||
return True
|
||||
```
|
||||
|
||||
### 3. 提示词模板
|
||||
|
||||
详见《数据资产盘点报告-大模型接口设计文档.md》中的提示词工程设计方案。
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ 注意事项
|
||||
|
||||
1. **长文本生成**: 需要使用支持长文本的模型（GPT-4 / 通义千问 Max）
|
||||
2. **Token 消耗**: 报告生成会消耗大量 Token，需要优化提示词
|
||||
3. **数据准确性**: 统计数据必须准确，基于输入数据
|
||||
4. **合规性**: 合规风险分析必须符合 PIPL、数据安全法等法规
|
||||
5. **建议可操作性**: 专家建议必须具体、可执行
|
||||
|
||||
---
|
||||
|
||||
## 📝 开发检查清单
|
||||
|
||||
- [ ] 大模型集成（GPT-4 / 通义千问 Max）
|
||||
- [ ] 分阶段生成策略实现
|
||||
- [ ] 四个章节的提示词工程
|
||||
- [ ] 数据验证引擎
|
||||
- [ ] 合规性验证
|
||||
- [ ] 错误处理和重试机制
|
||||
- [ ] 缓存机制（可选）
|
||||
- [ ] 日志记录
|
||||
- [ ] 单元测试
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [数据资产盘点报告-大模型接口设计文档](../数据资产盘点报告-大模型接口设计文档.md) - **详细设计文档**
|
||||
- [接口清单表格](../Python接口清单表格.md)
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md)
|
||||
150
docs/api/README.md
Normal file
150
docs/api/README.md
Normal file
@ -0,0 +1,150 @@
|
||||
# API 接口开发文档索引
|
||||
|
||||
## 📋 文档说明
|
||||
|
||||
本目录包含数据资源盘点系统中所有需要 Python 开发的接口的详细开发说明文档。每个接口都有独立的文档，包含完整的开发指导信息。
|
||||
|
||||
---
|
||||
|
||||
## 📚 接口文档列表
|
||||
|
||||
### 模块一：数据盘点智能分析服务
|
||||
|
||||
| 序号 | 接口名称 | 文档路径 | 是否大模型 | 优先级 | 工作量 |
|
||||
|------|---------|---------|-----------|--------|--------|
|
||||
| 1.1 | [文档解析接口](./01-parse-document.md) | `01-parse-document.md` | ❌ | 中 | 5 人日 |
|
||||
| 1.2 | [SQL 结果解析接口](./02-parse-sql-result.md) | `02-parse-sql-result.md` | ❌ | 低 | 2 人日 |
|
||||
| 1.3 | [业务表解析接口](./03-parse-business-tables.md) | `03-parse-business-tables.md` | ❌ | 中 | 3 人日 |
|
||||
| 1.4 | [数据资产智能识别接口 ⭐⭐⭐](./04-ai-analyze.md) | `04-ai-analyze.md` | ✅ **是** | **高** | **15 人日** |
|
||||
|
||||
### 模块二：场景挖掘智能推荐服务
|
||||
|
||||
| 序号 | 接口名称 | 文档路径 | 是否大模型 | 优先级 | 工作量 |
|
||||
|------|---------|---------|-----------|--------|--------|
|
||||
| 2.1 | [潜在场景推荐接口 ⭐⭐](./05-scenario-recommendation.md) | `05-scenario-recommendation.md` | ✅ **是** | **高** | **12 人日** |
|
||||
| 2.2 | [存量场景优化建议接口](./06-scenario-optimization.md) | `06-scenario-optimization.md` | ✅ **是** | 中 | 8 人日 |
|
||||
|
||||
### 模块三：数据资产盘点报告生成服务
|
||||
|
||||
| 序号 | 接口名称 | 文档路径 | 是否大模型 | 优先级 | 工作量 |
|
||||
|------|---------|---------|-----------|--------|--------|
|
||||
| 3.1 | [完整报告生成接口 ⭐⭐⭐](./07-generate-report.md) | `07-generate-report.md` | ✅ **是** | **高** | **20 人日** |
|
||||
|
||||
---
|
||||
|
||||
## 📊 文档内容结构
|
||||
|
||||
每个接口文档包含以下内容：
|
||||
|
||||
1. **接口基本信息**
|
||||
- 接口路径、请求方法
|
||||
- 功能描述、涉及页面
|
||||
- 工作量评估、优先级
|
||||
|
||||
2. **功能描述**
|
||||
- 详细的功能说明
|
||||
- 适用场景
|
||||
|
||||
3. **技术实现方案**
|
||||
- 技术栈推荐
|
||||
- 实现思路
|
||||
- 架构设计
|
||||
|
||||
4. **请求/响应格式**
|
||||
- 详细的请求参数说明
|
||||
- 响应格式定义
|
||||
- 字段说明
|
||||
|
||||
5. **代码实现示例**
|
||||
- FastAPI 实现代码
|
||||
- 关键逻辑示例
|
||||
- 最佳实践
|
||||
|
||||
6. **测试用例**
|
||||
- 单元测试示例
|
||||
- 集成测试建议
|
||||
|
||||
7. **注意事项**
|
||||
- 常见问题和解决方案
|
||||
- 性能优化建议
|
||||
- 安全注意事项
|
||||
|
||||
8. **开发检查清单**
|
||||
- 开发任务清单
|
||||
- 验收标准
|
||||
|
||||
---
|
||||
|
||||
## 🎯 快速导航
|
||||
|
||||
### 按优先级排序
|
||||
|
||||
**高优先级（核心功能）**:
|
||||
1. [数据资产智能识别接口](./04-ai-analyze.md) - 15 人日 ⭐⭐⭐
|
||||
2. [完整报告生成接口](./07-generate-report.md) - 20 人日 ⭐⭐⭐
|
||||
3. [潜在场景推荐接口](./05-scenario-recommendation.md) - 12 人日 ⭐⭐
|
||||
|
||||
**中优先级**:
|
||||
4. [文档解析接口](./01-parse-document.md) - 5 人日
|
||||
5. [业务表解析接口](./03-parse-business-tables.md) - 3 人日
|
||||
6. [存量场景优化建议接口](./06-scenario-optimization.md) - 8 人日
|
||||
|
||||
**低优先级**:
|
||||
7. [SQL 结果解析接口](./02-parse-sql-result.md) - 2 人日
|
||||
|
||||
### 按功能分类
|
||||
|
||||
**大模型接口（4个）**:
|
||||
- [数据资产智能识别接口](./04-ai-analyze.md)
|
||||
- [潜在场景推荐接口](./05-scenario-recommendation.md)
|
||||
- [存量场景优化建议接口](./06-scenario-optimization.md)
|
||||
- [完整报告生成接口](./07-generate-report.md)
|
||||
|
||||
**数据解析接口（3个）**:
|
||||
- [文档解析接口](./01-parse-document.md)
|
||||
- [SQL 结果解析接口](./02-parse-sql-result.md)
|
||||
- [业务表解析接口](./03-parse-business-tables.md)
|
||||
|
||||
---
|
||||
|
||||
## 📈 开发建议
|
||||
|
||||
### 第一阶段（MVP 版本）- 4 周
|
||||
|
||||
**推荐顺序**:
|
||||
1. [数据资产智能识别接口](./04-ai-analyze.md) - 核心功能
|
||||
2. [完整报告生成接口](./07-generate-report.md) - 核心功能（简化版）
|
||||
3. [文档解析接口](./01-parse-document.md) - 基础功能
|
||||
|
||||
### 第二阶段（完善版本）- 3 周
|
||||
|
||||
**推荐顺序**:
|
||||
1. [潜在场景推荐接口](./05-scenario-recommendation.md)
|
||||
2. [存量场景优化建议接口](./06-scenario-optimization.md)
|
||||
3. [业务表解析接口](./03-parse-business-tables.md)
|
||||
4. [SQL 结果解析接口](./02-parse-sql-result.md)
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [接口清单表格](../Python接口清单表格.md) - 接口总览和统计
|
||||
- [Python技术人员工作量文档](../Python技术人员工作量文档.md) - 详细工作量评估
|
||||
- [数据资产盘点报告-大模型接口设计文档](../数据资产盘点报告-大模型接口设计文档.md) - 报告生成接口详细设计
|
||||
|
||||
---
|
||||
|
||||
## 📞 联系方式
|
||||
|
||||
如有接口开发相关问题，请联系：
|
||||
- **Python 技术负责人**: [待填写]
|
||||
- **大模型技术顾问**: [待填写]
|
||||
