feat: IMAP mail sync with thread-to-contact matching (PRD §6.1)
Real IMAP connection via imapflow library: - Connects to IMAP server (IMAPFlow, TLS on port 993) - Fetches recent messages (configurable limit, default 20) - Extracts: from, fromEmail, subject, date, snippet (200 chars) - Thread-to-contact matching (PRD §6.1): 1. Match sender email against persons table 2. If no person match, extract domain and match against company.website 3. Returns matchedCompany + matchedPerson for each email - Audit-logged (action: crm.mailsync:imap) - Zod validated (host, port, user, password, folder, limit) - 502 on IMAP connection failure with detail - 400 on validation failure Tested: invalid creds → 502 'IMAP connection failed', missing host → 400
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---
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name: gaokao-fetch-volunteers
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description: >-
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调用高考智能推荐志愿表 API，根据考生基本信息及专业/城市/院校倾向（映射为 API 选填参数）
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获取冲稳保志愿列表，解析为 parsed.json。适用于获取推荐院校、冲稳保志愿表、志愿 API 调用。
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---
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# 获取推荐志愿表
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本 Skill 是流水线的**第二步**：读取 `student.json`，**提取并映射考生倾向到 API 选填参数**，调用志愿接口，输出 `parsed.json`。
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## 上下游
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- **上游**：[gaokao-collect-student-info](../gaokao-collect-student-info/SKILL.md) → `student.json`
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- **下游**：[gaokao-recommend-majors](../gaokao-recommend-majors/SKILL.md)、[gaokao-recommend-schools](../gaokao-recommend-schools/SKILL.md)、[gaokao-generate-report](../gaokao-generate-report/SKILL.md)
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## 环境准备
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```bash
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cd gaokao-fetch-volunteers
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python3 -m venv .venv && source .venv/bin/activate
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pip install -r requirements.txt
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```
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## 执行步骤
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### 1. 从辅助信息提取倾向（Agent 必做）
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读取 `student.json`，根据 [preference_mapping.md](preference_mapping.md) 将考生倾向补全/写入以下字段：
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| 字段 | 映射到 API |
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|------|-----------|
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| `preferred_universities` | `universitys` |
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| `preferred_provinces` / `preferred_cities` | `provinces` |
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| `preferred_tags` | `tags` |
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| `preferred_major_classes` | `majorClass` |
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提取来源：`interests`、`career_direction`、`preferred_cities`、`notes` 及对话中的院校/专业/层次偏好。
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若 Step1 已结构化写入，本步核对并补全；**调用 API 前倾向字段不得为空数组**（考生无偏好时除外）。
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### 2. 构建 API 请求
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```bash
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python3 scripts/build_api_request.py \
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-i output/student.json \
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-o output/api_request.json \
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--summary output/preference_summary.json
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```
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脚本将 `preferred_*` 转为 API 选填参数；`preferred_cities` 会自动推导 `provinces`（见 `preference_mapping.md`）。
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`build_api_request.py` 会调用 `province_config.validate_classify` 校验 `classify` 是否与省份模式匹配。
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### 2.5 调用前参数核对（必做）
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在调用 API 前，对照 [reference.md](reference.md) 核对 `student.json` / `api_request.json`：
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| 检查项 | 规则 |
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|--------|------|
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| `classify` | 新疆→文科/理科；3+1+2 省→物理/历史；3+3 省→综合。**传错会导致批次列表为空** |
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| `subjects` | 3+1+2：**完整三科**（首选+两门再选，如 `物理,化学,生物`）；3+3：完整三科；新疆不传；京沪津专科由脚本处理 |
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| `gradeType` | **仅北京/上海/天津**填本科或专科；其他省必须为 `null` 或不传 |
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| `score` | 新疆**必传**（无一分一段表，仅 rank 无效） |
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| `batch` | 可填泛称「本科批」/「专科批」，由 batch/list 解析为各省具体批次名 |
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| 西藏 | 测试环境不支持，应退回采集环节说明 |
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**批次 batch**：用户侧可填泛称 `本科批` / `专科批`，脚本通过 batch/list 解析为各省具体批次名（如山东 → `普通类一段`）。接口失败时使用 [reference.md](reference.md) 中的静态兜底表。
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### 3. 调用志愿 API（两阶段：批次 → 志愿列表）
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```bash
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python3 scripts/fetch_volunteers.py \
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--config output/api_request.json \
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-o output/parsed.json
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```
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或一步完成（内置构建逻辑）：
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```bash
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python3 scripts/fetch_volunteers.py \
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--student output/student.json \
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-o output/parsed.json
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```
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脚本会先调用 `batch/list` 根据分数与选科解析具体 `batch` 和 `volunteerType`，再请求志愿推荐接口。参数规范化由 `scripts/province_config.py` 自动完成。可用 `--no-auto-batch` 关闭自动解析。
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**两阶段 SOP**：
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```
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1. GET batch/list → 得到各省可选批次 + volunteerType
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2. POST intelligenceVolunteer → 用选中批次的 batch / volunteerType 拉志愿
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```
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京沪津在步骤 1 还需传 `gradeType`（脚本按 score / batch 自动推断）。
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### 4. 向用户说明
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结合 `preference_summary.json` 与 `parsed.json` 的 `stats`，说明：
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- 传入了哪些倾向参数（院校/省份/层次/专业类）
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- 冲/稳/保各多少所
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## API 选填参数说明
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| API 字段 | 含义 | 来源 |
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|----------|------|------|
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| `universitys` | 心仪高校 | `preferred_universities` |
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| `provinces` | 省份意向 | `preferred_provinces` 或由城市推导 |
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| `tags` | 院校属性 | `preferred_tags`（985/211 等） |
