A Scalable and Interoperable Platform for Transforming Building Information with Brick Ontology
To address poor scalability in cross-system data integration, high manual modeling costs, and data privacy risks induced by cloud-based transmission in building automation, this paper proposes a localized, semantics-driven building information modeling framework. Centered on the Brick ontology, the framework integrates hierarchical tree structures with graph data models and employs a lightweight transformation algorithm to achieve automatic semantic alignment and offline structured organization of heterogeneous building data—including sensor readings, actuator states, and spatial configurations. Unlike conventional cloud-dependent approaches, our method enables rapid configuration adaptation across multiple buildings, reduces manual modeling effort by approximately 65% (empirically measured), and eliminates external transmission of sensitive data, ensuring privacy compliance. Experimental results demonstrate superior performance over existing ontology-driven methods in both historical data retrieval efficiency and cross-platform interoperability.