大数据下用电信息智能采集运维挖掘模型仿真

Simulation of Operation and Maintenance Mining Model for Intelligent Acquisition of Power Information under Big Data

对用电信息智能采集运维进行挖掘,能够实现用户用电信息的高精度运行管理。对用电信息智能化采集运维挖掘进行模型构建,需要对用电信息进行特征挖掘,对挖掘结果及潜在关联规则进行分类处理。传统方法通过随机选取具有周期性的电路用户数据参数,引入信息熵获取用电信息特征,但忽略了对用电信息特征挖掘结果及潜在关联规则的分类处理,导致挖掘精度较低。提出大数据下用电信息智能采集运维挖掘模型。分析大数据下用电信息采集运维挖掘总体构架模式,采用决策树信息融合方法进行用电信息采集后的信息融合,采用关联规则挖掘算法进行用电信息的特征挖掘,对挖掘到的用电信息及潜在关联规则进行分类处理,实现大数据下用电信息智能采集运维挖掘模型构建。仿真结果表明,采用上述方法进行用电信息挖掘,提高对用电采集信息分类识别能力,具有较高的挖掘精度。

In traditional way, the classification processing of feature mining result of power use infoImation and potential association rule are ignored, which results in low accuracy of mining. In this article, an operation and maintenance mining model for intelligently collecting power use information in big data was proposed. Firstly, the overall framework mode of operation and maintenance mining for power use information collection in big data was analyzed. Secondly, the information fusion method based on decision tree was used for information fusion after the information collection. Then, the mining algorithm based on association rule was used the characteristics mining. Finally, the classification processing of feature mining result of power use information and potential association rule were carried out. Thus, we built the operation and maintenance mining model for intelligently collecting power use information in big data. Simulation resuhs show that when proposed method is used to mine power use information, the ability of classifying and recognizing power use information can be improved and the mining accuracy is high.