面向工艺工程原理图设计的智能体网页导航与检索增强AI：加速从材料发现到制造规模化的进程
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
AI总结：
针对材料发现到工业生产规模化中合规工艺原理图生成难的问题，提出融合智能体网页检索、Graph RAG知识图谱的两阶段自主框架，可低专家干预生成合规原理图，具备工业实用价值。
摘要：
工艺流程图（PFDs）以及工艺与仪表流程图（PIDs）是工业工艺设计、控制与安全领域的核心工具。然而，生成精准且符合规范的原理图仍是一项重大挑战，尤其是在自动化与数字化时代，将材料发现的突破性成果规模化应用到工业生产的过程中，这一问题更为突出。本文提出了一种自主智能体框架，通过知识获取与生成的两阶段方法应对上述挑战。该框架整合了多个专用子智能体，用于从公开在线资源中检索并合成多模态数据，并采用图检索增强生成（Graph RAG）范式构建本体知识图谱。这些能力可实现原理图生成与开放域问答（ODQA）任务的自动化，且具备较高的上下文准确性。大量实证实验表明，该框架仅需极少的专家干预即可生成符合规范的原理图，凸显了其在工业应用中的实用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
