分布式手机信令数据采集与分析技术研究
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摘要
针对智能手机端分布范围分散导致的大规模信令数据存储和实时分析处理困难的问题,文中设计了一种基于分布式计算的手机信令数据采集与分析技术方案。基于分布式信令数据采集技术结构,方案集成了4种分布式数据库以实现分布式信令的数据存储。另外,结合Spark技术,所设计的分布式信令数据分析模块可以实时对采集的信令进行分析和整合。该分布式信令数据采集与分析方案可以适用于大规模手机信令数据的采集和处理,为分布式信令监测解决方案的设计提供了参考。
Aiming at the problem of large-scale signaling data storage and real-time analysis caused by the dispersion of the distribution of smart phones, this paper designed a mobile phone signaling data acquisition and analysised technology based on distributed computing. This paper designed a distributed signaling data acquisition technology structure and integrates four distributed databases to realize distributed signaling data storage. In addition, combined with Spark technology, the distributed signaling data analysis module designed in this paper can analyze and integrate the collected signaling in real time. The distributed signaling data acquisition and analysised scheme designed in this paper could be applied to the collection and processing of large-scale mobile phone signaling data, which had certain significance for distributed signaling monitoring.
Aiming at the problem of large-scale signaling data storage and real-time analysis caused by the dispersion of the distribution of smart phones, this paper designed a mobile phone signaling data acquisition and analysised technology based on distributed computing. This paper designed a distributed signaling data acquisition technology structure and integrates four distributed databases to realize distributed signaling data storage. In addition, combined with Spark technology, the distributed signaling data analysis module designed in this paper can analyze and integrate the collected signaling in real time. The distributed signaling data acquisition and analysised scheme designed in this paper could be applied to the collection and processing of large-scale mobile phone signaling data, which had certain significance for distributed signaling monitoring.
引文
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[2] 詹益旺.基于手机信令的道路交通状态识别及预测研究[D].广州:华南理工大学,2017.Zhan Yiwang.Research on road traffic status recognition and prediction based on mobile signaling [D].Guangzhou:South China University of Technology,2017.
[3] 马彦力.三维GIS大数据量场景快速可视化关键技术研究[D].杭州:浙江大学,2013.Ma Yanli.Research on key technologies of rapid visualization of large data scene in 3D GIS [D].Hangzhou:Zhejiang University,2013.
[4] 任飞,秦雅娟,周华春,等.内容中心网络分布式移动性管理[J].北京邮电大学学报,2016,39(4):40-44.Ren Fei,Qin Yajuan,Zhou Huachun,et al.Content-centric network distributed mobility management [J].Journal of Beijing University of Posts and Telecommunications,2016,39(4):40-44.
[5] 张扬.信令监测系统存在的问题以及优化方案[J].电信技术,2012(10):19-22.Zhang Yang.Problems and optimization of signaling monitoring system[J].Telecommunication Technology,2012(10):19-22.
[6] 莫少莹.一种基于光纤通信信令采集实验教学系统的设计[D].南宁:广西大学,2015.Mo Shaoying.Design of an experimental teaching system for signal acquisition based on optical fiber communication[D].Nanning:Guangxi University,2015.
[7] 张帆.基于载波聚合技术的FDD-LTE网络设计方案[J].电子设计工程,2017,25(3):93-95.Zhang Fan.FDD-LTE network design scheme based on carrier aggregation technology[J].Electronic Design Engineering,2017,25(3):93-95.
[8] 张佳隆,刁鸣.LTE核心网EMM协议栈服务请求过程的研究与实现[J].电子科技,2014,27(3):25-27,30.Zhang Jialong,Diao Ming.Research and implementation of EMM protocol stack service request process in LTE core network [J].Electronic Science and Technology,2014,27(3):25-27,30.
[9] 董斌,杨迪,王铮,等.流计算大数据技术在运营商实时信令处理中的应用[J].电信科学,2015,31(10):165-171.Dong Bin,Yang Di,Wang Zheng,et al.Application of stream computing big data technology in operator real-time signaling processing [J].Telecommunications Science,2015,31(10):165-171.
[10] 朱丽雅.基于信令数据分离的服务合成技术研究[J].微计算机信息,2011,27(7):175-176.Zhu Liya.Research on service composition technology based on signaling data separation [J].Microcomputer Information,2011,27(7):175-176.
[11] 吴修权,钟其柱,梅艳.互联网业务质量智能拨测分析系统的研究及应用[J].电信工程技术与标准化,2018,31(7):49-54.Wu Xiuquan,Zhong Qizhu,Mei Yan.Research and application of intelligent distribution and analysis system for internet service quality[J].Telecommunication Engineering Technology and Standardization,2018,31(7):49-54.