As UAVs are increasingly deployed in complex scenarios such as disaster monitoring, emergency rescue, and power-line inspection, traditional command and control systems face severe challenges in intelligent decision-making, resource allocation, and elastic scalability. To address these issues, we first propose a distributed UAV command and control system based on large language models of the LLaMA2 family. The system adopts a “cloud–edge–terminal” architecture, using 5G as the backbone network and the Internet of Things as a supplement, with edge computing serving as the computing platform. LLMs of various parameter scales are deployed on demand at different hierarchical levels to support both training and inference, enabling intelligent decision-making and optimal resource allocation. Second, we establish a multidimensional system model that integrates computation, communication, and energy consumption, providing a theoretical analysis of network dynamics, resource constraints, and task heterogeneity. Furthermore, we develop an improved Grey Wolf Optimizer (ILGWO) that incorporates adaptive weights, an elite learning strategy, and Lévy flights to solve the multi-objective optimization problem posed by the system. Experimental results show that the proposed system improves task latency, energy efficiency, and resource utilization by 34.2%, 29.6%, and 31.8%, respectively, compared with conventional methods. Real-world field tests demonstrate that, in urban rescue scenarios, the system reduces response latency by 44.7% and increases coordination efficiency by 39.5%. This work offers a reference for the optimized design and practical deployment of UAV command and control systems in complex environments.
Han et al. (Fri,) studied this question.