面向轨迹挖掘的大语言模型建设路径探索与展望OA

Research on large language model(LLM)empowered trajectory mining is conducted to address the limitations of traditional models in cross-domain generalization and multi-task processing.The development status and classification of LLM are reviewed.Architecture optimization,domain fine-tuning,and scene tuning are analyzed to clarify the adapta-tion mechanisms for improving model adaptability and efficiency.Internal driving forces of LLM-driven trajectory mining are explored from three dimensions:trajectory semantic enhancement,vertical domain reinforcement,and spatio-temporal scenario adaptation.Based on this,a bottom-up architecture is proposed,which includes a general LLM layer,a domain-specific LLM layer,and a scenario-specific LLM layer.Its application value in trajectory data semantic enrichment,domain empowerment,and scenario specialization is elaborated.Finally,challenges such as insufficient multi-source data fusion and lack of domain credibility assurance are summarized,and future trends including cross-modal fusion,visual credibility analysis,and agent collaboration are highlighted.