Research on Aircraft Attitude Anomaly Auxiliary Decision-Making Method Based on Knowledge Graph and Predictive Model

A knowledge graph is constructed for flight test safety, which is conducive to enhancing the data deduction ability in flight test monitoring and offers efficient and highly complex decision-making support for safety monitoring. Based on this graph, an aircraft attitude predictive model is established by employing neural network technology. This model can accurately predict the changes in aircraft attitude under pilot manipulation, with a mean absolute error of 0.18 degrees in the predicted angle of attack values. By integrating the knowledge graph and the predictive model, an auxiliary decision-making method for abnormal aircraft attitude situations is proposed. This method calculates the safety manipulation space of the aircraft under different flight states through risk quantification technology, providing a theoretical basis for the pilots’ manipulation decisions in abnormal attitude situations. The research is verified based on simulation data, which not only enhances the scientific rigor and practicability of flight test safety monitoring in simulated scenarios but also provides new theoretical support and technical approaches for the field of flight safety.