The Piping and Instrumentation Diagrams (P&IDs) are foundational to the design, construction, and operation of workflows in the engineering and process industries. However, their manual creation is often labor-intensive, error-prone, and lacks robust mechanisms for error detection and correction. While recent advancements in Generative AI, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), have demonstrated significant potential across various domains, their application in automating generation of engineering workflows remains underexplored. In this work, we introduce a novel copilot for automating the generation of P&IDs from natural language descriptions. Leveraging a multi-step agentic workflow, our copilot provides a structured and iterative approach to diagram creation directly from Natural Language prompts. We demonstrate the feasibility of the generation process by evaluating the soundness and completeness of the workflow, and show improved results compared to vanilla zero-shot and few-shot generation approaches.

翻译：管道与仪表图（P&ID）是工程与流程工业中工作流设计、建造与运行的基础。然而，其人工创建过程通常劳动密集、易出错，且缺乏可靠的错误检测与修正机制。尽管生成式人工智能的最新进展，特别是大语言模型（LLM）与视觉语言模型（VLM），已在多个领域展现出巨大潜力，但它们在工程工作流自动化生成中的应用仍未被充分探索。本研究提出一种新型智能辅助系统，用于从自然语言描述自动生成P&ID图。通过采用多步骤智能体工作流，该辅助系统提供了一种从自然语言提示直接创建图表的结构化迭代方法。我们通过评估工作流的合理性与完整性，证明了该生成过程的可行性，并展示了相较于原始零样本与少样本生成方法的改进效果。