Design an AI-driven intelligent scheduling system for the logistics industry to address issues such as slow response and unreasonable resource allocation in traditional logistics scheduling. Construct a data-driven decision model using machine learning and deep learning algorithms, integrating four functional modules: transportation optimization, warehouse management, distribution network, and monitoring platform. The system achieves core functions including dynamic path planning, intelligent inventory prediction, and precise distribution scheduling, significantly improving logistics efficiency.