A Semantic and Multidimensional Analysis-based Charging Station Classification and Operation Method and its Application

Abstract

To address challenges in charging station operations such as low efficiency, resource imbalance, and inadequate fault prediction during the large-scale development of new energy vehicles, this study proposes a hierarchical operation and maintenance (O&M) method integrating semantic analysis, grid clustering, and uncertainty modeling. By consolidating multi-source O&M data, the method establishes a four-dimensional evaluation index system. It utilizes semantic models to extract information from unstructured customer service texts, employs grid clustering to identify regional service pressure, and incorporates polyhedral uncertainty theory to enhance evaluation robustness, ultimately forming a tiered O&M strategy. Practical applications demonstrate that this approach enables precise allocation of O&M resources, improves fault response efficiency, effectively reduces excessive O&M costs, and provides technical support for intelligent charging station management.