Iot-based power detection equipment management and control system

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2022-01-01 DOI:10.1515/jisys-2022-0127
Jintao Chen, Jianfeng Jiang, Binruo Zhu
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Abstract

Abstract The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.
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基于物联网的电力检测设备管理与控制系统
物联网的发展和应用范围也越来越广泛。特别是在功率测试改进系统的应用方面,取得了很大的进展。本文旨在研究如何分析基于物联网的系统检测设备。本文介绍了物联网和功率检测改进系统的基本理论知识。提出了基于物联网的聚类分析算法和支持向量机算法。在本文的实验中,专家传统检测系统的评分项目存在技术复杂、使用不便、智能不全等问题。其中,复杂技术最高得分8.6分,最低得分7分;“不便”的最高分数为8.6分,最低分数为8.3分。由此可见,相关专家认为,传统的功率检测改进系统不仅技术非常复杂,使用非常不方便,而且不完全智能化。因此,研究基于物联网的系统检测设备是非常有必要的。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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