<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>China’s tailing pond online monitoring technology started late, and the tailing pond is located in a harsh working environment, for the limitations of traditional manual monitoring of tailing pond, combined with the actual situation of Zhenhua Mining tailing pond. This paper constructs a risk monitoring index system and online monitoring early warning model based on (Language Model - Back Propagation, LM-BP) neural network to quantitatively assess tailing pond safety risks and analyze and judge safety risk trends. We extracted common indicators of regional tailing ponds, combined with meteorological data to establish a regional safety risk assessment model, integrated vulnerability of disaster-bearing bodies, environmental sensitivity and other influencing factors, realized regional risk coupling analysis, and dynamically built a risk cloud map. Based on the perspective of safety risk prevention and control, the integrity and accuracy of monitoring data are analyzed, the causes of early warning are inverted, alarm disposal mechanisms are established, and closed-loop management of early warning is realized to provide scientific auxiliary decision-making support for tailing pond safety supervisors.</p>
</abstract>

China’s tailing pond online monitoring technology started late, and the tailing pond is located in a harsh working environment, for the limitations of traditional manual monitoring of tailing pond, combined with the actual situation of Zhenhua Mining tailing pond. This paper constructs a risk monitoring index system and online monitoring early warning model based on (Language Model - Back Propagation, LM-BP) neural network to quantitatively assess tailing pond safety risks and analyze and judge safety risk trends. We extracted common indicators of regional tailing ponds, combined with meteorological data to establish a regional safety risk assessment model, integrated vulnerability of disaster-bearing bodies, environmental sensitivity and other influencing factors, realized regional risk coupling analysis, and dynamically built a risk cloud map. Based on the perspective of safety risk prevention and control, the integrity and accuracy of monitoring data are analyzed, the causes of early warning are inverted, alarm disposal mechanisms are established, and closed-loop management of early warning is realized to provide scientific auxiliary decision-making support for tailing pond safety supervisors.

Analysis of quantitative management of online intelligent monitoring of tailing ponds based on the perspective of safety prevention and control

Shijiazhuang University of Applied Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

China’s tailing pond online monitoring technology started late, and the tailing pond is located in a harsh working environment, for the limitations of...

Analysis of quantitative management of online intelligent monitoring of tailing ponds based on the perspective of safety prevention and control

Published Online: Jun 02, 2023

Page range: -

Received: May 18, 2022

Accepted: Oct 24, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00204

Keywords
tailings pond, online monitoring system, LM-BP neural network, early warning system, dynamic assessment model

© 2023 Wenjun Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of quantitative management of online intelligent monitoring of tailing ponds based on the perspective of safety prevention and control

Published Online: Jun 02, 2023

Page range: -

Received: May 18, 2022

Accepted: Oct 24, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00204

Keywordstailings pond, online monitoring system, LM-BP neural network, early warning system, dynamic assessment model

© 2023 Wenjun Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
tailings pond, online monitoring system, LM-BP neural network, early warning system, dynamic assessment model