A recent study conducted on biosecurity laboratory regulation utilized a research methodology comprising a literature review, factor analysis, and case studies to analyze risk factors, technologies, and challenges in laboratory regulation. Data acquisition was done through various channels including perception terminals and system-recorded files. An intelligent supervision system based on an Internet of Things (IoT) framework was designed with three layers offering features such as personnel behavior recognition, gesture recognition, and equipment management.
To ensure data security, the network architecture employed robust encryption methods and secure communication channels. Notable algorithms like YOLOv8 for target detection and OpenPose for human posture detection were utilized for accurate monitoring of personnel within laboratories. The study focused on enhancing algorithms for increased accuracy and operational efficiency, making significant improvements to meet stringent requirements.
The laboratory evaluation system consisted of eight key aspects evaluated by experts using the Analytic Hierarchy Process to determine overall risk assessment. The study also developed a comprehensive regulatory framework focusing on all supervisory elements and processes to ensure proactive safety management in laboratories.
The innovative research findings offer insights into the development of intelligent supervision systems for biological laboratories, emphasizing data security, accuracy in monitoring human behavior, and comprehensive risk management strategies. The study showcases advancements in algorithmic support, network architecture, and regulatory elements, providing a foundation for enhanced biosecurity laboratory regulations and safety measures.
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