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| `majorClass` | 专业类意向 | `preferred_major_classes` |
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完整 API 文档见 [reference.md](reference.md)。
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## 输出结构（parsed.json）
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| 字段 | 说明 |
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|------|------|
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| `profile` | 含传入的选填参数回显 |
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| `stats` | 冲/稳/保数量 |
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| `schools_by_type` | 分组院校列表 |
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| `request` | 实际 API 请求体（含倾向参数） |
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| `batch_resolution` | 批次解析来源、选中项与可选项 |
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## 故障排查
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| 现象 | 处理 |
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|------|------|
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| 推荐结果与倾向不符 | 检查 `api_request.json` 中选填参数是否正确 |
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| 没有可填报的批次 | 查 [reference.md](reference.md)；确认 classify 与省份模式一致 |
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| 批次列表为空 | classify 错误（如 3+3 省传了物理）— 脚本会明确报错 |
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| 西藏考生 | 测试环境不支持，需换省份或等待平台接入 |
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| 志愿接口 500 | 查 reference：gradeType/subjects 是否按省传对 |
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| 缺少 subjects | 3+3/3+1+2 必填选科；新疆/京沪津专科除外 |
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| subjects 只有两门 | 3+1+2 须传**完整三科**（含物理/历史），不能只传化学,生物 |
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| 倾向未传入 | 确认 Step1/本步已填写 `preferred_*` 字段 |
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|
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## 附加资源
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- [preference_mapping.md](preference_mapping.md) — 倾向 → API 参数映射规则
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- [reference.md](reference.md) — API、选科模式、批次接口踩坑速查
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- [scripts/province_config.py](scripts/province_config.py) — 各省 classify/subjects/gradeType 校验与规范化
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- [scripts/test_batch_api.py](scripts/test_batch_api.py) — 31 省批次接口回归测试
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- [examples/api_request_shandong.json](examples/api_request_shandong.json) — 含选填参数示例
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{
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"province": "山东",
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"classify": "综合",
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"subjects": "物理,化学,生物",
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"gradeType": null,
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"score": 650,
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"rank": null,
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"batch": "本科批",
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"universitys": "北京航空航天大学,华东师范大学,中山大学",
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"provinces": "北京,上海,广东,湖北",
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"tags": "985,211,双一流",
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"majorClass": "计算机类,电子信息类,自动化类,临床医学类",
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"universityNum": 30,
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"majorNum": 6,
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"isAdjust": true,
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"volunteerType": "ACADEMY_GROUP",
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"returnUniversityNum": 30,
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"intentionNum": 0
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}
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{
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"province": "北京",
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"classify": "综合",
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"subjects": "物理,化学,生物",
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"gradeType": "本科",
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"score": 650,
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"rank": null,
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"batch": "本科批",
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"universitys": null,
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"provinces": null,
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"tags": null,
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"majorClass": null,
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"universityNum": 30,
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"majorNum": 6,
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"isAdjust": true,
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"volunteerType": "ACADEMY_GROUP",
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"returnUniversityNum": 30,
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"intentionNum": 0
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}
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# 考生倾向 → API 选填参数映射参考
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Agent 在**第二步获取志愿前**，须从 `student.json` 辅助信息中提取倾向，写入结构化字段，再构建 API 请求。
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## 映射总表
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| student.json 字段 | API 字段 | 格式 | 说明 |
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|-------------------|----------|------|------|
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| `preferred_universities` | `universitys` | 逗号分隔字符串 | 心仪高校全称，如 `北京航空航天大学,华东师范大学` |
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| `preferred_provinces` | `provinces` | 逗号分隔字符串 | 意向就读省份；若未填，由 `preferred_cities` 自动推导 |
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| `preferred_cities` | `provinces`（推导） | — | 城市映射见下方；脚本 `build_api_request.py` 自动处理 |
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| `preferred_tags` | `tags` | 逗号分隔字符串 | 院校属性，如 `985,211,双一流` |
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| `preferred_major_classes` | `majorClass` | 逗号分隔字符串 | 教育部专业类名称，如 `计算机类,电子信息类` |
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## Agent 提取规则
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从自然语言中识别倾向，**写入 student.json 对应字段**（可在 Step1 采集时写入，或在 Step2 调用 API 前补全）：
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### 院校倾向 → `preferred_universities`
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用户提及的具体学校、「想上清北复交」「考虑国防科大」等。
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|
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### 城市倾向 → `preferred_cities` + `preferred_provinces`
|
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- 收集城市列表到 `preferred_cities`
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- 同时推导省份写入 `preferred_provinces`（可与城市映射结果合并去重）
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常见城市→省份：深圳/广州→广东，武汉→湖北，杭州→浙江，南京/苏州→江苏，成都→四川，西安→陕西。
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### 院校层次倾向 → `preferred_tags`
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| 用户表述 | tags 值 |
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|----------|---------|
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| 985 / 顶尖 | `985` |
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| 211 | `211` |
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| 双一流 | `双一流` |
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| 保研 | `保研资格` |
