摘要
为了提高工业电气设备大数据分析和识别能力,提出一种基于模糊信息熵特征提取的工业电气设备大数据特征识别方法。通过34980A数据采集器获取工业电气设备参数,结合信息流融合调度方法与期望频繁项(EFI)采样方法融合数据参数,并采用多分布的传感器阵列进行工业电气设备大数据采样,得到电气设备大数据。结合数据聚类方法,并根据大数据的个体差异度进行工业电气设备大数据信息流非线性特征重组。利用重组结果进行数学化处理,进行特征匹配,为特征识别提供可依基础,最终实现大数据的多特征识别。通过仿真结果表明,采用该方法进行工业电气设备大数据特征识别的精度较高,特征识别过程的收敛性较好,提高了工业电气设备的信息化管理和监测能力。
In order to improve the ability of big data analysis and recognition of industrial electrical equipment,a feature recognition method based on fuzzy information entropy feature extraction is proposed.The parameters of industrial electrical equipment are obtained by 34980A data collector,which are combined with the fusion scheduling method of information flow and the expected frequent term(EFI)sampling method.Big data sampling of industrial electrical equipment is carried out by using multi-distributed sensor array for the electrical equipment big data.Combined with the data clustering method,and according to the individual difference degree of big data,the nonlinear characteristics of big data information flow of industrial electrical equipment are reorganized.The reconstruction results are used for mathematical processing and feature matching to provide a reliable basis for feature recognition and finally realize big data’s multi-feature recognition.According to simulation,the proposed method has high precision in feature recognition of big data of industrial electrical equipment and excellent convergence in feature recognition process,which improves the information management and monitoring ability of industrial electrical equipment.
作者
马文
田园
MA Wen;TIAN Yuan(Information Center of Yunnan Power Grid Co.,Ltd.,Kunming 650000,China)
出处
《计算机技术与发展》
2020年第11期190-194,共5页
Computer Technology and Development
基金
云南省国家电网省级项目(5230JY18000H)。
关键词
工业电气设备
大数据
特征识别
信息熵
特征重组
industrial electrical equipment
big data
feature recognition
information entropy
feature recombination
