The rapid growth of remote sensing data in Low Earth Orbit (LEO) satellite networks is increasingly constrained by limited downlink capacity to terrestrial networks. Satellite edge computing alleviates this pressure by enabling in-orbit data processing. However, it introduces a new challenge of spatio-temporal resource fragmentation. Variations in onboard computing capability, constrained energy availability, and intermittent inter-satellite and satellite-ground connectivity lead to highly dynamic and uneven resource distribution, which degrades the performance of conventional static routing and scheduling approaches. To address this, we propose a Retrieval-Augmented Generation (RAG)-enhanced bi-level cognitive orchestration framework for knowledge-guided, multi-objective scheduling. The proposed framework explicitly decouples network control across two different operational scales: at the strategic upper level, a Large Language Model (LLM) leverages an offline-distilled Expert Knowledge Base (EKB) to dynamically infer preference weights based on a compact abstract-state descriptor of real-time network conditions. At the lower execution level, a fidelity-aware genetic scheduler utilizes these inferred weights to compute physically feasible, collision-free joint routing and task offloading schedules. Extensive evaluations on a high-fidelity Walker-Delta network testbed under mixed-criticality workloads demonstrate that the proposed framework effectively consolidates fragmented resources, achieving a 30.7% reduction in packet loss, a 30% improvement in energy efficiency over the most competitive learning-based baseline, and an 8.5% decrease in end-to-end latency, while maintaining robust performance under cascading node-failure scenarios.

翻译：低轨卫星网络中遥感数据的快速增长日益受到地面网络下行链路容量有限的制约。卫星边缘计算通过支持在轨数据处理缓解了这一压力，然而也引入了时空资源碎片化的新挑战。星载计算能力差异、受限的能源可用性以及星间和星地间歇性连接，导致资源分布高度动态且不均衡，从而降低了传统静态路由与调度方法的性能。为此，我们提出一种基于检索增强生成（RAG）增强的双层认知编排框架，用于知识引导的多目标调度。该框架显式地将网络控制解耦为两种不同运行尺度：在战略上层，大语言模型（LLM）利用离线提取的专家知识库（EKB），基于实时网络状态的紧凑抽象状态描述符动态推断偏好权重；在底层执行层，保真感知遗传调度器利用这些推断权重计算物理可行、无冲突的联合路由与任务卸载调度方案。在混合关键性工作负载下的高保真Walker-Delta网络测试平台上进行的广泛评估表明，该框架有效整合了碎片化资源，相比最具竞争力的基于学习的基线方法，实现了丢包率降低30.7%、能效提升30%，端到端延迟降低8.5%，并且在级联节点故障场景下仍保持稳健性能。