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Calculating method of index selection, weight optimization and channel planning of electric power payment channel analysis

A calculation method and electricity payment technology, applied in computing, computer parts, instruments, etc., can solve the hidden dangers of electricity bill recovery, unreasonable allocation of human resources, hidden dangers of power supply services, etc., to facilitate payment and improve the level of lean management. Effect

Inactive Publication Date: 2017-05-10
ELECTRIC POWER RES INST OF EAST INNER MONGOLIA ELECTRIC POWER +2
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Contradictions between user payment habits, payment needs and payment channel construction are prominent, and problems such as hidden dangers of electricity fee recovery, hidden dangers of power supply services, and unreasonable allocation of human resources are gradually emerging

Method used

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  • Calculating method of index selection, weight optimization and channel planning of electric power payment channel analysis
  • Calculating method of index selection, weight optimization and channel planning of electric power payment channel analysis
  • Calculating method of index selection, weight optimization and channel planning of electric power payment channel analysis

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Embodiment 1

[0050] figure 1It is a flow chart of the calculation method for the index selection, weight optimization and channel planning of the electric power payment channel analysis of the present invention, as figure 1 As shown, a calculation method for index selection, weight optimization and channel planning of electricity payment channel analysis includes the following steps:

[0051] Step 1 Obtain the data of the basic attribute information and payment habit attribute information of the payment user through the SG186 system or the questionnaire;

[0052] Step 2: Use the feature weight optimization method to optimize each weight in the individual user portrait to obtain the optimal individual user portrait, and establish a group user payment behavior portrait through a clustering algorithm;

[0053] Step 3 uses the K-nearest neighbor classification algorithm to establish an index evaluation system,

[0054] Step 4 uses the genetic annealing algorithm to calculate the weight value...

Embodiment 2

[0057] Obtain the data of the basic attribute information of the payment user and the attribute information of the payment habit

[0058] The purpose of the research on bill-paying customers is to objectively collect the research data of bill-paying customers and prepare for follow-up work. The research objects are mainly household-based customers who pay electricity bills, and each household is represented by a grid user number. The research method is mainly a combination of questionnaire survey and data research provided by power supply companies.

[0059] The questionnaire survey mainly collects the user's name, age, gender, home address, and payment habit information, and combines the user payment information provided by the power supply company to establish an individual user portrait. details as follows:

[0060] Name: Replaced by User ID

[0061] Age: According to the average age of the family and the analysis of the payment weight of each person in the family, the e...

Embodiment 3

[0117] K-Nearest Neighbor Classification Algorithm to Establish Index Evaluation System

[0118] The main idea of ​​the KNN classification algorithm is: first calculate the distance or similarity between the samples to be classified and the training samples of known categories, and find the K neighbors whose distance or similarity is closest to the sample data to be classified; category to determine the category of the sample data to be classified. If the K neighbors of the sample data to be classified belong to a category, then the sample to be classified also belongs to this category. Otherwise, score each candidate category, and determine the category of the sample data to be classified according to certain rules.

[0119] For a test sample, calculate its similarity with each sample in the training sample set, find the K most similar samples, and judge the category of the test sample according to the weighted distance sum. The specific algorithm steps are as follows:

[...

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Abstract

A calculating method of index selection, weight optimization and channel planning of electric power payment channel analysis is disclosed. The method comprises the following steps of step1, through a SG186 system or a questionnaire, acquiring data of basic attribute information and payment habit attribute information of a user who pays; step2, using a characteristic weight optimization method to optimize each weight in an individual user figure, acquiring an optimal individual user figure, and through a clustering algorithm, establishing a group user payment behavior figure; step3, using a K-nearest neighbor classification algorithm to establish an index evaluation system; step4, using a genetic annealing algorithm to calculate a weight value of each attribute index; and step5, determining whether the value is an optimal value and determining an optimal payment channel. In the invention, based on user payment large data, a lean management level of marketing electricity charge recovery work is increased and finally a payment service channel through which the user can pay conveniently and which is satisfied by the user is realized.

Description

technical field [0001] The invention relates to the field of electric power system communication, in particular to a calculation method for index selection, weight optimization and channel planning of electric power payment channel analysis. Background technique [0002] With the development of diversification of payment channels and payment methods, the original "single payment" mode of electric power agency business office payment has been broken. The preferred method is that individual business offices are overcrowded during peak payment periods. The contradiction between users' payment habits, payment needs and payment channel construction has become prominent, and problems such as hidden dangers of electricity fee recovery, hidden dangers of power supply services, and unreasonable allocation of human resources have gradually emerged. [0003] In 2012 when the United States proposed the "Big Data Research and Development Plan", the Chinese government also approved the "...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23G06F18/24147G06F18/214
Inventor 樊新李文杰石研陈爽王曦雯秦宇郑海涛陈永利徐宝锋孙萍董莹鞠凤学刘涛苑伟东刘文会曹爽马红波申少辉
Owner ELECTRIC POWER RES INST OF EAST INNER MONGOLIA ELECTRIC POWER
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