Digital Twin-Based Office Equipment Management and Personnel Detection System

In traditional office management, it is labor-intensive to perform real-time oversight on equipment and personnel. To address this challenge, this paper proposes a digital twin-based office management system. The system leverages the ESP8266 wireless module for device control and data collection, and employs the YOLOv5 deep learning model for real-time detection of employees' working conditions. Additionally, a virtual office environment is constructed using the Unity engine. The system implemented herein enables real-time monitoring and analysis of office utilization, and assists managers in optimally allocating resources to enhance resource utilization efficiency by leveraging intelligent sensing and decision-making technologies. The system incurs low hardware and software costs, minimal data transmission latency, and rapid response times across its modules. Moreover, through data masking techniques, the system can protect the privacy of office personnel while enabling real-time monitoring.