- **接口对接负责人**: [待填写]
|
||||
|
||||
---
|
||||
|
||||
## 📅 更新记录
|
||||
|
||||
| 版本 | 日期 | 更新内容 | 作者 |
|
||||
|------|------|---------|------|
|
||||
| v1.0 | 2025-01-XX | 初始版本，包含 7 个接口的完整开发说明文档 | AI Assistant |
|
||||
1020
docs/api/数据资产盘点报告-大模型接口设计文档.md
Normal file
1020
docs/api/数据资产盘点报告-大模型接口设计文档.md
Normal file
File diff suppressed because it is too large
Load Diff
@ -65,7 +65,7 @@ export const scenarioData: Scenario[] = [
|
||||
id: 1,
|
||||
name: '冷链物流路径优化',
|
||||
type: '降本增效',
|
||||
impact: 'High',
|
||||
recommendationIndex: 5,
|
||||
desc: '利用车辆轨迹与订单位置数据，优化配送路线，降低燃油成本。',
|
||||
dependencies: ['订单流水记录表', '门店测绘地理信息'],
|
||||
selected: true
|
||||
@ -74,7 +74,7 @@ export const scenarioData: Scenario[] = [
|
||||
id: 2,
|
||||
name: '精准会员营销',
|
||||
type: '营销增长',
|
||||
impact: 'High',
|
||||
recommendationIndex: 5,
|
||||
desc: '基于用户画像与历史交易行为，实现千人千面的优惠券发放。',
|
||||
dependencies: ['会员基础信息表', '订单流水记录表'],
|
||||
selected: true
|
||||
@ -83,7 +83,7 @@ export const scenarioData: Scenario[] = [
|
||||
id: 3,
|
||||
name: '供应链金融征信',
|
||||
type: '金融服务',
|
||||
impact: 'Medium',
|
||||
recommendationIndex: 3,
|
||||
desc: '将采购与库存数据作为增信依据，为上游供应商提供融资服务。',
|
||||
dependencies: ['库存变动表(未识别)', '采购订单表(未识别)'],
|
||||
selected: false
|
||||
@ -92,7 +92,7 @@ export const scenarioData: Scenario[] = [
|
||||
id: 4,
|
||||
name: '门店选址辅助模型',
|
||||
type: '决策支持',
|
||||
impact: 'Medium',
|
||||
recommendationIndex: 3,
|
||||
desc: '结合外部人口热力图与内部门店业绩，辅助新店选址决策。',
|
||||
dependencies: ['门店测绘地理信息', '外部人口数据'],
|
||||
selected: false
|
||||
|
||||
@ -4,8 +4,7 @@
|
||||
isPresentationMode ? 'p-0' : 'p-6'
|
||||
]">
|
||||
<!-- Project Header -->
|
||||
<div v-if="!isPresentationMode" class="mb-6 flex items-center justify-between">
|
||||
<div>
|
||||
<div v-if="!isPresentationMode" class="mb-6">
|
||||
<!-- Back to Project List Navigation -->
|
||||
<div
|
||||
class="flex items-center text-sm text-slate-500 mb-1 cursor-pointer hover:text-blue-600 transition-colors"
|
||||
@ -15,21 +14,6 @@
|
||||
<span>返回项目列表</span>
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex items-center space-x-4">
|
||||
<div class="flex -space-x-2">
|
||||
<div class="w-8 h-8 rounded-full bg-blue-100 border-2 border-white flex items-center justify-center text-xs font-bold text-blue-800" title="Project Manager">PM</div>
|
||||
<div class="w-8 h-8 rounded-full bg-purple-100 border-2 border-white flex items-center justify-center text-xs font-bold text-purple-800" title="Data Analyst">DA</div>
|
||||
<div class="w-8 h-8 rounded-full bg-gray-100 border-2 border-white flex items-center justify-center text-xs text-gray-500">+2</div>
|
||||
</div>
|
||||
<div class="h-6 w-px bg-slate-300"></div>
|
||||
<button
|
||||
@click="() => setIsPresentationMode(true)"
|
||||
class="flex items-center px-4 py-2 bg-slate-900 text-white rounded-md text-sm hover:bg-slate-700 shadow-lg transition-all"
|
||||
>
|
||||
<Sparkles :size="16" class="mr-2" /> 演示模式
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Stepper -->
|
||||
<div :class="[
|
||||
@ -93,15 +77,6 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 演示模式退出按钮 -->
|
||||
<button
|
||||
v-if="isPresentationMode"
|
||||
@click="setIsPresentationMode(false)"
|
||||
class="fixed top-4 right-4 z-50 bg-white/90 backdrop-blur text-slate-800 px-4 py-2 rounded-full shadow-lg font-medium text-sm flex items-center hover:bg-white"
|
||||
>
|
||||
<EyeOff :size="16" class="mr-2"/> 退出演示
|
||||
</button>
|
||||
|
||||
<!-- Main Workspace Area -->
|
||||
<div class="flex-1 bg-white border border-slate-200 rounded-lg shadow-sm overflow-hidden flex flex-col">
|
||||
<InventoryStep
|
||||
@ -114,6 +89,8 @@
|
||||
<ContextStep
|
||||
v-if="currentStep === 'context'"
|
||||
:set-current-step="handleStepClick"
|
||||
:existing-scenarios="existingScenarios"
|
||||
@update:scenarios="handleUpdateScenarios"
|
||||
/>
|
||||
|
||||
<ValueStep
|
||||
@ -122,6 +99,9 @@
|
||||
:selected-scenarios="selectedScenarios"
|
||||
:all-scenarios="initialScenarioData"
|
||||
:toggle-scenario-selection="toggleScenarioSelection"
|
||||
:existing-scenarios="existingScenarios"
|
||||
:selected-existing-scenarios="selectedExistingScenarios"
|
||||
:toggle-existing-scenario-selection="toggleExistingScenarioSelection"
|
||||
/>
|
||||
|
||||
<DeliveryStep
|
||||
@ -135,7 +115,7 @@
|
||||
<script setup lang="ts">
|
||||
import { ref, computed, watch } from 'vue';
|
||||
import { useRouter, useRoute } from 'vue-router';
|
||||
import { ArrowLeft, Sparkles, EyeOff, CheckCircle2 } from 'lucide-vue-next';
|
||||
import { ArrowLeft, CheckCircle2 } from 'lucide-vue-next';
|
||||
import InventoryStep from './engagement/InventoryStep.vue';
|
||||
import ContextStep from './engagement/ContextStep.vue';
|
||||
import ValueStep from './engagement/ValueStep.vue';
|
||||
