ARTICLE
Volume 10,Issue 7
31 March 2026
LLM-Empowered Sensor Data Analytics and Interactive Digital Twin for Building Facility Management
Tianlun Yang ,
Rong Chen ,
Haojie Tan ,
Georgios Kapogiannis ,
Byung-Gyoo Kang
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1
Department of Intelligent Interaction Design, NingboTech University, Ningbo 315040, Zhejiang, China,
2
Ningbo International Investment Consulting Co., Ltd., Ningbo 315040, Zhejiang, China,
3
Zhejiang Wanli Education Group, Ningbo 315040, Zhejiang, China,
4
Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, West Midlands, UK,
5
Department of Civil Engineering, University of Nottingham Ningbo China, Ningbo 315040, Zhejiang, China,
JERA 2026 , 10(2), 91–98;
https://doi.org/10.26689/jera.v10i2.14336
© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract
This study proposes an LLM-empowered edge-cloud digital twin architecture for intelligent FM. Edge servers acquire and pre-process sensor data, with Flask-based cloud middleware aggregating into hybrid data storage. The LLM performs semantic reasoning to generate operational decisions, relaying insights in real time to a Unity-engineered 3D visualization environment. BIM semantic parsing segregates geometric from non-graphical attributes, integrating multi-source data at room-floor granularity to establish a “perception-analysis-decision-execution” closed loop, thereby enhancing situational awareness and decision support for facility managers.
References
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