基于知识图谱的航天产品总装质量追溯方法研究

doi:10.3901/JME.260230

机械工程学报 ›› 2026, Vol. 62 ›› Issue (5): 88-99.doi: 10.3901/JME.260230

• 特邀专栏：信息驱动的总装拉动生产模式、技术及应用 • 上一篇

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基于知识图谱的航天产品总装质量追溯方法研究

郑小虎^1,2, 曹立俊³, 刘骁佳³, 杜思淇⁴, 武文强¹, 张洁^1,2, 丁司懿^1,2

1. 东华大学信息与智能科学学院上海 201620;
2. 上海工业大数据与智能系统工程技术研究中心上海 201620;
3. 上海航天精密机械研究所上海 201600;
4. 东华大学机械工程学院上海 201620

收稿日期:2025-02-25 修回日期:2025-12-02 发布日期:2026-04-23
作者简介:郑小虎,男,1983年出生,博士,副教授,博士研究生导师。主要研究方向为智能工艺与仿真、具身智能技术。E-mail:xhzheng@dhu.edu.cn
张洁,女,1963年出生,博士,教授,博士研究生导师。主要研究方向为智能制造系统、工业大数据。E-mail:mezhangjie@dhu.edu.cn
丁司懿(通信作者),男,1986年出生,博士,副教授,硕士研究生导师。主要研究方向为复杂产品制造质量控制、机电产品故障诊断。E-mail:dingsiy@dhu.edu.cn
基金资助:
国防科工局资助项目。

Research on Traceability Method of Aerospace Product Assembly Quality Based on Knowledge Graph

ZHENG Xiaohu^1,2, CAO Lijun³, LIU Xiaojia³, DU Siqi⁴, WU Wenqiang¹, ZHANG Jie^1,2, DING Siyi^1,2

1. School of Information and Intelligent Science, Donghua University, Shanghai 201620;
2. Shanghai Engineering Center of Industrial Big Data and Intelligent System, Shanghai 201620;
3. Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600;
4. College of Mechanical Engineering, Donghua University, Shanghai 201620

Received:2025-02-25 Revised:2025-12-02 Published:2026-04-23

摘要/Abstract

摘要： 针对航天产品总装过程多源异构数据整合困难、质量问题关联追溯效率低的问题，提出一种基于知识图谱的质量追溯方法。通过构建涵盖物流吊装、电缆网导通和综合测试关键场景的知识图谱，整合结构化与非结构化数据，实现跨环节数据的语义关联与深度推理。结合YOLOv8与DeepSORT算法实现物流吊装场景的实时目标检测与行为分析，利用K近邻算法对电缆网导通及综合测试场景的异常数据进行异常判断，基于知识图谱构建“异常-原因-解决方案”联动推理机制，突破传统质量追溯方法的数据孤岛限制，为总装质量问题定位与根因分析提供可解释性支持。案例验证表明，该方法可有效解决航天产品总装过程中的数据孤岛问题，提升异常定位与质量追溯的准确性与效率，为复杂总装场景的智能化质量管控提供技术支撑。

关键词: 知识图谱, 质量追溯, 航天产品总装, 多源数据融合

Abstract: To address the challenges of integrating multi-source heterogeneous data and inefficient association and tracing of quality issues in the assembly process of aerospace products, this study proposes a knowledge graph-based quality tracing method. By constructing a knowledge graph encompassing critical scenarios including logistics and hoisting, cable network conductivity, and comprehensive testing, we integrate structured and unstructured data to achieve cross-process semantic association and deep reasoning of data. The approach combines YOLOv8 and DeepSORT algorithms for real-time object detection and behavior analysis in logistics/hoisting scenarios, while employing the K-nearest neighbors (KNN) algorithm for anomaly detection in cable network conductivity and comprehensive testing scenarios. A collaborative reasoning mechanism of "anomaly-cause-solution" is established through the knowledge graph, breaking through the data silo limitations of traditional quality tracing methods and providing interpretable support for quality issue localization and root cause analysis in product assembly. Case validation demonstrates that this method effectively resolves data silo issues in aerospace product assembly, enhances the accuracy and efficiency of anomaly localization and quality tracing, and offers technical support for intelligent quality management in complex assembly scenarios.

Key words: knowledge graph, quality traceability, aerospace product final assembly, multi-source data fusion

中图分类号:

TP18

郑小虎, 曹立俊, 刘骁佳, 杜思淇, 武文强, 张洁, 丁司懿. 基于知识图谱的航天产品总装质量追溯方法研究[J]. 机械工程学报, 2026, 62(5): 88-99.

ZHENG Xiaohu, CAO Lijun, LIU Xiaojia, DU Siqi, WU Wenqiang, ZHANG Jie, DING Siyi. Research on Traceability Method of Aerospace Product Assembly Quality Based on Knowledge Graph[J]. Journal of Mechanical Engineering, 2026, 62(5): 88-99.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260230

http://www.cjmenet.com.cn/CN/Y2026/V62/I5/88

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基于知识图谱的航天产品总装质量追溯方法研究

Research on Traceability Method of Aerospace Product Assembly Quality Based on Knowledge Graph

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