一种支持现有制造系统数字孪生创建的基于图的知识表示与模式挖掘方法
A graph-based knowledge representation and pattern mining supporting the Digital Twin creation of existing manufacturing systems
AI总结：
针对棕地制造系统数字孪生创建中多源信息融合与模板识别难题，提出基于图的知识表示与模式挖掘方法，实现异构信息融合及自动模板识别。
摘要：
为现有制造系统（即棕地系统）创建数字孪生是一项具有挑战性的任务，原因在于需要关于棕地系统结构的专家知识以及实现数字模型所需的工作量。已有多种方法和技术被提出，它们至少部分地将棕地制造系统的信息数字化。数字孪生需要来自多个来源的关联信息。本文提出了一种基于图的方法，用于融合来自异构来源的信息。此外，该方法提供了一种通过图结构分析自动识别模板的方式，以促进对生成的数字孪生的进一步工作及其后续增强。
英文摘要：
The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of brownfield systems and the effort to realize the digital models. Several approaches and methods have already been proposed that at least partially digitalize the information about a brownfield manufacturing system. A Digital Twin requires linked information from multiple sources. This paper presents a graph-based approach to merge information from heterogeneous sources. Furthermore, the approach provides a way to automatically identify templates using graph structure analysis to facilitate further work with the resulting Digital Twin and its further enhancement.
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。
