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Security threshold setting algorithm of distributed optical fiber monitoring and sensing system based on big data in smart city

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Abstract

For such large-scale buildings and engineering facilities, health monitoring and accident early warning have the characteristics of long monitoring distance, wide range, strong real-time, and distributable characteristics, making traditional thermocouples, thermal resistances, strain gauges, and other sensors difficult to perform. In order to solve the above-mentioned problems, the research on the security threshold setting algorithm of the distributed optical fiber monitoring and sensing system based on big data is very important. Each component of the system is introduced, and the factors affecting system performance are analyzed, and the methods to improve system performance are summarized. The 30 km distributed optical fiber Raman temperature sensor system was designed, and the design and selection principles of each module were analyzed in detail. After on-site testing, all performance indicators have reached the design requirements. The laying of optical fibers and cables under different monitoring environments, the selection of monitoring key areas, and the setting of alarm thresholds are studied. Through the on-site simulation leak test, the system can accurately alarm and work normally, with an accuracy of 97.9%.

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The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. First Construction Engineering Limited Company of China Construction Third Bureau, Wuhan, Hubei, 430040, China

    Zhenghong Huang, Chunguang Mao, Shiyu Guan, Hui Tang, Guanghua Chen & Zaohong Liu

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  1. Zhenghong Huang
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  2. Chunguang Mao
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  3. Shiyu Guan
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  4. Hui Tang
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  5. Guanghua Chen
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  6. Zaohong Liu
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Correspondence to Chunguang Mao.

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Communicated by Oscar Sanjuán Martínez.

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Huang, Z., Mao, C., Guan, S. et al. Security threshold setting algorithm of distributed optical fiber monitoring and sensing system based on big data in smart city. Soft Comput 27, 5147–5157 (2023). https://doi.org/10.1007/s00500-021-06212-3

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  • DOI: https://doi.org/10.1007/s00500-021-06212-3

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