结合事故树与AHP-CBR的煤矿瓦斯爆炸风险预测方法

Risk Prediction Method Combining Fault Tree and AHP-CBR for Coal Mine Gas Explosion

  • 摘要: 为提高煤矿瓦斯爆炸风险预测的准确性,提出结合事故树分析(FTA)与层次分析法(AHP)-案例推理(CBR)的瓦斯爆炸风险预测方法。该方法的核心创新在于构建了“事故树-FTA与AHP-CBR的耦合机制”与“两级索引检索”模型。通过运用FTA识别出12个关键致灾因素,结合AHP动态权重厘定与CBR两级索引检索机制,实现对风险发生、隐患及影响范围的精准预测,在实例验证中准确率分别达到100%、95%和100%,为煤矿瓦斯爆炸风险防控提供有效的预测工具。

     

    Abstract: In order to improve the accuracy of risk prediction for coal mine gas explosion, proposes a risk prediction method for gas explosion that combines Fault Tree Analysis (FTA) and Analytic Hierarchy Process (AHP) - Case-Based Reasoning (CBR). The core innovation of this method lies in the construction of "coupling mechanism between fault tree-FTA and AHP-CBR" and "two-level index retrieval" model. 12 items of key disaster-causing factors are identified by using FTA, combined with AHP dynamic weight determination and CBR two-level index retrieval mechanism, accurate prediction of risk occurrence and hidden danger and influence scope is achieved. In instance verification, the accuracy rates reach 100%, 95%, and 100% respectively, which provides an effective prediction tool for gas explosion risk prevention and control in coal mines.

     

/

返回文章
返回