Intelligent fusion power communication scheduling method based on multi-modal semantics

Power communication scheduling is crucial for the stable and efficient operation of modern power systems, particularly with the rise of smart grids and renewable energy sources. Traditional scheduling methods struggle with issues like delay, bandwidth limitations, and data security. Research indicates that multi-modal semantic intelligent fusion technology can enhance the efficiency and reliability of power communication by integrating data from various modes through semantic understanding. This approach improves data processing and system responsiveness while boosting adaptability and robustness. Furthermore, intelligent algorithms optimize data processing and grid management. However, challenges remain, such as data integration complexity and high system maintenance demands. Therefore, we are developing an approach for efficient and secure power communication scheduling that incorporates optimization algorithms using reinforcement learning, considers the needs of diverse devices, and employs a multi-layer architectural design. This aims to address the evolving requirements of modern power grids.