摘要
针对传统的监控方法监测故障需要时间较长的问题,研究基于网络数据分析的物联网终端监控方法。对物联网终端数据进行过滤、提取数据特征、数据清洗等一系列处理后,将对数据集合进行分析。使用聚类算法对物联网终端数据进行聚类,通过计算样本数据间距离,得到离群因子阈值。根据离群因子阈值对物联网终端数据进行处理,得到物联网终端异常数据。对异常数据进行分类、监测、识别,完成对基于网络数据分析的物联网终端监控。通过与传统方法的对比实验,验证了本文设计的监控方法能够有效缩短物联网终端故障检测时间,具有更好的使用价值。
Aiming at the problem that traditional monitoring methods need a long time to monitor faults,this paper studies the monitoring methods of Internet of Things terminal based on network data analysis.After a series of processing,such as filtering,extracting data features and data cleaning,the data set will be analyzed.Clustering algorithm is used to cluster the terminal data of the Internet of Things,and the threshold of outlier factor is obtained by calculating the distance between sample data.According to the threshold of outlier factor,the terminal data of the Internet of Things are processed to get the abnormal data of the terminal of the Internet of Things.To classify,monitor and identify abnormal data,complete the monitoring of Internet of Things terminal based on network data analysis.Compared with traditional methods,the monitoring method designed in this paper can effectively shorten the time of terminal fault detection in the Internet of Things,and has better application value.
作者
王聪
Wang Cong(Pingdingshan Polytechnic College of Computer and Software Engineering,Pingdingshan Henan,467001)
出处
《电子测试》
2019年第17期57-58,128,共3页
Electronic Test
关键词
网络数据分析
物联网终端
监控研究
聚类
network data analysis
Internet of things terminal
monitoring research
clustering
