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LLM-Empowered Sensor Data Analytics and Interactive Digital Twin for Building Facility Management

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摘要 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.
出处 《Journal of Electronic Research and Application》 2026年第2期91-98,共8页 电子研究与应用
基金 supported by Ningbo International Investment Consulting Co.,Ltd.

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