Electric power user figure establishment and analysis method based on big data technology

Abstract

The invention discloses an electric power user figure establishment and analysis method based on the big data technology. The method comprises steps that the historical electricity information, basic attributes, the fee-paying information and the appeal information of electric power users are acquired; classification category sets of user figures are determined, an influence factor set of a classification result is determined, and a mapping relationship between the influence factor set and the classification set is determined; random extraction of the acquired data is carried out, one part of the data is taken as a training sample, and other data is taken as prediction sample; normalization processing, discretization processing and attribute reduction for the training sample and the prediction sample are carried out, and an influence factor set after correction is determined; the training sample is trained, ten-fold cross validation is taken as a test mode, an electric power user figure prediction model based on a naive Bayes classifier is established, data classification mining analysis on the prediction sample is carried out through utilizing the prediction model, and electric power user figures are acquired. The method is advantaged in that electric power electric quantity prediction and management can be facilitated.

Description

technical field [0001] The invention relates to a method for establishing and analyzing power user portraits based on big data technology. Background technique [0002] Nowadays, more and more industries are beginning to pay attention to the application of user portraits. However, because different industries have different industry backgrounds, application scenarios and user needs, user portraits in different industries cannot be considered the same. The use of user portraits in the financial and banking industries is because the consumption habits of the younger generation of customers have changed. They do not like to go to financial outlets to do business, but choose to use smart devices for financial consumption, and it is difficult to have a product that satisfies everyone at the same time. demand. The telecommunications industry needs to realize real-time and precise marketing through user portraits, such as traffic packages, phone bill packages, etc., and at the sam...

AI Technical Summary

Problems solved by technology

However, this solution has obvious defects in both business and technology: from a business point of view, the payment behavior is only a part of user portraits and credit ratings, and cannot be equated with it; from a technical point of view, the determination of the K value in the K-means algorithm is The key point is that the clustering effect is extremely sensitive to the K value, noise points, and outliers, and often requires many experiments to determine the optimal K value, which is inefficient; since each attribute has different influences on user portraits, we need to assign different weight, but the K-means algorithm cannot determine the specific weight, making the classification results unconvincing

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