In recent years, with the continuous improvement of rocket carrying capacity and the increasingly complex international space strategic competition, the number of spacecraft launches at home and abroad has shown explosive growth. Against the backdrop of a surge in global launch numbers, the automated analysis of spacecraft relevance has become a key foundational task in the process of digital development in spacecraft development fields, holding significant importance for precise bench marking of domestic and foreign models in the same field, collaborative model planning within the same series, intelligence analysis, and enhancement of research and development capabilities. However, there has been no related research in the field of aerospace aimed at this algorithmic task. Thus, this paper conducts research to address the demand for spacecraft relevance calculation. We model this demand as a statistical regression task and formulate data annotation rules and dataset construction methods accordingly. We construct diverse and discriminative sample features for three types of fields: text attributes with unstructured model properties, category attributes with limited values, and time/numeric attributes. Additionally, we design and implement a model for spacecraft relevance calculation based on ensemble learning along with its training methods. Experimental evaluations demonstrate that the corresponding algorithm achieves high levels of performance on indicators such as R2-Score and MSE. Furthermore, after being deployed online, it undergoes validation by spacecraft domain experts and meets the demand for automated analysis of spacecraft relevance.
Design and Implementation of Spacecraft Relevance Algorithm Based on Ensemble Learning
Lect. Notes Electrical Eng.
International Conference On Signal And Information Processing, Networking And Computers ; 2024 ; China September 10, 2024 - September 13, 2024
16.05.2025
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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