Prefetch and Push Method of Flight Information Based on Migration Workflow

Abstract

As the architecture of aircraft cockpit panels becomes more complicated and more flight data are placed onto the panels, trainee pilots require more time during flight training to learn and comprehend flight information. This problem lengthens flight training time and raises costs. This paper proposes a mechanism for prefetching and pushing flight information to facilitate flight training for trainee pilots. This paper addresses the challenges of a high quantity of data and the chaotic time-series relationship between distinct data in flight sequence data by building a migration workflow model in the aircraft cockpit environment and getting flight data with shorter time intervals. Then the flight data are input into the Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) prediction algorithm, which generates the prompt operation information and prediction information by analyzing the current flight data and predicting flight data of next stage. A case study of the takeoff stage is given. The experimental results of the prediction algorithm are given, which prove that the time-series flight data refined by the migration workflow model and MLP-LSTM algorithm have a better prediction effect compared with the LSTM algorithm.