Zhao Xianli, Wang Guixin
Research Center for Transformation and Development of Resource Exhausted Cities, Hubei Normal University, Huangshi, 435002 Hubei China.
School of Information Engineering, Wenzhou Business College University, Wenzhou, 325035 Zhejiang China.
Neural Comput Appl. 2023;35(12):8823-8832. doi: 10.1007/s00521-022-07696-2. Epub 2022 Aug 24.
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.
在当今全球新冠病毒肆虐的严峻形势下，应急资源调度仍存在效率问题，救援标准也存在不足。为了人民生命的幸福和安康，秉持人类命运共同体理念，城市公共卫生突发事件应急资源调度系统需要改进和完善。本文主要研究城市应急资源调度优化模型，利用深度强化学习算法构建应急资源分配系统框架，并使用深度Q网络路径规划算法对系统进行优化，以实现城市应急资源高效调度的优化升级目的。最后，通过仿真实验得出，所研究的深度学习算法有助于应急资源调度优化系统。然而，随着深度学习的逐步发展，其一些缺点日益明显。一个明显的缺陷是，构建基于深度学习的模型通常需要大量CPU计算资源，成本过高。
