Electricity usage trend abnormity suspicion analysis method and anti-electric-larceny monitoring system

Abstract

The invention discloses an electricity usage trend abnormity suspicion analysis method and an anti-electric-larceny monitoring system. The method includes the steps of electricity usage information state estimation, electricity usage abnormity suspicion analysis of low-voltage public transformer platform regions, suspect user filtering and positioning and key suspect user monitoring, and an anti-electric-larceny knowledge base is involved in the method. The step of electricity usage information state estimation is used for dealing with abnormities and deficiency of electric energy collection readings, asymmetry of standing book information and other abnormities. The step of electricity usage abnormity suspicion analysis of the low-voltage public transformer platform regions is used for analyzing electricity usage abnormities of users in the low-voltage public transformer platform regions. The step of suspect user filtering and positioning is used for filtering out suspect electric quantity major users and positioning the suspect major users. The step of key suspect user monitoring is used for tracking electricity usage abnormity users. The anti-electric-larceny knowledge base is used for accumulating anti-electric-larceny basic knowledge. The method is used for achieving the ultimate purposes of automatically positioning the suspect users by analyzing the electricity usage abnormities of the users in the low-voltage public transformer platform regions, forming a checking name list, lowering the technical analysis intensity of anti-electric-larceny staff and improving analysis efficiency.

Description

technical field [0001] The invention relates to the technical field of power grid anti-stealing electricity, in particular to a method for analyzing suspicions of abnormal electricity consumption trends and an anti-stealing electricity monitoring system. Background technique [0002] Driven by economic interests, the situation of power theft in the national power grid is becoming increasingly severe. Among them, the low-voltage station area, especially the low-voltage station area with a complicated power environment, is the hardest hit area for electricity theft. Since the implementation of the SG186 marketing business application, the marketing management system and the electricity consumption information collection system have accumulated rich and large amounts of user information data, providing a data basis for power companies to carry out anti-stealing analysis and monitoring work. However, in the analysis process, each data Information asymmetry exists between systems...

AI Technical Summary

Problems solved by technology

Since the implementation of the SG186 marketing business application, the marketing management system and the electricity consumption information collection system have accumulated rich and large amounts of user information data, providing a data basis for power companies to carry out anti-stealing analysis and monitoring work. However, in the analysis process, each data There are information asymmetry between systems, data with bad points, shortcomings, etc., which increases the difficulty of analysis, and the overall application of effective mining and analysis of these massive data appears to be relatively lagging behind

At present, the traditional anti-stealing technology analysis method generally performs threshold screening on massive data according to mature empirical rules to narrow the scope of suspect analysis, and then locks up suspect users through curve comparison, or directly conducts spot checks on large target groups, and The audit results depend to a certain extent on the skills and quality of the inspectors, and it is easy to miss the fish that slipped through the net. It is time-consuming and laborious, and the implementation effect is not ideal, forming the reality of "rich data, mature experience, but poor application"

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