As an important part of modern transportation, smart rail system need to handle a large number of delay-sensitive and task-intensive tasks in a high-speed mobile state. However, high-speed mobility challenges the traditional information processing modes a lot, such as service interruptions and information congestion. In order to solve these problems, we proposed a service migration strategy based on intelligent agent group cooperative prediction combined with edge computing service migration technology. The aim is to reduce system latency and overhead and ensure service continuity. Firstly, we constructed a cloud-edge-end collaborative scheduling network architecture model for distributed smart rail system is constructed, integrating mobility management and business orchestration to provide effective support for intelligent decision-making. Then we proposed an intelligent grouping collaborative migration strategy by consolidating resources for similar or identical tasks and employing a group negotiation mechanism, where the migration process is divided into four steps: detection, interaction, coordination and execution. Finally, a deep reinforcement learning algorithm is utilized to train multi-agent models for group collaborative prediction in edge migration strategies. The strategy dynamically adjusts task migration between edge nodes and the cloud based on real-time system status and task requirements to optimize its performance. Experimental results show that the proposed architecture and algorithm can effectively reduce total task delay and system overhead. Instead it can guarantee migration rate for task-intensive requirements, and effectively improve the reliability, effectiveness, and safety of smart rail system services. The present study lays a foundation for the future researches on applications of smart rail system.

智能轨道系统作为现代交通的重要组成部分，需要在高速移动状态下处理大量时延敏感、任务密集型任务。然而，高速移动给传统的信息处理模式带来了很大的挑战，例如服务中断、信息拥塞等。为了解决这些问题，我们提出了一种基于智能代理群体协同预测结合边缘计算服务迁移技术的服务迁移策略。目的是减少系统延迟和开销并确保服务连续性。首先，构建了分布式智慧轨道系统的云边端协同调度网络架构模型，将移动管理和业务编排融为一体，为智能决策提供有效支撑。然后，我们提出了一种智能分组协同迁移策略，通过整合相似或相同任务的资源并采用组协商机制，将迁移过程分为四个步骤：检测、交互、协调和执行。最后，利用深度强化学习算法来训练多智能体模型，以实现边缘迁移策略中的群体协作预测。该策略根据实时系统状态和任务需求动态调整边缘节点和云端之间的任务迁移，以优化其性能。实验结果表明，所提出的架构和算法可以有效降低总任务延迟和系统开销。反而可以保证任务密集型需求的迁移率，有效提高智慧轨道系统服务的可靠性、有效性和安全性。本研究为未来智能轨道系统的应用研究奠定了基础。