基于空闲资源碎片整合的测控数传一体化资源调度算法

doi:10.12305/j.issn.1001-506X.2026.06.16

Abstract

Abstract:

Aiming at the three prominent issues encountered in practical telemetry, tracking and control and data transmission scheduling, namely the integrated scheduling of telemetry, tracking and control and data transmission, the low efficiency of large-scale satellite resource scheduling, and the equipment resources fragmentization after scheduling, an optimization model for integrated telemetry, tracking and control and data transmission resource scheduling is established. Two optimization objectives, namely task satisfaction rate and idle resource fragmentation number, are proposed, and a three-stage heuristic solution algorithm is designed.The algorithm first generates an initial scheduling solution, then combines a backtracking search mechanism with heuristic strategies such as tabu list and random search to improve the task satisfaction rate. Finally, by establishing a feasible solution decision function, resource allocation is optimized to reduce the number of idle resource fragments. Experimental results show that compared with existing benchmark algorithms, the algorithm improves the solution efficiency by at least 4 times, increases the task satisfaction rate by at least 3.13%, and reduces the number of idle resource fragments by at least 18.05%, thereby validating its fundamental efficacy in resolving the tightly constrained joint scheduling of telemetry, tracking and control and data transmission

Key words: integration of telemetry, tracking and control and data transmission, resource scheduling, idle resource fragmentation, heuristic algorithm

CLC Number:

TP 18

Chenyu ZHOU, Chao ZHANG, Cong LI. Integrated resource scheduling algorithm for telemetry, tracking and control and data transmission based on idle resource fragmentation integration[J]. Systems Engineering and Electronics, 2026, 48(6): 1946-1964.

