Research on flight technology evaluation based on machine learning algorithm

Abstract

In China's civil aviation transportation industry, flight safety has been the focus of attention. In this paper, a flight technology assessment model and an automated early warning model are established for aviation safety. First, data pre-processing is performed. Then the suitable indicators are continuously screened by multiple machine learning classifications, and then the screened data are fitted to continuously screen the suitable indicators, and the aircraft technology assessment is found to be more suitable for the integrated learning classification model. Subsequently, three unoptimized optimal models were derived as LightGBM, XGboost and Random Forest classification models. The results of these models are then fused by Stacking model to combine their advantages to build the final aircraft technology assessment prediction model. For the automated early warning mechanism, the aviation early warning mechanism needs to be established first by subclassing these data with the K-mean clustering model and visualizing the key data items such as avg (COG NORM ACCEL) based on the normal distribution, combined with the differentiated distribution for each category to set the implausible warning level to establish the aviation automated early warning model.