基于盲均衡算法的网络大数据异常节点检测

A nomalous Node Detection of Network Big Data Based on Blind Equalization Algorithm

网络大数据具有复杂性、多样性,其在流动期间容易出现异常节点。工作人员需采用智能性能检测的方式,才能减少网络故障问题的发生。相关部门基于网络大数据提出了异常节点的检测方法,采用传感序列采集模型,应用盲均衡算法提取网络环境中的噪声均值,以展现网络大数据异常节点的特征。基于此,文中结合实际,简要分析了盲均衡算法,阐述了基于盲均衡算法的网络大数据异常节点检测方法,以期为相关部门的工作提供支持。

Network big data has complexity and diversity,and it is prone to abnormal nodes during the flow.Staff need to adopt intelligent performance detection methods to reduce the occurrence of network failure problems.Relevant departments have proposed a detection method for abnormal nodes based on network big data,using a sensor sequence acquisi-tion model,and applying a blind equalization algorithm to extract the mean noise in the network environment to show the characteristics of abnormal nodes in network big data.Based on this,this paper briefly analyzes the blind equalization algo-rithm combined with practice,and expounds the network big data abnormal node detection method based on the blind e-qualization algorithm,in order to provide support for the work of relevant departments.