在工业物联网中，移动边缘计算的可靠性很大程度上取决于无线信道条件。针对工业物联网中边缘计算任务卸载过程非完美信道状态信息对系统的影响，该文提出了一种数字孪生辅助的移动边缘计算能耗优化方法。对于工业物联网中的任务卸载问题，建立边缘计算系统中设备、信道的数字孪生模型，考虑非完美信道状态信息，联合优化卸载决策、发射功率、信道资源和计算资源，建立了系统总能耗最小化问题。为解决所提出的混合整数的非线性非凸问题，将概率时延约束进行转换，并将原问题分解为资源分配方案与卸载策略2个子问题，提出了一种基于连续凸逼近的联合优化算法，在数字孪生的辅助下联合优化卸载策略与资源分配方案。首先，将原问题进行松弛处理，以获得所有终端设备的资源分配方案与任务卸载优先级；然后，对各终端设备的卸载优先级进行降序排序，通过求解迭代优化问题获得完整的任务卸载方案。仿真结果表明，与其他基准方案相比，所提的计算卸载优化方案显著降低了系统的总能耗。

In the industrial Internet of Things, the reliability of mobile edge computing largely depends on the wireless channel conditions. In order to process the influence of imperfect channel state information to the system, this paper proposed a digital twin assisted mobile edge computing energy consumption optimization method. For the task offloading problem in industrial Internet of Things, a digital twin model of devices and channels in the edge computing system was established. Considering imperfect channel state information, the joint optimization of offloading decisions, transmission power, channel resources, and computational resources is performed with the aim of minimizing the total system energy consumption. To deal with the proposed nonlinear non convex problem of mixed integers, the probabilistic delay constraint was transformed and the original problem was decomposed into two sub-problems, and a joint optimization algorithm with the assistance of digital twins based on continuous convex approximation was proposed. Firstly, the original problem was relaxed to obtain resource allocation schemes and task offloading priorities. Then, the offloading priority of each terminal device was sorted in descending order. The complete task offloading scheme was obtained by solving the iterative optimization problem. Finally, simulation results show that, compared to other benchmark schemes, the proposed computational offloading optimization scheme significantly reduces the total energy consumption of the system.