Clustering Analysis of Water Usage Structures Based on Knowledge Graph

A key issue in the management of water resources in China is the adoption of a water supply total quantity control framework at the regional level based on socioeconomic development. This necessitates more effective management of water resource demand to achieve a balance between supply and demand. This paper aims to conduct an in-depth analysis of the industrial water usage structure, identify the primary influencing factors, and thereby provide insights for the design of demand management policies. Using a typical application scenario of "water extraction and supply" in a subsidiary of a water supply company in Beijing, a water knowledge graph incorporating multiple data sources is constructed. The study proposes a knowledge graph embedding model based on graph convolutional neural networks. Over the 36 months from 2019 to 2021, the water units in the accommodation, office, urban resident, healthcare, retail, education, catering, and residential services industries are clustered, and the water usage is analyzed. The results of this study are expected to provide references for optimizing urban water resource utilization.