基于时空元胞的冰冻圈多源异构数据融合与智能管理OA

[Background]Cryosphere research faces core challenges such as inconsistent spatiotemporal references and weak semantic correlations in multi-source heterogeneous data,which severely hinder the in-depth mining of data value.[Objective]This study proposes a spatiotemporal cell-based framework for multimodal data fusion and intelligent management,aiming to enhance the operability and knowledge discovery efficiency of massive scientific data through standardized spatiotempo-ral unit construction and semantic enhancement technologies.[Methods]First,based on unified spatiotemporal references,observation areas are divided into regularized spatiotemporal cells with preset resolutions,ensuring spatiotemporal consistency of meteorological,remote sensing,and other multimodal data through standardized processing.Second,a cell feature calculation model is designed to mine data variation patterns and interaction re-lationships within and across cells using statistical algorithms and association analysis.Furthermore,large lan-guage models and semantic embedding technologies are integrated to establish a"precise condition+semantic reasoning"dual-mode retrieval mechanism,supporting structured queries and natural language-driven deep corre-lation mining.[Conclusions]The framework effectively addresses multi-source data fusion and weak semantic correlation issues,significantly improving the intelligent management level of cryosphere scientific data.Re-searchers can dynamically explore data evolution patterns through interactive interfaces,supporting scientific dis-covery and decision-making,thereby providing efficient tool support for climate system research and sustainable development.