A new study focusing on biosecurity laboratory regulation has been conducted using a literature review method, factor analysis, and case study approach. The research aims to analyze risk factors, key technologies, and challenges in biosecurity laboratory regulation. Data acquisition is performed through perception terminals, system-recorded data, and updates. An early warning system is established to monitor personnel behavior, posture, qualifications, and attire. The study utilizes deep learning training datasets and an Internet of Things (IoT) application framework to create an intelligent supervision system for biological laboratories.
The system architecture consists of three layers: perception, network, and application. Regulatory elements and processing flow focus on five key risk areas and are structured into pre-approval, daily, and post-incident management stages. The network architecture prioritizes data transmission security in biological laboratories using encryption, authentication mechanisms, and network isolation.
The study employs the YOLOv8 algorithm for target detection and the OpenPose model for human posture detection. A laboratory evaluation index system based on organizational management, facilities, personnel, and waste management is utilized to assess laboratory risks. Overall, the research provides a comprehensive, integrated approach to managing biosecurity in laboratories, incorporating advanced technologies and algorithms to enhance safety and efficiency.
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