Implementing Digital Twins for Real-Time Library Operations Monitoring

Abstract

Increased user expectations for intelligent systems fuel enhanced digitization of daily activities in library systems. We propose a new framework RT-LTM, Real-Time Library Twin Monitoring, which aims to utilize Digital Twins for real-time monitoring, predictive maintenance, and tailored service optimization in library spaces. RT-LTM framework designs a virtual model of library assets such as books, RFID-tagged materials, HVAC systems, and previous behavior analytics to achieve real-time synchronization of physical and virtual systems. The architecture integrates IoT sensors, edge-cloud computing, and AI analytics for real-time monitoring of spatial efficiency, resource consumption, and maintenance forecasting. A university library case study demonstrates the active library's real-time monitoring efficacy, resource access, energy use, and user satisfaction. The results highlight the outstanding impact of Digital Twin technology in contemporary library systems and services management.