Optimization and Simulation of Monitoring Technology of Blasting Rock Movement Trajectory Based on the Improved SVM Algorithm

摘要

In most cases, the blasting object is the rock mass. Because the rock mass has the characteristics of anisotropy and inhomogeneity, there are often structural surfaces such as joints, fissures, faults, and weak interlayers, but details are basically impossible. Compared with the rock, these structural surfaces are weak parts, and the explosive energy required for breaking is smaller. It is difficult to take into account the existence of each weak side when the explosives are arranged in the blasthole. Therefore, after the explosive explodes in the rock mass, the explosive gas will first rush out from these weak parts, entraining individual fragments to form flying rocks. Aiming at the problem that it is difficult to accurately obtain the rock motion information in the blasting process of open-pit mines, this paper selects the kernel function to establish the support vector machine model and optimizes the parameters of the support vector machine model to obtain the optimal blasting rock trajectory prediction model. Under the protection of special protection devices, the information of the rock movement during the blasting process is collected at a higher frequency, and the analysis algorithm of the rock movement characteristics is researched on the basis of inertial navigation technology. The algorithm is used to analyze and output the rock movement. The curve of velocity, position, and kinetic energy provides a theoretical and technical basis for the study of rock movement law in the blasting process of open-pit mines. Through the experimental analysis, based on the cross-validation method, through the support vector machine model parameter optimization and comparison evaluation parameters, the optimal prediction model of the blasting rock trajectory is obtained as the support vector machine model based on the radial basis kernel function. The mean value of root mean square error and mean absolute error of this model are 0.102 and 0.0674, respectively, and the performance is the most robust among the three types of models.