Model construction of big data asset management system for digital power grid regulation

IF 2 4区计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS

Information Technology and Control Pub Date : 2023-12-22 DOI:10.5755/j01.itc.52.4.32642

Min Xu, Guanyu Zhang, Lin Duan

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引用次数: 0

Abstract

Abstract: There are many and complex big data for digital power grid regulation, which makes it more difficult to manage big data assets. Therefore, a model of big data asset management system for digital power grid regulation has been built. The model consists of three parts: data collection, data security storage and data index. The data acquisition architecture is designed, and the grey prediction method is used to fill the missing values and correct the abnormal values of the data acquisition results. Store the filled and amended data in the blockchain to ensure data security. The AR-tree index organization is used to realize the digital grid regulation big data index and achieve the goal of high-quality management of digital grid regulation big data assets. The experimental results show that the average recall and precision of this method are 96.9% and 97.9% respectively, and the data collection quality is high; After the application of this method, there is almost no non security data, and the proportion of security data is higher, which shows that this method can ensure the security of big data storage; The response time of digital power grid regulation big data index is less than 0.21s, and the index efficiency is higher.

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数字电网调控大数据资产管理系统模型构建

摘要：数字电网调控大数据多而复杂，增加了大数据资产管理的难度。因此，构建了数字电网调控大数据资产管理系统模型。该模型由数据采集、数据安全存储和数据索引三部分组成。设计了数据采集架构，采用灰色预测法对数据采集结果进行缺失值填充和异常值修正。将填充和修正后的数据存储在区块链中，确保数据安全。采用 AR 树索引组织实现数字电网监管大数据索引，实现数字电网监管大数据资产高质量管理的目标。实验结果表明，该方法的平均召回率和精度分别为96.9%和97.9%，数据采集质量较高；应用该方法后，几乎没有非安全数据，且安全数据比例较高，说明该方法能够保证大数据存储的安全性；数字电网监管大数据索引响应时间小于0.21s，索引效率较高。

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

Information Technology and Control 工程技术-计算机：人工智能

CiteScore

2.70

自引率

9.10%

发文量

审稿时长

12 months

期刊介绍： Periodical journal covers a wide field of computer science and control systems related problems including: -Software and hardware engineering; -Management systems engineering; -Information systems and databases; -Embedded systems; -Physical systems modelling and application; -Computer networks and cloud computing; -Data visualization; -Human-computer interface; -Computer graphics, visual analytics, and multimedia systems.