On-site emergency response in mine B.

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.煤矿瓦斯爆炸严重威胁矿工安全与作业可持续性，往往造成重大人员伤亡与生产损失。针对应急决策与预案准备环节的不足，本研究以B矿典型瓦斯爆炸事故为目标案例，提出了一种用于应急响应规划的基于案例推理（Case-Based Reasoning, CBR）模型。研究通过分析历史事故案例，提取并量化关键描述性与决策相关属性；采用基于云模型（Cloud Model）的权重赋值方法确定各特征的相对重要性，随后借助改进的K近邻（K-Nearest Neighbor, KNN）检索算法完成相似案例匹配；利用多群体遗传算法（Multi-Population Genetic Algorithm, MEA）优化反向传播（Backpropagation, BP）神经网络的权重与阈值，实现案例的适配与复用；进一步引入云模型对所提应急方案的有效性开展评估。仿真结果表明，该模型可生成可靠且实用的应急响应方案，所评估方案的评级处于“一般”与“良好”之间。最后，本研究明确了应急方案执行的实施与保障措施，可为煤矿企业提升瓦斯爆炸应急准备与响应效率提供科学有据的参考。