Multi-label co-occurrence network for stealing electricity type detection and research on forensics sequence rules

Runan Song, Yang Xue, P. Zhang, Yining Yang, Cong Wang
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Abstract

With the development of economy and the progress of technology, the anti-stealing and investigation work in power consumption management has become extremely complex and arduous. In the complex environment of the electricity stealing scene, due to the hidden electricity stealing and the complex on-site power distribution relations, it is difficult to carry out the on-site evidence collection work smoothly. This paper focuses on the stealing electricity type detection and the process of on-site evidence collection, deeply analyzes the key elements of electricity stealing, and constructs a stealing electricity type detection model based on multi label graph convolution network. It uses GRU network to mine the time sequence characteristics of power consumption, learns the co-occurrence relationship of electricity stealing labels based on GCN network, and combines the user attribute characteristics to ultimately improve the accuracy of stealing electricity type detection. Based on the above, by analyzing the key elements such as the historically collected data, we use the method of combining expert experience and statistical analysis to generate the forensics sequence rules of evidence collection for different types of electricity stealing, thus effectively improving the efficiency and quality of evidence collection for on-site staff. The experimental results demonstrate that the proposed methods perform favorably against the most frequently used methods.
多标签共现网络窃电型检测及取证顺序规则研究
随着经济的发展和技术的进步,用电管理中的防盗侦查工作变得极其复杂和艰巨。在窃电现场复杂的环境下,由于窃电的隐蔽性和现场配电关系的复杂性,使得现场取证工作难以顺利开展。本文以窃电类型检测和现场取证过程为研究重点,深入分析了窃电的关键要素,构建了基于多标签图卷积网络的窃电类型检测模型。利用GRU网络挖掘功耗的时间序列特征,学习基于GCN网络的窃电标签共现关系,结合用户属性特征,最终提高窃电类型检测的准确率。在此基础上,通过对历史采集数据等关键要素进行分析,采用专家经验与统计分析相结合的方法,生成不同类型窃电案件取证的取证顺序规则,有效提高现场工作人员取证的效率和质量。实验结果表明,所提出的方法比常用的方法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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