Construction and Optimization Methods for Unmanned Support Systems Empowered by Large Language Models

Autonomous unmanned aerial systems (UAS) have the capability to transport diverse mission payloads for accomplishing multiple operational objectives in crisis response environments. Nevertheless, during severe weather conditions, conventional UAS platforms utilizing legacy information frameworks encounter significant challenges in executing intelligence gathering, data broadcasting, and rapid payload delivery operations. To overcome these limitations, we present an autonomous support framework powered by advanced language models. The proposed system establishes a comprehensive three-dimensional communication and computational network leveraging fifth-generation wireless technology and distributed computing capabilities. The integrated language model delivers analytical intelligence and predictive situational awareness, while the system resource allocation challenge is mathematically formulated. An Enhanced Adaptive Learning Optimization (EALO) methodology is introduced to simultaneously optimize various critical system performance metrics. Experimental validation demonstrates that our approach substantially minimizes system power consumption and response delays, effectively addressing operational challenges in emergency deployment scenarios.