Through human-machine knowledge interaction, the interpretability of model decisions is improved, and the reliability and optimization problems of intelligent models are overcome by knowledge enhancement guided by expert feedback and evaluation information, so as to achieve continuous optimization of decision level and improvement of model generalization ability. Effectively improve the reliability level of the action scheme of the decision-making model, realize the recommendation of control actions under various complex operation scenarios, reduce the work burden of dispatchers, and improve the safety, stability and economy of power grid operation. Based on the feedback knowledge of power experts and the improved strategy gradient reinforcement learning algorithm, how to combine artificial intelligence model with expert logic is proposed. By constructing the memory reasoning framework of autonomous decision-making agents, the autonomous decomposition of regulatory tasks and automatic invocation of various executable small models are realized, and the decision-making level of artificial intelligence models is improved.