Figures/Tables 23

Fig.1

Table 1

Table 2

Fig.2

Table 3

Table 4

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 5

Table 6

Table 7

Fig.10

Table 8

Table 9

Table 10

Fig.11

Table 11

Fig.12

References 43

1	孙耀华, 冯昕澳, 彭木根. 低轨巨型星座组网: 挑战与关键技术[J]. 天地一体化信息网络, 2024, 5 (4): 57- 74.
	SUN Y H, FENG X A, PENG M G. Mega LEO satellite constellations networking: challenges and key technologies[J]. Space-Integrated-Ground Information Networks, 2024, 5 (4): 57- 74.
2	DU Y H, XING L N, CHEN Y G. Unified modeling and multi-strategy collaborative optimization for satellite task scheduling[J]. Control and Decision, 2019, 34 (9): 1847- 1856.
3	WU J, CHEN Y N, HE Y M, et al. Survey on autonomous task scheduling technology for earth observation satellites[J]. Journal of Systems Engineering and Electronics, 2022, 33 (6): 1176- 1189.
4	FERRARI B, CORDEAU J F, DELORME M, et al. Satellite scheduling problems: a survey of applications in earth and outer space observation[J]. Computers & Operations Research, 2025, 173, 106875.
5	LEE M, YU S, KWON K, et al. Mixed-integer linear programming model for scheduling missions and communications of multiple satellites[J]. Aerospace, 2024, 11 (1): 83. doi: 10.3390/aerospace11010083
6	CHEN X Y, REINELT G, DAI G M, et al. A mixed integer linear programming model for multi-satellite scheduling[J]. European Journal of Operational Research, 2019, 275 (2): 694- 707. doi: 10.1016/j.ejor.2018.11.058
7	贺仁杰. 成像侦察卫星调度问题研究[D]. 长沙: 国防科学技术大学, 2004.
	HE R J. Research on imaging reconnaissance satellite scheduling problem[D]. Changsha: National University of Defense Technology, 2004.
8	ZHANG Z J, HU F N, ZHANG N. Ant colony algorithm for satellite control resource scheduling problem[J]. Applied Intelligence, 2018, 48 (10): 3295- 3305. doi: 10.1007/s10489-018-1144-z
9	张娜, 柯良军, 冯祖仁. 一种新的卫星测控资源调度模型及其求解算法[J]. 宇航学报, 2009, 30 (5): 2140- 2145. doi: 10.3873/j.issn.1000-1328.2009.05.066
	ZHANG N, KE L J, FENG Z R. A new model for satellite TT&C resource scheduling and its solution algorithm[J]. Journal of Astronautics, 2009, 30 (5): 2140- 2145. doi: 10.3873/j.issn.1000-1328.2009.05.066
10	彭双. 适应动态需求的卫星观测任务星上连续自主规划方法[D]. 长沙: 国防科技大学, 2019.
	PENG S. An approach of autonomous satellite observation task onboard continuous planning for dynamic requests[D]. Changsha: National University of Defense Technology, 2019.
11	SONG Y J, ZHANG Z S, SONG B Y, et al. Improved genetic algorithm with local search for satellite range scheduling system and its application in environmental monitoring[J]. Sustainable Computing: Informatics and Systems, 2019, 21 (3): 19- 27. doi: 10.1016/j.suscom.2018.11.009
12	樊慧晶, 章文毅, 田妙苗, 等. 基于粒子群算法的卫星任务地面站资源调度方法[J]. 中国科学院大学学报, 2022, 39 (6): 801- 808.
	FAN H J, ZHANG W Y, TIAN M M, et al. A resource scheduling method for satellite mission ground station based on particle swarm optimization algorithm[J]. Journal of University of Science and Technology of China, 2022, 39 (6): 801- 808.
13	DENG B Y, JIANG C X, KUANG L L, et al. Two-phase task scheduling in data relay satellite systems[J]. IEEE Trans. on Vehicular Technology, 2018, 67 (2): 1782- 1793. doi: 10.1109/TVT.2017.2763150
14	ZHANG J W, XING L N. An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem[J]. Computers and Operations Research, 2022, 139 (3): 105626. doi: 10.1016/j.cor.2021.105626
