用AI自动解析燃气厂图纸，提升数字化效率
Case Study: Transformer-Based Solution for the Automatic Digitization of Gas Plants
- 结合OCR与视觉大模型，构建端到端图纸信息提取流程
- 文本信息识别准确率达91%，组件识别率93%，层级结构80%准确
- 适合能源基建、工业数字化领域研究与应用
能源转型是过去数十年的关键议题，其可持续未来离不开数字化、创新与新技术。本文由Engineering Ingegneria Informatica SpA开发，针对意大利及欧洲领先的天然气运输公司SNAM的能源基础设施，提出基于生成式人工智能的自动化工厂结构采集方案。燃气厂数字化需通过解读相关文档来记录所有关键信息。本工作旨在设计一种基于AI的有效解决方案，以自动化提取数字化所需信息，减轻MGM用户日常负担。输入为每份以PDF格式提供的P&ID图纸，系统采用OCR、视觉大模型（Vision LLM）、目标检测、关系推理与优化算法，输出包括结构化的设计数据概览和工厂的分层架构。为提升复杂组件间关系分析能力，我们改进了前沿场景图生成模型，引入全新Transformer架构。多技术协同有效克服了因缺乏标准化导致的数据多样性挑战。在设计数据文本提取上达到91%准确率；组件识别率达93%，层级结构提取准确率约80%。
原文摘要 · Abstract (English)
The energy transition is a key theme of the last decades to determine a future of eco-sustainability, and an area of such importance cannot disregard digitization, innovation and the new technological tools available. This is the context in which the Generative Artificial Intelligence models described in this paper are positioned, developed by Engineering Ingegneria Informatica SpA in order to automate the plant structures acquisition of SNAM energy infrastructure, a leading gas transportation company in Italy and Europe. The digitization of a gas plant consists in registering all its relevant information through the interpretation of the related documentation. The aim of this work is therefore to design an effective solution based on Artificial Intelligence techniques to automate the extraction of the information necessary for the digitization of a plant, in order to streamline the daily work of MGM users. The solution received the P&ID of the plant as input, each one in pdf format, and uses OCR, Vision LLM, Object Detection, Relational Reasoning and optimization algorithms to return an output consisting of two sets of information: a structured overview of the relevant design data and the hierarchical framework of the plant. To achieve convincing results, we extend a state-of-the-art model for Scene Graph Generation introducing a brand new Transformer architecture with the aim of deepening the analysis of the complex relations between the plant's components. The synergistic use of the listed AI-based technologies allowed to overcome many obstacles arising from the high variety of data, due to the lack of standardization. An accuracy of 91\% has been achieved in the extraction of textual information relating to design data. Regarding the plants topology, 93\% of components are correctly identified and the hierarchical structure is extracted with an accuracy around 80\%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。