深空环境中基于云边端协同的任务卸载方法

Task Offloading Method Based on Cloud-Edge-End Cooperation in Deep Space Environment

深空探测是当今世界航天任务的重要领域,深空探测自主技术对未来进行大规模的深空探测具有重大意义。由于深空环境复杂且未知,通信时延长,星上计算资源有限,深空探测自主技术面临严峻挑战。针对此问题,提出了一种面向深空探测任务的数字孪生云边端协同框架,通过云边端协同的任务卸载,为深空探测自主技术提供更加高效的资源服务。首先将复杂深空探测任务分解为多个具有依赖关系的子模块,然后在虚拟空间层分别建立环绕器覆盖时间模型、协同计算模型和模块依赖模型,最后基于以上模型构建了相应的目标优化问题。优化目标是在模块依赖、环绕器的有效通信服务时间以及着陆巡视器发射功率控制约束条件下,最小化着陆巡视器完成深空探测任务的能耗和时间。为了解决该优化问题,提出了一种自适应遗传算法,以确定最优的执行策略交由物理空间层的着陆巡视器执行。仿真结果表明,所提出的自适应遗传算法可以有效减少任务完成时间和能耗。此外,将所提的云边端协同计算模式与另外3种计算模式进行了对比,结果表明,在完成相同目标的情况下,所提的云边端协同框架具有更高的资源利用率。

Deep space exploration is a significant area of space missions in the modern world,and future large-scale deep space exploration will be greatly impacted by autonomous deep space exploration technologies.The autonomous technology of deep space exploration faces severe challenges because of the complicated and uncharted deep space environment,lengthy deep space communication time,and constrained on-board computing capacity.To address this issue,a cloud-edge-end cooperation architecture for deep space exploration tasks using digital twins is developed,which can offer more efficient resource services for deep space exploration autonomous technologies.Firstly,the complex deep space exploration task is decomposed into multiple sub-modules with dependencies.Secondly,the orbiter coverage time model,the collaborative computing model,and the task dependency model are established in the virtual space layer.Finally,based on the aforementioned models,the corresponding target optimization pro-blem is proposed.The optimization objective is to minimize the energy consumption and time of the landing rover for completing the deep space exploration mission under the constraints of module dependence,the effective communication service time of the orbiter and the transmit power control of the landing rover.In order to solve this optimization problem,an adaptive genetic algorithm is proposed,so that the optimal execution strategy for the landing rover in the physical space layer can be determined.Si-mulation results show that the proposed adaptive genetic algorithm can effectively reduce the mission completion time and energy consumption.Additionally,the proposed cloud-edge-end cooperation computing model is contrasted with the other three computing models,and the results reveal that,when it is used to achieve the same objective,the proposed cloud-edge-end cooperation framework has a greater resource utilization.