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| 师范 | `师范` |
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| 医学强校 | 结合具体院校，或 tag 填 `医学院`（若 API 支持） |
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|
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### 专业倾向 → `preferred_major_classes`
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|
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结合 `interests`、`career_direction`、`subject_scores` 推断：
|
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|
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| 兴趣/就业方向 | 建议 majorClass |
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|--------------|-----------------|
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| 编程、互联网、AI | `计算机类,软件工程,电子信息类` |
|
||||
| 电子、芯片、通信 | `电子信息类,微电子科学与工程,集成电路` |
|
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| 医学、临床 | `临床医学类,口腔医学类,药学类` |
|
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| 机械、制造、航天 | `机械类,航空航天类,自动化类` |
|
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| 经济、金融、管理 | `经济学类,金融学类,工商管理类` |
|
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| 法学 | `法学类` |
|
||||
| 师范、教育 | `教育学类,中国语言文学类,数学类` |
|
||||
|
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> 使用教育部本科专业类标准名称；多个类用英文逗号分隔，不超过 5 个为宜。
|
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|
||||
### 禁忌与排除 → `notes` + 不传参
|
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|
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「不接受偏远地区」等**排除性**偏好写入 `notes`，供后续推荐 Skill 使用；**不要**把排斥省份填入 `provinces`。
|
||||
|
||||
## 工作流
|
||||
|
||||
```
|
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student.json（含倾向字段）
|
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→ Agent 核对/补全 preferred_* 字段
|
||||
→ build_api_request.py
|
||||
→ api_request.json
|
||||
→ fetch_volunteers.py
|
||||
→ parsed.json
|
||||
```
|
||||
|
||||
## 山东示例
|
||||
|
||||
考生：兴趣编程/AI，意向北京上海深圳，希望 985，专业偏计算机。
|
||||
|
||||
```json
|
||||
{
|
||||
"preferred_cities": ["北京", "上海", "深圳"],
|
||||
"preferred_provinces": ["北京", "上海", "广东"],
|
||||
"preferred_universities": ["北京航空航天大学", "华东师范大学"],
|
||||
"preferred_tags": ["985", "211"],
|
||||
"preferred_major_classes": ["计算机类", "电子信息类", "自动化类"]
|
||||
}
|
||||
```
|
||||
|
||||
生成的 API 片段：
|
||||
|
||||
```json
|
||||
{
|
||||
"universitys": "北京航空航天大学,华东师范大学",
|
||||
"provinces": "北京,上海,广东",
|
||||
"tags": "985,211",
|
||||
"majorClass": "计算机类,电子信息类,自动化类"
|
||||
}
|
||||
```
|
||||
Executable
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||||
# 智能推荐志愿表 API 速查
|
||||
|
||||
## 两阶段调用流程
|
||||
|
||||
`fetch_volunteers.py` 默认先调批次接口，再调志愿推荐接口：
|
||||
|
||||
1. **批次列表** `GET /zp/volunteer/batch/list?province=山东&classify=综合&score=651`
|
||||
2. **志愿推荐** `POST /zp/volunteer/intelligenceVolunteer`（请求体中的 `batch`、`volunteerType` 来自第 1 步选中项）
|
||||
|
||||
`classify` 取值：`文科`、`理科`、`物理`、`历史`、`综合`，按省份与选科模式填写（见下文）。
|
||||
|
||||
批次选中规则：精确批次名 → 按 `gradeType`/本科批·专科批过滤 → 在达线批次中取控制线最高者。
|
||||
|
||||
解析结果 `parsed.json` 含 `batch_resolution` 字段，记录批次来源与可选项。
|
||||
|
||||
## 批次列表接口
|
||||
|
||||
| 项 | 值 |
|
||||
|----|-----|
|
||||
| 路径 | `GET /zp/volunteer/batch/list` |
|
||||
| 参数 | `province`、`classify`、`score`；京沪津另加 `gradeType` |
|
||||
| 响应 | `{ status, message, result: [{ batch, score, gradeType, type, ... }] }` |
|
||||
|
||||
`result[].type` 对应志愿接口的 `volunteerType`（如 `MAJOR_GROUP`）。`gradeType` 仅用于筛选批次；**仅京沪津**才写入志愿请求体。
|
||||
|
||||
## 志愿推荐接口
|
||||
|
||||
| 项 | 值 |
|
||||
|----|-----|
|
||||
| 路径 | `POST /zp/volunteer/intelligenceVolunteer` |
|
||||
| Content-Type | `application/json` |
|
||||
| 认证 | 无（公网 skill-test 接口） |
|
||||
| 默认 BASE_URL | `https://publicapi.chatglm.cn/chatglm_public/skill-test` |
|
||||
| 完整地址 | `{BASE_URL}/zp/volunteer/intelligenceVolunteer` |
|
||||
|
||||
## 请求体字段
|
||||
|
||||
| 字段 | 类型 | 必填 | 说明 |
|
||||
|------|------|------|------|
|
||||
| province | string | ✓ | 高考省份 |
|
||||
| classify | string | ✓ | 文科/理科/物理/历史/综合 |
|
||||
| score | integer | ✓ | 高考成绩 |
|
||||
| batch | string | ✓ | 填报批次（可由 batch/list 自动解析） |
|
||||
| subjects | string \| null | 模式相关 | 逗号分隔选科 |
|
||||
| gradeType | string \| null | 京沪津 | 本科 / 专科 |
|
||||
| rank | integer \| null | | 位次 |
|
||||
| universitys | string \| null | | 心仪高校，`,` 分隔；来自 `student.preferred_universities` |
|
||||
| provinces | string \| null | | 省份意向；来自 `preferred_provinces` 或由 `preferred_cities` 推导 |
|
||||
| tags | string \| null | | 院校属性意向；来自 `preferred_tags`（如 985,211） |
|
||||
| majorClass | string \| null | | 专业类意向；来自 `preferred_major_classes` |
|
||||
| universityNum | integer | | 推荐院校数，默认 30 |
|
||||
| majorNum | integer | | 每组专业数，默认 6 |
|
||||
| isAdjust | boolean | | 是否调剂 |
|
||||
| volunteerType | string | | ACADEMY_GROUP / ACADEMY_MAJOR / MAJOR_GROUP |
|
||||
| returnUniversityNum | integer | | 已填志愿数 |
|
||||
| intentionNum | integer | | 已填意向数 |
|
||||
|
||||
## 省份与选科模式（实测规律）
|
||||
|
||||
### 老高考（仅新疆）
|
||||
|
||||
| 字段 | 值 |
|
||||
|------|-----|
|
||||
| classify | `文科` 或 `理科`（二选一，不能填物理/历史/综合） |
|
||||
| subjects | 不传或 `null`（**不要**传字符串 `"null"`） |
|
||||
| gradeType | 不传 |
|
||||
| score | **必传**（新疆无一分一段表，仅传 rank 无法反查分数） |
|
||||
|
||||
### 3+1+2（23 省）
|
||||
|
||||
广东,江苏,河北,湖北,湖南,福建,辽宁,重庆,甘肃,黑龙江,吉林,安徽,江西,贵州,广西,云南,内蒙古,四川,宁夏,山西,河南,陕西,青海
|
||||
|
||||
| 字段 | 值 |
|
||||
|------|-----|
|
||||
| classify | `物理` 或 `历史`（对应 3+1+2 首选科目） |
|
||||
| subjects | **完整三科**，逗号分隔：`首选科目,再选1,再选2`。须包含与 `classify` 一致的首选科目，**不能只传两门再选** |
|
||||
|
||||
例：`classify=物理`，`subjects=物理,化学,生物`（✓）；`subjects=化学,生物`（✗ 缺少物理）
|
||||
|
||||
再选科目仅限：化学、生物、政治、地理。
|
||||
| gradeType | 不传 |
|
||||
|
||||
### 3+3（6 省）
|
||||
|
||||
上海,北京,天津,山东,浙江,海南
|
||||
|
||||
| 字段 | 值 |
|
||||
|------|-----|
|
||||
| classify | 固定 `综合` |
|
||||
| subjects | 三科逗号分隔；浙江可选 `技术` |
|
||||
| gradeType | **仅京沪津**需要：`本科` / `专科` |
|
||||
|
||||
**满分差异**：山东/浙江 750；海南 900；上海本科 660；北京/天津本科 750；京沪津专科语数外 450。
|
||||
|
||||
### 京沪津 gradeType 规则
|
||||
|
||||
批次接口 `batch/list` 与志愿接口**均需**传 `gradeType`：
|
||||
|
||||
```
|
||||
GET .../batch/list?province=北京&classify=综合&score=650&gradeType=本科
|
||||
```
|
||||
|
||||
未填时脚本按以下规则推断：`batch=专科批` → 专科；`score≤450` → 专科；否则 → 本科。
|
||||
|
||||
| 场景 | subjects | gradeType |
|
||||
|------|----------|-----------|
|
||||
| 北京本科 | `物理,化学,生物` | `本科` |
|
||||
| 北京专科 | 字符串 `"null"`（API 要求，非 JSON null） | `专科` |
|
||||
| 浙江/山东/海南 | 三科组合 | 不传 |
|
||||
|
||||
### 西藏
|
||||
|
||||
测试环境**平台未接入**，任意 classify 均返回 `status=500`，非参数错误。脚本会在校验阶段直接提示不支持。
|
||||
|
||||
### 常见踩坑（批次接口）
|
||||
|
||||
| 现象 | 原因 | 处理 |
|
||||
|------|------|------|
|
||||
| `status=0` 但 `result=[]` | classify 与省份模式不匹配（如山东传 `物理`） | 按上表改 classify |
|
||||
| 志愿接口 500（山东等） | 误传 `gradeType` | 仅京沪津传，其他省移除 |
|
||||
| 志愿接口 500（山东等） | 缺少 `subjects` 或只有两门 | 3+1+2 须传**完整三科**（含物理/历史） |
|
||||
| 志愿接口 500（新疆） | `subjects` 为字符串 `"null"` | 不传 subjects 字段 |
|
||||
| 北京专科 500 | subjects 为 JSON null 或省略 | 改为字符串 `"null"` |
|
||||
|
||||
`scripts/province_config.py` 封装了上述校验与参数规范化；`scripts/test_batch_api.py` 可批量回归 31 省。
|
||||
|
||||
### 批次静态兜底（batch/list 失败时）
|
||||
|
||||
| 省份 | batch |
|
||||
|------|-------|
|
||||
| 天津 | 本科批A段 |
|
||||
| 浙江 | 普通类一段 |
|
||||
| 山东 | 普通类一段 |
|
||||
| 四川、云南 | 本科批B段 |
|
||||
| 甘肃 | 本科批 |
|
||||
| 宁夏 | 本科批B段 |
|
||||
| 新疆 | 本科一批 |
|
||||
| 西藏 | 测试环境暂不支持（2222010 高考省份错误） |
|
||||
|
||||
## 响应结构
|
||||
|
||||
公网 skill-test 接口返回 `{ status, message, result }`（`status=0` 为成功），脚本会自动转换为统一格式。
|
||||
|
||||
```json
|
||||
{
|
||||
"status": 0,
|
||||
"message": "success",
|
||||
"result": {
|
||||
"province": "...",
|
||||
"schoolList": [
|
||||
{
|
||||
"universityName": "",
|
||||
"universityCode": "",
|
||||
"universityMajorGroup": "",
|
||||
"enrollProbability": 20,
|
||||
"type": "CHONG",
|
||||
"majorList": [ { "majorName": "", "claim": "", "type": "CHONG" } ]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
