摘 要 随着车联网技术日趋成熟,在得到准确、实时的各种车辆数据的基础上,智能、便捷、高效的车联网应用应运而生,从而实现了对车队车辆的位置、里程、速度、油耗等关键性数据进行有效地监控和管理,达到了高效管理、成本降低、智能运营、出行便捷、安全行车的目的。车联网在车队管理及安全运营所体现的巨大效益,已经为人们充分认可。 本文介绍了基于车联网数据实时计算的车队油耗管理系统的系统设计、实现方法及实现的功能等,包括数据流、数据存储、后台数据分析处理以及前台界面展示等的设计与实现。系统使用 Kafka集群用于车辆原始数据的实时接收,使用 Jstorm 系统用于车辆原始数据的实时处理,本系统的后台主要通过对车队车辆数据的分析、处理及存储实现各个功能,采用 PostgreSQL 数据库,前台展示界面使用 JSP 等相关技术进行实现,并引入ECharts 图表进行数据图表展示。 本课题的研究结果,实现了对车队车辆信息管理、对 OBD原始数据进行数据质量统计、对车辆剩余油量的填充处理、对车辆油量事件的判别等功能,经测试,可应用于车联网应用中。 59631 毕业论文关键词:车联网;实时接收;实时处理;车辆数据;油耗管理
Abstract With development of the vehicle networking technology, intelligent, convenient and efficient vehicle networking applications come into being based on the accurate and real-time vehicle data, consequently, effective monitoring and management of the location, mileage, speed, fuel consumption and other key data of the vehicles are realized, so that the purpose of efficient management, cost reduction, intelligent operation, travel convenience and safe driving are achieved. The huge benefit of vehicle networking in fleet management and safety operation has been fully recognized by people. This paper introduces the system design, implementation method and functions of the management system of fleet’s fuel consumption based on vehicle networking data and real-time calculation, including the design and implementation of the data flow, data storage, data analysis, backstage processing and front-end interface display, etc. This system uses the Kafka cluster for the real-time receiving of vehicles' original data, and uses Jstorm system for real-time processing of vehicles' original data. The backstage of this system realizes each function mainly through the analysis, processing and storage of the vehicles’ data, using the PostgreSQL database, realizes the front-end interface through using JSP and other relevant technology, and introduces ECharts chart to show the data. The research results of this topic achieve many functions, such as management of vehicles’ information, data quality statistics of OBD primary data, filling processing of vehicles’ remaining fuel and the discriminant of vehicles’ fuel events, after testing, these functions can be applied to vehicle networking applications.
Keywords:vehicle networking; real-time receiving; real-time processing; vehicle data; fuel consumption management