摘要：

钢轨是铁路运输系统的重要基础设施, 其安全性对列车运行安全至关重要。定期检测钢轨的状态可以帮助及时发现潜在的缺陷和损坏。机器视觉检测近年来逐步运用到钢轨检测中。然而, 因为铁路上网络和计算资源的限制, 检测工作只能在普通列车非运行时间段开展, 不能进行实时检测。针对以上问题, 采用终端-边缘端-云端架构, 提出在列车上每隔一段距离装载高速摄像机, 并将列车收集到的检测图片任务合理卸载至提前缓存的预训练检测模型的终端、轨边的边缘服务器和云服务器进行处理。基于检测任务的组成是离散的, 考虑检测任务分配比例、CPU计算能力和任务优先级约束时延的约束条件, 以检测任务时延作为优化目标构建目标函数, 将任务卸载处理问题表述为最大最小化模型问题。最后通过遗传算法(GA)获取最优任务分配比例、最优CPU计算能力任务分配以及最优最小任务时延。实验结果表明, 在列车拍摄频率为200 Hz生成单个检测任务的情况下, GA的协同卸载比基于二进制云端、边缘端和本地的响应时延分别减少了1 287、515、875 ms; 在检测任务数为10个情况下, 基于GA的协同卸载比基于粒子群算法和蚁群算法的响应时延分别减少了2.440、3.520 s。该方法在不同卸载方案中具有明显的时延优化作用。

关键词: 钢轨检测, 任务分配, 协同卸载, 最大最小化模型, 遗传算法

Abstract:

Rail is an important infrastructure of railway transportation system, and its safety is very important to train operation. Regular inspection of rail conditions can help detect potential defects and damages in a timely manner. In recent years, machine vision has been gradually applied to rail inspection. However, owing to the limitations of network and computing resources on railway cars, detection work can only be carried out during the nonrunning time of ordinary trains and real-time detection cannot be performed. To solve the aforementioned problems, a terminal-edge-cloud architecture is adopted. This study proposes mounting high-speed cameras on a train at certain positions. The detection image tasks collected by these cameras are carried to the terminal of the pretrained detection model (cached in advance), the edge server of the rail side, and the cloud server for processing. Based on the discrete composition of the detection tasks and considering the constraints of the detection task distribution ratio, CPU computing power, and task priority constraint time delay, the detection task time delay is used as the optimization objective to construct the objective function. Moreover, the task unloading processing problem is expressed as a maximum-minimization model problem. Finally, a Genetic Algorithm (GA) is used to obtain the optimal task allocation ratio, CPU computing power, task allocation, and minimum task time delay. The experimental results show that in the case of generating a single detection task with a train capturing frequency of 200 Hz, the response time delay based on genetic algorithm collaborative unloading is reduced by 1 287, 515, and 875 ms in terms of the binary cloud, edge, and local response time delays. In the case of 10 detection tasks, the response time delay based on genetic algorithm collaborative unloading is reduced by 2.440 and 3.520 s compared to particle swarm optimization and ant colony optimization, respectively. This method has significant time delay optimization effects in different unloading scenarios.

Key words: rail detection, task allocation, collaborative unloading, maximum-minimization model, Genetic Algorithm (GA)