旧版接口格式 `{ code, msg, body }`（`code=200`）同样兼容。
|
||||
|
||||
`type` 枚举：`CHONG` 冲、`WEN` 稳、`BAO` 保、`NAN` 难、`YI` 易。
|
||||
|
||||
`score` / `parityScore` 常为 JSON 字符串，如 `[{"2025":"660,2153,60"}]`（分,位次,计划数）。
|
||||
+2
@@ -0,0 +1,2 @@
|
||||
requests>=2.28.0
|
||||
jinja2>=3.1.0
|
||||
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
Binary file not shown.
+182
@@ -0,0 +1,182 @@
|
||||
#!/usr/bin/env python3
|
||||
"""从 student.json 构建 API 请求体，将考生倾向映射到选填参数。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
# 意向城市 → API provinces（省份意向）
|
||||
CITY_TO_PROVINCE: dict[str, str] = {
|
||||
"北京": "北京",
|
||||
"上海": "上海",
|
||||
"天津": "天津",
|
||||
"重庆": "重庆",
|
||||
"深圳": "广东",
|
||||
"广州": "广东",
|
||||
"东莞": "广东",
|
||||
"佛山": "广东",
|
||||
"武汉": "湖北",
|
||||
"成都": "四川",
|
||||
"杭州": "浙江",
|
||||
"宁波": "浙江",
|
||||
"南京": "江苏",
|
||||
"苏州": "江苏",
|
||||
"无锡": "江苏",
|
||||
"西安": "陕西",
|
||||
"长沙": "湖南",
|
||||
"郑州": "河南",
|
||||
"济南": "山东",
|
||||
"青岛": "山东",
|
||||
"合肥": "安徽",
|
||||
"厦门": "福建",
|
||||
"福州": "福建",
|
||||
"大连": "辽宁",
|
||||
"沈阳": "辽宁",
|
||||
"哈尔滨": "黑龙江",
|
||||
"长春": "吉林",
|
||||
"昆明": "云南",
|
||||
"南宁": "广西",
|
||||
"海口": "海南",
|
||||
"石家庄": "河北",
|
||||
"太原": "山西",
|
||||
"南昌": "江西",
|
||||
"兰州": "甘肃",
|
||||
"贵阳": "贵州",
|
||||
"呼和浩特": "内蒙古",
|
||||
"乌鲁木齐": "新疆",
|
||||
"银川": "宁夏",
|
||||
"西宁": "青海",
|
||||
"拉萨": "西藏",
|
||||
}
|
||||
|
||||
DEFAULT_API_OPTIONS: dict[str, Any] = {
|
||||
"universityNum": 30,
|
||||
"majorNum": 6,
|
||||
"isAdjust": True,
|
||||
"volunteerType": "ACADEMY_GROUP",
|
||||
"returnUniversityNum": 30,
|
||||
"intentionNum": 0,
|
||||
}
|
||||
|
||||
|
||||
def _join_csv(values: list[str] | None) -> str | None:
|
||||
if not values:
|
||||
return None
|
||||
cleaned = [str(v).strip() for v in values if str(v).strip()]
|
||||
return ",".join(cleaned) if cleaned else None
|
||||
|
||||
|
||||
def cities_to_provinces(cities: list[str] | None) -> list[str]:
|
||||
"""意向城市去重映射为省份列表。"""
|
||||
if not cities:
|
||||
return []
|
||||
provinces: list[str] = []
|
||||
seen: set[str] = set()
|
||||
for city in cities:
|
||||
key = str(city).strip().replace("市", "").replace("省", "")
|
||||
if not key:
|
||||
continue
|
||||
province = CITY_TO_PROVINCE.get(key, key)
|
||||
if province not in seen:
|
||||
seen.add(province)
|
||||
provinces.append(province)
|
||||
return provinces
|
||||
|
||||
|
||||
def student_to_api_payload(student: dict[str, Any]) -> dict[str, Any]:
|
||||
"""将 student.json 转为 API 请求体。"""
|
||||
preferred_provinces = student.get("preferred_provinces") or []
|
||||
if not preferred_provinces:
|
||||
preferred_provinces = cities_to_provinces(student.get("preferred_cities"))
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"province": student.get("province"),
|
||||
"classify": student.get("classify"),
|
||||
"subjects": student.get("subjects"),
|
||||
"gradeType": student.get("gradeType"),
|
||||
"score": student.get("score"),
|
||||
"rank": student.get("rank"),
|
||||
"batch": student.get("batch"),
|
||||
"universitys": _join_csv(student.get("preferred_universities")),
|
||||
"provinces": _join_csv(preferred_provinces),
|
||||
"tags": _join_csv(student.get("preferred_tags")),
|
||||
"majorClass": _join_csv(student.get("preferred_major_classes")),
|
||||
**DEFAULT_API_OPTIONS,
|
||||
}
|
||||
|
||||
overrides = student.get("api_overrides") or {}
|
||||
for key, value in overrides.items():
|
||||
if value is not None:
|
||||
payload[key] = value
|
||||
|
||||
return payload
|
||||
|
||||
|
||||
def summarize_preferences(student: dict[str, Any], payload: dict[str, Any]) -> dict[str, Any]:
|
||||
"""返回倾向映射摘要，便于 Agent 向用户说明。"""
|
||||
return {
|
||||
"universitys": payload.get("universitys"),
|
||||
"provinces": payload.get("provinces"),
|
||||
"tags": payload.get("tags"),
|
||||
"majorClass": payload.get("majorClass"),
|
||||
"sources": {
|
||||
"preferred_universities": student.get("preferred_universities"),
|
||||
"preferred_cities": student.get("preferred_cities"),
|
||||
"preferred_provinces": student.get("preferred_provinces"),
|
||||
"preferred_tags": student.get("preferred_tags"),
|
||||
"preferred_major_classes": student.get("preferred_major_classes"),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description="从 student.json 构建 API 请求 JSON")
|
||||
parser.add_argument("-i", "--input", type=Path, required=True, help="student.json 路径")
|
||||
parser.add_argument("-o", "--output", type=Path, required=True, help="输出 api_request.json")
|
||||
parser.add_argument(
|
||||
"--summary",
|
||||
type=Path,
|
||||
help="可选：输出倾向映射摘要 JSON（供 Agent 核对）",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
student = json.loads(args.input.read_text(encoding="utf-8"))
|
||||
except (OSError, json.JSONDecodeError) as e:
|
||||
print(f"错误: 无法读取 student.json — {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
payload = student_to_api_payload(student)
|
||||
try:
|
||||
from province_config import validate_classify, validate_subjects_for_volunteer_api
|
||||
|
||||
validate_classify(str(payload.get("province", "")), payload.get("classify"))
|
||||
validate_subjects_for_volunteer_api(payload)
|
||||
except ValueError as e:
|
||||
print(f"错误: {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
required = ["province", "classify", "score"]
|
||||
missing = [k for k in required if not payload.get(k)]
|
||||
if missing:
|
||||
print(f"错误: student.json 缺少 API 必填字段 {missing}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.output.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"已生成 API 请求: {args.output}")
|
||||
|
||||
if args.summary:
|
||||
summary = summarize_preferences(student, payload)
|
||||
args.summary.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"已生成倾向映射摘要: {args.summary}")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
+569
@@ -0,0 +1,569 @@
|
||||
#!/usr/bin/env python3
|
||||
"""调用智能推荐志愿表 API，解析响应并输出结构化 JSON。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
from urllib.error import HTTPError, URLError
|
||||
from urllib.parse import urlencode
|
||||
from urllib.request import Request, urlopen
|
||||
|
||||
from province_config import (
|
||||
PROVINCES_REQUIRING_GRADE_TYPE,
|
||||
batch_list_query_params,
|
||||
sanitize_payload_for_api,
|
||||
validate_classify,
|
||||
validate_subjects_for_volunteer_api,
|
||||
)
|
||||
|
||||
# ========== 接口配置（修改此处即可切换环境）==========
|
||||
BASE_URL = "https://publicapi.chatglm.cn/chatglm_public/skill"
|
||||
API_PATH = "/zp/volunteer/intelligenceVolunteer"
|
||||
BATCH_LIST_PATH = "/zp/volunteer/batch/list"
|
||||
# =====================================================
|
||||
|
||||
TYPE_LABELS = {
|
||||
"CHONG": "冲",
|
||||
"WEN": "稳",
|
||||
"BAO": "保",
|
||||
"NAN": "难",
|
||||
"YI": "易",
|
||||
}
|
||||
|
||||
PROVINCE_BATCH_FALLBACK: dict[str, str] = {
|
||||
"天津": "本科批A段",
|
||||
"浙江": "普通类一段",
|
||||
"山东": "普通类一段",
|
||||
"四川": "本科批B段",
|
||||
"云南": "本科批B段",
|
||||
"甘肃": "本科批",
|
||||
"宁夏": "本科批B段",
|
||||
"新疆": "本科一批",
|
||||
}
|
||||
|
||||
|
||||
def resolve_batch_fallback(province: str | None, batch: str | None) -> str | None:
|
||||
"""批次 API 失败时的省份静态兜底。"""
|
||||
if not province:
|
||||
return batch
|
||||
mapped = PROVINCE_BATCH_FALLBACK.get(province)
|
||||
if not mapped:
|
||||
return batch
|
||||
if batch is None:
|
||||
return mapped
|
||||
if str(batch).strip() == "本科批":
|
||||
return mapped
|
||||
return batch
|
||||
|
||||
|
||||
def batch_list_url(
|
||||
params: dict[str, Any],
|
||||
base_url: str | None = None,
|
||||
) -> str:
|
||||
root = (base_url or BASE_URL).rstrip("/")
|
||||
path = BATCH_LIST_PATH if BATCH_LIST_PATH.startswith("/") else f"/{BATCH_LIST_PATH}"
|
||||
query = urlencode(params)
|
||||
return f"{root}{path}?{query}"
|
||||
|
||||
|
||||
def call_batch_list_api(
|
||||
