基于Kafka和Storm的车辆套牌实时分析存储系统

城市机动车数量、出行量的增加，使得车辆套牌现象屡禁不止.交管部门为了解决套牌监测的难题，采用传统的识别方式（如基于人工识别、基于牌照识别、基于射频识别等）.然而面对海量的日志记录，这些方式普遍存在效率低、实时性差的问题.为此引入大数据技术，提出一个基于Kafka和Storm的车辆套牌实时分析存储系统.Kafka可以作为中间件进行缓存，提高数据采集和数据分析的同步性，还能避免数据丢失；Storm框架可以实现日志信息的实时计算，然后将套牌车辆信息存入指定文档中.整个系统具有实时、分布式存储、稳定、可扩展等特性.

Real-Time Analysis Storage System for Fake Plate Vehicle Based on Kafka and Storm

With the increase of the number of motor vehicles and vehicle traffic in the city, the phenomenon of fake plate vehicle appears repeatedly. In order to solve the problem of fake plate monitoring, the traffic management departments adopt traditional identification methods, such as manual identification, license plate recognition, radio frequency identification, etc. Nevertheless, facing the massive log records, these methods generally have problems of low efficiency and poor real-time performance. So big data technology was introduced, and fake plate vehicle real-time analysis storage system based on Kafka and Storm was proposed. Kafka can be used as a middleware for caching, improving the synchronization of data collection and data analysis, and avoiding data loss. The Storm framework can realize real-time calculation of log information, and then store the information of the fake plate vehicles in the specified document. The entire system has real-time, distributed storage, stability, scalability, and so on.