Knowledge graph modeling method for product manufacturing process based on human-cyber-physical fusion
Knowledge graph modeling method for product manufacturing process based on human-cyber-physical fusion
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DOI：
10.1016/j.aei.2023.102183
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Chen Ding;Fei Qiao;Juan Liu;Dongyuan Wang
中科院分区：
文献类型：
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作者：
Chen Ding;Fei Qiao;Juan Liu;Dongyuan Wang
产品制造过程中产生的数据通常以不同的格式分布，引发知识碎片化和信息脱节。为了解决这个问题，我们提出了一种产品制造过程的知识图建模方法。首先，详细分析了人-网络-物理（HCP）要素的概念。在本体建模过程中以形式化的方式定义与HCP相关的类、属性和关系。其次，通过知识提取、知识融合和知识推理三个步骤构建产品制造过程（KGM/PMP）的知识图模型。在构建 KGM/PMP 模型时，提出了一种名为 BERT-D’BiGRU-CRF 的深度学习方法，用于自动从制造数据中提取知识。此外，还设计了一套推理规则来推断新知识。最后通过案例研究验证了所提方法的有效性。通过与其他四种方法的性能比较，验证了 BERT-D’BiGRU-CRF 方法在知识提取方面的有效性。通过开发原型系统证明了知识图模型的适用性。通过该系统，可以快速、准确地为需求者提供制造知识。
The data generated in the product manufacturing process are usually distributed in different formats, triggering fragmented knowledge and disconnected information. To address this problem, we present a knowledge graph modeling method for the product manufacturing process. First, the concepts of human–cyber–physical (HCP) elements are analyzed in detail. The HCP-related classes, attributes, and relations are defined in a formalized manner in the ontology modeling process. Second, a knowledge graph model for the product manufacturing process (KGM/PMP) is constructed by three steps, including knowledge extraction, knowledge fusion, and knowledge reasoning. When constructing the KGM/PMP model, a deep learning method called BERT-D’BiGRU-CRF is presented to automatically extract knowledge from the manufacturing data. Moreover, a set of reasoning rules are designed to infer new knowledge. Finally, a case study is carried out to validate the effectiveness of the proposed method. The validity of the BERT-D’BiGRU-CRF method on knowledge extraction is verified by comparing performance with four other methods. The applicability of the knowledge graph model is demonstrated through developing a prototype system. With this system, manufacturing knowledge can be provided for the demanders rapidly and accurately.