海事AIoT的代理RAG:自然语言访问结构化数据
Oxana Sachenkova1, Melker Andreasson1, Dongzhu Tan1
1Computer Science and Media Technology Department, Linnaeus University, SE-391 31 Kalmar, Sweden.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
灯塔机器人通过使用代理检索增强生成 (RAG) 提供安全的自然语言访问传感器数据来增强海上AIoT. 该系统确保了关键工业应用的数据隐私和可验证的分析.
科学领域:
- 海上运营的海上运营.
- 事物的人工智能 (AIoT)
- 数据科学数据科学数据科学
背景情况:
- 现代海事运营越来越依赖传感器数据,以提高效率和决策.
- 大型语言模型 (LLM) 提供了高级功能,但在工业环境中需要安全,受限制的数据访问.
- 检索增强生成 (RAG) 对于数据最小化,隐私和敏感应用程序中的监管合规性至关重要.
研究的目的:
- 介绍灯塔机器人,一种新的Agentic RAG系统,用于自然语言访问海上传感器数据.
- 解决AIoT领域可验证的自主数据分析的需求,重点关注海上用例.
- 展示安全的,有限的背景数据交互和工业AIoT的数值准确性验证.
主要方法:
- 开发了一个详细的架构,将LLM与专业数据库和编码代理集成在一起.
- 将自然语言查询转换为可执行任务,包括用于时间序列分析的Python代码生成和用于关系数据库的SQL查询.
- 实施安全的,有限的上下文RAG,以使敏感数据远离LLM提示,并确保可审计的工具使用.
- 创建了一个测试套件,包括24个问题,涉及不同复杂度和数据交互类型,以评估系统性能.
主要成果:
- 灯塔机器人展示了强大的,可控的数据访问,具有高事实准确性.
- 专有的克劳德3.7模型实现了近90%的事实正确性.
- 开源的Qwen 72B模型实现了66%的整体正确性,在简单的检索和聚合查询中准确率为99%.
- 该系统成功生成了用于分析的Python代码,并执行了复杂的SQL查询.
结论:
- 对于海上环境中的AIoT来说,安全的,有限的背景RAG是必不可少的.
- 灯塔机器人为AIoT领域的可验证的自主数据分析提供了可行的解决方案.
- 该系统显示了在工业环境中探索性数据分析的成本效益自动化的潜力.
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