@ -162,6 +142,31 @@ const selectedScenarios = ref<Scenario[]>(
|
||||
initialScenarioData.filter(s => s.selected).map(s => ({ ...s }))
|
||||
);
|
||||
|
||||
// 存储从ContextStep传来的已有场景数据
|
||||
interface ExistingScenario {
|
||||
name: string;
|
||||
description: string;
|
||||
image?: string;
|
||||
}
|
||||
|
||||
const existingScenarios = ref<ExistingScenario[]>([]);
|
||||
const selectedExistingScenarios = ref<number[]>([]); // 存储已选择的已有场景索引
|
||||
|
||||
const handleUpdateScenarios = (scenarios: ExistingScenario[]) => {
|
||||
existingScenarios.value = scenarios;
|
||||
// 当场景数据更新时，重置选择状态
|
||||
selectedExistingScenarios.value = [];
|
||||
};
|
||||
|
||||
const toggleExistingScenarioSelection = (index: number) => {
|
||||
const currentIndex = selectedExistingScenarios.value.indexOf(index);
|
||||
if (currentIndex > -1) {
|
||||
selectedExistingScenarios.value.splice(currentIndex, 1);
|
||||
} else {
|
||||
selectedExistingScenarios.value.push(index);
|
||||
}
|
||||
};
|
||||
|
||||
// Set inventory mode function
|
||||
const setInventoryMode = (mode: InventoryMode) => {
|
||||
inventoryMode.value = mode;
|
||||
|
||||
@ -182,6 +182,18 @@
|
||||
class="flex-1 p-1.5 border border-slate-200 rounded text-xs focus:ring-2 focus:ring-blue-500 focus:border-blue-500 outline-none"
|
||||
placeholder="简述"
|
||||
/>
|
||||
<button
|
||||
@click="handleScenarioImageUpload(index)"
|
||||
:class="[
|
||||
'p-1.5 rounded transition-colors flex-shrink-0',
|
||||
scenario.image
|
||||
? 'text-green-600 hover:text-green-800 hover:bg-green-50'
|
||||
: 'text-blue-600 hover:text-blue-800 hover:bg-blue-50'
|
||||
]"
|
||||
:title="scenario.image ? '重新上传截图' : '上传截图'"
|
||||
>
|
||||
<ImageIcon :size="14" :class="{ 'opacity-75': scenario.image }"/>
|
||||
</button>
|
||||
<button
|
||||
@click="removeScenario(index)"
|
||||
class="p-1 text-slate-400 hover:text-red-500 transition-colors flex-shrink-0"
|
||||
@ -190,15 +202,6 @@
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div
|
||||
@click="handleImageUpload"
|
||||
class="mt-2 border-2 border-dashed border-slate-300 rounded p-2 text-center cursor-pointer hover:bg-white transition-colors"
|
||||
>
|
||||
<p class="text-xs text-slate-500 flex items-center justify-center">
|
||||
<ImageIcon :size="12" class="mr-1"/> 上传现有报表/大屏截图
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -215,7 +218,7 @@
|
||||
</template>
|
||||
|
||||
<script setup lang="ts">
|
||||
import { ref, watch } from 'vue';
|
||||
import { ref, watch, onMounted } from 'vue';
|
||||
import {
|
||||
Server,
|
||||
ClipboardList,
|
||||
@ -229,11 +232,24 @@ import { Step } from '@/types';
|
||||
import { useToastStore } from '@/stores/toast';
|
||||
import IndustryTree, { type IndustryNode } from '@/components/IndustryTree.vue';
|
||||
|
||||
interface Props {
|
||||
setCurrentStep: (step: Step) => void;
|
||||
interface Scenario {
|
||||
name: string;
|
||||
description: string;
|
||||
image?: string; // 上传的截图 URL 或 base64
|
||||
}
|
||||
|
||||
const props = defineProps<Props>();
|
||||
interface Props {
|
||||
setCurrentStep: (step: Step) => void;
|
||||
existingScenarios?: Scenario[]; // 从父组件传入的已有场景（用于恢复数据）
|
||||
}
|
||||
|
||||
const props = withDefaults(defineProps<Props>(), {
|
||||
existingScenarios: () => []
|
||||
});
|
||||
|
||||
const emit = defineEmits<{
|
||||
(e: 'update:scenarios', scenarios: Scenario[]): void;
|
||||
}>();
|
||||
const toast = useToastStore();
|
||||
|
||||
// 必填项
|
||||
@ -384,15 +400,26 @@ const storageOptions: StorageOption[] = [
|
||||
}
|
||||
];
|
||||
|
||||
interface Scenario {
|
||||
name: string;
|
||||
description: string;
|
||||
}
|
||||
|
||||
const scenarios = ref<Scenario[]>([
|
||||
const scenarios = ref<Scenario[]>(
|
||||
props.existingScenarios.length > 0
|
||||
? [...props.existingScenarios]
|
||||
: [
|
||||
{ name: '月度销售经营报表', description: '统计各区域门店的月度GMV，维度单一' },
|
||||
{ name: '物流配送监控大屏', description: '实时展示车辆位置，但缺乏预警功能' }
|
||||
]);
|
||||
]
|
||||
);
|
||||
|
||||
// 监听场景变化，同步到父组件
|
||||
watch(scenarios, (newScenarios) => {
|
||||
emit('update:scenarios', [...newScenarios]);
|
||||
}, { deep: true });
|
||||
|
||||
// 组件挂载时，如果有传入的已有场景，同步一次
|
||||
onMounted(() => {
|
||||
if (props.existingScenarios.length > 0) {
|
||||
emit('update:scenarios', [...props.existingScenarios]);
|
||||
}
|
||||
});
|
||||
|
||||
const addScenario = () => {
|
||||
scenarios.value.push({ name: '', description: '' });
|
||||
@ -402,9 +429,65 @@ const removeScenario = (index: number) => {
|
||||
scenarios.value.splice(index, 1);
|
||||
};
|
||||
|
||||
const handleImageUpload = () => {
|
||||
// TODO: 实现图片上传功能
|
||||
toast.info('图片上传功能待实现');
|
||||
const handleScenarioImageUpload = (index: number) => {
|
||||
// 创建隐藏的文件输入元素
|
||||
const input = document.createElement('input');
|
||||
input.type = 'file';
|
||||
