Multimodal large model construction technology for intelligent safety supervision of water diversion projects
WANG Lihu
LIU Xuemei
LI Hairui
CHEN Xiaonan
Abstract：With the comprehensive development of the"sky-space-earth-water-project"integrated monitoring system,safety management data of water diversion projects exhibit characteristics of multi-source heterogeneity,large volume,and dynamic complexity.Traditional analysis and mining methods based on single-modality data face significant limitations in the context of intelligent safety supervision.By integrating multimodal large models with knowledge graph technology,an intelligent supervision paradigm of"perception-cognition-decision"is proposed.Based on standards and specifications,risk and emergency management materials,inspection texts and images,and multispectral remote sensing imagery,a multimodal large model is fine-tuned and combined with a dynamic prompting strategy to construct a multimodal knowledge graph for engineering safety.Retrieval augmented generation(RAG)and the structured knowledge within the knowledge graph are employed to enhance the model's reliability and reasoning capability in specialized domains.A collaborative multi-agent decision chain construction method is introduced,enabling the coupling of model capabilities through dynamic task orchestration to support risk identification,assessment,and contingency planning in safety management.Experimental results show that the proposed method achieves high accuracy in multimodal knowledge extraction,providing effective support for intelligent safety supervision of water diversion projects.
Keywords：intelligent supervisionmultimodal large modelmultimodal knowledge graphintelligent safety supervisionwater diversion projects
Publication Date：2025-06-12
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 8-19 )