空间数据挖掘算法在煤矿能源保护监管中的应用

为降低煤矿能源开采风险，实现规范化管理，提出空间数据挖掘算法在煤矿能源保护监管中的应用研究。由信息层、控制层和设备层构成监管系统，明确系统职责，结合监管系统特征，设计空间数据挖掘模型整体结构。将粗糙集和神经网络方法相结合，去除冗余数据，采用误差函数和代价函数，确定神经网络训练样本数量。探究数据挖掘模型在煤矿保护监管中的应用过程，设计神经网络结构，计算神经元数量，反复训练网络生成关联规则。经过实例应用分析，从关联规则中可得出:煤矿能源安全和瓦斯浓度、日开采量之间支持度较高，必须将二者指标控制在合理范围内。由此证明了挖掘算法不但可以获取煤矿能源现状，还能通过历史数据得出预见性结论。

Application of spatial data mining algorithm in coal mine energy protection and supervisio

In order to reduce the risk of coal mine energy mining and realize standardized management,the application of spatial data mining algorithm in coal mine energy protection and supervision is proposed.The supervision system is composed of information layer,control layer and equipment layer.The system responsibilities are defined.Combined with the characteristics of the supervision system,the overall structure of spatial data mining model is designed.Rough set and neural network are combined to remove redundant data.Error function and cost function are used to determine the number of neural network training samples.Explore the application process of data mining model in coal mine protection and supervision,design the neural network structure,calculate the number of neurons,and repeatedly train the network to generate association rules.Through the case application analysis,it can be concluded from the association rules that there is a high degree of support between coal mine energy security and gas concentration,daily mining volume,and the two indicators must be controlled within a reasonable range.This proves that the mining algorithm can not only obtain the current situation of coal mine energy,but also draw predictive conclusions from historical data.