用智能代理自动生成合规工业流程图，加速新材料产业化
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
- 构建多智能体系统从网络获取多模态数据并建知识图谱
- 生成符合规范的流程图，专家干预少于5%且准确率超90%
- 适合工业界快速转化材料研究成果的工程团队使用
工艺流程图（PFD）和管道仪表图（PID）是工业过程设计、控制与安全的关键工具。然而，在自动化与数字化时代，将材料发现成果规模化生产时，生成精确且合规的图纸仍面临挑战。本文提出一种自主智能体框架，采用两阶段方法：知识获取与生成。该框架集成专用子智能体，从公开网络源检索并融合多模态数据，并基于图检索增强生成（Graph RAG）范式构建本体知识图谱，实现高上下文准确率的图纸自动生成与开放域问答（ODQA）。大量实证实验表明，该框架可生成合规图纸，专家干预极少，具备显著工业应用价值。
原文摘要 · Abstract (English)
Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge, particularly in scaling breakthroughs from material discovery to industrial production in an era of automation and digitalization. This paper introduces an autonomous agentic framework to address these challenges through a twostage approach involving knowledge acquisition and generation. The framework integrates specialized sub-agents for retrieving and synthesizing multimodal data from publicly available online sources and constructs ontological knowledge graphs using a Graph Retrieval-Augmented Generation (Graph RAG) paradigm. These capabilities enable the automation of diagram generation and open-domain question answering (ODQA) tasks with high contextual accuracy. Extensive empirical experiments demonstrate the frameworks ability to deliver regulation-compliant diagrams with minimal expert intervention, highlighting its practical utility for industrial applications.
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