Electric power credit investigation evaluation method based on big data model

Abstract

The invention relates to an electric power credit investigation evaluation method based on a big data model. The method comprises the steps of collecting internal enterprise power data and external enterprise operation data; building credit loss power utilization enterprise features and high-risk credit loss power utilization enterprise features, building an enterprise credit investigation evaluation model for enterprise credit scoring by adopting an AHP analytic hierarchy process and a TOPSIS comprehensive evaluation method, determining a threshold value, and determining a user credit ratingaccording to a score range. According to the method, the dimension is more comprehensive, and the data reliability is higher; the method is advanced, the result is more accurate, and the enterprise credit analysis dimension is increased.

Description

technical field [0001] The invention relates to a power credit evaluation method based on a big data model. Background technique [0002] The construction of enterprise credit system is of great significance in promoting the high-quality development of the power industry, building a new industry supervision and governance model, and maintaining a fair and just power market order. There are generally the following problems in the construction of the current credit reporting system: First, the traditional enterprise credit system fails to include industry-wide data, and the evaluation dimension is not comprehensive; second, the traditional method of identifying untrustworthy enterprises does not use big data technology, and the degree of manual intervention is high, and its rationality needs to be The third is that the traditional credit evaluation system generally uses existing data directly, can only distinguish between untrustworthy and untrustworthy enterprises, and cannot...

AI Technical Summary

Problems solved by technology

There are generally some problems in the construction of the current credit reporting system: First, the traditional enterprise credit system fails to include the data of the whole industry, and the evaluation dimension is not comprehensive; second, the traditional identification method of untrustworthy enterprises does not use big data technology, and the degree of manual intervention is high, and the rationality remains to be seen. Third, the traditional credit evaluation system generally directly uses existing data, which can only distinguish between untrustworthy and untrustworthy enterprises, and cannot effectively evaluate potentially high-risk untrustworthy enterprises, and the effect of untrustworthy governance and credit classification management is not good

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