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Acta Aeronautica et Astronautica Sinica ›› 2026, Vol. 47 ›› Issue (2): 332097.doi: 10.7527/S1000-6893.2025.32097

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Incremental TAS scheduling method with DM-KM grouping

Shilong JING1, Rui SHI2, Xuan ZHOU3, Jiawei YAN4, Feng HE1()   

  1. 1.School of Electronic and Information Engineer,Beihang University,Beijing 100083,China
    2.Key Laboratory of Science and Technology on Space Physics,China Academy of Launch Vehicle Technology,Beijing 100076,China
    3.School of Information Engineering,Minzu University of China,Beijing 100081,China
    4.Research & Development Center,China Academy of Lunch Vehicle Technology,Beijing 100076,China
  • Received:2025-04-09 Revised:2025-05-06 Accepted:2025-06-16 Online:2025-07-28 Published:2025-07-25
  • Contact: Feng HE E-mail:fenghe@buaa.edu.cn
  • Supported by:
    National Natural Foundation of China(U2333213)

Abstract:

In large-scale, time-sensitive networking applications for spacecraft, the Time-Aware Scheduling (TAS) scheduling often faces challenges such as relatively low solving scale and slow speed. This paper proposes a DM-KM traffic grouping algorithm based on a distance matrix and K-means clustering, and integrated with it, designs an incremental TAS scheduling method. First, a traffic network model is constructed, using a weighted comprehensive distance matrix based on the entropy weight method to represent the correlations between traffic flows. Then, an incremental scheduling algorithm combined with DM-KM traffic grouping is designed and implemented. The proposed grouping method achieves high intra-group similarity and low inter-group similarity, which effectively improves the solving speed of the incremental scheduling. Experimental results show that compared with the existing DoC-KM and CILP-KM grouping algorithms, the DM-KM algorithm achieves better schedulability while maintaining a high solving speed in a 1000-traffic scheduling scenario. Compared with other scheduling algorithms, the solving scale can be improved by up to 32.36%, providing a grouping and incremental scheduling solution for Time-Sensitive Networking (TSN) in large-scale spacecraft networks.

Key words: Time-Sensitive Network (TSN), Time-Aware Shaper (TAS), flow grouping, incremental solving framework, on-board network

CLC Number: