本地可部署的基于工具的LLM多智能体框架用于甲烷排放分析自动化
原标题：A Locally Deployable Tool-Grounded LLM Multi-agent Framework for Automating Methane Emission Analysis and Reporting
AI 摘要
该研究提出一个本地可部署的、基于工具的大语言模型多智能体框架，用于自动化甲烷排放分析与报告。框架通过LLM智能体协调现场测量、气象数据、传感器处理、高斯羽流反演和报告生成，在多种实地环境中实现92.0%的工作流路由和参数提取准确率、85.0%的排放率估算成功率及95.0%的报告生成成功率，将工作流时间从小时级缩短至分钟级，并减少数据泄露风险。
正文节选
A Locally Deployable Tool-Grounded LLM Multi-agent Framework for Automating Methane Emission Analysis and Reporting Abstract Methane field monitoring requires the integration of sampling design, meteorological interpretation, sensor processing, plume analysis, visualization, and reporting, but these steps are often distributed across separate expert-driven workflows. We developed a locally deployable, tool-grounded large language model (LLM) multi-agent framework for our low-cost methane sensing