input.accept = 'image/*';
|
||||
input.style.display = 'none';
|
||||
|
||||
const cleanup = () => {
|
||||
if (input.parentNode) {
|
||||
document.body.removeChild(input);
|
||||
}
|
||||
};
|
||||
|
||||
input.onchange = (e: Event) => {
|
||||
const target = e.target as HTMLInputElement;
|
||||
const file = target.files?.[0];
|
||||
|
||||
if (file) {
|
||||
// 验证文件类型
|
||||
if (!file.type.startsWith('image/')) {
|
||||
toast.error('请选择图片文件');
|
||||
cleanup();
|
||||
return;
|
||||
}
|
||||
|
||||
// 验证文件大小（例如：最大 5MB）
|
||||
const maxSize = 5 * 1024 * 1024; // 5MB
|
||||
if (file.size > maxSize) {
|
||||
toast.error('图片大小不能超过 5MB');
|
||||
cleanup();
|
||||
return;
|
||||
}
|
||||
|
||||
// 读取文件为 base64 或上传到服务器
|
||||
const reader = new FileReader();
|
||||
reader.onload = (event) => {
|
||||
const result = event.target?.result as string;
|
||||
if (scenarios.value[index]) {
|
||||
scenarios.value[index].image = result;
|
||||
toast.success('截图上传成功');
|
||||
}
|
||||
cleanup();
|
||||
};
|
||||
reader.onerror = () => {
|
||||
toast.error('图片读取失败，请重试');
|
||||
cleanup();
|
||||
};
|
||||
reader.readAsDataURL(file);
|
||||
} else {
|
||||
cleanup();
|
||||
}
|
||||
};
|
||||
|
||||
input.oncancel = () => {
|
||||
cleanup();
|
||||
};
|
||||
|
||||
document.body.appendChild(input);
|
||||
input.click();
|
||||
};
|
||||
|
||||
const handleUploadPlatformList = () => {
|
||||
@ -436,6 +519,9 @@ const handleSubmit = () => {
|
||||
return;
|
||||
}
|
||||
|
||||
// 提交前同步场景数据到父组件
|
||||
emit('update:scenarios', [...scenarios.value]);
|
||||
|
||||
errors.value = {};
|
||||
toast.success('背景调研信息已保存，开始生成场景挖掘建议');
|
||||
props.setCurrentStep('value');
|
||||
|
||||
414
src/pages/engagement/DataAssetDetail.vue
Normal file
414
src/pages/engagement/DataAssetDetail.vue
Normal file
@ -0,0 +1,414 @@
|
||||
<template>
|
||||
<div class="data-asset-detail min-h-screen bg-gray-50">
|
||||
<!-- 页面头部 -->
|
||||
<div class="bg-white border-b border-app-border shadow-sm sticky top-0 z-50">
|
||||
<div class="max-w-7xl mx-auto px-6 py-4">
|
||||
<div class="flex items-center justify-between">
|
||||
<div class="flex items-center gap-4">
|
||||
<button
|
||||
@click="goBack"
|
||||
class="text-app-text-secondary hover:text-app-primary transition-colors"
|
||||
>
|
||||
<ArrowLeft :size="20" />
|
||||
</button>
|
||||
<div>
|
||||
<h1 class="text-2xl font-bold text-app-text">{{ assetTitle }}</h1>
|
||||
<p class="text-sm text-app-text-secondary mt-1">{{ assetSubtitle }}</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex gap-3">
|
||||
<button
|
||||
class="px-4 py-2 border border-app-border rounded-lg text-app-text hover:bg-app-hover transition-colors"
|
||||
@click="handleExport"
|
||||
>
|
||||
导出报告
|
||||
</button>
|
||||
<button
|
||||
class="px-4 py-2 bg-app-primary text-white rounded-lg hover:bg-blue-700 transition-colors"
|
||||
@click="handlePrint"
|
||||
>
|
||||
打印
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 内容区域 -->
|
||||
<div class="max-w-7xl mx-auto px-6 py-8">
|
||||
<!-- 1. 数据资产简介 -->
|
||||
<section class="mb-8">
|
||||
<div class="bg-white rounded-lg border border-app-border shadow-sm p-6">
|
||||
<div class="flex items-center gap-3 mb-6">
|
||||
<div class="w-8 h-8 rounded-lg bg-app-primary text-white flex items-center justify-center font-bold">
|
||||
1
|
||||
</div>
|
||||
<h2 class="text-xl font-bold text-app-text">数据资产简介</h2>
|
||||
</div>
|
||||
<div class="prose max-w-none">
|
||||
<div class="text-app-text-secondary text-sm leading-relaxed" v-html="assetIntroduction"></div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- 2. 数据资源目录 -->
|
||||
<section class="mb-8">
|
||||
<div class="bg-white rounded-lg border border-app-border shadow-sm p-6">
|
||||
<div class="flex items-center justify-between mb-6">
|
||||
<div class="flex items-center gap-3">
|
||||
<div class="w-8 h-8 rounded-lg bg-app-primary text-white flex items-center justify-center font-bold">
|
||||
2
|
||||
</div>
|
||||
<h2 class="text-xl font-bold text-app-text">数据资源目录</h2>
|
||||
</div>
|
||||
<button
|
||||
class="px-4 py-2 bg-app-primary text-white rounded-lg hover:bg-blue-700 transition-colors text-sm"
|
||||
@click="handleDownloadAllTables"
|
||||
>
|
||||
全部下载
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div class="space-y-3">
|
||||
<div
|
||||
v-for="(table, index) in dataTables"
|
||||
:key="index"
|
||||
class="border border-app-border rounded-lg p-4 hover:border-app-primary transition-colors"
|
||||
>
|
||||
<div class="flex items-center justify-between">
|
||||
<div class="flex items-center gap-3 flex-1">
|
||||
<div class="w-10 h-10 rounded-lg bg-blue-50 text-blue-600 flex items-center justify-center">
|
||||
<Database :size="20" />
|
||||
</div>
|
||||
<div class="flex-1">
|
||||
<h3 class="font-semibold text-app-text">{{ table.name }}</h3>
