With the large-scale integration of renewable energy sources and the continuous growth in electricity demand, the operating environment of power grids has become increasingly complex and variable. This change has introduced numerous uncertainties, making it difficult for traditional model-based or purely data-driven grid scheduling methods to formulate accurate and reliable scheduling plans. To address this challenge, this paper proposes an adaptive cruising method for grid scheduling that integrates data and knowledge. Firstly, a mathematical model for the adaptive cruising problem in power system dispatch is constructed, and a deep reinforcement learning training framework is built based on this model. Furthermore, this article proposes an improved proximal policy optimization algorithm for offline training of dispatch agent. By integrating professional knowledge related to scheduling regulations, the proposed algorithm can guide agents to learn towards optimization. Finally, an in-depth analysis of simulation examples validates the effectiveness and superiority of the proposed method.