params: dict[str, Any],
|
||||
base_url: str | None = None,
|
||||
timeout: int = 60,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""GET 批次列表，返回 result 数组。"""
|
||||
url = batch_list_url(params, base_url)
|
||||
req = Request(url, method="GET", headers={"Accept": "*/*"})
|
||||
try:
|
||||
with urlopen(req, timeout=timeout) as resp:
|
||||
charset = resp.headers.get_content_charset() or "utf-8"
|
||||
text = resp.read().decode(charset)
|
||||
except HTTPError as e:
|
||||
detail = e.read().decode("utf-8", errors="replace")
|
||||
raise RuntimeError(f"批次接口 HTTP {e.code}: {detail}") from e
|
||||
except URLError as e:
|
||||
raise RuntimeError(f"批次接口网络失败: {e.reason}") from e
|
||||
|
||||
raw = json.loads(text)
|
||||
if raw.get("status") != 0:
|
||||
raise ValueError(f"批次接口异常: status={raw.get('status')}, message={raw.get('message')}")
|
||||
result = raw.get("result")
|
||||
if not isinstance(result, list):
|
||||
raise ValueError("批次接口返回格式异常：result 非数组")
|
||||
if not result:
|
||||
province = params.get("province")
|
||||
classify = params.get("classify")
|
||||
grade_type = params.get("gradeType")
|
||||
hint = f"classify={classify}"
|
||||
if grade_type:
|
||||
hint += f", gradeType={grade_type}"
|
||||
raise ValueError(
|
||||
f"批次列表为空（{province}，{hint}）。"
|
||||
f"请确认 classify 与省份选科模式一致（见 reference.md）"
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def select_batch_from_list(
|
||||
items: list[dict[str, Any]],
|
||||
user_score: int,
|
||||
preferred_batch: str | None = None,
|
||||
grade_type: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""从批次列表中选取与分数、层次最匹配的一项。"""
|
||||
if not items:
|
||||
raise ValueError("没有可填报的批次")
|
||||
|
||||
candidates = list(items)
|
||||
|
||||
# 1. 精确批次名（非泛称「本科批」）
|
||||
if preferred_batch and preferred_batch.strip() not in ("本科批", "专科批"):
|
||||
exact = [x for x in candidates if x.get("batch") == preferred_batch]
|
||||
if exact:
|
||||
return exact[0]
|
||||
|
||||
# 2. 按 gradeType / 泛称本科批·专科批 过滤
|
||||
if grade_type:
|
||||
filtered = [x for x in candidates if x.get("gradeType") == grade_type]
|
||||
if filtered:
|
||||
candidates = filtered
|
||||
elif preferred_batch == "本科批":
|
||||
filtered = [x for x in candidates if x.get("gradeType") == "本科"]
|
||||
if filtered:
|
||||
candidates = filtered
|
||||
elif preferred_batch == "专科批":
|
||||
filtered = [x for x in candidates if x.get("gradeType") == "专科"]
|
||||
if filtered:
|
||||
candidates = filtered
|
||||
|
||||
# 3. 分数达线：取控制线下考生分数仍达标的批次中，控制线最高者（偏本科一段）
|
||||
qualified = [x for x in candidates if user_score >= int(x.get("score") or 0)]
|
||||
pool = qualified if qualified else candidates
|
||||
return max(pool, key=lambda x: int(x.get("score") or 0))
|
||||
|
||||
|
||||
def apply_batch_item_to_payload(payload: dict[str, Any], batch_item: dict[str, Any]) -> None:
|
||||
"""将选中批次写入志愿请求体。"""
|
||||
payload["batch"] = batch_item.get("batch")
|
||||
if batch_item.get("type"):
|
||||
payload["volunteerType"] = batch_item["type"]
|
||||
province = payload.get("province")
|
||||
if (
|
||||
province in PROVINCES_REQUIRING_GRADE_TYPE
|
||||
and batch_item.get("gradeType")
|
||||
and not payload.get("gradeType")
|
||||
):
|
||||
payload["gradeType"] = batch_item["gradeType"]
|
||||
|
||||
|
||||
def resolve_batch_via_api(
|
||||
payload: dict[str, Any],
|
||||
base_url: str | None = None,
|
||||
) -> tuple[str, dict[str, Any], list[dict[str, Any]]]:
|
||||
"""调用批次接口并选定 batch，返回 (batch名, 选中项, 全部项)。"""
|
||||
params = batch_list_query_params(payload)
|
||||
items = call_batch_list_api(params, base_url=base_url)
|
||||
selected = select_batch_from_list(
|
||||
items,
|
||||
user_score=int(payload["score"]),
|
||||
preferred_batch=payload.get("batch"),
|
||||
grade_type=payload.get("gradeType"),
|
||||
)
|
||||
apply_batch_item_to_payload(payload, selected)
|
||||
return selected.get("batch") or "", selected, items
|
||||
|
||||
|
||||
def _parse_json_field(value: Any) -> Any:
|
||||
if value is None or value == "":
|
||||
return None
|
||||
if isinstance(value, (dict, list)):
|
||||
return value
|
||||
if isinstance(value, str):
|
||||
text = value.strip()
|
||||
if not text:
|
||||
return None
|
||||
try:
|
||||
return json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
return value
|
||||
return value
|
||||
|
||||
|
||||
def parse_score_history(score_str: Any) -> list[dict[str, str]]:
|
||||
"""将 score / parityScore 字段解析为 [{year, score, rank, plan_num}, ...]。"""
|
||||
parsed = _parse_json_field(score_str)
|
||||
if not isinstance(parsed, list):
|
||||
return []
|
||||
|
||||
rows: list[dict[str, str]] = []
|
||||
for item in parsed:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
for year, triple in item.items():
|
||||
parts = str(triple).split(",")
|
||||
rows.append(
|
||||
{
|
||||
"year": str(year),
|
||||
"score": parts[0] if len(parts) > 0 else "-",
|
||||
"rank": parts[1] if len(parts) > 1 else "-",
|
||||
"plan_num": parts[2] if len(parts) > 2 else "-",
|
||||
}
|
||||
)
|
||||
return rows
|
||||
|
||||
|
||||
def parse_parity_scores(parity_str: Any) -> list[dict[str, str]]:
|
||||
parsed = _parse_json_field(parity_str)
|
||||
if not isinstance(parsed, list):
|
||||
return []
|
||||
rows: list[dict[str, str]] = []
|
||||
for item in parsed:
|
||||
if isinstance(item, dict):
|
||||
for year, score in item.items():
|
||||
rows.append({"year": str(year), "score": str(score)})
|
||||
return rows
|
||||
|
||||
|
||||
def normalize_major(major: dict[str, Any]) -> dict[str, Any]:
|
||||
type_code = major.get("type") or ""
|
||||
return {
|
||||
"major_name": major.get("majorName") or "",
|
||||
"major_code": major.get("majorCode") or "",
|
||||
"major_num": major.get("majorNum"),
|
||||
"claim": major.get("claim") or "",
|
||||
"study_year": major.get("studyYear") or "",
|
||||
"study_cost": major.get("studyCost") or "",
|
||||
"major_class": major.get("majorClass") or "",
|
||||
"major_remarks": major.get("majorRemarks") or "",
|
||||
"plan_num": major.get("planNum"),
|
||||
"enroll_probability": major.get("enrollProbability"),
|
||||
"type": type_code,
|
||||
"type_label": TYPE_LABELS.get(type_code, type_code or "—"),
|
||||
"score_history": parse_score_history(major.get("score")),
|
||||
"parity_scores": parse_parity_scores(major.get("parityScore")),
|
||||
}
|
||||
|
||||
|
||||
def normalize_school(school: dict[str, Any], index: int) -> dict[str, Any]:
|
||||
type_code = school.get("type") or ""
|
||||
majors = school.get("majorList") or []
|
||||
return {
|
||||
"index": index,
|
||||
"university_name": school.get("universityName") or "",
|
||||
"university_code": school.get("universityCode") or "",
|
||||
"major_group": school.get("universityMajorGroup") or "",
|
||||
"logo": school.get("logo") or "",
|
||||
"category_name": school.get("categoryName") or "",
|
||||
"property_name": school.get("propertyName") or "",
|
||||
"tags": school.get("tags") or "",
|
||||
"city_name": school.get("cityName") or "",
|
||||
"province_name": school.get("provinceName") or "",
|
||||
"enroll_probability": school.get("enrollProbability"),
|
||||
"lowest_score": school.get("lowestScore"),
|
||||
"type": type_code,
|
||||
"type_label": TYPE_LABELS.get(type_code, type_code or "—"),
|
||||
"plan_num": school.get("planNum"),
|
||||
"level": school.get("level") or "",
|
||||
"is_adjust": school.get("isAdjust"),
|
||||
"score_history": parse_score_history(school.get("score")),
|
||||
"parity_scores": parse_parity_scores(school.get("parityScore")),
|
||||
"majors": [normalize_major(m) for m in majors],
|
||||
}
|
||||
|
||||
|
||||
def group_schools_by_type(schools: list[dict[str, Any]]) -> dict[str, list[dict[str, Any]]]:
|
||||
order = ["CHONG", "WEN", "BAO", "YI", "NAN"]
|
||||
grouped: dict[str, list[dict[str, Any]]] = {k: [] for k in order}
|
||||
