Risk quantitative classification method of non-coal underground mine based on multi-source data

Chao Wang

doi:10.1117/12.2691863

23 August 2023 Risk quantitative classification method of non-coal underground mine based on multi-source data

Chao Wang

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127842N (2023) https://doi.org/10.1117/12.2691863
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

In order to solve the problem of inadequate risk management and control caused by inaccurate risk assessment lack of data support. A risk quantitative classification method for non-coal underground mine is proposed, risk quantitative indexes are constructed from three aspects: accident probability, exposure frequency, and risk consequence. Accident probability, exposure frequency and risk consequence are derived from mine risk list database, real-time monitoring dynamic database and historical accident static database respectively. According to the risk value, the risk classification control mode of 'one risk controller corresponds to one risk' is realized. An example application of an iron mine shows that the roof fall and rib spalling in 1404 mining area is a major risk, the collapse of - 180 excavation face is a major risk, and object strike of wind shaft is a general risk. Finally, the evaluation method is applied to the non-coal mine risk monitoring and early warning system, and the risk information management and control is realized through the system reminder and SMS push mode.

Citation Download Citation

Chao Wang "Risk quantitative classification method of non-coal underground mine based on multi-source data", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127842N (23 August 2023); https://doi.org/10.1117/12.2691863

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