Research on intelligent early warning of substation fire based on multi-sensor fusion algorithm

Zhenxi Zhao; Xing Li; Hongfeng Li; Biao Chen; Jianqiang Hua; Bo Di; Biao Yang

doi:10.1117/12.2683947

14 June 2023 Research on intelligent early warning of substation fire based on multi-sensor fusion algorithm

Zhenxi Zhao, Xing Li, Hongfeng Li, Biao Chen, Jianqiang Hua, Bo Di, Biao Yang

Author Affiliations +

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127081Y (2023) https://doi.org/10.1117/12.2683947
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

Aiming at the problem of untimely fire prevention and control due to missed and false alarms in the traditional fire early warning system of substations, an intelligent fire classification and early warning algorithm based on multi-sensor information fusion is proposed in this paper. Different from the fire warning with a single sensor, firstly, the algorithm proposed in this paper combines the temperature, CO concentration and smoke sensors to build a multi-sensing fusion layer of the fire detection model, which improves the detection sensitivity to a certain extent. Then, the algorithm uses support vector machine (SVM) to classify and warn fires based on the feature information collected by the multi-sensor fusion layer. Finally, the experimental verification is carried out based on the national standard test fire dataset. The experimental results show that the proposed model can effectively and accurately classify and predict the occurrence of fire, and improve the accuracy of fire early warning decision-making to a certain extent.

Citation Download Citation

Zhenxi Zhao, Xing Li, Hongfeng Li, Biao Chen, Jianqiang Hua, Bo Di, and Biao Yang "Research on intelligent early warning of substation fire based on multi-sensor fusion algorithm", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127081Y (14 June 2023); https://doi.org/10.1117/12.2683947

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KEYWORDS

Fire

Data modeling

Education and training

Performance modeling

Sensors

Statistical modeling

Carbon monoxide

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