Multi-source RAG对空间数据存在的问题

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+1. 方法A、方法B都用于解决同一个问题，融合A+B方法，
+代表论文：A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters;
+Hydra: Deadline-Aware and Efficiency-Oriented  Scheduling for Deep Learning Jobs on  Heterogeneous GPUs
+说明A、B方法各自的优点与缺点，再提出如何将A+B结合，实现更好的效果。
+2. 方法A直接用在B领域会存在问题，
+代表论文：MultiRAG: A Knowledge-Guided Framework for Mitigating Hallucination in Multi-Source Retrieval Augmented Generation;
+TrajMesa: A Distributed NoSQL-Based Trajectory  Data Management System
+3. 论文A考虑了A因素，论文b考虑了B因素，联合考虑A+B，
+代表论文：