Data-Driven Urban Natural Gas Pipeline Risk Factor Identification and Correlation Analysis
As an important infrastructure to meet the needs of people's life,the safety of the urban gas pipeline network is of great sig-nificance to protect the property and life safety of the general public.With 810 cases of domestic urban gas pipeline leakage and explo-sion accidents as samples,the text mining technology is used to process the sample cases,and the key risk factors leading to gas pipe-line leakage are identified according to the TF-IDF algorithm with word frequency statistics;the visualization of the relationship between risk factors is realized through the co-occurrence analysis,and the centrality indicators are calculated to determine the set of risk fac-tors;the association rules between the safety risk factors of gas pipelines are revealed based on the Apriori algorithm.Based on Apriori algorithm,the correlation rules between gas pipeline safety risk factors are revealed.It is found that in the process of identifying the key risk factors of urban gas pipelines,the text mining method is basically the same as the traditional method;in the analysis of the second-ary factors,the aging of pipeline equipment,staff training,safety protection measures,and imperfect approval procedures in the process of safety production show a stronger correlation with gas accidents compared with the previous studies,which provides a new perspective for the management of pipeline safety.
urban gas pipelinerisk factorstext miningco-occurrence analysisassociation rule mining
万方数据
维普
