Data Operation Scheduling Method for Multiagent Collaboration in Power Grid Scene

To solve the problems of high sensitivity to data quality and limited edge computing resources, a data operation scheduling method based on multi-agent collaboration and reinforcement learning is proposed. The distributed double-layer reinforcement learning framework is constructed innovatively. The upper layer optimizes the adjustable load scheduling in the time dimension with the minimum cost of multi-agent interaction. The lower level adjusts the task allocation strategy in real time by continuous reinforcement learning based on the complementarity of the agents. Experimental results show that the proposed method improves the power data fitting degree and makes it close to 1, and effectively solves the problem of data point offset.