在分布式光伏传感器网络中可靠的通信:一个大语言模型驱动的方法
1Power Dispatch Control Center, Guizhou Power Grid Co., Ltd., Guiyang 550000, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究引入了一种用于监控分布式光伏 (DPV) 系统的新框架,通过优化数据传输和大型语言模型 (LLM) 诊断来提高可靠性. 该方法显著降低了延迟时间,并改善了工业能源解决方案的故障分析.
科学领域:
- 能源系统工程 能源系统工程
- 工业物联网中的人工智能
- 网络优化 网络优化
背景情况:
- 分布式光伏 (DPV) 系统提供可持续的工业能源,但面临监控挑战.
- 当前的监控系统在数据传输限制和及时故障检测方面扎.
研究的目的:
- 为可靠的DPV系统监控开发一个层次优化框架.
- 通过智能数据管理和诊断,尽量减少DPV系统的延迟和停机时间.
主要方法:
- 在网络层集成基于hysteresis的流量塑造.
- 应用大型语言模型 (LLM) 驱动的诊断与基于物理的提示工程进行零射击根本原因分析.
- 对关键故障数据传输的优先安排.
主要成果:
- 在10 Mbps网关带宽下,P50延迟减少了 46.08%至 49.87%.
- 在没有广泛的培训数据的情况下,证明了有效的零射击根本原因分析.
- 通过详细的LLM-powered评估,实现了精确的故障诊断.
结论:
- 拟议的框架提高了DPV系统监控的可靠性和效率.
- 基于LLM的诊断为工业物联网中的故障分析提供了强大的,数据高效的方法.
- 这种综合解决方案解决了DPV系统管理中的关键技术限制.
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