Civil aviation safety risk intelligent early warning model based on text mining and multi-model fusion

Abstract

Aviation unsafe events often lead to major casualties and property losses. Aviation safety risk intelligent early warning is an important means to ensure the safe and reliable operation of aircraft. Therefore, an intelligent early warning model is urgently needed to quickly predict the risk level and identify potential risks to take targeted measures to realize the active management of safety. To realize the above process, the text mining method is used to extract key risk information from unsafe event reports and input it into the intelligent early warning model to predict its risk level, further constructing the priority processing index to achieve a rapid decision, and finally realize the intelligent safety management process of features extraction to early warning levels identification and then to priority processing. First, domain dictionary and Chinese stop word list are constructed to process the massive text data in the unsafe event’s report. Further, TF-IDF and TextRank are fused to extract key risk information and convert it into feature vectors. Second, the IHT algorithm is used to alleviate the sample class imbalance problem. After that, input the balanced risk information into an improved stacking multi-model fusion algorithm to accurately identify the early warning level and improve the level of active management and control via priority processing index ranking. The effectiveness and feasibility of the proposed method are demonstrated by testing the unsafe event text data of some aircraft maintenance companies and airlines, which promotes the practical application value of text mining technology in the aviation field.