关键词: 地下金属矿山, 采矿成本, 成本预测, 预测模型, 成本影响因素

Abstract: In order to solve the problems of the core components of modern mine mining costs have changed and difficulty of cost control have increased,analyzed the key influencing factors of mining costs,constructed a cost prediction model based on PCABP neural network,formed a cost accounting method that is suited to modern mines.In response to the changes in mining costs caused by mechanized mining,increased mining depths,and differences in working conditions,obtained the characteristics of mining cost components of modern mining enterprises,based on this,applied the job costing method to account for the costs of each mining unit in detail.By summarizing the change pattern of mining cost,it was concluded that the mining cost is mainly influenced by 10 key index factors,such as the size of operating space,temperature,depth and transportation distance of the quarry,as well as workers′ efficiency,working experience,equipment shoveling efficiency,service life of equipment,fuel consumption rate and oil consumption rate,using the principal component analysis method to reduce the dimensionality of the influencing factors,extracted the principal components as cost prediction variables,and established the cost prediction model based on PCABP neural network.Taking the cost data of an underground metal mine in Shandong Province as an example,the model is trained and validated,and the prediction results were also analyzed and evaluated.The results show that the average relative error between the predicted and actual values of PCABP neural network is 3.80%,which indicates that the model has good prediction effect and high accuracy,which provides a basis for cost control and cost planning of modern mining enterprises.

Key words: underground metal mine,mining cost,cost prediction,prediction model,cost affecting factor