数据与知识融合驱动的空间对接机构数字孪生试验  

Data and Knowledge Fusion-driven Digital Twin Experiment for Space Docking Mechanisms

作　　者：王昭 于游游 金旭龙 徐子锋 高增桂 杨娜 刘丽兰[1] WANG Zhao;YU Youyou;JIN Xulong;XU Zifeng;GAO Zenggui;YANG Na;LIU Lilan(School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China;Shanghai Aerospace System Engineering Institute,Shanghai 201109,China)

机构地区：[1]上海大学机电工程与自动化学院,上海200444 [2]上海宇航系统工程研究所,上海201109

出　　处：《上海航天(中英文)》2025年第2期144-156,176,共14页Aerospace Shanghai(Chinese&English)

基　　金：工信部高质量发展专项基金资助项目(2023ZY01007)。

摘　　要：在航天任务中,空间对接机构的分离性能直接影响任务的稳定性与可靠性。基于数字孪生技术,结合数据与知识融合方法,构建了空间对接机构的数字孪生实验平台。采用贝叶斯优化算法提升预测模型对组件退化与分离性能耦合关系的学习能力,建立了高精度分离性能预测模型。通过沙普利可加性特征解释(SHAP)可解释性分析,揭示了关键组件对分离性能的影响规律。经实验该平台通过实时仿真、动态监测与分离性能预测,能够提高了试验效率与可靠性,助力对接任务的优化与安全保障。In space missions,the separation performance of docking mechanisms directly affects the stability and reliability of the missions.In this paper,a digital twin experimental platform for space docking mechanisms is established based on the digital twin technology,along with the fusion method of data and knowledge.The Bayesian optimization algorithm is used to enhance the predictive model’s ability to learn the coupling relationship between the component degradation and separation performance,and a high-precision separation performance prediction model is established.The shapley additive explanations(SHAP)interpretability analysis is adopted to reveal the impacts of key components on the separation performance.The experimental results demonstrate that the platform improves the testing efficiency and reliability through real-time simulation,dynamic monitoring,and separation performance prediction,supporting the optimization and safety assurance of docking missions.

关 键 词：数字孪生 空间对接机构 分离性能预测 贝叶斯优化 

分 类 号：V416.5[航空宇航科学与技术—航空宇航推进理论与工程] TP18[自动化与计算机技术—控制理论与控制工程] TP391.9[自动化与计算机技术—控制科学与工程]