Research on Intelligent Management and Scheduling Optimization System of Unmanned Commercial Resources Based on Cloud Computing

This project studies the link scheduling method of unmanned vehicle networking based on collaborative resource allocation to solve the problems of low real-time information transmission rate and low throughput rate in vehicle-connected networks. This project aims to establish a two-stage collaborative communication model, utilizing the value function to depict the maximum capacity of each node in the vehicular ad-hoc network (VANET) and formulating a corresponding optimization model. The multi-choice knapsack approach is applied to enhance network transmission rates, with an exhaustive search method employed to fine-tune the quantity of wireless network nodes. Compared to conventional distributed sensing techniques and channel propagation-based vehicle-mounted systems, the proposed methodology in this project is expected to accelerate the link speeds within VANETs by 8.82% and 7.54%, and the overall system throughput by 11.21% and 12.96%, so as to better meet the requirements of the vehicle-mounted wireless communication system for data transmission. Compared with the distributed unmanned intelligent scheduling method, it has greater advantages.