Big data technology-based mining and analysis of application and installation in power business expanding

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI:10.1109/CSAIEE54046.2021.9543251

Haihong Liang, X. Cui, Ling Zeng, W. Zheng, Yang Dong

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Abstract

A large amount of data information is generated in the informatization construction of the application and installation in power business expanding of the power system. The traditional data analysis method of the application and installation in power business expanding only establishes a single analysis model for the data, and does not clarify the deep relationship of the data, which leads to the ineffective use of the archival data. For this reason, the mining analysis of the application and installation in power business expanding based on big data technology is proposed. Based on the establishment of the data warehouse of the application and installation in power business expanding, the data of the application and installation in power business expanding are processed by using the combined prediction model. After improving k-means clustering by genetic algorithm, data mining was performed to obtain the relationship between the archive data. The experimental results show that the studied analysis method not only has high data processing efficiency, but also can effectively shorten the application and installation in power business expanding process and improve the economic efficiency of enterprises when applied to actual power operation.

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基于大数据技术的挖掘与分析在电力业务拓展中的应用与安装

电力系统在电力业务扩展的应用和安装的信息化建设过程中产生了大量的数据信息。传统的电力企业扩产应用安装数据分析方法只对数据建立了单一的分析模型，没有厘清数据之间的深层关系，导致档案数据利用效率低下。为此，提出了基于大数据技术在电力企业扩容中的应用与安装的挖掘分析。在建立电力企业扩容应用安装数据仓库的基础上，采用组合预测模型对电力企业扩容应用安装数据进行处理。在遗传算法改进k-means聚类后，进行数据挖掘，获取档案数据之间的关系。实验结果表明，所研究的分析方法不仅具有较高的数据处理效率，而且应用于实际电力运行时，可以有效缩短在电力业务拓展过程中的应用和安装时间，提高企业的经济效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)

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