基于多模态大模型的水利地理智能体研究与应用

Research on application of a multimodal large model-based hydro-geospatial intelligent agent

摘要:

针对水利业务系统中自然语言交互能力不足、多源异构数据融合效率低及专业决策智能化水平有限等问题，提出一种基于多模态大模型的水利地理空间智能体框架（Hydro-GIA）。该框架构建包含基础设施层、数据与知识层、认知与计算层、编排与执行层、应用与交互层的 5 层体系，通过提示词构建任务执行规范，结合多模态提示嵌入策略（MPE）增强领域语义理解，并采用树形结构分配机制进行任务精准调度，最终实现从自然语言指令到多模态结果生成的全流程闭环。基于 Qwen 3 与 DeepSeek 模型构建 8 类专项智能体，在地震灾害分析、洪涝预演评估和智能在线制图等典型场景中得到应用与验证。结果表明，Hydro-GIA 能够有效提升水利业务中的交互效率、 数据融合与智能决策水平。

Abstract:

To address the challenges of inadequate natural language interaction capability，low efficiency in multisource 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.