Space-based systems providing remote sensing, communication, and navigation services are essential to the economy and national defense. Users’ demand for satellites has increased sharply in recent years, but resources such as storage, energy, and computation are limited. Therefore, an efficient resource scheduling strategy is urgently needed to satisfy users’ demands maximally and get high task execution benefits. A hierarchical scheduling method is proposed in this work, which combines improved ant colony optimization and an improved deep Q network. The proposed method considers the quality of current task execution and resource load balance. The entire resource scheduling process contains two steps, task allocation and resource scheduling in the timeline. The former mainly implements load balance by improved ant colony optimization, while the latter mainly accomplishes the high task completion rate by an improved deep Q network. Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. In the simulation scenarios, the proposed method can achieve up to 97.3% task completion rate, with almost 50% of the CPU runtime required by HAW and HADRT. Furthermore, this method has successfully implemented load balance.

Distributed collaboration for satellite swarm has good robustness, parallelism, and execution capability, but its performance is still somehow limited. How to reduce the reliance of distributed collaboration on a large amount of information interaction and computational support becomes a difficult point to constrain the collaboration of a satellite swarm. Stigmergy is a bionic-based distributed general collaboration mechanism in swarm intelligence, which can effectively improve the communication efficiency and break the bottleneck of communication of swarm system. In this paper, based on the stigmergy mechanism, an implicit communication method containing multiple semantic information for satellite swarm is established. Meanwhile, based on the implicit communication method, a satellite swarm collaboration mechanism (SSCM) is proposed. Finally, a satellite swarm collaborative task scenario is simulated based on SSCM. In a discrete grid map, multiple satellites collaborate in a distributed manner via digital pheromones for the “detection-imaging” task chain. The simulation results show that SSCM performs well in task execution rate and target coverage, with improvements of up to 30.8% and 44% compared to others. The simulation experiments validate the robustness and applicability of SSCM, and the experiment has some scalability in problem scale.