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随着城市轨道交通系统智能化程度的提升,传统算力资源分配模式已难以应对多源异构数据处理、实时决策支持等新型需求。提出基于大语言模型(largelanguagemodels,LLMs)的城轨云算力资源平台架构,构建了融合知识图谱的混合调度算法,通过动态资源分配优化策略提升了算力资源利用率。实验表明,该平台在处理突发客流预测、设备故障诊断等典型场景时,缩短了响应时间,提高了模型推理准确度,为智慧城轨建设提供了新型算力基础设施解决方案。

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