AI Air Conditioner Area Recognition for Occupant-Based Airflow Control
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Solution Overview
Problem
Existing air conditioners face challenges in accurately controlling airflow based on occupant location, leading to inefficiencies in cooling and comfort due to limitations in human body detection and space recognition, which affects cooling efficiency and user comfort.
Innovation Solution
An air conditioner system incorporating a camera, area recognition module using machine learning and deep learning algorithms to classify indoor spaces into living and non-living areas, and an airflow controller that adjusts airflow direction and strength based on occupant location and space classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If camera-based human body detection is used to control airflow, then automation is improved, but measurement precision deteriorates due to inaccuracy in detecting occupant locations
Solution Approach 1:
The patent introduces an area recognition module as an intermediary between the camera detection and airflow control. This module processes the detected occupant locations and determines living areas, acting as a mediator that improves the overall system accuracy by compensating for camera detection limitations through additional processing and multiple detection methods
Solution Approach 2:
The patent divides the indoor space into multiple areas and further segments them into living areas and non-living areas based on occupant detection results. This segmentation allows the system to apply different airflow control strategies to different zones, improving overall control precision by addressing each area's specific needs rather than treating the entire space uniformly
2Device complexity
If simple occupant detection is used, then device complexity is reduced, but adaptability deteriorates because the system cannot recognize indoor space characteristics and user patterns
Solution Approach 1:
The patent implements a learning module that performs preliminary actions by continuously learning and storing user patterns, space characteristics, and occupancy behaviors over time. This pre-learning process enables the system to adapt to specific environments and user preferences without requiring complex real-time analysis, thereby improving adaptability while maintaining relatively simple device architecture
Solution Approach 2:
The patent incorporates feedback mechanisms where the area recognition module continuously monitors occupant locations and adjusts living area definitions based on detected patterns. The system uses feedback from occupancy detection to refine its understanding of space usage and user behavior, enabling adaptive airflow control that responds to changing conditions without requiring overly complex hardware
3Device complexity
If airflow is controlled without considering living area classification, then device complexity is minimized, but loss of energy increases due to cooling non-living areas
Solution Approach 1:
The patent applies local quality by differentiating between living areas and non-living areas, applying different airflow control strategies to each. The system directs cooling resources preferentially to living areas where occupants are present, while reducing or eliminating airflow to non-living areas, thereby improving energy efficiency by matching cooling provision to actual space utilization patterns
Solution Approach 2:
The patent implements partial action by providing airflow only to the extent necessary for occupied living areas rather than uniformly to the entire space. The area recognition module determines which areas require cooling and applies airflow selectively, avoiding the excessive action of cooling unused spaces, thus reducing energy loss without requiring a complete redesign of the airflow control system
Data Source
AI summary
An artificial intelligence air conditioner may include a camera to obtain an image, an area recognition module for recognizing an area in which an occupant is located in an indoor space divided into a plurality of areas from the image obtained by the camera and distinguishing a living area with respect to the plurality of areas based on a result of recognizing a location of the occupant, and an airflow controller controlling airflow based on the distinguished living area, whereby airflow optimized for each living area of the indoor space may be controlled.