Abstract: To solve the problems of low scheduling efficiency and high energy consumption in State Grid data center resource allocation in existing cloud computing environments, this paper proposes a flexible migration algorithm for arithmetic power based on improved ant colony algorithm. Firstly, a computational migration model for State Grid cloud data centers is constructed to model the energy consumption of resource scheduling in data centers. Subsequently, by integrating the bacterial foraging optimization algorithm, we enhanced the initial pheromone distribution in the fundamental ant colony optimization algorithm and reformulated both the heuristic function and pheromone evaporation factor. Simulation outcomes demonstrate that, compared with existing models, the algorithm proposed in this paper can find a more optimal scheduling solution for computing resources. This algorithm not only abbreviates the task execution duration but also culminates in a substantial 36.6% reduction in energy consumption in the State Grid data centers.