[1]马 文,田 园.基于聚类方法的工业电气设备大数据特征识别[J].计算机技术与发展,2020,30(11):190-194.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 035]
　MA Wen,TIAN Yuan.Feature Recognition of Big Data of Industrial Electrical Equipment Based on Clustering Method[J].,2020,30(11):190-194.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 035]
点击复制

基于聚类方法的工业电气设备大数据特征识别()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年11期
页码:
190-194
栏目:
应用开发研究
出版日期:
2020-11-10

文章信息/Info

Title:
Feature Recognition of Big Data of Industrial Electrical Equipment Based on Clustering Method
文章编号:
1673-629X(2020)11-0190-05
作者:
马 文; 田 园
云南电网有限责任公司信息中心,云南 昆明 650000
Author(s):
MA Wen; TIAN Yuan
Information Center of Yunnan Power Grid Co. ,Ltd. ,Kunming 650000,China
关键词:
工业电气设备; 大数据; 特征识别; 信息熵; 特征重组
Keywords:
industrial electrical equipment; big data; feature recognition; information entropy; feature recombination
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 11. 035
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。
Abstract:
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 pro-posed. 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 reo-rganized. 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 multifeature 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.

相似文献/References:

[1]严霄凤,张德馨.大数据研究[J].计算机技术与发展,2013,(04):168.
　YAN Xiao-feng,ZHANG De-xin.Big Data Research[J].,2013,(11):168.
[2]王雷,陈彦先,袁哲,等. 面向预拌混凝土行业的云计算[J].计算机技术与发展,2014,24(08):14.
　WANG Lei,CHEN Yan-xian,YUAN Zhe JI Xu. Research on Cloud Computing for Ready-mixed Concrete Industry[J].,2014,24(11):14.
[3]金宗泽,冯亚丽,文必龙,等. 大数据分析流程框架的研究[J].计算机技术与发展,2014,24(08):117.
　JIN Zong-ze,FENG Ya-l,WEN Bi-long,et al. Research on Framework of Big Data Analytic Process[J].,2014,24(11):117.
[4]张也弛,周文钦,石润华. 一种面向云的大数据完整性检测协议[J].计算机技术与发展,2014,24(09):68.
　ZHANG Ye-chi,ZHOU Wen-qin,SHI Run-hua. A Big Data Integrity Checking Protocol for Cloud[J].,2014,24(11):68.
[5]谢怡,王航,刘新瀚,等. 大数据环境下数据读取关键技术研究[J].计算机技术与发展,2015,25(02):113.
　XIE Yi,WANG Hang,LIU Xin-han,et al. Research on Data Reading Techniques Based on Big Data Environment[J].,2015,25(11):113.
[6]付燕平,罗明宇,刘其军. 大数据三维模型快速显示技术研究[J].计算机技术与发展,2015,25(05):87.
　FU Yan-ping,LUO Ming-yu,LIU Qi-jun. Research on Fast Display Technology for Big Data Three-dimensional Model[J].,2015,25(11):87.
[7]赵震,任永昌. 大数据时代基于云计算的电子政务平台研究[J].计算机技术与发展,2015,25(10):145.
　ZHAO Zhen,REN Yong-chang. Research on E-government Platform Based on Cloud Computing in Big Data Era[J].,2015,25(11):145.
[8]胡存刚,程莹. 基于粒子群算法的大数据智能搜索引擎的研究[J].计算机技术与发展,2015,25(12):14.
　HU Cun-gang,CHENG Ying. Research on Big Data Intelligent Search Engine Based on PSO[J].,2015,25(11):14.
[9]孔钦,叶长青,孙赟.大数据下数据预处理方法研究[J].计算机技术与发展,2018,28(05):1.[doi:10．3969/j．issn．1673－629X．2018．05．001]
　KONG Qin,YE Changqing,SUN Yun.Research on Data Preprocessing Methods for Big Data[J].,2018,28(11):1.[doi:10．3969/j．issn．1673－629X．2018．05．001]
[10]杨明,李铁冰,姜茸,等.基于AHP 的大数据可用性及挖掘方案模型研究[J].计算机技术与发展,2018,28(05):51.[doi:10.3969/j.issn.1673－629X.2018.05.012]
　YANG Ming,LI Tie-bing,JIANG Rong,et al.Research on Model of Big Data Usability and Mining Strategy Based on AHP[J].,2018,28(11):51.[doi:10.3969/j.issn.1673－629X.2018.05.012]

更新日期/Last Update: 2020-11-10