|
||||
<p class="text-xs text-app-text-secondary mt-1">{{ table.description }}</p>
|
||||
<div class="flex gap-2 mt-2">
|
||||
<span class="text-xs px-2 py-1 bg-gray-100 text-gray-600 rounded">
|
||||
{{ table.rows }} 行
|
||||
</span>
|
||||
<span class="text-xs px-2 py-1 bg-gray-100 text-gray-600 rounded">
|
||||
{{ table.columns }} 列
|
||||
</span>
|
||||
<span class="text-xs px-2 py-1 bg-gray-100 text-gray-600 rounded">
|
||||
{{ table.size }}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex gap-2">
|
||||
<button
|
||||
class="px-3 py-1.5 border border-app-border rounded-lg text-app-text hover:bg-app-hover transition-colors text-sm"
|
||||
@click="viewTable(table)"
|
||||
>
|
||||
<Eye :size="16" class="inline-block mr-1" />
|
||||
在线浏览
|
||||
</button>
|
||||
<button
|
||||
class="px-3 py-1.5 bg-app-primary text-white rounded-lg hover:bg-blue-700 transition-colors text-sm"
|
||||
@click="downloadTable(table)"
|
||||
>
|
||||
<Download :size="16" class="inline-block mr-1" />
|
||||
下载
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- 3. 场景优化建议 -->
|
||||
<section class="mb-8">
|
||||
<div class="bg-white rounded-lg border border-app-border shadow-sm p-6">
|
||||
<div class="flex items-center gap-3 mb-6">
|
||||
<div class="w-8 h-8 rounded-lg bg-app-primary text-white flex items-center justify-center font-bold">
|
||||
3
|
||||
</div>
|
||||
<h2 class="text-xl font-bold text-app-text">场景优化建议</h2>
|
||||
</div>
|
||||
<div class="prose max-w-none">
|
||||
<div class="text-app-text-secondary text-sm leading-relaxed space-y-4">
|
||||
<div
|
||||
v-for="(suggestion, index) in optimizationSuggestions"
|
||||
:key="index"
|
||||
class="border-l-4 border-amber-500 bg-amber-50 p-4 rounded-r-lg"
|
||||
>
|
||||
<h4 class="font-semibold text-app-text mb-2">{{ suggestion.title }}</h4>
|
||||
<p class="text-sm leading-6">{{ suggestion.content }}</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- 4. 潜在场景评估 -->
|
||||
<section class="mb-8">
|
||||
<div class="bg-white rounded-lg border border-app-border shadow-sm p-6">
|
||||
<div class="flex items-center gap-3 mb-6">
|
||||
<div class="w-8 h-8 rounded-lg bg-app-primary text-white flex items-center justify-center font-bold">
|
||||
4
|
||||
</div>
|
||||
<h2 class="text-xl font-bold text-app-text">潜在场景评估</h2>
|
||||
</div>
|
||||
<div class="prose max-w-none">
|
||||
<div class="space-y-4">
|
||||
<div
|
||||
v-for="(scenario, index) in potentialScenarios"
|
||||
:key="index"
|
||||
class="border border-app-border rounded-lg p-5 hover:shadow-md transition-shadow"
|
||||
>
|
||||
<div class="flex items-start justify-between mb-3">
|
||||
<h4 class="font-semibold text-app-text text-lg">{{ scenario.name }}</h4>
|
||||
<div class="flex items-center gap-1 px-3 py-1 rounded bg-blue-50">
|
||||
<span class="text-xs font-bold text-blue-700">推荐指数</span>
|
||||
<div class="flex items-center gap-0.5">
|
||||
<span
|
||||
v-for="i in 5"
|
||||
:key="i"
|
||||
:class="[
|
||||
'text-xs',
|
||||
i <= scenario.recommendationIndex ? 'text-blue-600' : 'text-gray-300'
|
||||
]"
|
||||
>
|
||||
★
|
||||
</span>
|
||||
</div>
|
||||
<span class="text-xs font-bold text-blue-700 ml-1">{{ scenario.recommendationIndex }}/5</span>
|
||||
</div>
|
||||
</div>
|
||||
<p class="text-sm text-app-text-secondary leading-6 mb-3">{{ scenario.description }}</p>
|
||||
<div class="flex gap-2 flex-wrap">
|
||||
<span
|
||||
v-for="(tag, tagIndex) in scenario.tags"
|
||||
:key="tagIndex"
|
||||
class="text-xs px-2 py-1 bg-blue-50 text-blue-700 rounded border border-blue-200"
|
||||
>
|
||||
{{ tag }}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</div>
|
||||
|
||||
<!-- 表格浏览弹窗 -->
|
||||
<Teleport to="body">
|
||||
<div
|
||||
v-if="viewingTable"
|
||||
class="fixed inset-0 bg-black/50 z-50 flex items-center justify-center p-4"
|
||||
@click.self="closeTableView"
|
||||
>
|
||||
<div class="bg-white rounded-lg shadow-xl max-w-6xl w-full max-h-[90vh] flex flex-col">
|
||||
<div class="flex items-center justify-between p-4 border-b border-app-border">
|
||||
<h3 class="text-lg font-bold text-app-text">{{ viewingTable?.name }}</h3>
|
||||
<button
|
||||
@click="closeTableView"
|
||||
class="text-app-text-secondary hover:text-app-text"
|
||||
>
|
||||
<X :size="20" />
|
||||
</button>
|
||||
</div>
|
||||
<div class="flex-1 overflow-auto p-4">
|
||||
<div class="bg-gray-50 rounded-lg p-4 text-center text-app-text-secondary">
|
||||
<p class="mb-4">表格预览功能开发中...</p>
|
||||
<p class="text-sm">表格：{{ viewingTable?.name }}</p>
|
||||
<p class="text-sm">行数：{{ viewingTable?.rows }} | 列数：{{ viewingTable?.columns }}</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</Teleport>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
<script setup lang="ts">
|
||||
import { ref, computed } from 'vue'
|
||||
