Method and system for detecting abnormal user based on water-electricity ratio and support vector clustering

Abstract

The invention discloses a method and a system for detecting an abnormal user based on a water-electricity ratio and support vector clustering. The method comprises the following steps: firstly, acquiring water and electricity data of the user; secondly, obtaining non-zero consumption data in the water and electricity data; thirdly, calculating the water-electricity ratio of the non-zero consumption data; fourthly, inputting the water-electricity ratio to a support vector classifier for performing classification to obtain classification data; finally, judging whether the classification data is a noise cluster or not, and if the classification data is the noise cluster, the classification data is abnormal data; if the classification data is not the noise cluster, the classification data is normal data. The electricity consumption and the water consumption are effectively integrated with a water-electricity ratio or electricity-water ratio method and serve as input vectors for support vector clustering analysis in sequence for performing detection, so that the false detection rate and the missing detection rate of the water consumption and the electricity consumption during separate detection can be effectively avoided. A support vector clustering design can obtain relatively high classification accuracy by utilizing relatively good distinguishing performance. With a method for dynamically setting a support vector kernel function and a maximum number of iterations, the sensitivity of the detection method can be improved.

Description

technical field [0001] The invention relates to the technical field of detecting abnormal users of water and electricity consumption, in particular to a method and system for detecting abnormal users based on water-electricity ratio and support vector clustering. Background technique [0002] With the continuous development of information technology, intelligent collection terminals are more and more widely used, which greatly reduces the workload of manual door-to-door meter reading. At present, most urban areas can basically realize the data collection of water and electricity consumption through automatic centralized copying, providing a solid foundation for subsequent management and billing. However, due to various reasons such as the network, the stability of the collection terminal, electricity and water theft by users, and other reasons, abnormal data collection will inevitably occur. If the abnormal data is not detected and processed in time, it will bring economic l...

AI Technical Summary

Problems solved by technology

Since the data of water consumption and electricity consumption are detected separately, and there is no combination and comprehensive analysis of the two data, the analysis results are prone to local convergence, resulting in inaccurate detection results.

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