Mobile Customer Restroom Perception
Mobile customer restroom perception is achieved through a 'perception-analysis-response' system built with sensors, the Internet of Things (IoT), and intelligent algorithms. Its core lies in real-time monitoring of restroom environments and equipment status, optimizing management efficiency and user experience through data-driven approaches. The system can be divided into the following three layers:
The perception layer serves as the foundation for data collection, relying on multiple types of sensors to digitally monitor environments and equipment. Air quality monitoring uses ammonia and hydrogen sulfide sensors to capture odor concentrations in real time, providing a basis for ventilation and deodorization. Combining human infrared and door magnetic sensors accurately identifies toilet stall occupancy status, displaying 'occupied/available' information on door-mounted screens or mobile devices to reduce user waiting times. Liquid level sensors monitor water tank levels to prevent water outages, while energy consumption meters record water and electricity usage, supporting energy-saving analysis. Additionally, intelligent toilet stall monitoring devices and consumable inventory modules convert equipment status into digital signals, automatically triggering restocking alerts when toilet paper levels fall below a threshold.
The transmission layer ensures stable data upload through IoT protocols and mobile communication networks. Raw data collected by sensors is aggregated by gateway devices and transmitted to cloud-based management platforms via low-latency, high-bandwidth networks like 5G. The characteristics of 5G networks enable data continuity in high-traffic scenarios (e.g., large events) or areas with weak signals (e.g., remote scenic spots), preventing management delays caused by network interruptions.
The application layer leverages cloud-based algorithms for data analysis and intelligent responses. The platform extracts value through multi-dimensional analysis: for example, combining foot traffic and cleaning supply consumption data, algorithms predict peak times and generate cleaning scheduling recommendations; comparing historical energy consumption records, abnormal water or electricity usage is flagged as potential equipment failures, prompting timely maintenance. Specific functions include: an environmental control system that automatically activates ventilation or deodorization equipment based on air quality; a supply management system that prompts water replenishment or consumable replacement based on liquid levels and inventory; safety monitoring that triggers alarms for abnormal sensor data (e.g., leaks, equipment failures); and service guidance that updates toilet stall status in real time via external displays, optimizing user experience.
Through closed-loop management, this system not only enhances restroom operational efficiency but also enables a shift from reactive maintenance to proactive prevention through data-driven decision support, providing a typical example for smart city public services.
