基于图谱特征提取的宽带城域光纤网络异常识别方法

Anomaly recognition method of broadband metropolitan optical fiber network based on map feature extraction

为了实现对宽带城域光纤网络异常信息的识别,提出基于图谱特征提取的宽带城域光纤网络异常识别方法。构建宽带城域光纤网络异常信息特征检测模型,采用大数据融合方法进行宽带城域光纤网络的异常图谱特征分析,采用统计相关性分析方法进行宽带城域光纤网络的输出负载均衡控制,提取宽带城域光纤网络大数据的异常特征量,采用模糊自相关性融合聚类分析方法进行宽带城域光纤网络监控数据的聚类处理,建立宽带城域光纤网络输出信息的谱特征检测模型,通过负载均衡控制方法,实现宽带城域光纤网络的异常识别。仿真结果表明,采用该方法进行宽带城域光纤网络异常信息识别的实时性较好,时延为150 ms以下,时间开销为21 ms,识别精度达到97.8%,整体性能较优越。

In order to recognize the abnormal information of broadband metropolitan optical fiber network,an abnormal recognition method of broadband metropolitan optical fiber network based on map feature extraction is proposed.The abnormal information feature detection model of broadband metro optical fiber network is constructed,the abnormal map feature analysis of broadband metro optical fiber network is carried out by using big data fusion method,the output load balance control of broadband metro optical fiber network is carried out by using statistical correlation analysis method,the abnormal feature quantity of big data of broadband metro optical fiber network is extracted,the monitoring data of broadband metro optical fiber network is clustered by using fuzzy autocorrelation fusion clustering analysis method,and the spectral feature detection model of output information of broadband metro optical fiber network is established. The simulation results show that this method has good real-time performance in identifying abnormal information in broadband metropolitan optical fiber network,with a time delay of less than 150 ms,a time cost of 21 ms,an identification accuracy of 97. 8%,and superior overall performance.