针对当前车主保养记录分散、提醒缺乏针对性的核心痛点,结合2024年中国汽车后市场多样化发展趋势,设计并实现汽车保养智能记录与推荐系统。系统以智能记录和精准提醒为核心,构建感知层、数据层、应用层三层架构：感知层采集图片单据,集成YOLO v8定位并校正单据区域,使用OCR 技术提取VIN码;数据层采用MySQL主从架构、Redis缓存与阿里云OSS,存储车辆档案和保养记录等信息;应用层基于三维因子生成精准提醒,并结合用户的反馈动态迭代系统消息提醒策略。测试结果表明,系统单据关键信息识别准确率达99.5%,记录查询响应≤1秒,可有效减少人工录入操作,解决传统保养管理效率低、提醒针对性不强的问题,为车主提供轻量化、高易用性的一站式保养管理服务。

Aiming at the core pain points of current car owners, such as scattered maintenance records and lack of targeted reminders, and combining with the diversified development trend of China's automobile aftermarket in 2024, an intelligent record and recommendation system for automobile maintenance is designed and implemented. With intelligent recording and accurate reminders as the core, the system constructs a three-layer architecture consisting of a perception layer, a data layer, and an application layer: The perception layer collects image documents, integrates YOLOv8 to locate and correct document areas, and uses OCR (Optical Character Recognition) technology to extract VIN (Vehicle Identification Number) codes. The data layer adopts MySQL master-slave architecture, Redis cache, and Alibaba Cloud OSS (Object Storage Service) to store information such as vehicle files and maintenance records. The application layer generates accurate reminders based on three-dimensional factors and dynamically iterates the system message reminder strategy in combination with user feedback.Test results show that the recognition accuracy of key information in system documents reaches 99.5%, and the record query response time is less than 1 second. The system can effectively reduce manual entry operations, solve the problems of low efficiency of traditional maintenance management and lack of targeted reminders, and provide car owners with a lightweight and highly user-friendly one-stop maintenance management service.