15	XU Y J, LIU X L, HE R J, et al. Multi-satellite scheduling framework and algorithm for very large area observation[J]. Acta Astronautica, 2020, 167 (2): 93- 107. doi: 10.1016/j.actaastro.2019.10.041
16	SONG Y J, OU J W, WITOLD P, et al. Generalized model and deep reinforcement learning-based evolutionary method for multitype satellite observation scheduling[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2023, 52 (4): 2576- 2589.
17	YAO X Y, PAN X G, ZHANG T, et al. Knowledge-guided evolutionary algorithm for multi-satellite resource scheduling optimization[J]. Future Generation Computer Systems, 2024, 156 (7): 130- 141. doi: 10.1016/j.future.2024.03.006
18	TIAN M M, MA G B, HUANG P, et al. Optimizing satellite ground station facilities scheduling for RSGS: a novel model and algorithm[J]. International Journal of Digital Earth, 2023, 16 (1): 3949- 3972. doi: 10.1080/17538947.2023.2259870
19	YANG X Y, HU M, HUANG G, et al. Multi-layer objective model and progressive optimization mechanism for multi-satellite imaging mission planning in large-scale target scenarios[J]. Applied Sciences, 2024, 14 (19): 8597. doi: 10.3390/app14198597
20	SONG Y J, SONG B Y, XING L, et al. Improved artificial bee colony algorithm for satellite-ground cooperative observation planning problem[J]. Control and Decision, 2022, 37 (3): 555- 564.
21	LI X X, GUO X, TANG H T, et al. An improved cuckoo search algorithm for the hybrid flow-shop scheduling problem in sand casting enterprises considering batch processing[J]. Computers & Industrial Engineering, 2023, 176 (2): 108921.
22	JIANG X M, GUO Y Y, ZHANG Y, et al. An evolutionary task scheduling algorithm using fuzzy fitness evaluation method for communication satellite network[J]. Swarm and Evolutionary Computation, 2025, 92 (2): 101830- 101830. doi: 10.1016/j.swevo.2024.101830
23	XU Y, JIAO Y Y, PAN X G, et al. An efficient scheduling method for satellite TT&C resources[C]//Proc. of the 7th International Conference on Big Data and Information Analytics, 2021: 340−349.
24	李夏苗, 陈新江, 伍国华, 等. 考虑断点续传的中继卫星调度模型及启发式算法[J]. 航空学报, 2019, 40 (11): 274- 289.
	LI X M, CHEN X J, WU G H, et al. Scheduling model and heuristic algorithm for tracking and data relay satellite considering break-point transmission[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40 (11): 274- 289.
25	REN B, LIU J P, LI Z Y, et al. Satellite requirement preference driven TT&C resources scheduling algorithm for time sensitive missions[C]//Proc. of the IEEE 3rd International Conference on Electronic Information and Communication Technology, 2020: 15−19.
26	CHEN X Y, GERHARD R, DAI G M, et al. Priority-based and conflict-avoidance heuristics for multi-satellite scheduling[J]. Applied Soft Computing, 2018, 69 (8): 177- 191. doi: 10.1016/j.asoc.2018.04.021
27	WU G H, LIU J H, MA M, et al. A two-phase scheduling method with the consideration of task clustering for earth observing satellites[J]. Computers and Operations Research, 2013, 40 (7): 1884- 1894. doi: 10.1016/j.cor.2013.02.009
28	刘润滋, 盛敏, 唐成圆, 等. 基于任务拆分聚合的中继卫星系统任务规划方法[J]. 通信学报, 2017, 38 (S1): 110- 117.
	LIU R Z, SHENG M, TANG C Y, et al. Tasking planning based on task splitting and merging in relay satellite network[J]. Journal on Communications, 2017, 38 (S1): 110- 117.
