让工程师用自然语言与管道仪表图对话，减少错误理解。
Talking like Piping and Instrumentation Diagrams (P&IDs)
- 将管道仪表图转为带属性的图结构数据
- 结合大模型实现精准问答与信息提取
- 适合流程工业工程师和安全分析人员使用
我们提出一种方法，使用户能通过自然语言与管道仪表图（P&IDs）交互。具体而言，采用DEXPI数据模型将P&IDs表示为带标签的属性图，并通过pyDEXPI Python工具包完成转换。随后构建P&ID知识图谱，并利用基于图的检索增强生成（graph-RAG）技术将其集成至大语言模型（LLMs）。该方法使用户可通过自然语言查询P&IDs中的上下文信息，提升信息获取准确性，缓解模型幻觉问题。同时，借助大模型的海量知识，可解析工艺信息，辅助工程师日常任务。未来该工作还可能拓展至其他生成式人工智能应用，如AI辅助HAZOP分析。
原文摘要 · Abstract (English)
We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&IDs are cast into a graph representation from the DEXPI format using our pyDEXPI Python package. 2) A tool for generating P&ID knowledge graphs from pyDEXPI. 3) Integration of the P&ID knowledge graph to LLMs using graph-based retrieval augmented generation (graph-RAG). This approach allows users to communicate with P&IDs using natural language. It extends LLM's ability to retrieve contextual data from P&IDs and mitigate hallucinations. Leveraging the LLM's large corpus, the model is also able to interpret process information in PIDs, which could help engineers in their daily tasks. In the future, this work will also open up opportunities in the context of other generative Artificial Intelligence (genAI) solutions on P&IDs, and AI-assisted HAZOP studies.
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