摘要： 针对现有公安违法取证分析系统存在效率低下、功能单一的问题,提出基于云平台和大数据的违法取证分析系统。利用MongoDB存储原始海量的车辆轨迹数据,采用MapReduce分布式计算对轨迹数据进行挖掘与筛选,通过大数据平台上的系统调度器对系统各层资源进行有效调度并监控系统运行状态。基于提取的离散轨迹数据对违反规定时间行驶、违法规定路线行驶、疲劳驾驶3种交通违法行为进行取证分析,从而找出违法车辆。实验结果表明,当车辆轨迹数据规模较大时,该系统仍具有较高的运行效率及稳定性。

关键词: 大数据, 数据挖掘, 交通监控, 取证分析, 并行处理

Abstract: Aiming at the problems of low efficiency and single function of the existing illegal forensics analysis system for public security,this paper proposes an illegal forensics analysis system based on cloud platform and big data.It uses MongoDB to store original and mass vehicle trajectory data and MapReduce distributed computing to mine and screen trajectory data.The system scheduler on big data platform is used to schedule the resources of the system and monitor the running state of the system.Based on the extraction of discrete trajectory data for violation of the provisions of time travel,illegal provisions of the route,and fatigue driving for forensic analysis to find illegal vehicles.Experimental result shows that,when the vehicle trajectory data scale is large,the system still has high efficiency and stability.

Key words: big data, data mining, traffic monitoring, forensic analysis, parallel processing

中图分类号: 

• TP39