面向设备运维的人机物三元融合知识图谱构建方法

doi:10.3901/JME.2025.17.215

摘要/Abstract

摘要： 设备运维是保障生产正常进行的重要基础，现有的智能运维技术主要依赖信号分析、数据挖掘或专家知识重用。然而，随着设备自动化和集成化程度的提高，其各类运行异常的表征信号、多源致因和维护方案之间的关系呈现出更高的模糊性和复杂性，将信号、数据和知识进行融合分析是提高设备运维精度和效率的关键。为此，采用知识图谱技术将“人”、“机”、“物”三元数据融合来支撑复杂设备的异常诊断和维护方案决策，提高运维智能化程度、避免决策片面性。首先，对设备运维领域人机物三元数据进行定义并完成三元本体设计，指导知识图数据层的构建。其次，对人机物三元数据进行预处理并搭建了统一混合注意力机制联合抽取模型(Joint entity and relation extraction model with mixed attention，MAREL)从三元数据中自动抽取知识，并建立三元知识之间的关联关系，以此实现人机物三元数据的融合；MAREL模型将任务拆解为两个关联的解码模块来解决实体重叠问题，利用混合注意力机制增强模型的长文本处理能力，在中文数据集SKE上的测试证明MAREL具有优异的性能。最后，以某汽车生产机器人设备运维人机物知识图谱的构建为例，验证了所提方法的有效性，结果表明知识图谱能够将人机物三元数据有效融合，为工业设备的智能运维提供支持。

关键词: 设备运维, 人机物, 知识图谱, 数据融合, 本体, 联合抽取

Abstract: Equipment maintenance is an important basis to ensure normal production, the existing intelligent maintenance technologies mainly rely on signal analysis, data mining or expert knowledge reuse. However, with the improvement of the automation and integration degree of production equipment, the relationships among the characteristic signals of various operating anomalies, multi-source causes and maintenance schemes present higher fuzziness and complexity, the integration analysis of signals, data and knowledge is the key to improve the accuracy and efficiency of equipment maintenance. Therefore, knowledge graph technology is used to integrate the ternary data of “human”, “cyber” and “physical” to support the abnormal diagnosis and maintenance scheme decision of complex equipment, improve the intelligent degree of equipment maintenance, and avoid the one-sidedness of decision. Firstly, the ternary man-machine object data in the field of equipment maintenance is defined and the ternary ontology design is completed to guide the construction of knowledge graph data layer. Secondly, preprocessing is conducted on the ternary data of human-cyber-physical and a unified joint entity and relation extraction model with mixed attention，MAREL is built to automatically extract knowledge from the ternary data of human-cyber-physical, and to establish associative relationships between them, thereby achieving the fusion of ternary human-cyber-physical data; MAREL dissolves the task into two related decoding modules to solve the entity overlap problem, and the mixed attention mechanism is used to enhance the long text processing capability of the model, the test on the Chinese data set SKE proves that MAREL has excellent performance. Finally, the construction of human-cyber-physical knowledge graph for the maintenance of robot equipment in an automobile production workshop is taken as an example, the effectiveness of the proposed method is verified, results show that the knowledge graph can effectively integrate the ternary data of “human”, “cyber” and “physical”, and provide decision support for intelligent equipment maintenance.

Key words: equipment maintenance, human-cyber-physical, knowledge graph, data fusion, ontology, joint extraction

中图分类号:

TP319
TH17

杨波, 申小玉, 王时龙, 何彦, 杜卡泽. 面向设备运维的人机物三元融合知识图谱构建方法[J]. 机械工程学报, 2025, 61(17): 215-232.

YANG Bo, SHEN Xiaoyu, WANG Shilong, HE Yan, DU Kaze. A Human-cyber-physical Data Fusion Knowledge Graph Construction Method for Equipment Maintenance[J]. Journal of Mechanical Engineering, 2025, 61(17): 215-232.

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