29	辛立强, 张超, 赵灵芝, 等. 面向复杂关联测控需求的冲突规避调度算法[J]. 系统工程与电子技术, 2022, 44 (5): 1581- 1588.
	XIN L Q, ZHANG C, ZHAO L Z, et al. Conflict avoidance scheduling algorithm for complex associated TT&C requirements[J]. Systems Engineering and Electronics, 2022, 44 (5): 1581- 1588.
30	CHEN X Y, TIAN T, DAI G M, et al. Deep reinforcement learning-based resource allocation method for multi-satellite scheduling[J]. Computers & Operations Research, 2025, 181, 107088.
31	SHANG M H, YUAN R G, SONG B, et al. Joint observation and transmission scheduling of multiple agile satellites with energy constraint using improved ACO algorithm[J]. Acta Astronautica, 2025, 230, 92- 103. doi: 10.1016/j.actaastro.2025.02.008
32	LIU Y, ZHANG S Y, HU H Y. A simulated annealing algorithm with tabu list for the multi-satellite downlink schedule problem considering waiting time[J]. Aerospace, 2022, 9 (5): 235. doi: 10.3390/aerospace9050235
33	XU L Z, YU C H, WU B, et al. A hybrid genetic algorithm for ground station scheduling problems[J]. Applied Sciences, 2024, 14 (12): 5045- 5045. doi: 10.3390/app14125045
34	孙刚, 彭双, 陈浩, 等. 面向测控数传资源一体化场景的卫星地面站资源多目标优化方法[J]. 航空学报, 2022, 43 (9): 661- 677.
	SUN G, PENG S, CHEN H, et al. Multi-objective optimization method oriented to integrated scenario of TT&C resources and data transmission resources[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (9): 661- 677.
35	LI Y, GUO X, MENG Z, et al. A hierarchical resource scheduling method for satellite control system based on deep reinforcement learning[J]. Electronics, 2023, 12 (19): 3991. doi: 10.3390/electronics12193991
36	DAI Q, LIU Z. ModEnPBT: a modified backtracking ensemble pruning algorithm[J]. Applied Soft Computing Journal, 2013, 13 (11): 4292- 4302. doi: 10.1016/j.asoc.2013.06.023
37	BERGSTRA J, BENGIO Y. Random search for hyper-parameter optimization[J]. Journal of Machine Learning Research, 2012, 13, 281- 305.
38	NAVARRO A C, HITSCHFELD-KAHLER N, MATEU L. A survey on parallel computing and its applications in data-parallel problems using GPU architectures[J]. Communications in Computational Physics, 2014, 15 (2): 285- 329. doi: 10.4208/cicp.110113.010813a
39	FIEME B, FIGUEIREDO J, SOUSA J M C, et al. Agent-based hybrid tabu-search heuristic for dynamic scheduling[J]. Engineering Applications of Artificial Intelligence, 2023, 126, 107146.
40	伍国华, 王天宇. 基于自适应模拟退火的大规模星座测控资源调度算法[J]. 航空学报, 2023, 44 (12): 265- 286.
	WU G H, WANG T Y. Large-scale constellation TT&C resource scheduling algorithm based on adaptive simulated annealing[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (12): 265- 286.
41	HOLTE R C, FELNER A, SHARON G, et al. MM: a bidirectional search algorithm that is guaranteed to meet in the middle[J]. Artificial Intelligence, 2017, 252, 232- 266. doi: 10.1016/j.artint.2017.05.004
42	LU W L, GAO W H, LIU B Y, et al. Parallel dual adaptive genetic algorithm: a method for satellite constellation task assignment in time-sensitive target tracking[J]. Advances in Space Research, 2024, 74 (10): 5192- 5213. doi: 10.1016/j.asr.2024.07.044
43	WANG T Y, GU Y, WANG H L, et al.. Adaptive variable neighborhood search algorithm with Metropolis rule and tabu list for satellite range scheduling problem[J]. Computers & Operations Research, 2024, 170 (10): 106757. doi: 10.1016/j.cor.2024.106757

