【摘要】 在云边协同智能监控系统的边缘计算设备上部署行为识别算法时，由于缺乏合理的任务卸载方案，使得系统的计算资源分配不均，从而导致系统运行功耗不稳定，并影响识别的速度及准确率。为解决上述问题，设计了一种基于场景人物数量的任务卸载方案，以优化云边协同智能监控系统的运行稳定性和识别效果。首先，对智能监控系统的运行参数进行了采集，确定了其功耗曲线和识别性能。然后，设计了轻量型人物数量识别模块，并通过程序编写实现了基于场景人物数量的监控任务分类。接着，测试了不同视频采样率对智能监控系统功耗和识别性能的影响，并确定了最佳采样率分配方案。最后，在复兴号动车组生产线的智能监控系统上对所提出的任务卸载方案进行了测试。结果显示，相较于现有的并行式任务卸载方案，基于场景人物数量的任务卸载方案使该生产线智能监控系统的平均识别准确率提高了0.53%，平均延迟缩短了1.56%，平均功耗降低了14.47%，有效地提高了系统的运行稳定性。研究结果对云边协同智能监控系统运行稳定性和识别效果的优化具有重要意义，可为其性能提升提供理论依据和工程支持。更多还原

【Abstract】 When the behavior recognition algorithm is deployed on the edge computing device of the cloud edge collaborative intelligent monitoring system, due to the lack of a reasonable task offloading scheme, the computing resources of the system are distributed unevenly, which leads to unstable system operation power consumption and affects the speed and accuracy of recognition. To solve the above problems, a task offloading scheme based on the number of scene characters has been designed to optimize the operational stability and recognition effect of the cloud edge collaborative intelligent monitoring system. Firstly, the operating parameters of the intelligent monitoring system were collected,and its power consumption curve and recognition performance were determined. Next, a lightweight character number recognition module was designed, and the classification of monitoring tasks based on the number of scene characters was realized by programming. Then, the influence of different video sampling rates on the power consumption and recognition performance of the intelligent monitoring system was tested, and the optimal sampling rate allocation scheme was determined. Finally, the proposed task offloading scheme was tested on the intelligent monitoring system for the production line of Fuxing electric multiple units. The results showed that compared with the existing parallel task offloading scheme, the task offloading scheme based on the number of scene characters improved the average recognition accuracy of the intelligent monitoring system of the production line by 0.53%,reduced average delay by 1.56%, and reduced average power consumption by 14.47%, which effectively improved the operational stability of the system. The research results are of great significance for optimizing the operational stability and recognition effect of the cloud edge collaborative intelligent monitoring system, and can provide theoretical basis and engineering support for its performance improvement.更多还原