State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology
College of Safety Science and Engineering, Anhui University of Science and Technology

为了有效地减少煤矿安全事故发生,制订科学的预防灾害措施,以近11年的煤矿安全事故相关数据为统计分析样本,通过对事故等级和事故类型2个要素进行深入分析,研究我国煤矿安全事故发生的规律和特点。以瓦斯、放炮、水害、运输、顶板、机电、火灾和其他事故发生起数作为样本数据,构建灰色神经网络在线预测模型,并基于2021年数据进行验证。结果表明,一般事故最多,其次是较大事故和重大事故;顶板、运输、机电和其他事故起数整体呈现上升趋势,顶板事故最多;灰色神经网络模型平均相对误差和均方根误差分别为0.161和2.902,与灰色模型相比分别降低了0.234和2.945。因此,采用灰色神经网络模型对煤矿安全事故进行预测的精度更高、稳定性更好。

In order to effectively reduce the occurrence of coal mine safety accidents, and to formulate scientific measures of disaster prevention, this study takes the relevant data of coal mine safety accidents in recent 11 years as statistical analysis samples, and studies the rules and characteristics of coal mine safety accidents in China by making analysis of two elements which are accident grades and types. With the occurrence of gas accidents, blasting, water hazards, transportation, roof, electromechanical, fire and other accidents as sample data, the grey neural network online prediction model was constructed and verified based on the data of 2021. The results show that general accidents are the most frequent, followed by larger accidents and major accidents; the number of roof, transportation, electromechanical and other accidents shows an overall upward trend, and the roof accidents are the most frequent; the mean relative error and root mean square error of the grey neural network model are 0. 161 and 2. 902, respectively, which are reduced by 0. 234 and 2. 945 compared to the grey model. Therefore, the grey neural network model is used to predict coal mine safety accidents, with higher accuracy and better stability.

国家科技重大专项(2016ZX05068);安徽省高校协同创新项目(GXXT-2020-057)

杨静,蔡峰,封居强,等. 煤矿安全事故分析与预测研究[J]. 矿业安全与环保,2023,50(5):144-148.

YANG Jing,CAI Feng,FENG Juqiang,et al. Research on analysis and prediction of coal mine safety accidents[J]. Mining Safety & Environmental Protection,2023,50(5):144-148.