6G无人机群结合AI与语义通信，实现高效基础设施自动巡检。
AI and Semantic Communication for Infrastructure Monitoring in 6G-Driven Drone Swarms
- 用6G低延迟通信与边缘AI协同，构建大规模无人机群巡检系统。
- 基于大模型生成结构化报告，故障检测速度显著提升。
- 适合电力、交通等高危设施的自动化巡检场景。
在多个工业领域，使用无人机监测关键基础设施正日益普及。组织需求推动这一发展，以降低费用、加速流程并减少巡检人员面临的风险。然而，传统监测系统存在严重瓶颈：5G网络难以满足大规模无人机协同所需的低时延和高可靠性，而人工巡检仍成本高昂且效率低下。本文提出一种6G赋能的无人机群系统，集成超可靠低时延通信、边缘AI与语义通信，实现巡检自动化。通过采用大语言模型（LLMs）生成结构化输出与报告，该框架预计相比现有方法可降低巡检成本并提升故障检测速度。
原文摘要 · Abstract (English)
The adoption of unmanned aerial vehicles to monitor critical infrastructure is gaining momentum in various industrial domains. Organizational imperatives drive this progression to minimize expenses, accelerate processes, and mitigate hazards faced by inspection personnel. However, traditional infrastructure monitoring systems face critical bottlenecks-5G networks lack the latency and reliability for large-scale drone coordination, while manual inspections remain costly and slow. We propose a 6G-enabled drone swarm system that integrates ultra-reliable, low-latency communications, edge AI, and semantic communication to automate inspections. By adopting LLMs for structured output and report generation, our framework is hypothesized to reduce inspection costs and improve fault detection speed compared to existing methods.
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