Abnormal monitoring Method of Radar Operation and Maintenance Information Based On Risk Extraction
Authors: 
Wei Wu, Jiarui Zhang, Kun YuAuthors Info & Claims
Wei Wu, Jiarui Zhang, Kun YuAuthors Info & ClaimsArticle No.: 11, Pages 1 - 8
Abstract
Accelerating the integrated development of mechanization, informatization and intelligence in weapons and equipment is the only way for the armed forces to realize the modernization of weapons and equipment. However, due to reasons such as network security, data security, technical reliability and so on, the intelligent progress of military units themselves is very slow. In the field of integrated radar system support, the operation and maintenance information of the radar system is usually sent by each component device to the operation and maintenance software in the integrated electronic chassis, and the operation and maintenance data is displayed by the display and control software. The effective method of automatic monitoring of operation and maintenance information is lacking through the observation of abnormal situations by the on-duty personnel, and professional operation and maintenance personnel are lacking when anomalies occur. It is difficult to locate the fault and requires remote technical personnel to locate the problem, which is time-consuming and labor-intensive. In this paper, aiming at this problem, we developed an anomaly monitoring method for radar system operation and maintenance information that can perform anomaly prediction and anomaly factor analysis, describes the event extraction and anomaly prediction methods in detail, and verified the feasibility of the anomaly prediction method through comparative experiments.
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- Abnormal monitoring Method of Radar Operation and Maintenance Information Based On Risk Extraction
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Published In
August 2023
378 pages
ISBN:9798400708701
DOI:10.1145/3627341
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 15 December 2023
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ICCVIT 2023
ICCVIT 2023: International Conference on Computer, Vision and Intelligent Technology
August 25 - 28, 2023
Chenzhou, China
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ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
Overall Acceptance Rate 54 of 142 submissions, 38%
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