Distributed power marketing strategy method and system based on big data

Abstract

Provided are a distributed power marketing strategy method and system based on big data. According to the method, by using the broadband carrier of an electricity information collection system to achieve automatic meter reading, meter number data is transmitted back in real time. By conducting distributed processing on user profiles, electricity price number, distribution data, meter number and other big data information, electric charge measurement is completed. By conducting distributed processing on marketing strategy management and an electric charge measurement result, strategy application is generated. Finally, strategy execution on a marketing strategy application result is completed, and an execution result is fed back. The method includes the steps of using the broadband carrier to conduct meter number data collection, distributed-electric charge measurement of the big data and distributed processing, and thus generating the marketing strategy application result, an executionstrategy application and a returned strategy execution result. According to the technical scheme, the problems that traditional power marketing strategy is not high in automation degree and poor in performance are solved, and the distributed processing mode base on the big data is used in power marketing strategy processing, so that the power marketing industry personnel' working efficiency is greatly improved.

Description

technical field [0001] The invention relates to a distributed power marketing strategy method and system based on big data, belonging to the technical field of power marketing. Background technique [0002] Since the problem of electricity consumption is closely related to every resident, the volume of electricity consumption is huge, and the issues related to electricity fee recovery are complicated. Therefore, in recent years, power companies have continuously changed their marketing strategies and related technical means in order to strengthen the risk management and control of electricity fee recovery, and at the same time further alleviate the grassroots. Employee workload. However, the number of residential users is large, and the meter code data changes every second. At the same time, with the data generated by automatic meter reading, electricity cost calculation, reminders, early warnings, power outages and restorations, and special affairs strategies, the data volu...

AI Technical Summary

Problems solved by technology

However, the number of residential users is large, and the meter code data changes every second. At the same time, with the data generated by automatic meter reading, electricity cost calculation, reminders, early warnings, power outages and restorations, and special affairs strategies, the data volume grows geometrically. Therefore, how To solve the power marketing strategy problem with tens of millions or even hundreds of millions of users, it has become a serious problem to construct a reasonable power marketing strategy method and system

At present, the traditional power marketing strategy system generally has the following disadvantages: (1) The calculation of electricity bills is slow, and the hourly data volume processing is less than 500,000 households per second; (2) The marketing strategies that can be provided are limited and the degree of automation is not high , requiring human intervention; (3) adopting a centralized processing method, in the face of local anomalies generated by large-volume data processing, it is easy to induce overall anomalies, and the ability to automatically repair is poor; (4) data storage and analysis performance is low, and policy response takes a long time

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