目前提出的舰船通信网络失效节点自动识别方法耗时时间较长，冗余节点关闭率较低。为了解决上述问题，提出基于数据挖掘的舰船通信网络失效节点自动识别方法。确定目标数据集，得到特征提取最优解集合和整体最优解集合，识别提取异常数据特征，建立异常数据聚类集合，引入数据密度系数计算$ i $节点数据聚类结果优化数值。构建失效节点自动识别函数，得到检索半径的适应目标函数，深入挖掘数据，通过统一化处理实现网络失效节点自动识别。实验结果表明，基于数据挖掘的舰船通信网络失效节点自动识别方法的识别准确率在99%以上，耗时时间低于4 s。
The current proposed method for automatic identification of failed nodes in ship communication network is time-consuming, and redundant nodes have a low shutdown rate. In order to solve the above problems, a new method for automatic identification of failed nodes in ship communication network is studied based on data mining. Determine the target data set, obtain the feature extraction optimal solution set and the overall optimal solution set, identify and extract the abnormal data features, establish the abnormal data clustering set, and introduce the data density coefficient to calculate the optimal value of the node data clustering result. The automatic identification function of the failed node is constructed, the adaptive objective function of the retrieval radius is obtained, the data mining is carried out, and the automatic identification of the network failure node is realized through unified processing. The experimental results show that the time-consuming time of the automatic identification method of the failure node of the ship communication network based on data mining is less than 4 s.
2022,44(19): 146-149 收稿日期：2022-05-17
DOI：10.3404/j.issn.1672-7649.2022.19.029
分类号：TP391.4
作者简介：陈文庆(1981-)，男，硕士，副教授，研究方向为大数据技术及计算机多媒体技术
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