基于多模态大模型的水利地理智能体研究与应用OA

To address the challenges of inadequate natural language interaction capability,low efficiency in multi-source heterogeneous data fusion,and limited intelligent decision-making support in water conservancy systems,this paper proposed a multimodal large model-based hydro-geospatial intelligent agent framework(Hydro-GIA).The framework established a five-layer architecture comprising the infrastructure layer,data and knowledge layer,cognitive and computing layer,orchestration and execution layer,and application and interaction layer.By incorporating prompts to define task execution specifications,a multimodal prompt embedding strategy(MPE)to enhance domain-specific semantic understanding,and a tree-structured allocation mechanism for precise task scheduling,the framework achieved a complete closed-loop process from natural language instructions to multimodal result generation.Based on Qwen 3 and DeepSeek models,eight specialized agents were developed and validated through typical application scenarios,including earthquake disaster analysis,flood pre-assessment,and intelligent online mapping.The results demonstrate that Hydro-GIA effectively enhances interaction efficiency,data fusion capability,and intelligent decision-making in water conservancy applications.