基于D-S证据融合和直觉模糊贝叶斯网络双向推理的景区游客拥挤踩踏故障诊断分析

An intuitionistic fuzzy Bayesian network bidirection reasoning model for stampede fault diagnosis analysis of scenic spots integrating the D-S evidence theory

针对不确定条件下景区游客拥挤踩踏故障诊断问题,本文提出一种新的直觉模糊贝叶斯网络双向推理模型.首先,利用直觉模糊集表示专家对贝叶斯网络节点先验概率信息的模糊语言判断,并基于模糊可能性-概率变换公式,得到不同专家给出的节点先验概率值.其次,运用D-S证据合成规则进行信息融合,得到节点先验概率值.最后,结合贝叶斯网络模型,实现贝叶斯双向推理和重要度分析,并以华山景区为例进行实证分析.研究结果表明,本文方法可有效克服“去模糊化”方法导致的信息损失,为解决不确定环境下故障诊断和贝叶斯推理提供崭新途径.

In order to diagnose the root causes of stampede accident in scenic spots under uncertainty,this paper proposes an intuitionistic fuzzy Bayesian network bidrection reasoning model.First,we use multigranularity trapezoidal intuitionistic fuzzy sets to express experts’judgements about fault possibilities of root nodes,and extend the fuzzy possibility-probability transformation function to obtain the failure prior probabilities of root nodes.Second,we use the D-S evidence theory to conduct information fusion and obtain the aggregated prior probability values of root nodes.We perform Bayesian network bidirection reasoning to conduct Bayesian network updating analysis and important analysis.And then,the proposed approach is implemented in the empirical study in the Mount Hua scenic spot,China.The proposed method can overcome the information loss caused by the previous defuzzication method in estimating the prior probabilities of root nodes,and has advantages in solving the problem of fault diagnosis and Bayesian inference under uncertainty.