用计算机视觉自动连接管道图中的线条，提升设备互联建模效率。
Advanced Integration of Discrete Line Segments in Digitized P&ID for Continuous Instrument Connectivity
- 通过视觉模型检测线条后进行智能合并，构建完整连接关系
- 将分离线条整合为连续路径，实现设备与管线的精准关联
- 适合工业数字化、知识图谱构建及自动化流程设计场景
管道和仪表图（P&ID）是工厂的核心蓝图，展示了工艺设备、控制仪表以及流体与控制信号的连接关系。当前依赖人工从图纸中提取信息，耗时3至6个月，易出错且高度依赖专家经验，需多次审核。数字化P&ID的关键在于将计算机视觉模型识别出的离散线段进行融合，以建立仪器间的完整连接。本文提出一种线段整合方法，将检测到的线段合并为连续路径，从而实现设备与管线的准确关联。最终生成可存储于知识图谱中的结构化信息，支持最优路径查找、系统循环检测、传递闭包计算等高级分析任务。
原文摘要 · Abstract (English)
Piping and Instrumentation Diagrams (P&IDs) constitute the foundational blueprint of a plant, depicting the interconnections among process equipment, instrumentation for process control, and the flow of fluids and control signals. In their existing setup, the manual mapping of information from P&ID sheets holds a significant challenge. This is a time-consuming process, taking around 3-6 months, and is susceptible to errors. It also depends on the expertise of the domain experts and often requires multiple rounds of review. The digitization of P&IDs entails merging detected line segments, which is essential for linking various detected instruments, thereby creating a comprehensive digitized P&ID. This paper focuses on explaining how line segments which are detected using a computer vision model are merged and eventually building the connection between equipment and merged lines. Hence presenting a digitized form of information stating the interconnection between process equipment, instrumentation, flow of fluids and control signals. Eventually, which can be stored in a knowledge graph and that information along with the help of advanced algorithms can be leveraged for tasks like finding optimal routes, detecting system cycles, computing transitive closures, and more.
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