煤矿燃气爆炸事故的应急响应计划的案例性推理
Dongling Ma1, Jingwen Kuang2, Minyuan Zhang3
1School of Management, Wuhan Technology and Business University, Wuhan, China.
PloS one
|September 11, 2025
概括
本研究引入了基于案例的推理 (CBR) 模型,以改进煤矿煤气爆炸事件的应急响应计划. 该模型提供可靠的计划,增强矿工安全和作战准备.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 风险管理 风险管理
背景情况:
- 煤矿燃气爆炸带来了重大的安全和运营风险.
- 目前的紧急决策和准备战略存在明显的缺陷.
研究的目的:
- 开发和验证基于案例的推理 (CBR) 模型,以加强煤矿的应急响应计划.
- 为应对改善应对气体爆炸事件的准备工作的迫切需要.
主要方法:
- 利用历史事故数据提取和量化案例属性.
- 采用云模型进行特征加权和改进的K-近邻 (KNN) 算法进行案例检索.
- 应用了多种群遗传算法 (MEA) 来优化反向传播 (BP) 神经网络,以适应病例和重复使用.
主要成果:
- 拟议的CBR模型产生了可靠和实用的应急响应计划.
- 应急计划的有效性使用云模型进行了评估,得到了"公平"和"良好"之间的评级.
结论:
- 开发的CBR模型提供了一种以科学为基础的方法,以提高煤矿天然气爆炸准备.
- 建议采取实施和保护措施,以改善应急计划的执行和整体安全.
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