Construction of Knowledge Map Based on Large Model and Decision Support of Power System Planning

In this paper, a method combining deep learning and natural language processing (NLP) technology is proposed. Through the fusion of large model and knowledge map, structured knowledge is extracted from massive and multi-source power system data, and a knowledge map with rich semantic relations is constructed. In this study, BERT model is used to identify entities, extract relationships and identify attributes. Through the steps of knowledge fusion and map construction, the uniqueness of entities and the unified representation of relationships are ensured. At the same time, new relationships or attributes are inferred by using graph neural network (GNN) method to improve the knowledge map. In addition, a hierarchical power system planning decision support system (DSS) is designed, including data layer, knowledge layer, application layer and user layer, supported by large model and AI technology. Through case analysis, the application of the system in power system planning of large cities is demonstrated, including knowledge extraction from technical documents, knowledge fusion, risk assessment and optimization analysis, which significantly improves the accuracy, efficiency and safety of decision-making. This study not only improves the intelligence and automation level of power system planning, but also provides scientific decision support for the planning and operation of power system, which is of great significance to the sustainable development of power industry.