Research on the Application of Knowledge Graph Technology in the Testing Field of New Energy Motors

The testing of new energy motors serves as a critical safeguard for ensuring operational performance and reliability, constituting an essential validation mechanism for compliance with technical specifications. With advancements in new energy motor technology, the exponential growth of testing-related datasets has exposed limitations in current data management frameworks, particularly in analytical efficiency and knowledge utilization. As an advanced knowledge engineering paradigm, knowledge graph (KG) offer transformative solutions for overcoming these challenges. This paper systematically investigates the methodological framework and implementation strategies of KG technology in new energy motor testing ecosystems. A hybrid deep neural architecture integrating BERT embeddings with BiLSTM-CRF networks is proposed for automated knowledge extraction from heterogeneous testing data. Subsequently, a seven-phase ontological engineering methodology is developed to formalize domain-specific knowledge representation. Post-ontology construction, we introduce a synergistic approach combining TransE-based knowledge embedding with an optimized Personalized PageRank algorithm to enable context-aware semantic search capabilities. Empirical case studies demonstrate the framework's efficacy, achieving a 41.2% improvement in testing workflow efficiency and 91.06% precision in multi-criteria semantic queries compared to conventional relational database systems.