An in-depth understanding of the gravity flow behaviour of broken ore is beneficial to improving the ore recovery in sublevel caving mines. In this study, granular dolomites were used as an analogue to experimentally explore the effect of the physical properties of fragments on the gravity flow behaviour of broken ore. Five groups of granular dolomites were prepared first. Then, the particle size, particle size distribution, and particle morphology (e.g., sphericity, circularity, and fractal dimension) in each group were measured following the standard particle morphology test. After that, the angle of repose and the isolated extraction zone (IEZ) of each group were determined by performing funnel and isolated draw experiments, respectively. The experimental results highlighted the insubstantial effect of particle size and the significant effect of particle morphology on the angle of repose and isolated extraction zone of granular dolomite. It was found that a wider particle size distribution yielded an isolated extraction zone with a nonuniform shape and a smaller mass drawn because of the percolation of finer particles and the jamming of larger particles. Further, a linear negative correlation was revealed between the repose angle and the IEZ mass. Establishing the relationship between the repose angle and the IEZ shape would be of great significance. In this regard, the findings of this study can aid a preliminary analysis.

The safety of blasting site in open-pit mine can be greatly improved by risk early warning. Therefore, an intelligent real-time risk early warning method of blasting site in open-pit mine based on deep learning was proposed in this research. The mobile wireless webcams, H.264 video compression algorithm, and real-time transport protocol were applied to achieve real-time video acquisition and transmission of blasting site in open-pit mine. A single-stage deep neural network DG-YOLOv3 was proposed in this research. DG-YOLOv3 is an improvement of Gaussian YOLOv3, among which Darknet41 is used to improve the model’s detection speed and detection accuracy of small targets. To further improve the performance (i.e., speed and accuracy) of risk early warning, the surveillance videos were first split into pictures by frame. Then, the pictures were processed by weighted average grayscale and contrast limited adaptive histogram equalization. Experiments show that the mean average precision of DG-YOLOv3 proposed in this paper reaches 87.45 and the detection speed reaches 56.82 frames per second, which has better accuracy and speed compared with other algorithms. In addition, DG-YOLOv3 has good robustness in complex scenarios. Based on the detection results, the intelligent real-time risk early warning of the blasting site in open-pit mine is achieved finally.

Selecting reasonable blasting parameters of ore and rock is an important measure to achieve good blasting effect. In the mining process, rock fragmentation is an important index to evaluate the blasting effect, which directly affects the technical scheme, equipment selection, economic effect, and other issues of the mine and even seriously threatens the sustainable safety production of the mine. With the rapid development of information technology, the development of computer intelligent image recognition technology is becoming more and more perfect, and its role is becoming more and more important. Based on the neural network method, this paper studies the computer intelligent image recognition technology. In this paper, the GA-BP network image recognition model is established by combining genetic algorithm with BP algorithm and analyzing the principles of intelligent image recognition, image pattern recognition, and BP neural network learning algorithm. On the basis of experimental analysis, the average accuracy of prediction can reach 67.4%. For the efficiency analysis of computer mathematical analysis, it will generally reach 64.3%. In this paper, taking the lump rate and blasting cost as the optimization objective function, the comparison and selection of multiple schemes of production blasting design are carried out, which provides quantitative decision-making basis for the rational selection of production blasting design parameters.

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.