Air conditioner
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Solution Overview
Problem
Conventional air conditioners face limitations in dynamically adapting to changes in the surrounding environment, such as temperature, occupancy, and humidity, as they lack the ability to efficiently classify and respond to varying air blowing areas within a space.
Innovation Solution
The integration of a camera and machine-learning network in the air conditioner allows for the classification of air blowing areas into intensive and non-intensive zones based on occupant distance and direction, enabling the air conditioner to operate in optimized modes that adjust airflow and energy consumption accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the air conditioner operates in intensive operation mode to quickly cool or heat the indoor space, then the cooling or heating speed is improved, but the energy consumption increases
Solution Approach 1:
The patent divides the indoor space into multiple air-blowable areas and further segments each area into intensive and non-intensive zones based on occupant presence. The air conditioner then applies different operation modes to different segments: intensive operation mode for areas with occupants and non-intensive operation mode for areas without occupants. This segmentation allows the system to achieve fast cooling/heating where needed while conserving energy in unoccupied areas.
Solution Approach 2:
The patent implements local quality by applying different operation intensities to different spatial locations based on their specific needs. The intensive operation mode is applied locally to air-blowable areas where occupants are detected, while non-intensive operation mode is applied to areas without occupants. This localized approach ensures that energy-intensive operations are performed only where necessary to maintain comfort, rather than uniformly across the entire indoor space.
2Stability of the object's composition
If the air conditioner blows air to all areas uniformly, then the temperature distribution is improved, but the energy consumption increases
Solution Approach 1:
The patent segments the indoor space into multiple air-blowable areas and further divides each area into intensive and non-intensive zones based on detected occupant presence. This segmentation enables differentiated air blowing strategies: intensive air blowing to areas with occupants and reduced or no air blowing to areas without occupants, thereby maintaining adequate temperature distribution while reducing overall energy consumption.
Solution Approach 2:
The patent applies local quality by providing intensive cooling or heating only to specific local areas where occupants are present, rather than uniformly treating the entire indoor space. The system adjusts air blowing intensity locally based on real-time occupancy detection, ensuring comfortable temperatures in occupied zones while minimizing energy expenditure in unoccupied zones.
3Adaptability or versatility
If the air conditioner uses camera and machine-learning network to classify air-blowable areas, then the adaptability to environment changes is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a camera and machine-learning network as intermediary components that bridge the gap between the physical environment (occupant presence, distance, direction) and the air conditioner's control system. These intermediaries automatically detect and classify air-blowable areas, enabling the system to adapt to environmental changes without requiring complex manual configuration or user intervention. The intermediary components handle the complexity of environmental analysis, allowing the air conditioner to respond intelligently to changing conditions.
4Use of energy by moving object
If the air conditioner switches between intensive and non-intensive operation modes, then the energy efficiency is improved, but the control complexity increases
Solution Approach 1:
The patent implements feedback control by continuously monitoring occupant presence, distance, and direction using the camera and machine-learning network, and automatically adjusting the air conditioner's operation mode accordingly. When occupants are detected in an air-blowable area, the system switches to intensive operation mode; when no occupants are present, it transitions to non-intensive operation mode. This feedback mechanism enables automatic energy-efficient control without requiring complex user input or manual adjustment.
Solution Approach 2:
The patent enables the air conditioner to self-manage its operation by automatically classifying air-blowable areas and selecting appropriate operation modes based on real-time environmental data. The system uses its own integrated camera and machine-learning capabilities to perform occupancy detection and area classification, then autonomously decides when to switch between intensive and non-intensive operation modes, eliminating the need for external control systems or complex user programming.
Data Source
AI summary
A method of operating an air conditioner, including: obtaining an image acquired by a camera; determining a distance and a direction of an occupant relative to the air conditioner, based on the image; using at least one machine-learning network to classify an air-blowable space of the air conditioner into an intensive air blowing area and a non-intensive air blowing area, based on the distance and the direction of the occupant; controlling the air conditioner to operate in an intensive operation mode with respect to the intensive air blowing area; and controlling the air conditioner to operate in a non-intensive operation mode with respect to the intensive air blowing area and the non-intensive air blowing area based on completion of the intensive operation mode. A time duration of the intensive operation mode is smaller than a time duration of the non-intensive operation mode.