基于多模态知识图谱的数字孪生变电站应用研究

igital twin technology has been widely applied in the power engineering sector, effectively achieving real-time correlation between equipment data and models, as well as visualizing the status of equipment. However, as the volume of data in substations increases and the forms of data collection diversify, including structured data, unstructured data, and various industry standards and operational guidelines, most current applications have not fully exploited these rich data resources. To address this issue, this study proposes a method based on multimodal knowledge graph technology, aimed at constructing a holistic monitoring and intelligent decision-making system to enhance the data analysis capabilities and decision intelligence of substations comprehensively. This system provides an all-encompassing data analysis platform, supporting fault handling and decision-making, and pushes substation management towards a more intelligent and refined direction to meet the complexities and diverse needs of future power systems.