基于多智能体协同的高寒山区道路结冰数据工程预警OA

[Objective]This study focuses on the limitations of traditional road icing warning methods in high-altitude mountainous environments,including poor adaptability,low coordination efficiency,and difficulties in practical implementation.It aims to explore and validate a new warning approach that integrates multi-agent collaboration with data engineering,with the goal of improving the accuracy,timeliness,and deployability of road icing warn-ings in such regions and providing a practical solution for engineering applications.[Methods]A new data engi-neering paradigm is established through the integration of large-scale artificial intelligence models,leveraging their advanced capabilities in semantic understanding,multimodal fusion,and adaptive reasoning to reconstruct the data engineering value chain.A standardized and automated"data-method-analysis-decision"intelligent pipe-line is developed to enable end-to-end collaboration,encompassing data selection,engineering analysis,and deci-sion support.This approach transforms data analysis from a one-off task into a continuously scalable and reusable engineering process.[Conclusions]This study proposes a fusion approach combining intelligent agents with data engineering and applies it to a road icing warning simulation on the G217 Duku Highway.By continuously ana-lyzing video data from cameras and integrating multi-source heterogeneous data from along the route,this ap-proach improves the accuracy and timeliness of road icing warnings while enhancing data reusability.The results indicate that the method is feasible for practical implementation in high-altitude,complex environments.It pro-vides an efficient and reliable solution for road icing disaster warning and effectively promotes the transformation of data from a"resource"into a valuable"asset."