Research On Flight Diversion Strategy In Dangerous Weather

Air transportation is an area that is particularly susceptible to meteorological factors.When the weather conditions are bad and they are also distributed over a large area and cannot meet the normal navigation conditions of the aircraft,the existing air control deployment methods often allow the flight to wait passively until the area where the planned route passes is restored to capacity.This situation brings great inconvenience to people’s travel,and even causes certain economic losses.Dangerous weather has become an important reason for flight delays.Therefore,the study of space traffic management strategies under dangerous weather has important practical significance and practical value.After researching the distribution of dangerous weather such as thunderstorms,this paper summarized a variety of distributions.Mainly aiming at the problem of sailing deployment in scattered points of dangerous weather,an improved reinforcement learning algorithm is proposed,and the improved algorithm is combined with the heuristic algorithm.First of all,in view of the poor adaptability of the existing Q-Learning algorithm to the unknown state of the environmental information,a Q-Learning algorithm based on the conditional selection model is proposed.Although in terms of algorithm efficiency slightly reduced,The improved algorithm improved the Agent’s ability to adapt to the environment.Next,the improved Q-Learning algorithm is combined with the heuristic algorithm to build an obstacle avoidance simulation platform,and then the flight in dangerous weather can be rerouted.The research in this paper mainly includes the following contents:(1)Analyze the status of flight diversion under dangerous weather and the applicability of reinforcement learning to route planning problems,especially in the areas of route obstacle avoidance and flight operations.(2)Preprocess based on simulated weather data to extract dangerous weather areas.According to different distribution characteristics,dangerous weather is divided into block distribution,strip distribution and scattered distribution,and corresponding diversion algorithms and diversion strategies are proposed.(3)Aiming at the dangerous weather situation with scattered points,an algorithm model combining an improved Q-Learning algorithm and heuristic algorithm is proposed.Applying the algorithm to a simulation system,comparing with the simulation data of the aircraft’s randomly generated diversion path,it is found that the optimal solution reduces the flight time and flight distance by 4.66%and 3.66%,respectively,compared with the random solution.To sum up,based on the algorithm optimization under reinforcement learning,a safer diversion path in line with aircraft performance is obtained under dangerous weather,which shows that the dangerous weather diversion path planning based on reinforcement learning has a good experimental effect.