Design of an intelligent disinfection control system based on an STM32 single-chip microprocessor by using the YOLO...

Area of Science:

• Engineering
• Computer Science
• Public Health

Background:

• The COVID-19 pandemic necessitates enhanced sanitation and epidemic prevention in public spaces.
• Traditional disinfection methods in public areas are often inefficient and lack dynamic adaptation.

Purpose of the Study:

• To design and implement an intelligent, closed-loop disinfection control system for public spaces, specifically public toilets.
• To dynamically optimize disinfection strategies based on real-time crowd density and environmental factors.

Main Methods:

• Utilized an STM32 microcontroller as the central control unit.
• Integrated modules for image acquisition, spraying, disinfectant level control, access control, voice broadcast, display, and data storage.
• Employed the You Only Look Once (YOLO) algorithm for accurate human target recognition and crowd density estimation.
• Implemented Proportion Integration Differentiation (PID) and logical control for managing disinfectant concentration and liquid levels.
• Developed an access control system optimized based on crowd density thresholds.

Main Results:

• The system effectively monitors disinfectant levels and crowd density using sensors and image processing.
• The YOLO algorithm enabled dynamic crowd density assessment, informing real-time spraying strategy adjustments.
• Optimized access control parameters based on crowd density, enhancing safety and efficiency.
• Achieved intelligent, full-cycle, closed-loop disinfection control.

Conclusions:

• The developed intelligent disinfection system offers a dynamic and optimized approach to sanitation in public toilets.
• This system effectively reduces the risk of virus transmission by intelligently managing disinfection based on occupancy.
• The integration of microcontrollers, AI algorithms, and sensor networks provides a robust solution for public health challenges.