Abstract: In view of the characteristics of forest fires, such as strong sudden occurrence and difficult disposal, and the current situation that helicopter task scheduling efficiency needs to be improved in fire rescue, in order to reduce the loss caused by forest fires, the optimization method of helicopter bucket fire extinguishing task scheduling was studied. Combined with fire area, fire situation, rescue force and other factors, a grid model of fire airspace was established. Aiming at the dynamic changes of fire sites in forest fire scenes, a dynamic task scheduling optimization method based on deep reinforcement learning (DQN) is proposed. The method continuously updates the global environment, forms a dynamic decision basis, and generates a task scheduling scheme by quantifying the burning time constraints of the fire grid and the water required for extinguishing the fire in real time. Improve the efficiency of helicopter bucket fire fighting. The simulation results show that compared with the greedy algorithm in the traditional algorithm, the proposed method can reduce the fire loss rate by 15.4% under the dynamic change of fire site, and effectively reduce the resource loss caused by forest fires.

Key words: task scheduling, deep reinforcement learning, emergency rescue, dynamic decision, helicopter bucket