Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Risk Level Assessment and CO Prediction of Underground Mines for Poisoning and Asphyxiation Accidents
To effectively prevent the occurrence of poisoning and asphyxiation accidents in underground mines, this paper establishes an evaluation index system for the factors influencing accidents, constructs a combined assignment model to solve the problem of low accuracy of assignment results caused by a single algorithm, predicts the CO concentration after blasting because CO poisoning is the main cause of accidents, explores the accuracy of different time series prediction methods, and projects the required ventilation after blasting to ensure the safe operation of personnel. Firstly, starting from “man-machine-environment-management”, social factors are introduced to build an evaluation index system. Secondly, three combinatorial allocation models were compared, namely rough set theory–G1 method (RS-G1), entropy method–G1 method (Entropy-G1), and CRITIC method–G1 method (CRITIC-G1). The best model was selected and the allocation rating model was constructed in combination with the cloud model, and the mine risk level was evaluated by using the model. Thirdly, the GM(1,1) model, the quadratic exponential smoothing method, and the ARIMA model were compared by calculating posterior differences and errors, and the method with the highest accuracy was selected for predicting CO concentration. The results show that the inclusion of social assessment indexes in the assessment index system makes the consideration of assessment indexes more comprehensive. The RS-G1 combined assignment model achieved higher accuracy than other combined assignment models, and the GM(1,1) model had the highest accuracy and the best prediction effect. The results of the study can help provide targeted prevention and management measures for poisoning and asphyxiation accidents in underground mines.
Risk Level Assessment and CO Prediction of Underground Mines for Poisoning and Asphyxiation Accidents
To effectively prevent the occurrence of poisoning and asphyxiation accidents in underground mines, this paper establishes an evaluation index system for the factors influencing accidents, constructs a combined assignment model to solve the problem of low accuracy of assignment results caused by a single algorithm, predicts the CO concentration after blasting because CO poisoning is the main cause of accidents, explores the accuracy of different time series prediction methods, and projects the required ventilation after blasting to ensure the safe operation of personnel. Firstly, starting from “man-machine-environment-management”, social factors are introduced to build an evaluation index system. Secondly, three combinatorial allocation models were compared, namely rough set theory–G1 method (RS-G1), entropy method–G1 method (Entropy-G1), and CRITIC method–G1 method (CRITIC-G1). The best model was selected and the allocation rating model was constructed in combination with the cloud model, and the mine risk level was evaluated by using the model. Thirdly, the GM(1,1) model, the quadratic exponential smoothing method, and the ARIMA model were compared by calculating posterior differences and errors, and the method with the highest accuracy was selected for predicting CO concentration. The results show that the inclusion of social assessment indexes in the assessment index system makes the consideration of assessment indexes more comprehensive. The RS-G1 combined assignment model achieved higher accuracy than other combined assignment models, and the GM(1,1) model had the highest accuracy and the best prediction effect. The results of the study can help provide targeted prevention and management measures for poisoning and asphyxiation accidents in underground mines.
Risk Level Assessment and CO Prediction of Underground Mines for Poisoning and Asphyxiation Accidents
Jie Liu (Autor:in) / Qian Ma (Autor:in) / Wanqing Wang (Autor:in) / Guanding Yang (Autor:in) / Haowen Zhou (Autor:in) / Xinyue Hu (Autor:in) / Liangyun Teng (Autor:in) / Xuehua Luo (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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