other: list[dict[str, Any]] = []
|
||||
for school in schools:
|
||||
t = school.get("type") or ""
|
||||
if t in grouped:
|
||||
grouped[t].append(school)
|
||||
else:
|
||||
other.append(school)
|
||||
if other:
|
||||
grouped["_OTHER"] = other
|
||||
return grouped
|
||||
|
||||
|
||||
def normalize_api_response(raw: dict[str, Any]) -> dict[str, Any]:
|
||||
"""兼容新旧两种响应信封：{code,msg,body} 与 {status,message,result}。"""
|
||||
if "result" in raw and "status" in raw:
|
||||
status = raw.get("status")
|
||||
msg = raw.get("message") or ""
|
||||
if status != 0:
|
||||
raise ValueError(f"API 返回异常: status={status}, message={msg}")
|
||||
return {"code": 200, "msg": msg or "OK", "body": raw.get("result") or {}}
|
||||
return raw
|
||||
|
||||
|
||||
def _empty_to_none(value: Any) -> Any:
|
||||
if value == "" or value is None:
|
||||
return None
|
||||
return value
|
||||
|
||||
|
||||
def parse_api_response(raw: dict[str, Any]) -> dict[str, Any]:
|
||||
raw = normalize_api_response(raw)
|
||||
code = raw.get("code")
|
||||
msg = raw.get("msg") or ""
|
||||
if code != 200:
|
||||
raise ValueError(f"API 返回异常: code={code}, msg={msg}")
|
||||
|
||||
body = raw.get("body") or {}
|
||||
schools_raw = body.get("schoolList") or []
|
||||
schools = [normalize_school(s, i + 1) for i, s in enumerate(schools_raw)]
|
||||
grouped = group_schools_by_type(schools)
|
||||
|
||||
stats = {
|
||||
"total": len(schools),
|
||||
"chong": len(grouped.get("CHONG", [])),
|
||||
"wen": len(grouped.get("WEN", [])),
|
||||
"bao": len(grouped.get("BAO", [])),
|
||||
}
|
||||
|
||||
return {
|
||||
"meta": {
|
||||
"generated_from": "intelligenceVolunteer",
|
||||
"api_code": code,
|
||||
"api_msg": msg,
|
||||
},
|
||||
"profile": {
|
||||
"province": body.get("province"),
|
||||
"classify": body.get("classify"),
|
||||
"subjects": _empty_to_none(body.get("subjects")),
|
||||
"grade_type": _empty_to_none(body.get("gradeType")),
|
||||
"score": body.get("score"),
|
||||
"rank": body.get("rank"),
|
||||
"batch": body.get("batch"),
|
||||
"volunteer_type": body.get("volunteerType"),
|
||||
"is_adjust": body.get("isAdjust"),
|
||||
"university_num": body.get("universityNum"),
|
||||
"major_num": body.get("majorNum"),
|
||||
"universitys": body.get("universitys"),
|
||||
"provinces": body.get("provinces"),
|
||||
"tags": body.get("tags"),
|
||||
"major_class": body.get("majorClass"),
|
||||
},
|
||||
"stats": stats,
|
||||
"schools": schools,
|
||||
"schools_by_type": {
|
||||
TYPE_LABELS.get(k, k): grouped[k]
|
||||
for k in grouped
|
||||
if grouped[k] and not k.startswith("_")
|
||||
},
|
||||
"raw_body": body,
|
||||
}
|
||||
|
||||
|
||||
def load_payload_from_student(student_path: Path) -> dict[str, Any]:
|
||||
from build_api_request import student_to_api_payload
|
||||
|
||||
student = json.loads(student_path.read_text(encoding="utf-8"))
|
||||
return student_to_api_payload(student)
|
||||
|
||||
|
||||
def build_request_payload(args: argparse.Namespace) -> dict[str, Any]:
|
||||
if args.student:
|
||||
return load_payload_from_student(args.student)
|
||||
if args.config:
|
||||
with open(args.config, encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"province": args.province,
|
||||
"classify": args.classify,
|
||||
"subjects": args.subjects,
|
||||
"gradeType": args.grade_type,
|
||||
"score": args.score,
|
||||
"rank": args.rank,
|
||||
"batch": args.batch,
|
||||
"universitys": args.universitys,
|
||||
"provinces": args.provinces,
|
||||
"tags": args.tags,
|
||||
"majorClass": args.major_class,
|
||||
"universityNum": args.university_num,
|
||||
"majorNum": args.major_num,
|
||||
"isAdjust": args.is_adjust,
|
||||
"volunteerType": args.volunteer_type,
|
||||
"returnUniversityNum": args.return_university_num,
|
||||
"intentionNum": args.intention_num,
|
||||
}
|
||||
return payload
|
||||
|
||||
|
||||
def api_url(base_url: str | None = None) -> str:
|
||||
root = (base_url or BASE_URL).rstrip("/")
|
||||
path = API_PATH if API_PATH.startswith("/") else f"/{API_PATH}"
|
||||
return root + path
|
||||
|
||||
|
||||
def call_api(
|
||||
payload: dict[str, Any],
|
||||
base_url: str | None = None,
|
||||
timeout: int = 60,
|
||||
) -> dict[str, Any]:
|
||||
url = api_url(base_url)
|
||||
data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
|
||||
req = Request(
|
||||
url,
|
||||
data=data,
|
||||
method="POST",
|
||||
headers={
|
||||
"Content-Type": "application/json",
|
||||
"Accept": "*/*",
|
||||
},
|
||||
)
|
||||
try:
|
||||
with urlopen(req, timeout=timeout) as resp:
|
||||
charset = resp.headers.get_content_charset() or "utf-8"
|
||||
text = resp.read().decode(charset)
|
||||
except HTTPError as e:
|
||||
detail = e.read().decode("utf-8", errors="replace")
|
||||
raise RuntimeError(f"HTTP {e.code}: {detail}") from e
|
||||
except URLError as e:
|
||||
raise RuntimeError(f"网络请求失败: {e.reason}") from e
|
||||
|
||||
return json.loads(text)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description="获取并解析智能推荐志愿表")
|
||||
parser.add_argument(
|
||||
"--student",
|
||||
type=Path,
|
||||
help="student.json 路径（自动映射倾向到 API 选填参数，优先于 --config）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config",
|
||||
type=Path,
|
||||
help="完整请求 JSON 文件路径",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--base-url",
|
||||
default=os.environ.get("ZP_API_BASE_URL", BASE_URL),
|
||||
help=f"API 根地址（默认脚本内 BASE_URL: {BASE_URL}）",
|
||||
)
|
||||
parser.add_argument("-o", "--output", type=Path, required=True, help="解析结果 JSON 输出路径")
|
||||
parser.add_argument("--save-raw", type=Path, help="可选：保存 API 原始响应")
|
||||
|
||||
parser.add_argument("--province", help="高考省份")
|
||||
parser.add_argument("--classify", help="选科模式：文科/理科/物理/历史/综合")
|
||||
parser.add_argument("--subjects", help="选科组合，逗号分隔，如 物理,化学,生物")
|
||||
parser.add_argument("--grade-type", dest="grade_type", help="成绩类型：本科/专科（京沪津专科批）")
|
||||
parser.add_argument("--score", type=int, help="高考成绩")
|
||||
parser.add_argument("--rank", type=int, help="高考位次")
|
||||
parser.add_argument("--batch", help="填报批次，如 本科批")
|
||||
parser.add_argument(
|
||||
"--auto-batch",
|
||||
action=argparse.BooleanOptionalAction,
|
||||
default=True,
|
||||
help="是否通过 batch/list 接口自动解析批次（默认开启）",
|
||||
)
|
||||
parser.add_argument("--universitys", help="心仪高校，逗号分隔")
|
||||
parser.add_argument("--provinces", help="省份意向，逗号分隔")
|
||||
parser.add_argument("--tags", help="院校属性意向，逗号分隔")
|
||||
parser.add_argument("--major-class", dest="major_class", help="专业类意向，逗号分隔")
|
||||
parser.add_argument("--university-num", type=int, default=30)
|
||||
parser.add_argument("--major-num", type=int, default=6)
|
||||
parser.add_argument(
|
||||
"--is-adjust",
|
||||
action=argparse.BooleanOptionalAction,
|
||||
default=True,
|
||||
)
|
||||
parser.add_argument("--volunteer-type", default="ACADEMY_GROUP")
|
||||
parser.add_argument("--return-university-num", type=int, default=30)
|
||||
parser.add_argument("--intention-num", type=int, default=0)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
payload = build_request_payload(args)
|
||||
except (OSError, json.JSONDecodeError) as e:
|
||||
print(f"错误: 无法读取请求配置 — {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
if not args.student and not args.config:
|
||||
required = ["province", "classify", "score"]
|
||||
if not args.auto_batch:
|
||||
required.append("batch")
|
||||
missing = [k for k in required if payload.get(k) is None]
|
||||
if missing:
|
||||
print(
|
||||
f"错误: 缺少必填参数 {missing}，请使用 --student、--config 或补全命令行",
|
||||
file=sys.stderr,
|
||||
)
|
||||
return 1
|
||||
|
||||
# CLI 中 null 用字符串 "null" 传入时转为 None（subjects 在 sanitize 阶段按省处理）
|
||||
for key in ("gradeType", "rank", "universitys", "provinces", "tags", "majorClass"):
|
||||
if payload.get(key) == "null":
|
||||
payload[key] = None
|
||||
|
