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.

中文翻译：

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基于GA-BP神经网络的矿山爆破质量预测分析方法

选择合理的矿岩爆破参数是取得良好爆破效果的重要措施。在采矿过程中，碎石是评价爆破效果的重要指标，直接影响矿山的技术方案、设备选型、经济效果等问题，甚至严重威胁矿山的可持续安全生产。随着信息技术的飞速发展，计算机智能图像识别技术的发展越来越完善，其作用也越来越重要。本文基于神经网络方法，研究计算机智能图像识别技术。在本文中，将遗传算法与BP算法相结合，分析智能图像识别、图像模式识别、BP神经网络学习算法的原理，建立GA-BP网络图像识别模型。在实验分析的基础上，预测的平均准确率可以达到67.4%。对于计算机数学分析的效率分析，一般会达到64.3%。本文以块料率和爆破成本为优化目标函数，对多个生产爆破设计方案进行比较和选择，为合理选择生产爆破设计参数提供量化决策依据。在实验分析的基础上，预测的平均准确率可以达到67.4%。对于计算机数学分析的效率分析，一般会达到64.3%。本文以块料率和爆破成本为优化目标函数，对多个生产爆破设计方案进行比较和选择，为合理选择生产爆破设计参数提供量化决策依据。在实验分析的基础上，预测的平均准确率可以达到67.4%。对于计算机数学分析的效率分析，一般会达到64.3%。本文以块料率和爆破成本为优化目标函数，对多个生产爆破设计方案进行比较和选择，为合理选择生产爆破设计参数提供量化决策依据。