一种可本地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
A Locally Deployable Tool-Grounded LLM Multi-agent Framework for Automating Methane Emission Analysis and Reporting
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中文总结 AI 辅助
本研究开发了一种可本地部署的基于工具的LLM多智能体框架，用于自动化甲烷排放分析与报告，在多类真实场景测试中表现优异，能缩短工作流时间、降低人力需求并提升数据安全性。
中文摘要 AI 辅助
甲烷现场监测需要整合采样设计、气象解读、传感器处理、羽流分析、可视化及报告生成等环节，但这些步骤通常分散在不同的专家驱动工作流中。我们为低成本甲烷传感与现场监测活动开发了一种可本地部署、基于工具的大语言模型（LLM）多智能体框架。该框架将LLM智能体作为工作流协调者，关联现场测量数据、气象数据、确定性传感器处理程序、高斯羽流反演及报告生成，而非直接估算甲烷浓度或排放量。在不同真实环境（如污水处理设施、垃圾填埋场、油气站点）开展的大量现场部署表明，本框架在工作流路由与参数提取上可达到92.0%的准确率，在排放速率估算与羽流预测上成功率为85.0%，在实际运行条件下生成可编辑报告的成功率达95.0%。与手动及通用LLM辅助工作流相比，它将工作流时间从小时级缩短至分钟级，降低了手动协调与提示工程需求，同时保留了可追溯的基于羽流的输出。此外，多数处理可在本地执行，减少了敏感设施与现场数据暴露给云服务的风险。这些结果表明，基于工具的LLM协调可降低甲烷现场监测在时间、人力、易用性及数据安全方面的障碍。
英文摘要
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 and field-monitoring campaigns. The framework uses LLM agents as workflow coordinators that link field measurements, meteorological data, deterministic sensor-processing routines, Gaussian plume inversion, and report generation, rather than directly estimating methane concentrations or emissions. Extensive field deployments across diverse real-world environments (e.g., wastewater treatment facilities, landfills, and oil and gas sites) demonstrate that our framework can achieve 92.0\% accuracy in workflow routing and parameter extraction, 85.0\% success in emission-rate estimation and plume prediction, and 95.0\% success in generating editable reports under practical operating conditions. Compared with manual and general-purpose LLM-assisted workflows, it reduced workflow time from hours-level to minutes-level, lowered manual coordination and prompt-engineering requirements, and retained traceable plume-based outputs. In addition, most processing can be performed locally, reducing exposure of sensitive facility and field data to cloud services. These results indicate that tool-grounded LLM coordination can reduce the time, labor, usability, and data-security barriers of methane field monitoring.
