Summary
Built backend capabilities for medical paper retrieval, filtering, subscription, deep reading, payment points, and mini-program APIs.
Business Value
Greatly improved efficiency for researchers to access frontier information.
Engineering Depth
Demonstrates requirements analysis and system architecture in a vertical domain (Medical).
Evidence
实习证明
Delivery record · Confidence Medium · Verified 2026-02-10
- Evidence level: strict review (core sections only show verifiable metrics)
- Source type: Project delivery record
- Source link: no public link provided, review against delivery records
- Verified at: 2026-02-10 (127 days ago, fresh evidence)
Rationale: Medium confidence: missing a public source link.
Repository · Confidence High · Verified 2026-03-31
- Evidence level: strict review (core sections only show verifiable metrics)
- Source type: Repository / code records
- Source link: no public link provided, review against delivery records
- Verified at: 2026-03-31 (78 days ago, fresh evidence)
Rationale: High confidence: organized under strict evidence rules, traceable to repository or code records, verified 78 days ago.
Background
医学专家需要持续跟踪骨科等方向的最新论文,但通用搜索入口噪音大、订阅和深度阅读流程割裂,难以形成稳定科研工作流。
Challenge
系统既要稳定抓取和入库 PubMed 论文,又要支持面向小程序的搜索、收藏、订阅、深度分析、支付积分和用户行为治理,业务链路较长。
Action and Results
Solution
- 数据底座:以 Django + MySQL 建模论文、主题、订阅、收藏、推送历史等核心实体,并通过 PubMed spider、DOI/期刊补全命令和 Celery 任务维护数据更新。
- 搜索与分析:提供关键词检索、AI 关键词生成、AI 搜索、深度分析与个性化推荐接口,降低从问题到论文集合的检索成本。
- 用户与运营:实现收藏、订阅频率/渠道配置、推送历史、积分记录、VIP、邀请码与反馈体系。
- 交易闭环:接入微信支付,支持会员/积分商品购买和支付回调处理,支撑小程序持续运营。
Result
形成面向医学专家的小程序后端闭环,覆盖论文抓取、检索、AI 分析、订阅推送与会员积分能力,可支撑日常科研信息获取与运营迭代。
Key Signals
Connected paper crawling, search APIs, and AI-assisted analysis. Supported subscription tasks and recommendation flows. Integrated WeChat Pay, points, VIP, and invitation-code operations. Tech Stack
DjangoMySQLRedisCeleryPubMedOpenAIWeChat Mini ProgramWeChat Pay