Occupancy-Adaptive Air Purification Control for Indoor Health
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Solution Overview
Problem
Conventional air quality management systems in indoor spaces lack intelligence and adaptability, failing to optimally manage environmental conditions such as air quality, especially in areas with changing occupancy and poor ventilation, which can lead to the spread of airborne diseases.
Innovation Solution
An intelligent environment management system that includes air quality monitoring and purification devices connected to a cloud platform, using sensors to collect data on air quality parameters and occupancy changes, and employing machine learning algorithms to adjust air purification intensity based on these inputs, ensuring optimal air quality through network communication and control of air purification modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate devices are used for air quality monitoring and purification, then device functionality is provided, but system intelligence and adaptability are lacking
Solution Approach 1:
The patent combines air quality monitoring sensors, occupancy sensors, and air purification modules into an integrated environment management system. The monitoring device and purification device are merged into a single coordinated system that shares data and control mechanisms, enabling intelligent adaptation to environmental conditions while maintaining functional separation of monitoring and purification tasks.
Solution Approach 2:
The environment management system performs multiple functions through a unified architecture: it monitors air quality parameters, detects occupancy changes, processes data through machine learning algorithms, and controls air purification. This multi-functional system replaces the need for separate standalone monitoring and purification devices, providing adaptability across different environmental scenarios.
2Productivity
If conventional air quality management systems are used, then basic air purification is provided, but optimal management of environmental conditions cannot be achieved
Solution Approach 1:
The system continuously monitors air quality parameters and occupancy information, feeds this data to machine learning algorithms that process the information, and automatically adjusts air purification settings based on the processed insights. This closed-loop feedback mechanism enables optimal air quality management by dynamically adapting purification intensity to actual environmental conditions rather than operating on fixed schedules or manual settings.
Solution Approach 2:
The environment management system performs self-optimization through machine learning algorithms that automatically analyze sensor data and adjust purification parameters without human intervention. The system serves itself by making intelligent decisions about when and how to purify air based on real-time conditions, eliminating the need for manual control while achieving optimal performance.
3Reliability
If air purification intensity is increased to maintain optimal air quality, then air quality improves, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts air purification intensity based on real-time environmental conditions and occupancy levels. Rather than operating at constant high intensity, the purification module modulates its performance to match actual air quality needs, ensuring reliable air quality maintenance while minimizing unnecessary energy consumption during periods when air quality is already acceptable or occupancy is low.
Solution Approach 2:
The machine learning algorithms analyze multiple parameters including air quality measurements, occupancy information, and environmental conditions to determine optimal purification settings. By changing purification intensity parameters dynamically based on the combined analysis of these factors, the system maintains air quality reliability while optimizing energy usage according to actual demand conditions.
Data Source
AI summary
Methods, systems, and devices for monitoring air quality and controlling air purification in an environment are described herein. The method can include receiving from a device a plurality of air quality parameters for the environment and receiving an indication representing a potential change in occupancy in the environment. In response to the indication of a potential change in occupancy, the method can include determining an air quality target that is based at least in part on at least one air quality parameter in the plurality of air quality parameters and the potential change in occupancy. The air quality parameter can be representative of the quality of air to be achieved in the environment for the change in occupancy. The method can include transmitting instructions to control purification in the environment based at least in part on the air quality target.