面向列车自主运行的边缘智能系统

Edge Intelligent system for autonomous train operation

摘要:

随着城市轨道交通网络的建设和发展不断推进，人们对运输能力的需求日益增加，车载计算能力短缺的现象已成为重要问题，探索新的解决方案变得非常必要。近年来，边缘智能(EI)作为一种全新的领域应运而生。通过EI，可以将复杂的计算任务卸载到轨道旁的计算服务器上，从根本上解放车载设备的计算能力，使车载设备专注于执行简单、低能耗的计算任务，将大部分计算工作交给边缘服务器完成。基于上述理念，本文提出一个全新的EI列车自主控制系统。该系统借助谷歌Kubernetes高可靠性边缘计算平台，实现列车自主控制算法；采用线性二次高斯(LQG)控制算法，对列车自主控制过程进行建模，并利用云上安全计算保障整个系统的高可靠性。同时，该系统能够有效避免局部故障的影响，因此在通信包间隔延迟性能方面表现出卓越的性能。经过大量实验验证，提出的EI列车自主控制系统具有较高的运行可靠性和数据安全性，同时具备较低的通信包间隔延迟性能。证明了该列车自主运行控制系统可以显著提高列车运行的效率和安全性，从而提高列车运行的质量。

Abstract:

With the continuous advancement of the construction and development of urban rail transit networks, people's demand for transportation capacity is increasing day by day. Therefore, the shortage of on-board computing power has become one of the important issues, making it necessary to explore new solutions. In recent years, Edge Intelligence(EI) has emerged as a new field. Through edge intelligence, complex computing tasks can be offloaded to trackside computing servers, fundamentally liberating the computing power of onboard equipment. In this case, we can focus the onboard devices on performing simple, low energy computing tasks, while leaving most of the computing work to edge servers. Based on the above idea, this paper proposes a new train autonomous control system, which uses Google Kubernetes high reliability edge computing platform to realize the train autonomous control algorithm. In addition, we use the Linear Quadratic Gaussian(LQG) algorithm to model the train autonomous control process and utilize cloud security computing to ensure the high reliability of the entire system. At the same time, due to its ability to effectively avoid the impact of local faults, the system also exhibits excellent performance in terms of communication packet interval delay performance. After extensive experimental verification, we can conclude that the proposed train autonomous control system has high operational reliability and data security, as well as low communication packet delay performance. This result further proves that using the autonomous train operation control system can significantly improve the efficiency and safety of train operation, thereby improving the quality of train operation.