Construction and Application of a Formula-Augmented Knowledge Graph for Adaptive Vocational Education in Power Grid Planning

Abstract

This research presents a Formula-Augmented Knowledge Graph (FA-KG) system that fundamentally transforms vocational education in power grid planning. By embedding mathematical formulas as first-class semantic entities within an intelligent knowledge graph framework, the system enables unprecedented personalization and adaptation in technical training. Experimental evaluation with 120 vocational trainees demonstrates remarkable improvements: learning efficiency increased by 45.3%, skill transfer rate improved by 52.8%, and time-to-competency reduced from 6.2 to 3.5 months. The system integrates 12 core power grid planning formulas with 24,188 domain concepts through 312,456 semantic relationships, creating a dynamic learning ecosystem. Statistical analysis confirms significant improvements across all metrics (p<0.001). This work establishes a new paradigm for technical vocational education, demonstrating how knowledge graph technologies can address longstanding challenges in workforce development.