智能终端海量数据采集与实时分析设计和应用研究
Design and Application of Massive Data Acquisition and Real-Time Analysis of Intelligent Terminal
摘要:
针对各种场景下的海量终端的部署,本文设计了一套海量数据采集与实时分析系统,具体在数据采集模块,通过借助Kafka消息队列,实现数据的高并发接入;在数据分析模块,借助大数据流处理系统Storm,在保证高可靠性的前提下,实现数据的实时处理,并通过相应的优化设计,解决海量终端接入网络时的高并发访问与数据处理需求;通过可视化设计以及实验验证本文方法的有效性,系统具有低延迟,高吞吐,可拓展等特点,能够满足车联网海量数据处理要求,具有很强的实用价值,目前本文提出的方法已经应用在实际场景中,为20多万台北斗定位终端提供服务。
Abstract:
For the deployment of mass terminals in various scenarios, this paper designs a set of mass data acquisition and real-time analysis system. In the data acquisition module, with the help of Kafka message queue, the high concurrent access of data is realized; in the data analysis module, with the help of the big data stream processing system storm, the real-time data processing is realized on the premise of high reliability, and through the corresponding optimization design, the high concurrent access and data processing requirements of massive terminals accessing the network are solved; through the visual design and experimental verification, the effectiveness of this method, the system has the characteristics of low latency, high throughput, scalability, and can meet the requirements of massive data processing in the Internet of vehicles, which has strong practical value. At present, the method proposed in this paper has been applied in the actual scene, providing services for more than 200,000 BD-based positioning terminals.
参考文献
|
[1]
|
林平荣, 陈泽荣, 施晓权. 高并发多线程竞争共享资源架构[J]. 计算机工程与设计, 2020, 41(11): 3282-3288.
|
|
[2]
|
袁喆, 文继荣, 魏哲巍, 刘家俊, 姚斌, 郑凯. 大数据实时交互式分析[J]. 软件学报, 2020, 31(1): 162-182.
|
|
[3]
|
戴琳琳. 大数据背景下车联网的应用分析[J]. 电子技术与软件工程, 2020, 187(17): 169-170.
|
|
[4]
|
刘静, 吴仲城, 李芳, 张春风, 陈杰. 基于Boost.Asio的智能车载终端数据采集系统[J]. 计算机应用与软件, 2018(2): 248-255.
|
|
[5]
|
狄程, 杨中国, 韩燕波, 等. 面向流数据的实时处理及服务化系统[J]. 重庆大学学报, 2020, 43(7): 75-83.
|
|
[6]
|
王绪亮, 聂铁铮, 唐欣然, 黄菊, 李迪, 闫铭森, 刘畅. 流式数据处理的动态自适应缓存策略研究[J]. 计算机科学, 2020, 47(11): 130-135.
|
|
[7]
|
McCreadie, R., Macdonald, C., Ounis, I., et al. (2013) Scalable Distributed Event Detection for Twitter. 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, 6-9 October 2013, 543-549. [Google Scholar] [CrossRef]
|
|
[8]
|
Nair, L.R. and Shetty, S.D. (2018) Applying Spark Based Machine Learning Model on Streaming Big Data for Health Status Prediction. Computers & Electrical Engineering, 65, 393-399. [Google Scholar] [CrossRef]
|
|
[9]
|
杨立鹏, 张仰森, 张雯, 等. 基于Storm实时流式计算框架的网络日志分析方法[J]. 计算机科学, 2019, 46(9): 183-190.
|
|
[10]
|
董楠楠, 单晓欢, 牟有静. 基于Hadoop和MapReduce的大数据处理系统设计与实现[J]. 信息通信, 2020(6): 29-31.
|
|
[11]
|
孔德丽, 屈会雪, 卞志勇. 浅析基于Hadoop的高校大数据云平台设计[J]. 机械制造与自动化, 2020, 49(1): 101-102. [Google Scholar] [CrossRef]
|