基于物联网和运行大数据的实验室设备异常状态预警方法

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guokai Zheng, Lu-xia Yi
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引用次数: 0

摘要

为了提高设备异常状态预警效果,缩短预警时间,本文设计了一种基于物联网和运行大数据的实验室设备异常状态预警方法。采集物联网环境下实验室设备的运行状态数据,并对采集到的运行状态数据进行降维处理。降维后提取实验室设备运行大数据的异常特征。在迭代更新的基础上,将实时特征分析结果与异常特征集进行对比,并根据异常启动预警响应程序。实验结果表明,该方法最大虚警率仅为1.34%,应用时异常状态响应始终保持在4.0 s以下,充分证明该方法有效实现了设计预期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An early warning method of abnormal state of laboratory equipment based on Internet of things and running big data
In order to improve the early warning effect of equipment abnormal state and shorten the early warning time, this paper designs an early warning method of laboratory equipment abnormal state based on the Internet of things and running big data. Collect the running status data of laboratory equipment in the environment of Internet of things, and implement dimension reduction processing on the collected running status data. After the dimensionality reduction, extract the abnormal characteristics of big data of laboratory equipment running. On the basis of iterative update, the real-time feature analysis results are compared with the abnormal feature set, and the early warning response program is started according to the abnormal. According to the experimental results, the maximum false alarm rate of this method is only 1.34%, and the abnormal state response is always kept below 4.0 s when applied, which fully proves that this method effectively realizes the design expectation.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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