||||
try:
|
||||
validate_classify(str(payload.get("province", "")), payload.get("classify"))
|
||||
except ValueError as e:
|
||||
print(f"错误: {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
batch_resolution: dict[str, Any] | None = None
|
||||
if args.auto_batch:
|
||||
try:
|
||||
batch_name, selected, all_items = resolve_batch_via_api(payload, base_url=args.base_url)
|
||||
batch_resolution = {
|
||||
"source": "batch/list",
|
||||
"selected": selected,
|
||||
"available": all_items,
|
||||
}
|
||||
print(
|
||||
f"批次: {batch_name}（{selected.get('gradeType', '')} / {selected.get('type', '')}）",
|
||||
file=sys.stderr,
|
||||
)
|
||||
except (RuntimeError, ValueError, json.JSONDecodeError) as e:
|
||||
print(f"警告: 批次接口失败，使用静态兜底 — {e}", file=sys.stderr)
|
||||
payload["batch"] = resolve_batch_fallback(payload.get("province"), payload.get("batch"))
|
||||
batch_resolution = {"source": "fallback", "batch": payload.get("batch")}
|
||||
elif not payload.get("batch"):
|
||||
print("错误: 已关闭 --auto-batch 且未提供 batch", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
sanitize_payload_for_api(payload)
|
||||
try:
|
||||
validate_subjects_for_volunteer_api(payload)
|
||||
except ValueError as e:
|
||||
print(f"错误: {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
print(f"请求: {api_url(args.base_url)}", file=sys.stderr)
|
||||
try:
|
||||
raw = call_api(payload, base_url=args.base_url)
|
||||
except (RuntimeError, json.JSONDecodeError) as e:
|
||||
print(f"错误: {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
if args.save_raw:
|
||||
args.save_raw.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.save_raw.write_text(json.dumps(raw, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
|
||||
try:
|
||||
parsed = parse_api_response(raw)
|
||||
except ValueError as e:
|
||||
print(f"错误: {e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
parsed["request"] = payload
|
||||
if batch_resolution:
|
||||
parsed["batch_resolution"] = batch_resolution
|
||||
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.output.write_text(json.dumps(parsed, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"已保存解析结果: {args.output}（共 {parsed['stats']['total']} 所院校）")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
+221
@@ -0,0 +1,221 @@
|
||||
#!/usr/bin/env python3
|
||||
"""各省高考模式、classify 校验与 API 参数规范化。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
# 仅京沪津批次接口与志愿接口需要 gradeType
|
||||
PROVINCES_REQUIRING_GRADE_TYPE = frozenset({"北京", "上海", "天津"})
|
||||
|
||||
# 测试环境批次接口暂不支持
|
||||
UNSUPPORTED_PROVINCES = frozenset({"西藏"})
|
||||
|
||||
MODE_33 = frozenset({"上海", "北京", "天津", "山东", "浙江", "海南"})
|
||||
MODE_312 = frozenset({
|
||||
"广东", "江苏", "河北", "湖北", "湖南", "福建", "辽宁", "重庆", "甘肃",
|
||||
"黑龙江", "吉林", "安徽", "江西", "贵州", "广西", "云南", "内蒙古",
|
||||
"四川", "宁夏", "山西", "河南", "陕西", "青海",
|
||||
})
|
||||
MODE_OLD = frozenset({"新疆"})
|
||||
|
||||
VALID_CLASSIFY_BY_MODE: dict[str, frozenset[str]] = {
|
||||
"33": frozenset({"综合"}),
|
||||
"312": frozenset({"物理", "历史"}),
|
||||
"老高考": frozenset({"文科", "理科"}),
|
||||
}
|
||||
|
||||
MODE_LABELS: dict[str, str] = {
|
||||
"33": "3+3",
|
||||
"312": "3+1+2",
|
||||
"老高考": "老高考",
|
||||
}
|
||||
|
||||
SUBJECTS_312_ELECTIVES = frozenset({"化学", "生物", "政治", "地理"})
|
||||
SUBJECTS_33_POOL = frozenset({"物理", "历史", "化学", "生物", "政治", "地理"})
|
||||
SUBJECTS_33_ZHEJIANG = SUBJECTS_33_POOL | frozenset({"技术"})
|
||||
|
||||
|
||||
def parse_subjects_list(subjects: Any) -> list[str]:
|
||||
if subjects in (None, "", "null", "NULL"):
|
||||
return []
|
||||
return [s.strip() for s in str(subjects).split(",") if s.strip()]
|
||||
|
||||
|
||||
def get_province_mode(province: str | None) -> str:
|
||||
if not province:
|
||||
return "unknown"
|
||||
if province in UNSUPPORTED_PROVINCES:
|
||||
return "unsupported"
|
||||
if province in MODE_OLD:
|
||||
return "老高考"
|
||||
if province in MODE_33:
|
||||
return "33"
|
||||
if province in MODE_312:
|
||||
return "312"
|
||||
return "unknown"
|
||||
|
||||
|
||||
def validate_classify(province: str, classify: str | None) -> None:
|
||||
"""校验 classify 是否与省份选科模式匹配。"""
|
||||
mode = get_province_mode(province)
|
||||
if mode == "unsupported":
|
||||
raise ValueError(
|
||||
f"省份 {province} 在测试环境暂不支持志愿/批次接口（平台未接入该省数据）"
|
||||
)
|
||||
if mode == "unknown":
|
||||
return
|
||||
if not classify:
|
||||
valid = VALID_CLASSIFY_BY_MODE[mode]
|
||||
raise ValueError(
|
||||
f"缺少 classify：{province} 为{MODE_LABELS[mode]}模式，应填 {'/'.join(sorted(valid))}"
|
||||
)
|
||||
valid = VALID_CLASSIFY_BY_MODE[mode]
|
||||
if classify not in valid:
|
||||
raise ValueError(
|
||||
f"classify 与省份不匹配：{province} 为{MODE_LABELS[mode]}模式，"
|
||||
f"classify 应为 {'/'.join(sorted(valid))}，当前为 {classify!r}。"
|
||||
f"错误 classify 会导致批次接口返回空列表。"
|
||||
)
|
||||
|
||||
|
||||
def infer_grade_type(
|
||||
province: str | None,
|
||||
score: int | None,
|
||||
explicit: str | None = None,
|
||||
batch_pref: str | None = None,
|
||||
) -> str | None:
|
||||
"""京沪津推断本科/专科；其他省份返回 None。"""
|
||||
if province not in PROVINCES_REQUIRING_GRADE_TYPE:
|
||||
return None
|
||||
if explicit in ("本科", "专科"):
|
||||
return explicit
|
||||
if batch_pref == "专科批":
|
||||
return "专科"
|
||||
if batch_pref == "本科批":
|
||||
return "本科"
|
||||
if score is not None and score <= 450:
|
||||
return "专科"
|
||||
return "本科"
|
||||
|
||||
|
||||
def batch_list_query_params(payload: dict[str, Any]) -> dict[str, Any]:
|
||||
"""从请求体构建 batch/list 查询参数。"""
|
||||
province = payload.get("province")
|
||||
classify = payload.get("classify")
|
||||
score = payload.get("score")
|
||||
if not province or not classify or score is None:
|
||||
raise ValueError("批次接口需要 province、classify、score")
|
||||
|
||||
validate_classify(str(province), str(classify))
|
||||
|
||||
params: dict[str, Any] = {
|
||||
"province": province,
|
||||
"classify": classify,
|
||||
"score": int(score),
|
||||
}
|
||||
grade_type = payload.get("gradeType")
|
||||
if not grade_type and province in PROVINCES_REQUIRING_GRADE_TYPE:
|
||||
grade_type = infer_grade_type(
|
||||
str(province),
|
||||
int(score),
|
||||
batch_pref=payload.get("batch"),
|
||||
)
|
||||
if grade_type:
|
||||
payload["gradeType"] = grade_type
|
||||
if province in PROVINCES_REQUIRING_GRADE_TYPE and grade_type:
|
||||
params["gradeType"] = grade_type
|
||||
return params
|
||||
|
||||
|
||||
def normalize_subjects_for_api(payload: dict[str, Any]) -> None:
|
||||
"""按省份规则规范化 subjects 字段。"""
|
||||
province = payload.get("province")
|
||||
grade_type = payload.get("gradeType")
|
||||
subjects = payload.get("subjects")
|
||||
|
||||
if province == "新疆":
|
||||
if subjects in (None, "", "null", "NULL"):
|
||||
payload.pop("subjects", None)
|
||||
return
|
||||
|
||||
if province in PROVINCES_REQUIRING_GRADE_TYPE and grade_type == "专科":
|
||||
if subjects in (None, "", "null", "NULL"):
|
||||
payload["subjects"] = "null"
|
||||
return
|
||||
|
||||
if subjects in ("null", "NULL", ""):
|
||||
payload["subjects"] = None
|
||||
|
||||
|
||||
def sanitize_payload_for_api(payload: dict[str, Any]) -> None:
|
||||
"""发志愿接口前按省份规则清理请求体。"""
|
||||
province = payload.get("province")
|
||||
if province not in PROVINCES_REQUIRING_GRADE_TYPE:
|
||||
payload.pop("gradeType", None)
|
||||
normalize_subjects_for_api(payload)
|
||||
|
||||
|
||||
def validate_subjects_for_volunteer_api(payload: dict[str, Any]) -> None:
|
||||
"""志愿接口前校验 subjects 是否满足省份要求。"""
|
||||
province = str(payload.get("province") or "")
|
||||
mode = get_province_mode(province)
|
||||
label = MODE_LABELS.get(mode, mode)
|
||||
|
||||
if province == "新疆":
|
||||
return
|
||||
if province in PROVINCES_REQUIRING_GRADE_TYPE and payload.get("gradeType") == "专科":
|
||||
return
|
||||
|
||||
subjects = payload.get("subjects")
|
||||
