Construction of Knowledge Platform for Water Distribution Scheduling in Irrigation Areas Based on Large Language Models

To enhance the efficiency of water allocation scheduling and improve the utilization of water resources in irrigation districts, this paper presents a knowledge platform based on a large language model (LLM) for irrigation district water allocation scheduling. This platform addresses key challenges such as low recall rates for water management knowledge retrieval, insufficient response accuracy, and limited interaction methods. By systematically organizing diverse types of water management knowledge, we develope structured knowledge collection templates and utilized LLMs to extract essential knowledge elements. Knowledge segments are summarized into knowledge tags and abstracts. Differentiated storage strategies are implemented based on knowledge types and characteristics, with mapping relationships established between related knowledge units. The application of a two-stage progressive similarity search technique significantly enhances knowledge recall capabilities. Furthermore, targeted fine-tuning of the large language model improves its semantic understanding of water management terminology. Experimental results demonstrate a 93% accuracy rate for knowledge-based question answering. After fine-tuning, the model achieves 90% semantic comprehension in irrigation district scheduling scenarios, enabling accurate user intent recognition and facilitating collaborative scheduling among intelligent agents. Thus this platform provides an efficient and intelligent decision-support tool for water allocation scheduling in irrigation districts, showcasing broad application prospects.