上海航天（中英文）2025，Vol.42Issue(2)：144-156,176,14.DOI:10.19328/j.cnki.2096-8655.2025.02.014
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
摘要
Abstract
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Key words
digital twin/space docking mechanism/separation performance prediction/Bayesian optimization引用本文复制引用
王昭,于游游,金旭龙,徐子锋,高增桂,杨娜,刘丽兰..数据与知识融合驱动的空间对接机构数字孪生试验[J].上海航天（中英文）,2025,42(2):144-156,176,14.基金项目
工信部高质量发展专项基金资助项目(2023ZY01007) （2023ZY01007）
