Deep Q networks-based optimization of emergency resource scheduling for urban public health events

In today's severe situation of the global new crown virus raging, there are still efficiency problems in emergency resource scheduling, and there are still deficiencies in rescue standards. For the happiness and well-being of people's lives, adhering to the principle of a community with a shared future for mankind, the emergency resource scheduling system for urban public health emergencies needs to be improved and perfected. This paper mainly studies the optimization model of urban emergency resource scheduling, which uses the deep reinforcement learning algorithm to build the emergency resource distribution system framework, and uses the Deep Q Network path planning algorithm to optimize the system, to achieve the purpose of optimizing and upgrading the efficient scheduling of emergency resources in the city. Finally, through simulation experiments, it is concluded that the deep learning algorithm studied is helpful to the emergency resource scheduling optimization system. However, with the gradual development of deep learning, some of its disadvantages are becoming increasingly obvious. An obvious flaw is that building a deep learning-based model generally requires a lot of CPU computing resources, making the cost too high.