Indoor air quality control method and control apparatus using intelligent air cleaner
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Solution Overview
Problem
Conventional air cleaners lack the ability to adaptively adjust their air cleaning intensity based on predicted dust concentration patterns in indoor spaces, leading to inefficient air quality control.
Innovation Solution
An intelligent air cleaner system that analyzes and predicts dust concentration patterns by receiving and processing data from indoor spaces, adjusting air cleaning intensities accordingly through a cloud server or processor, and transmitting scheduling results to the air cleaner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If air cleaner uses fixed reference value for dust concentration control, then control logic is simple, but air cleaning efficiency cannot be optimized for different living patterns
Solution Approach 1:
The system performs preliminary analysis of dust concentration patterns from historical data to predict future high dust concentration periods. This allows the air cleaner to proactively adjust its operation schedule before dust problems occur, optimizing cleaning efficiency without requiring complex real-time decision-making
Solution Approach 2:
The system continuously collects dust concentration data, analyzes patterns, and uses this feedback to dynamically adjust the air cleaner's reference values and operation schedule. This closed-loop control enables adaptation to different living patterns while maintaining manageable system complexity through automated learning
2Reliability
If air cleaner operates at high intensity continuously, then dust removal effectiveness is maximized, but energy consumption increases
Solution Approach 1:
The system determines specific time periods when dust concentration is predicted to be high and schedules high-intensity air cleaning operations only during those periods. During low-risk periods, the air cleaner operates at reduced intensity or remains idle, significantly reducing energy consumption while maintaining dust removal effectiveness when needed
Solution Approach 2:
The system dynamically changes the reference value for dust concentration control based on predicted patterns and living patterns. By adjusting this parameter, the air cleaner optimizes its operation intensity to match actual dust generation risks, achieving effective dust removal only when necessary and reducing energy waste during low-risk periods
Data Source
AI summary
An indoor air quality control method using an intelligent air cleaner is disclosed. An indoor air quality control method using an intelligent air cleaner according to an embodiment of the present invention classifies dust concentration data received from the air cleaner according to predetermined criteria and performs dust concentration pattern analysis. Particularly, dust concentration patterns for respective time periods are analyzed to control the air cleaning intensity of the air cleaner in advance of a predicted time, thereby facilitating efficient indoor air quality management. The intelligent air cleaner and the indoor air quality control method using the same of the present invention can be associated with artificial intelligence modules, devices related with the 5G service, and the like.