Electric charge recovery risk prevention and control method based on risk level

Abstract

The invention discloses an electric charge recovery risk prevention and control method based on risk levels. The method comprises the steps: determining the risk levels of a user according to a preset rule, and enabling the risk levels to comprise a high risk level, a medium risk level and a low risk level; and electric quantity monitoring is carried out on the low-risk-level users. An electricity charge risk evaluation model and an application function are constructed based on data such as customer electricity utilization direct behaviors and associated behaviors, customer credit analysis, electricity utilization trend analysis, industry foreground information evaluation and emergency evaluation are carried out by means of methods such as service personnel investigation and survey, basic interviews and rule induction, Meanwhile, a data mining algorithm is adopted to establish an electricity charge risk evaluation model and an application function. Factual risk customers and potential risk customers are calculated and outputted.

Description

technical field [0001] The invention relates to the field of power supply systems, in particular to a method for preventing and controlling risks of electricity charge recovery based on risk levels. Background technique [0002] Electricity charge recovery business is the key business of power supply enterprise marketing. The business model of using electricity first and paying later creates many potential risks for electricity fee recovery, which directly affects the economic benefits of enterprises. With the development of the market economy, customers' demand for electricity is increasing day by day, and the risk prevention and control of electricity fee recovery is facing greater challenges. [0003] Measures taken by power companies to strengthen electricity fee recovery include: increasing publicity efforts to increase awareness of electricity payment; improving and promoting payment methods and payment channels; setting up special electricity fee collection teams; es...

AI Technical Summary

Problems solved by technology

[0004] The customer’s identification of electricity bill recovery risks mainly relies on the experience and judgment of business personnel, which cannot avoid the influence of subjective factors, and lacks objective and standardized evaluation and analysis tools and means based on data; A large number of customers to be reminded do not have a scientific and unified customer differentiation arrangement and work plan, and are still dominated by personal experience, which affects work efficiency

This model has seriously affected the further improvement and optimization of electricity fee recovery work

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