import { useRoute, useRouter } from 'vue-router'
|
||||
import { ArrowLeft, Database, Eye, Download, X } from 'lucide-vue-next'
|
||||
import { useToastStore } from '@/stores/toast'
|
||||
|
||||
const route = useRoute()
|
||||
const router = useRouter()
|
||||
const toast = useToastStore()
|
||||
|
||||
const assetType = computed(() => route.params.type as string)
|
||||
const viewingTable = ref<any>(null)
|
||||
|
||||
// 根据资产类型设置内容
|
||||
const assetTitle = computed(() => {
|
||||
if (assetType.value === 'customer360') {
|
||||
return '消费者全景画像 (Customer 360)'
|
||||
} else if (assetType.value === 'inventory') {
|
||||
return '供应链动态库存模型 (Global Inventory View)'
|
||||
}
|
||||
return '数据资产详情'
|
||||
})
|
||||
|
||||
const assetSubtitle = computed(() => {
|
||||
if (assetType.value === 'customer360') {
|
||||
return 'Customer 360 Profile - 消费者数据资产'
|
||||
} else if (assetType.value === 'inventory') {
|
||||
return 'Global Inventory View - 供应链数据资产'
|
||||
}
|
||||
return ''
|
||||
})
|
||||
|
||||
const assetIntroduction = computed(() => {
|
||||
if (assetType.value === 'customer360') {
|
||||
return `
|
||||
<p class="mb-4"><strong>资产构成：</strong>核心依赖 <code class="bg-gray-100 px-2 py-1 rounded">Dim_Customer</code>（客户维度表）与 <code class="bg-gray-100 px-2 py-1 rounded">Fact_Sales</code>（销售事实表），并整合了线上电商 ID、线下门店会员卡号及社交媒体账号。</p>
|
||||
<p class="mb-4"><strong>应用场景：</strong>旨在构建 OneID 体系，支持计算客户生命周期价值（CLV），进行精准营销（如针对流失风险自动触发挽留策略），提升复购率。</p>
|
||||
<div class="bg-red-50 border-l-4 border-red-500 p-4 rounded-r-lg mt-4">
|
||||
<p class="text-red-700 font-semibold mb-2">⚠️ 数据合规风险提示</p>
|
||||
<ul class="text-sm space-y-1 ml-4 list-disc">
|
||||
<li>共识别出 <strong>12 项</strong>敏感个人信息（SPI）</li>
|
||||
<li>部分字段未进行脱敏处理，存在内部泄露风险</li>
|
||||
<li>部分场景未取得用户的"单独同意"，违反 PIPL 规定</li>
|
||||
</ul>
|
||||
</div>
|
||||
`
|
||||
} else if (assetType.value === 'inventory') {
|
||||
return `
|
||||
<p class="mb-4"><strong>资产构成：</strong>核心为 <code class="bg-gray-100 px-2 py-1 rounded">Fact_Inventory</code>（库存事实表），采用周期性快照设计，包含 <code class="bg-gray-100 px-2 py-1 rounded">Date_Key</code>、<code class="bg-gray-100 px-2 py-1 rounded">Warehouse_Key</code>、<code class="bg-gray-100 px-2 py-1 rounded">Quantity_On_Hand</code> 等关键字段。</p>
|
||||
<p class="mb-4"><strong>应用场景：</strong>作为资金占用分析和补货策略的核心依据，旨在实现从"单点库存优化"向"多级库存协同"转变，降低库存周转天数。</p>
|
||||
<div class="bg-red-50 border-l-4 border-red-500 p-4 rounded-r-lg mt-4">
|
||||
<p class="text-red-700 font-semibold mb-2">⚠️ 数据合规风险提示</p>
|
||||
<ul class="text-sm space-y-1 ml-4 list-disc">
|
||||
<li>识别出 <strong>3 类</strong>"重要数据"目录</li>
|
||||
<li>存在跨境传输风险，未申报数据出境安全评估</li>
|
||||
<li>当前 T+1 更新机制导致数据滞后</li>
|
||||
</ul>
|
||||
</div>
|
||||
`
|
||||
}
|
||||
return ''
|
||||
})
|
||||
|
||||
const dataTables = computed(() => {
|
||||
if (assetType.value === 'customer360') {
|
||||
return [
|
||||
{ name: 'Dim_Customer', description: '客户维度表', rows: '2,400,000', columns: '45', size: '1.2 GB' },
|
||||
{ name: 'Fact_Sales', description: '销售事实表', rows: '58,600,000', columns: '32', size: '8.5 GB' },
|
||||
{ name: 'Dim_Product', description: '产品维度表', rows: '125,000', columns: '28', size: '156 MB' },
|
||||
{ name: 'Fact_Customer_Behavior', description: '客户行为事实表', rows: '145,800,000', columns: '18', size: '12.3 GB' }
|
||||
]
|
||||
} else if (assetType.value === 'inventory') {
|
||||
return [
|
||||
{ name: 'Fact_Inventory', description: '库存事实表（周期性快照）', rows: '12,850,000', columns: '24', size: '3.8 GB' },
|
||||
{ name: 'Dim_Warehouse', description: '仓库维度表', rows: '1,250', columns: '35', size: '2.1 MB' },
|
||||
{ name: 'Dim_Product', description: '产品维度表', rows: '125,000', columns: '28', size: '156 MB' },
|
||||
{ name: 'Fact_Inventory_Transaction', description: '库存交易事实表', rows: '89,600,000', columns: '20', size: '15.2 GB' }
|
||||
]
|
||||
}
|
||||
return []
|
||||
})
|
||||
|
||||
const optimizationSuggestions = computed(() => {
|
||||
if (assetType.value === 'customer360') {
|
||||
return [
|
||||
{
|
||||
title: '标签体系优化',
|
||||
content: '建议构建多维度标签体系，整合 RFM 模型、行为标签、偏好标签等，提升画像精度。'
|
||||
},
|
||||
{
|
||||
title: '实时性提升',
|
||||
content: '当前客户行为数据存在 T+1 延迟，建议引入流式计算技术，实现近实时的客户画像更新。'
|
||||
}
|
||||
]
|
||||
} else if (assetType.value === 'inventory') {
|
||||
return [
|
||||
{
|
||||
title: '时效性优化',
|
||||
content: '从 T+1 更新机制升级为 T+0 实时更新，引入流式处理技术，满足实时供应链优化需求。'
|
||||
},
|
||||
{
|
||||
title: '多级库存协同',
|
||||
content: '建议基于现有数据构建多级库存协同模型，实现从单点优化向全局优化的转变。'
|
||||
}
|
||||
]
|
||||
}
|
||||
return []
|
||||
})
|
||||
|
||||
const potentialScenarios = computed(() => {
|
||||
if (assetType.value === 'customer360') {
|
||||
return [
|
||||
{
|
||||
name: '精准营销推荐',
|
||||
description: '基于客户画像进行个性化商品推荐，提升转化率和客单价',
|
||||
recommendationIndex: 5,
|
||||
tags: ['精准营销', '个性化推荐', '提升转化率']
|
||||
},
|
||||
{
|
||||
name: '客户流失预警',
|
||||
