基于Apriori算法的民航不正常事件诱因关联性分析OA

In order to explore the causes of high-frequency abnormal events and their correlation with each other.This paper combines the idea of shell model,while sorting out abnormal events in the ASIS system in 2024,selects some common causes,and determines a multi-dimensional and two-level cause system that includes 2 levels,4 dimensions,and 88 indicators.Then the APRIORI algorithm was selected to mine the data from January to October 2024,and the chi-square test was used to conduct statistical testing on the results obtained by the APRIORI algorithm to prove the scientific nature of the results.Finally,the data from November to December 2024 is used to test the reliability of the algorithm model.It was concluded that using GPS interferencein single-cause abnormal events,like abort approach,go-around,and bird strike,the support scores were significantly frequent items,with support scores of 0.207,0.146,and 0.138 respectively;in multi-cause abnormal events(abort approach,go-around,bird strike)and(deviation from command altitude,turbulence)are typical causal combinations that lead to the abnormal event.