A Multi-Agent and Hybrid RAG-Based Framework for Security Evaluation and Intelligent Strategy Generation in Regional Water Resource Management

To address the issues of low intelligence and weak knowledge support in traditional water resource security risk analysis and response strategy generation, this paper proposes a novel framework based on hybrid retrieval augmentation and multi-agent collaboration. First, the proposed method integrates the DPSIR-CRITIC-TOPSIS framework with an obstacle degree model to construct an evaluation agent. This agent enables the intelligent assessment of regional water resource security and the precise extraction of key obstacle factors. Second, a water resource security knowledge graph and a vector knowledge base are constructed utilizing textual data, including policies and regulations, technical standards, the academic literature, and typical case studies. A hybrid retrieval augmentation mechanism—integrating graph reasoning, dual-path recall, and relation expansion—is designed to enhance the precision and relevance of the generated risk response strategies. Finally, a collaborative workflow comprising a master control agent alongside evaluation, retrieval, generation, and review agents is established to iteratively optimize the strategies through cross-validation and compliance reviews. In an empirical case study utilizing multi-year data from Henan Province and its 18 prefecture-level cities, experimental results demonstrate that the proposed method significantly outperforms baseline models across multi-dimensional semantic evaluation metrics, as well as BLEU-4 and ROUGE-L scores. The multi-agent collaborative architecture developed in this study, which integrates data-driven evaluation with knowledge-based hybrid retrieval augmentation, significantly elevates the intelligence level of water resource security assessment. It provides robust technical support for the analysis of regional water resource security situations and the intelligent generation of actionable response strategies.