Ma Dongling, Kuang Jingwen, Zhang Minyuan
School of Management, Wuhan Technology and Business University, Wuhan, China.
School of Accounting, Wuhan Qingchuan University, Wuhan, China.
PLoS One. 2025 Sep 11;20(9):e0331711. doi: 10.1371/journal.pone.0331711. eCollection 2025.
Gas explosions in coal mines pose a serious threat to miner safety and operational sustainability, often resulting in significant casualties and production losses. To address the deficiencies in emergency decision-making and preparedness, this study proposes a case-based reasoning (CBR) model for emergency response planning, using a representative gas explosion incident at Mine B as the target case. Historical accident cases were analyzed to extract and quantify key descriptive and decision-related attributes. A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. The cloud model was further introduced to evaluate the effectiveness of the proposed emergency plans. Simulation results demonstrate that the model yields reliable and practical emergency responses, with the evaluated plan rated between "fair" and "good." Finally, this study outlines implementation and safeguard measures for emergency plan execution, offering a scientifically grounded reference for coal mine enterprises to enhance gas explosion preparedness and response efficiency.
煤矿瓦斯爆炸对矿工安全和运营可持续性构成严重威胁，常常导致重大人员伤亡和生产损失。为解决应急决策和准备方面的不足，本研究以B矿一次具有代表性的瓦斯爆炸事故为目标案例，提出了一种基于案例推理（CBR）的应急响应规划模型。分析历史事故案例以提取和量化关键描述性及决策相关属性。采用基于云模型的加权方法确定特征的相对重要性，随后进行改进的K近邻（KNN）检索以匹配相似案例。使用多种群遗传算法（MEA）优化反向传播（BP）神经网络的权重和阈值，以进行案例适配和重用。进一步引入云模型评估所提应急计划的有效性。仿真结果表明，该模型产生可靠且实用的应急响应，评估的计划评级在“一般”和“良好”之间。最后，本研究概述了应急计划执行的实施和保障措施，为煤矿企业加强瓦斯爆炸防范和响应效率提供了科学依据。
