Ontology-Driven Automatic Scoring of Mechanization Rate in Power Grid Construction Projects Using Large Language Models

Driven by the global energy transition, mechanized construction—characterized by enhanced safety, efficiency, and quality—is becoming the mainstream approach in power grid development. Mechanization assessment serves as a critical tool for guiding and optimizing this process, yet current practices remain largely manual, resulting in inefficiency, time-consuming operations, and a lack of real-time insights, which severely limit its practical utility for dynamic project guidance. To address these challenges, this study proposes a novel framework that integrates semantic technology (i.e., ontology) and large language models (LLMs). The framework first constructs a semantic model of the power grid construction domain using ontology. An LLM is then employed to convert multi-source project data into structured ontological instances. Building on this, mechanization assessment criteria are formalized into machine-executable Semantic Web Rule Language (SWRL) rules, which enable automated reasoning and scoring through an ontological reasoner. Furthermore, the LLM is utilized to generate comprehensive and intelligible assessment reports based on the reasoning outputs. To validate the proposed method, 126 real-world project cases were applied to the system. The results demonstrate a 96% accuracy rate in mechanization assessment outcomes compared to expert evaluations. The approach facilitates an objective, standardized, and dynamic evaluation of construction mechanization levels, providing a foundation for intelligent and scalable management models in power grid construction.