Feature analysis of index parameters while drilling and regression analysis prediction of rock mass hardness based on principal component clustering analysis

In order to analyze the characteristics of index parameters while drilling in open-pit mines and accurately predict the difficulty of drilling and the characteristics of on-site rock mass, the principal component clustering analysis method is used to simplify the complex model of dimensionality reduction, and the regression relationship between rock mass hardness and drilling rig characteristic index is established. Through the principal component clustering analysis method, four principal components covering 85.162% of the total information are extracted to replace the original eight variables. Based on the obtained principal component score and the cluster pedigree map output by SPPS, a new method for the perception classification while drilling in the open air is provided. At the same time, the reliability of the regression model based on the Platts hardness coefficient f and the drilling rig characteristic index is verified.