description: '通过行为分析识别流失风险客户，提前进行挽留策略',
|
||||
recommendationIndex: 5,
|
||||
tags: ['流失预警', '客户挽留', '行为分析']
|
||||
}
|
||||
]
|
||||
} else if (assetType.value === 'inventory') {
|
||||
return [
|
||||
{
|
||||
name: '智能补货策略',
|
||||
description: '基于历史数据和外部因素（天气、促销等）构建智能补货模型',
|
||||
recommendationIndex: 5,
|
||||
tags: ['智能补货', '预测模型', '库存优化']
|
||||
},
|
||||
{
|
||||
name: '多级库存协同',
|
||||
description: '实现中央仓、区域仓、门店的多级库存协同优化',
|
||||
recommendationIndex: 4,
|
||||
tags: ['多级协同', '库存优化', '供应链']
|
||||
}
|
||||
]
|
||||
}
|
||||
return []
|
||||
})
|
||||
|
||||
const goBack = () => {
|
||||
router.back()
|
||||
}
|
||||
|
||||
const viewTable = (table: any) => {
|
||||
viewingTable.value = table
|
||||
}
|
||||
|
||||
const closeTableView = () => {
|
||||
viewingTable.value = null
|
||||
}
|
||||
|
||||
const downloadTable = (table: any) => {
|
||||
toast.success(`正在下载表格：${table.name}`)
|
||||
// TODO: 实现下载功能
|
||||
console.log('Download table:', table)
|
||||
}
|
||||
|
||||
const handleDownloadAllTables = () => {
|
||||
toast.success('正在打包下载所有表格...')
|
||||
// TODO: 实现批量下载功能
|
||||
console.log('Download all tables')
|
||||
}
|
||||
|
||||
const handleExport = () => {
|
||||
toast.info('报告导出功能开发中...')
|
||||
}
|
||||
|
||||
const handlePrint = () => {
|
||||
window.print()
|
||||
}
|
||||
</script>
|
||||
|
||||
<style scoped>
|
||||
.data-asset-detail {
|
||||
min-height: 100vh;
|
||||
}
|
||||
|
||||
@media print {
|
||||
.data-asset-detail .bg-white {
|
||||
break-inside: avoid;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
File diff suppressed because it is too large
Load Diff
@ -4,11 +4,70 @@
|
||||
<!-- Content: New Scenarios -->
|
||||
<div class="w-full p-8 overflow-y-auto">
|
||||
<div class="max-w-7xl mx-auto">
|
||||
<!-- 已有场景清单部分 -->
|
||||
<div class="mb-8">
|
||||
<div class="flex items-center justify-between mb-6">
|
||||
<h3 class="text-xl font-bold text-slate-900 flex items-center">
|
||||
<ClipboardList :size="18" class="mr-2 text-blue-500"/>
|
||||
已有场景清单 (存量)
|
||||
<span class="ml-3 px-3 py-1 bg-blue-100 text-blue-700 text-xs rounded-full">
|
||||
{{ selectedExistingCount }} 已选
|
||||
</span>
|
||||
</h3>
|
||||
</div>
|
||||
<div class="grid grid-cols-3 gap-6 mb-8">
|
||||
<div
|
||||
v-for="(scenario, index) in displayExistingScenarios"
|
||||
:key="`existing-${index}`"
|
||||
@click="toggleExistingScenarioSelection(index)"
|
||||
:class="[
|
||||
'cursor-pointer rounded-xl border-2 p-6 transition-all relative flex flex-col group',
|
||||
isExistingScenarioSelected(index)
|
||||
? 'border-blue-500 bg-white shadow-md'
|
||||
: 'border-slate-200 bg-white hover:border-blue-300 hover:shadow-sm'
|
||||
]"
|
||||
>
|
||||
<CheckCircle2
|
||||
v-if="isExistingScenarioSelected(index)"
|
||||
class="absolute top-4 right-4 text-blue-500"
|
||||
:size="24"
|
||||
fill="currentColor"
|
||||
:class="['text-white']"
|
||||
/>
|
||||
|
||||
<div class="mb-4 pr-8">
|
||||
<div class="flex items-center space-x-2 mb-2">
|
||||
<span class="text-[10px] font-bold uppercase tracking-wider text-slate-500 border border-slate-200 px-1.5 py-0.5 rounded">存量场景</span>
|
||||
</div>
|
||||
<h4 class="text-lg font-bold text-slate-900 leading-tight">{{ scenario.name || '未命名场景' }}</h4>
|
||||
</div>
|
||||
|
||||
<p class="text-sm text-slate-600 mb-4 line-clamp-3 flex-grow">{{ scenario.description || '暂无描述' }}</p>
|
||||
|
||||
<div v-if="scenario.image" class="mb-3 pb-3 border-b border-slate-100">
|
||||
<div class="flex items-center text-xs text-slate-500">
|
||||
<ImageIcon :size="12" class="mr-1"/>
|
||||
<span>已上传截图</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="pt-4 border-t border-slate-100 mt-auto">
|
||||
<div :class="[
|
||||
'w-full py-2 rounded-lg text-sm font-bold text-center transition-colors',
|
||||
isExistingScenarioSelected(index) ? 'bg-blue-50 text-blue-700' : 'bg-slate-100 text-slate-600'
|
||||
]">
|
||||
{{ isExistingScenarioSelected(index) ? '已加入规划' : '加入场景规划' }}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="flex justify-between items-center mb-6">
|
||||
<h3 class="text-xl font-bold text-slate-900 flex items-center">
|
||||
AI 推荐潜在场景清单
|
||||
<span class="ml-3 px-3 py-1 bg-blue-100 text-blue-700 text-xs rounded-full">
|
||||
{{ selectedCount }} 已选
|
||||
{{ selectedAiCount }} 已选
|
||||
</span>
|
||||
</h3>
|
||||
<div class="flex items-center gap-3">
|
||||
@ -56,34 +115,28 @@
|
||||
<div class="mb-4 pr-8">
|
||||
<div class="flex items-center space-x-2 mb-2">
|
||||
<span class="text-[10px] font-bold uppercase tracking-wider text-slate-500 border border-slate-200 px-1.5 py-0.5 rounded">{{ scen.type }}</span>
|
||||
<span :class="[
|
||||
'text-[10px] font-bold uppercase tracking-wider px-1.5 py-0.5 rounded',
|
||||
scen.impact === 'High' ? 'bg-green-100 text-green-700' : 'bg-amber-100 text-amber-700'
|
||||
