吉林大学学报(信息科学版) ›› 2022, Vol. 40 ›› Issue (2): 269-274.

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电力物联网用户侧数据深度挖掘方法研究

颜远海   

  1. 广州华商学院 数据科学学院, 广州 511300
  • 收稿日期:2021-07-09 出版日期:2022-06-11 发布日期:2022-06-12
  • 作者简介:颜远海(1985— ), 男, 江西吉安人, 广州华商学院讲师, 主要从事数据可视化和数据分析算法研究, ( Tel) 86- 18924273591(E-mail)yan85028@ 163. com.
  • 基金资助:
    广东省普通高校人文社科 2017 年“创新强校工程冶基金资助项目(2017KQNCX266)

Research on Deep Mining Method of User Side Data for Power Internet of Things

YAN Yuanhai   

  1. College of Data Science, Guangzhou Huashang College, Guangzhou 511300, China
  • Received:2021-07-09 Online:2022-06-11 Published:2022-06-12

摘要: 针对在电力物联网中用户侧数据处于相对孤立的位置, 导致数据关联规则挖掘难度增加的问题, 提出了 基于关联规则映射的电力物联网用户侧数据深度挖掘方法。 该方法以用户侧数据网状拓扑的有向图结构为 基础, 根据关联属性组分析数据集的关联映射关系, 利用相互关系矩阵挖掘数据集的关联规则。 引入极值规范 化策略与径向基函数神经网络, 构建无量纲方法与离散聚类方法, 通过隐藏层神经元网络中心获取与连接权重 计算等训练阶段, 按照 K 均值聚类流程完成数据预处理, 根据显性与隐性的不同用户侧数据类型以及用户鄄 项目评分矩阵与兴趣度矩阵, 实现数据深度挖掘。 实验结果表明, 该方法可以用较短的时间完成挖掘任务, 不同规模数据集处理效果更好, 且能在较小的内存空间内完成数据深度挖掘。

关键词: 关联规则; , 关联映射; , 电力物联网; , 用户侧; , 数据挖掘

Abstract: In the power Internet of Things, user-side data is in a relatively isolated position, which makes it more difficult to mine data association rules. Therefore, a deep mining method of user-side data in the power Internet of Things based on association rule mapping is proposed. Based on the directed graph structure of user-side data mesh topology, the association mapping relationship among data sets is analyzed according to the association attribute group. And the association rules among data sets are mined using the correlation matrix. Extreme value standardization strategy and radial basis function neural network are introduced, and the dimensionless method and discrete clustering method are built. Through the hidden layer neural network is obtained. According to the K-means clustering process, data preprocessing, data types according to the different users of dominant and recessive side matrix, score matrix and users-project scale, deep data mining is realized. Experimental results show that this method can complete the mining task in a relatively short time, the processing effect of different data sets is better, and the data depth mining can be completed in a small memory space.

Key words: association rules; , association mapping; , power Internet of things; , user side; , data mining

中图分类号: 

  • TP391. 44