参数符号	参数名称
Subidx	子任务号
Type	任务类型
SatObj	卫星参数
S_n	卫星编号
TPT	建链时间
TET	拆链时间
revCstr	弧段选择约束
MME	最小仰角
Pri	优先级
ScopeDef₀	允许范围下移值
ScopeDef₁	最希望时刻/圈号
ScopeDef₂	允许范围上移值
SDA	可用设备集合

参数符号	参数名称
d_n	设备编码
Fresus	工作频段
SusF	设备支持功能
FT	禁用时间段集合

参数符号	参数名称
Dayidx	天号
rev	总圈号
Flag	升降轨状态
KeyPoint	站点预报

约束类型	约束条件	约束表达式
子任务约束	最小仰角约束	${\mathrm{F.KeyPoint}}.{ {\mathrm{kp}} }_{2 } .{ {\mathrm{Angle}} }_2 \geqslant {\mathrm{D T}}_{n, k, j }. {\mathrm{M M E}} $
	任务设备约束	$F . d_n \in {\mathrm{D T}}_{n, k, j} . {\mathrm{S D A }}$
	预报时间约束	$\begin{gathered}\forall{\mathrm{ f o r}}\; b_l \subseteq d_{m }. {\mathrm{F T}}\left[\mathrm{F.KeyPoint}. {\mathrm{k p}}_1 . T s_1-{\mathrm{D T}}_{n, k, j .} {\mathrm{T P T}},\right. \\\left.\mathrm{F.KeyPoint}. {\mathrm{k p}}_{3 }. T_{s 3}+{\mathrm{D T}}_{n, k, j } .{\mathrm{T E T}}\right] \cap {\mathrm{for}}\; b_l=\varnothing\left(d_m=F . d_n\right)\end{gathered} $
子任务间约束	同一设备时间占用冲突	$\begin{gathered}\forall\left(W_{n, k, j}, W_{n^{\prime}, k^{\prime}, j^{\prime}}\right)\left[W_{n, k, j} . {\mathrm{t t s}}, W_{n, k, j} . {\mathrm{t t e}}\right] \cap\left[W_{n^{\prime}, k^{\prime}, j^{\prime} }. {\mathrm{t t s}}, W_{n^{\prime}, k^{\prime}, j^{\prime}} . {\mathrm{t t e}}\right] =\\\varnothing\left(W_{n, k, j} . d_n=W_{n^{\prime}, k^{\prime}, j^{\prime} } . d_n, W_{n, k, j }. S_n \neq W_{n^{\prime}, k^{\prime}, j^{\prime} } . S_n\right)\end{gathered} $
子任务间约束	同一卫星圈号占用冲突	$\begin{gathered}\forall\left(W_{n, k, j}, W_{n^{\prime}, k^{\prime}, j^{\prime}}\right) W_{n, k, j} . {\mathrm{r e v}} \neq W_{n^{\prime}, k^{\prime}, j^{\prime}} . {\mathrm{r e v}} \\\left(W_{n, k, j }. S_n=W_{n^{\prime}, k^{\prime}, j^{\prime} }. S_n, {\mathrm{D T}}_{n, k, j } . { {\mathrm{Type}} }={\mathrm{D T}}_{n^{\prime}, k^{\prime}, j^{\prime} }. { {\mathrm{Type}} }\right)\end{gathered} $

实验组别	测控任务总数	数传任务总数
1	12800	7040
2	12800	7040
3	12800	7040
4	12800	7040
5	15680	10176
6	16000	10432

Integrated resource scheduling algorithm for telemetry, tracking and control and data transmission based on idle resource fragmentation integration

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 23

References 43

Related Articles 15

Recommended Articles

Metrics

Comments

实验组别	TAAFV			CAACLR			IACLR
实验组别	TTC	DDT	$ {G_{sr}} $	TTC	DDT	$ {G_{sr}} $	TTC	DDT	$ {G_{sr}} $
1	12777	6954	0.9934	12775	6966	0.9941	12800	7023	0.9989
2	12789	6930	0.9924	12767	6935	0.9918	12800	7016	0.9984
3	12780	6910	0.9907	12762	6922	0.9907	12800	7011	0.9981
4	12793	6972	0.9953	12788	6973	0.9951	12800	7036	0.9997
5	15675	9302	0.9574	15677	9345	0.9596	15680	9462	0.9653
6	15680	9366	0.9393	15689	9411	0.9417	16000	9520	0.9567

算法模块	组合算法1	组合算法2	组合算法3	组合算法4
模块①	×	×	×	√
模块②	√	√	√	√
模块③	×	×	√	√
模块④	√	√	√	√
模块⑤	×	√	√	√

实验组别	SA			GA			RBIA
实验组别	TTC	DDT	$ {G_{sr}} $	TTC	DDT	$ {G_{sr}} $	TTC	DDT	$ {G_{sr}} $
1	12800	7002	0.99756	12800	7015	0.99814	12800	7040	1
2	12800	6988	0.99666	12800	7010	0.99776	12800	7040	1
3	12800	6998	0.99730	12800	7014	0.99806	12800	7040	1
4	12800	7015	0.99839	12800	7002	0.99717	12800	7040	1
5	15680	9512	0.96781	15680	9529	0.96464	15680	10126	0.99757
6	16000	9602	0.96066	16000	9616	0.95573	16000	10298	0.99364

实验组别	优化前任务满足率	优化前碎片数量	优化后任务满足率	优化后碎片数量
1	1	10640	1	6928
2	1	10500	1	6657
3	1	10626	1	6283
4	1	10515	1	4881
5	0.99757	14709	0.99809	12031
6	0.99364	14343	0.99431	11753

