Space Surveillance Network (SSN) task scheduling plays a crucial role in maintaining the catalog of Resident Space Objects (RSO). However, various emergencies, such as RSO maneuvering, collisions, or rocket launch, may disrupt the original scheduled scheme. Therefore, it is essential to rapidly regenerating emergency schemes while minimizing disturbance to the initial scheduling scheme. This paper introduces an Emergency Task Scheduling model, referred to as MM-ETS, which aims to Maximize observation profits and Minimize disturbance. This model incorporates constraints related to observability, tasks, and resources, which are derived from practical applications. Additionally, a Hierarchical Distributed Dynamic Emergency Scheduling algorithm, encompassing Task assignment, Conflict resolution, Resource negotiation, and Center collaboration (HD-TCRC-DES), is proposed. The presented algorithm is activated by emergencies and a Rolling Horizon Strategy (RHS) is employed to break down long-term, large-scale problems into short-term, small-scale problems, thus improving feasibility and emergency response capabilities. At each layer of the proposed algorithm, heuristic rules are utilized to solve the new scheme, which helps allocate the computational load to resource nodes, and quickly adjust the initial scheduling scheme. Experimental scenarios involving 15 ground observation resources and 1000 emergency tasks are constructed, and the simulation results demonstrate that the proposed method can enhance Comprehensive Benefits Indicators (CBI) by approximately 24.65%, 24.7%, 24.91%, and 30.5% compared to the baselines. Consequently, it is suitable for SSN emergency task scheduling.

中文翻译：

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基于事件触发的空间监视网络应急调度与多层次规划

空间监视网络（SSN）任务调度在维护驻留空间对象（RSO）目录方面发挥着至关重要的作用。然而，各种紧急情况，例如RSO机动、碰撞或火箭发射等，都可能扰乱原定计划。因此，快速重新生成应急方案，同时最小化对初始调度方案的干扰至关重要。本文介绍了一种紧急任务调度模型，简称MM-ETS，其目标是最大化观测收益和最小化干扰。该模型结合了与可观测性、任务和资源相关的约束，这些约束源自实际应用。此外，还提出了一种分层分布式动态紧急调度算法，包括任务分配、冲突解决、资源协商和中心协作（HD-TCRC-DES）。该算法由突发事件启动，采用滚动地平线策略（RHS）将长期、大规模问题分解为短期、小规模问题，从而提高可行性和应急响应能力。在所提出算法的每一层，利用启发式规则来求解新方案，这有助于将计算负载分配给资源节点，并快速调整初始调度方案。构建了涉及15个地面观测资源和1000个应急任务的实验场景，仿真结果表明，与基线相比，该方法可以将综合效益指标（CBI）提高约24.65％、24.7％、24.91％和30.5％。因此，它适合SSN紧急任务调度。