Artificial intelligence, wireless communication, heterogeneous sensors, human–machine integration, and other emerging technologies provide effective technical support for the intelligent digital transformation of grid operation and maintenance modes. Exoskeleton devices can take on larger weight loads for the human body to improve operational efficiency, safety, and security for operation scenarios that require long time assistance, such as carrying and lifting in grid operation and maintenance. In this study, for the power-assisted optimization problem of a multisource sensor industrial exoskeleton device, a workspace optimization model of a four-degree-of-freedom industrial upper limb exoskeleton is constructed on the basis of the principle of positive kinematics and the graphical solution method, and an improved tuna swarm optimization algorithm (ITSO) based on population hierarchy, elite backward learning, and genetic variation is proposed for the solution of the constructed model. The Tent chaos mapping mechanism is introduced to improve the population diversity on the basis of the traditional tuna algorithm, and the population hierarchy mechanism, elite backward learning, and genetic variation operator are introduced to further improve the global optimization capability of the algorithm. The designed algorithm is compared with the particle swarm algorithm, genetic algorithm, gray wolf algorithm, and other cutting-edge intelligent algorithms in cross-sectional simulation experiments, and the results show that the optimal search ability of ITSO is improved by 0.12, 0.16, 0.08, and 0.05% on average, respectively, compared with the other algorithms, which verify the feasibility of the model and the algorithm designed in this study for solving the exoskeleton power-assisted problem.