基于值导数GRU的电力信息通信数据流量异常检测方法研究

Research on Anomaly Detection Method of Power InformationCommunication Data Traffic Based on Value Derivative GRU

为提高电力信息通信数据流量异常检测的准确度和灵敏度,提出基于值导数门控循环单元(GRU)的电力信息通信数据流量异常检测方法。采用小波变换方法,对电力信息通信数据流量进行降噪处理。利用改进聚类算法,对降噪后电力信息通信数据流量进行聚类处理。将聚类后电力信息通信数据流量输入值导数GRU模型,以实现电力信息通信流量异常检测。试验结果表明:该方法具有较好的电力信息通信数据流量降噪效果,能够有效提高电力信息通信数据流量异常检测准确度和灵敏度。该方法可用于网络通信、物联网中,对信息通信技术的发展具有重要意义。

To improve the accuracy and sensitivity of power information communication data traffic anomaly detection,the power information communication data traffic anomaly detection method based on value derivative gated recurrent unit (GRU) is proposed.The wavelet transform method is used to reduce the noise of power information communication data traffic.Utilizing the improved clustering algorithm,the noise reduction power information communication data traffic is clustered.The clustered power information communication data traffic is input into the value derivative GRU model to realize the power information communication traffic anomaly detection.The experimental results show that the method has better noise reduction effect of power information communication data traffic and can effectively improve the accuracy and sensitivity of power information communication data traffic anomaly detection.The method can be used in network communication and Internet of Things,which is of great significance to the development of information and communication technology.