Research on large model question answering agent technology of geospatial information based on "one map" platform of Changjiang water conservancy

The existing large models and intelligent agents powered by large models are predominantly trained on general datasets, making them prone to hallucination issues such as low relevance of answers, inaccuracies, and lack of real-time capability.To address these challenges, this paper proposes a technical framework for a large language model-based intelligent agent for geospatial information question answering, built upon the "one map" platform of Changjiang water conservancy (GLMA).This framework employs a large model agent to interpret users′ natural language queries and execute corresponding tasks on the "One Map" platform.The large language model then generates final responses based on both the query and the task outcomes.To enhance the accuracy of task assignment, a tree-structured task allocation system is introduced during the task dispatch phase, significantly improving task retrieval and parameter generation capabilities.Furthermore, this paper introduces a geospatial information question-answering dataset and corresponding evaluation metrics to validate the performance of GLMA.Compared to state-of-the-art Chinese open-source models such as Baichuan2, Llama3.1, ChatGLM4, and Qwen2.5, GLMA achieves the best results in terms of task allocation accuracy and answer correctness.This research demonstrates strong extensibility and lays a foundation for future studies on large model agents in other professional domains.