]">
|
||||
{{ scen.impact }} Impact
|
||||
<div class="flex items-center gap-1 px-2 py-0.5 rounded bg-blue-50">
|
||||
<span class="text-[10px] font-bold text-blue-700">推荐指数</span>
|
||||
<div class="flex items-center gap-0.5">
|
||||
<span
|
||||
v-for="i in 5"
|
||||
:key="i"
|
||||
:class="[
|
||||
'text-xs',
|
||||
i <= scen.recommendationIndex ? 'text-blue-600' : 'text-gray-300'
|
||||
]"
|
||||
>
|
||||
★
|
||||
</span>
|
||||
</div>
|
||||
<span class="text-[10px] font-bold text-blue-700 ml-1">{{ scen.recommendationIndex }}/5</span>
|
||||
</div>
|
||||
</div>
|
||||
<h4 class="text-lg font-bold text-slate-900 leading-tight">{{ scen.name }}</h4>
|
||||
</div>
|
||||
|
||||
<p class="text-sm text-slate-600 mb-4 line-clamp-3 flex-grow">{{ scen.desc }}</p>
|
||||
|
||||
<div class="mb-6 bg-slate-50 rounded-lg p-3 border border-slate-100">
|
||||
<p class="text-xs font-bold text-slate-500 mb-2 flex items-center">
|
||||
<Database :size="12" class="mr-1"/> 核心数据支撑
|
||||
</p>
|
||||
<div class="flex flex-wrap gap-1.5">
|
||||
<span
|
||||
v-for="(dep, i) in scen.dependencies"
|
||||
:key="i"
|
||||
class="text-[10px] bg-white border border-slate-200 px-1.5 py-0.5 rounded text-slate-600 flex items-center"
|
||||
>
|
||||
<LinkIcon :size="8" class="mr-1 text-blue-400"/>
|
||||
{{ dep }}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="pt-4 border-t border-slate-100 mt-auto">
|
||||
<div :class="[
|
||||
'w-full py-2 rounded-lg text-sm font-bold text-center transition-colors',
|
||||
@ -105,29 +158,71 @@ import { computed } from 'vue';
|
||||
import {
|
||||
ArrowRight,
|
||||
CheckCircle2,
|
||||
Database,
|
||||
Link as LinkIcon,
|
||||
RefreshCw
|
||||
RefreshCw,
|
||||
ClipboardList,
|
||||
Image as ImageIcon
|
||||
} from 'lucide-vue-next';
|
||||
import { Step, Scenario } from '@/types';
|
||||
import { useToastStore } from '@/stores/toast';
|
||||
|
||||
interface ExistingScenario {
|
||||
name: string;
|
||||
description: string;
|
||||
image?: string;
|
||||
}
|
||||
|
||||
interface Props {
|
||||
setCurrentStep: (step: Step) => void;
|
||||
selectedScenarios: Scenario[];
|
||||
allScenarios: Scenario[];
|
||||
toggleScenarioSelection: (scenarioId: number) => void;
|
||||
existingScenarios?: ExistingScenario[];
|
||||
selectedExistingScenarios?: number[];
|
||||
toggleExistingScenarioSelection: (index: number) => void;
|
||||
}
|
||||
|
||||
const props = defineProps<Props>();
|
||||
const props = withDefaults(defineProps<Props>(), {
|
||||
existingScenarios: () => [],
|
||||
selectedExistingScenarios: () => []
|
||||
});
|
||||
|
||||
const toast = useToastStore();
|
||||
|
||||
const selectedCount = computed(() => props.selectedScenarios.length);
|
||||
// 虚拟已有场景数据（用于演示）
|
||||
const mockExistingScenarios: ExistingScenario[] = [
|
||||
{
|
||||
name: '月度销售经营报表',
|
||||
description: '统计各区域门店的月度GMV，维度单一',
|
||||
image: undefined
|
||||
},
|
||||
{
|
||||
name: '物流配送监控大屏',
|
||||
description: '实时展示车辆位置，但缺乏预警功能',
|
||||
image: undefined
|
||||
}
|
||||
];
|
||||
|
||||
// 如果传入的场景数据为空，则使用虚拟数据
|
||||
const displayExistingScenarios = computed(() => {
|
||||
return props.existingScenarios.length > 0 ? props.existingScenarios : mockExistingScenarios;
|
||||
});
|
||||
|
||||
const selectedAiCount = computed(() => props.selectedScenarios.length);
|
||||
const selectedExistingCount = computed(() => props.selectedExistingScenarios.length);
|
||||
const selectedCount = computed(() => selectedAiCount.value + selectedExistingCount.value);
|
||||
|
||||
const isScenarioSelected = (scenarioId: number) => {
|
||||
return props.selectedScenarios.some(s => s.id === scenarioId);
|
||||
};
|
||||
|
||||
const isExistingScenarioSelected = (index: number) => {
|
||||
return props.selectedExistingScenarios.includes(index);
|
||||
};
|
||||
|
||||
const toggleExistingScenarioSelection = (index: number) => {
|
||||
props.toggleExistingScenarioSelection(index);
|
||||
};
|
||||
|
||||
const handleRerunRecommendation = () => {
|
||||
// TODO: 实现重新推荐逻辑
|
||||
toast.info('正在重新生成场景推荐，请稍候...');
|
||||
|
||||
@ -35,6 +35,16 @@ export const routes: Array<RouteRecordRaw> = [
|
||||
keepAlive: false,
|
||||
hidden: true
|
||||
}
|
||||
},
|
||||
{
|
||||
path: '/data-inventory/asset/:type',
|
||||
name: 'dataAssetDetail',
|
||||
component: () => import('@/pages/engagement/DataAssetDetail.vue'),
|
||||
meta: {
|
||||
title: '数据资产详情',
|
||||
keepAlive: false,
|
||||
hidden: true
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
@ -39,7 +39,7 @@ export interface Scenario {
|
||||
id: number;
|
||||
name: string;
|
||||
type: string;
|
||||
impact: 'High' | 'Medium' | 'Low';
|
||||
recommendationIndex: number; // 推荐指数 1-5
|
||||
desc: string;
|
||||
dependencies: string[];
|
||||
selected: boolean;
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user