[1]	Dawang WANG, Zhifeng LU, Guohua WU. Research on resource scheduling of fire control radar networking based on adaptive variable neighborhood search [J]. Systems Engineering and Electronics, 2025, 47(2): 496-507.
[2]	Daozhi WEI, Zhaoyu ZHANG, Jiahao XIE, Ning LI. Multi-sensor cross cueing technique based on improved Actor-Critic algorithm [J]. Systems Engineering and Electronics, 2023, 45(6): 1624-1632.
[3]	Yao LIU, Yangsheng XIA, Jianmai SHI, Chao CHEN, Jincai HUANG. Path planning method for multi-area coverage by cooperated ground vehicle multi-drone [J]. Systems Engineering and Electronics, 2023, 45(5): 1380-1390.
[4]	Kaikai CUI, Rongwei CUI, Wei HAN, Fang GUO, Yulin WANG, Jie LIU. Carrier aircraft recovery sequencing scheduling technology based on MGP algorithm [J]. Systems Engineering and Electronics, 2023, 45(10): 3192-3206.
[5]	Changxiao ZHAO, Ershuai LI, Feng HE, Peng WANG. Bandwidth allocation and optimization of time-sensitive traffic in TSN [J]. Systems Engineering and Electronics, 2022, 44(6): 2027-2034.
[6]	Jiawei ZHANG, Fengchen QIAN, Junqiang YANG, Qian ZHAO, Zhengrong ZHANG. Survey on routing and spectrum allocation algorithm in elastic optical networks [J]. Systems Engineering and Electronics, 2022, 44(6): 2001-2010.
[7]	Liqiang XIN, Chao ZHANG, Lingzhi ZHAO, Jianping LIU. Conflict avoidance scheduling algorithm for complex associated TT & C requirements [J]. Systems Engineering and Electronics, 2022, 44(5): 1581-1588.
[8]	Wenge XING, Chuanrui ZHOU, Cheng ZHOU. Research on key technology of detection and communication integration for phased array radar [J]. Systems Engineering and Electronics, 2022, 44(10): 3053-3058.
[9]	Zhihai YAO, Jianping LIU, Jianping WANG, Wei LI, Bo REN. Evaluation index system of ground-based TT&C resource scheduling [J]. Systems Engineering and Electronics, 2020, 42(8): 1751-1758.
[10]	Yadong WANG, Quan SHI, Wei XIA, Cai CHEN. Structure optimization of spare parts supply network based on hyper heuristic algorithm [J]. Systems Engineering and Electronics, 2020, 42(3): 620-629.
[11]	Yi DU, Kefei LIAO, YANG Shan OU, Yijun CHEN. Two-dimensional resource adaptive scheduling algorithm for ISAR imaging system [J]. Systems Engineering and Electronics, 2020, 42(2): 339-345.
[12]	WANG Xun, YAO Peiyang, ZHANG Jieyong, WAN Lujun, JIAO Zhiqiang, CHEN Jieyu. Research on problem of Holonic-C2 organization resource scheduling [J]. Systems Engineering and Electronics, 2019, 41(3): 564-570.
[13]	LIU Xinbo, WANG Buhong, LIU Shuaiqi, YANG Zhixian, ZHAO Zhiyuan. Heuristic algorithm for secure virtual network embedding [J]. Systems Engineering and Electronics, 2018, 40(3): 676-681.
[14]	DING Ding, AI Lihua, LUO Siwei, XU Baomin. User behavior-based resource scheduling mechanism for cloud computing with feedback control [J]. Systems Engineering and Electronics, 2018, 40(1): 209-216.
[15]	CUI Feng-zhe, WANG Xiu-kun, TENG Hong-fei. Dual-system cooperative co-evolutionary algorithm for non-separable function [J]. Systems Engineering and Electronics, 2016, 38(11): 2660-2669.

参数	数量
种群数量	100
交叉概率	0.9
变异概率	0.1

参数	数量
初始温度	100
降温系数	0.99
最低温度	0.1