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

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.