Abstract
Man‐machine efficacy evaluations of typical work in the safe mining of high‐altitude alpine metal mines are associated with fuzziness, multiple indexes, and large subjective components. This results in difficulties in the prediction of the typical work efficiency in high‐altitude alpine metal mining areas. In this study, ergonomic theory was applied to establish the evaluation index system of typical work efficiency in high‐altitude alpine metal mining areas by studying the cooperative relationship between operators, working machines, working environment, and design variables. First, we investigated the collaborative relationship between workers, operating machinery, operating environment, and design variables in order to establish the evaluation index system of typical work efficiency in high‐altitude alpine metal mining areas. Second, principal component analysis (PCA) was integrated with the fusion entropy weight method to (i) analyze the coupling correlation and overlapping effects between the factors influencing efficiency at different altitudes and (ii) to determine the key influencing factors. Third, a model based on the sequence generative adversarial network genetic algorithm backpropagation (SeqGAN‐GABP) hybrid algorithm was established to predict the trends in the operating efficiency of typical work types in high‐altitude alpine metal mining areas. Finally, three high‐altitude alpine metal mines in Xinjiang were selected as representative examples to verify the proposed framework by comparing it with other state‐of the art models (multiple linear regression prediction model, backpropagation (BP) neural network model, and genetic algorithm back propagation (GA‐BP) neural network model). Results determine the average relative error of each model as 2.74%, 1.97%, 1.29%, and 1.02%, respectively, indicating the greater accuracy of our proposed method in predicting the efficiency of typical work types in high‐altitude alpine mining areas. This study can provide a scientific basis for the establishment of mining safety judgment standards in high‐altitude alpine areas.
Highlights
China possesses a highly developed mineral resource industry and the energy and production materials typically originate from mineral resources [1]
Highaltitude alpine areas are rich in metal mineral resources, yet they are accompanied by extreme environmental characteristics such as low pressure and hypoxia, cold and dry weather, and poor geological conditions. is leads to poor mining conditions, complex disaster mechanisms, limited safety criteria, and difficulties in the quantitative analysis of operating efficiency [3]. erefore, the ability to accurately estimate the operating efficiency of high-altitude alpine mining areas and predict its laws plays a key role in the rationality of mining safety decisions and has become a key problem for mining in high-altitude alpine mining areas
Where max and min are the upper and lower bounds of the chromosome genes, respectively, g is the number of iterations, and r is a random number between intervals [0, 1]. e optimal initial weights and thresholds processed via the genetic algorithm are substituted into the BP neural network for predictions
Summary
China possesses a highly developed mineral resource industry and the energy and production materials typically originate from mineral resources [1]. Erefore, based on the aforementioned literature, in the current study, we employ ergonomics theory to establish an operation efficiency evaluation index system for typical work types in high-altitude alpine mining areas. Principal component analysis (PCA) and the entropy weight method are fused to analyze the coupling correlation and overlapping influence of numerous factors affecting efficiency at different elevations and to determine the key indexes affecting operating efficiency under high-altitude alpine conditions. A neural network model (SeqGAN-GABP hybrid algorithm model) is established based on the optimization of the generative adversarial network (GAN) and the genetic algorithm (GA) for backpropagation (BP) to predict the trends in the typical work efficiency in high-altitude alpine metal mines. Taking three high-altitude alpine metal mines in Xinjiang as typical examples of work involving air drillers, an evaluation index system for air drill operations is established based on the proposed hybrid algorithm. Taking three high-altitude alpine metal mines in Xinjiang as typical examples of work involving air drillers, an evaluation index system for air drill operations is established based on the proposed hybrid algorithm. e key factors influencing efficiency are obtained and the variations in the pneumatic drill operations are subsequently analyzed. e relative error rates of the four prediction methods are compared, demonstrating an improvement in the operation efficiency prediction accuracy of typical work types under high-altitude alpine conditions
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
