| Gas safety is an important topic in urban safety management,which is related to the safety of people’s lives and property.In recent years,with the continuous acceleration of urban construction in China,issues such as overdue use of pipelines,illegal occupation,and inadequate operation and maintenance management have become increasingly prominent.There have been relatively serious gas accidents in multiple places,causing casualties and property losses,affecting daily supply and safe operation.In order to improve the management level of gas safety operation and maintenance,this paper constructs a gas pipeline safety situation awareness model based on the characteristics and safety requirements of gas pipelines,which can accurately predict the safety situation of gas pipelines and provide intelligent gas safety situation awareness services.This paper mainly predicts and analyzes the safety situation of gas pipelines based on Bayesian network(BN),and studies the structure learning and parameter learning of BN in combination with the general situation of the actual business data of domestic urban gas pipeline safety situation awareness.This article first analyzes and studies the statistics and typical events of domestic gas pipeline leakage accidents in recent years,and establishes a multi-level indicator system for the cause chain of gas pipeline leakage based on fault trees,which serves as the main foundation for subsequent BN structure learning.Then,in response to the fact that some gas safety situational awareness data in real scenarios can only provide a small dataset,which makes it impossible to generate more accurate BN structural problems through the K2 algorithm based on the traditional Bayesian Information Criterion(BIC)scoring function.A structural constraint model based on connection probability distribution was established to represent expert knowledge,Then,the traditional BIC scoring function is improved through this constraint model,and the K2 algorithm is optimized to improve the accuracy of the BN structure.After determining the BN structure,this paper adds constraints to the Maximum a Posteriori(MAP)algorithm.By forming a parameter constraint space from prior knowledge and using MC method to sample parameters,a certain number of parameters that meet the parameter constraints are obtained.Then,the product of the average value of the sampled parameters and the equivalent sample size is taken as the hyperparameter of the Dirichlet prior distribution,so as to optimize the MAP algorithm,More accurate calculation of BN node probability distribution parameters.Finally,this article conducts experimental verification of the algorithm model based on real data of a certain city’s gas pipeline from three aspects:performance,parameters,and diagnostic reasoning.The experimental results show that the structure learning and parameter learning algorithms studied in this paper can not only introduce expert knowledge into the learning process under small dataset conditions,thereby improving learning effectiveness,but also have certain adaptability to incomplete expert knowledge. |