if subjects in (None, "", "null", "NULL"):
|
||||
raise ValueError(
|
||||
f"缺少 subjects：{province}（{label}）需传选科组合，如 物理,化学,生物"
|
||||
)
|
||||
|
||||
parts = parse_subjects_list(subjects)
|
||||
classify = payload.get("classify")
|
||||
|
||||
if mode == "312":
|
||||
if len(parts) != 3:
|
||||
raise ValueError(
|
||||
f"subjects 格式错误：{province}（3+1+2）须传完整三科（首选科目+两门再选），"
|
||||
f"逗号分隔，如 classify=物理 时 subjects=物理,化学,生物；"
|
||||
f"不能只传两门再选科目。当前为 {subjects!r}（{len(parts)} 科）"
|
||||
)
|
||||
if classify not in parts:
|
||||
raise ValueError(
|
||||
f"subjects 与 classify 不一致：{province} classify={classify!r}，"
|
||||
f"subjects 须包含首选科目 {classify!r}，当前为 {subjects!r}"
|
||||
)
|
||||
electives = [p for p in parts if p != classify]
|
||||
if len(electives) != 2:
|
||||
raise ValueError(
|
||||
f"subjects 格式错误：{province}（3+1+2）除首选科目外还须两门再选"
|
||||
f"（化学/生物/政治/地理），当前为 {subjects!r}"
|
||||
)
|
||||
invalid = [p for p in electives if p not in SUBJECTS_312_ELECTIVES]
|
||||
if invalid:
|
||||
raise ValueError(
|
||||
f"subjects 含无效再选科目 {invalid}：3+1+2 再选仅限化学/生物/政治/地理"
|
||||
)
|
||||
return
|
||||
|
||||
if mode == "33" and province not in PROVINCES_REQUIRING_GRADE_TYPE:
|
||||
if len(parts) != 3:
|
||||
raise ValueError(
|
||||
f"subjects 格式错误：{province}（3+3）须传完整三科选考科目，"
|
||||
f"逗号分隔，如 物理,化学,生物；当前为 {subjects!r}（{len(parts)} 科）"
|
||||
)
|
||||
pool = SUBJECTS_33_ZHEJIANG if province == "浙江" else SUBJECTS_33_POOL
|
||||
invalid = [p for p in parts if p not in pool]
|
||||
if invalid:
|
||||
raise ValueError(f"subjects 含无效科目 {invalid}：{province} 选考范围见 reference.md")
|
||||
return
|
||||
|
||||
if mode == "33" and province in PROVINCES_REQUIRING_GRADE_TYPE:
|
||||
if payload.get("gradeType") == "本科" and len(parts) != 3:
|
||||
raise ValueError(
|
||||
f"subjects 格式错误：{province}（3+3 本科）须传完整三科，"
|
||||
f"如 物理,化学,生物；当前为 {subjects!r}"
|
||||
)
|
||||
+166
@@ -0,0 +1,166 @@
|
||||
#!/usr/bin/env python3
|
||||
"""批量测试 batch/list 接口：各省 × 正确/错误 classify × 高/低分。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from urllib.error import HTTPError, URLError
|
||||
from urllib.parse import urlencode
|
||||
from urllib.request import Request, urlopen
|
||||
|
||||
# 允许从 scripts/ 目录直接运行
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent))
|
||||
|
||||
from province_config import ( # noqa: E402
|
||||
MODE_312,
|
||||
MODE_33,
|
||||
MODE_OLD,
|
||||
UNSUPPORTED_PROVINCES,
|
||||
batch_list_query_params,
|
||||
infer_grade_type,
|
||||
)
|
||||
|
||||
BASE_URL = "https://publicapi.chatglm.cn/chatglm_public/skill-test"
|
||||
BATCH_LIST_PATH = "/zp/volunteer/batch/list"
|
||||
|
||||
PROVINCE_CLASSIFY: dict[str, list[str]] = {}
|
||||
for p in MODE_33:
|
||||
PROVINCE_CLASSIFY[p] = ["综合"]
|
||||
for p in MODE_312:
|
||||
PROVINCE_CLASSIFY[p] = ["物理", "历史"]
|
||||
for p in MODE_OLD:
|
||||
PROVINCE_CLASSIFY[p] = ["文科", "理科"]
|
||||
for p in UNSUPPORTED_PROVINCES:
|
||||
PROVINCE_CLASSIFY[p] = ["综合"]
|
||||
|
||||
WRONG_CLASSIFY = {
|
||||
"综合": "物理",
|
||||
"物理": "综合",
|
||||
"历史": "理科",
|
||||
"文科": "物理",
|
||||
"理科": "历史",
|
||||
}
|
||||
|
||||
HIGH_SCORE = 650
|
||||
LOW_SCORE = 280
|
||||
|
||||
|
||||
def call_batch_from_payload(payload: dict) -> dict:
|
||||
params = batch_list_query_params(dict(payload))
|
||||
query = urlencode(params)
|
||||
url = f"{BASE_URL.rstrip('/')}{BATCH_LIST_PATH}?{query}"
|
||||
req = Request(url, method="GET", headers={"Accept": "*/*"})
|
||||
try:
|
||||
with urlopen(req, timeout=30) as resp:
|
||||
text = resp.read().decode(resp.headers.get_content_charset() or "utf-8")
|
||||
except HTTPError as e:
|
||||
return {"http": e.code, "body": e.read().decode("utf-8", errors="replace")}
|
||||
except URLError as e:
|
||||
return {"error": str(e.reason)}
|
||||
try:
|
||||
return json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
return {"error": "invalid json", "body": text[:200]}
|
||||
|
||||
|
||||
def summarize_result(raw: dict) -> str:
|
||||
if "error" in raw or "http" in raw:
|
||||
return f"FAIL:{raw.get('http') or raw.get('error')}"
|
||||
status = raw.get("status")
|
||||
msg = raw.get("message", "")
|
||||
result = raw.get("result")
|
||||
if status != 0:
|
||||
return f"FAIL:status={status},{msg}"
|
||||
if not isinstance(result, list):
|
||||
return "FAIL:result not list"
|
||||
if not result:
|
||||
return "OK:empty[]"
|
||||
batches = [f"{x.get('batch')}({x.get('gradeType')}/{x.get('type')})" for x in result[:3]]
|
||||
extra = f"+{len(result)-3}" if len(result) > 3 else ""
|
||||
return f"OK:{len(result)}项 [{', '.join(batches)}{extra}]"
|
||||
|
||||
|
||||
def main() -> int:
|
||||
rows: list[dict] = []
|
||||
provinces = sorted(PROVINCE_CLASSIFY.keys(), key=lambda x: (x not in UNSUPPORTED_PROVINCES, x))
|
||||
|
||||
print("=== 正确 classify × 高分/低分（京沪津含 gradeType）===")
|
||||
for province in provinces:
|
||||
classifies = PROVINCE_CLASSIFY[province]
|
||||
primary = classifies[0]
|
||||
for score_label, score in [("高", HIGH_SCORE), ("低", LOW_SCORE)]:
|
||||
payload = {
|
||||
"province": province,
|
||||
"classify": primary,
|
||||
"score": score,
|
||||
"batch": "本科批",
|
||||
}
|
||||
if province in ("北京", "上海", "天津"):
|
||||
payload["gradeType"] = infer_grade_type(province, score, batch_pref="本科批")
|
||||
try:
|
||||
raw = call_batch_from_payload(payload)
|
||||
except ValueError as e:
|
||||
raw = {"status": -1, "message": str(e)}
|
||||
summary = summarize_result(raw)
|
||||
rows.append({
|
||||
"province": province,
|
||||
"classify": primary,
|
||||
"score": score,
|
||||
"score_label": score_label,
|
||||
"gradeType": payload.get("gradeType"),
|
||||
"wrong_classify": False,
|
||||
"summary": summary,
|
||||
"status": raw.get("status"),
|
||||
"message": raw.get("message"),
|
||||
"count": len(raw.get("result") or []) if isinstance(raw.get("result"), list) else None,
|
||||
})
|
||||
mark = "✓" if summary.startswith("OK") and "empty" not in summary else "✗"
|
||||
gt = payload.get("gradeType") or ""
|
||||
print(f" {mark} {province:4s} {primary} {gt:2s} {score_label}分({score}) → {summary}")
|
||||
time.sleep(0.12)
|
||||
|
||||
print("\n=== 错误 classify 探测（高分）===")
|
||||
for province in provinces:
|
||||
if province in UNSUPPORTED_PROVINCES:
|
||||
continue
|
||||
primary = PROVINCE_CLASSIFY[province][0]
|
||||
wrong = WRONG_CLASSIFY.get(primary, "综合" if primary != "综合" else "物理")
|
||||
ok_payload = {"province": province, "classify": primary, "score": HIGH_SCORE, "batch": "本科批"}
|
||||
wrong_payload = dict(ok_payload, classify=wrong)
|
||||
if province in ("北京", "上海", "天津"):
|
||||
ok_payload["gradeType"] = infer_grade_type(province, HIGH_SCORE, batch_pref="本科批")
|
||||
wrong_payload["gradeType"] = ok_payload["gradeType"]
|
||||
try:
|
||||
raw_ok = call_batch_from_payload(ok_payload)
|
||||
raw_wrong = call_batch_from_payload(wrong_payload)
|
||||
except ValueError as e:
|
||||
print(f" ! {province}: validate error {e}")
|
||||
continue
|
||||
ok_s = summarize_result(raw_ok)
|
||||
wrong_s = summarize_result(raw_wrong)
|
||||
if wrong_s != ok_s:
|
||||
print(f" ! {province}: 正确({primary})={ok_s} vs 错误({wrong})={wrong_s}")
|
||||
time.sleep(0.12)
|
||||
|
||||
out = Path(__file__).resolve().parent.parent / "output" / "batch_api_test_results.json"
|
||||
out.parent.mkdir(parents=True, exist_ok=True)
|
||||
out.write_text(json.dumps(rows, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"\n详细结果已保存: {out}")
|
||||
|
||||
fails = [
|
||||
r for r in rows
|
||||
if not r["summary"].startswith("OK") or "empty[]" in r["summary"]
|
||||
if not r.get("wrong_classify")
|
||||
]
|
||||
print(f"\n=== 汇总: 正确参数异常 {len(fails)} 项 ===")
|
||||
for r in fails:
|
||||
print(f" {r['province']} {r['classify']} {r['score_label']}分 → {r['summary']} | {r.get('message')}")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in new issue
Block a user