Method and system for providing air conditioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional air conditioning systems lack adaptability to varying installation settings and operating environments, relying on manual settings and preprogrammed profiles, which limits their ability to optimize operation and leads to energy wastage and compromised user comfort.
Innovation Solution
The integration of computer vision and machine learning enables an air conditioning system to obtain images of its surroundings, analyze them to determine environmental factors, and adjust operation profiles and control parameters in real-time, optimizing airflow and temperature settings based on the detected layout and object states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional air conditioners use manual settings and preprogrammed operation profiles, then the device complexity is reduced and ease of operation is improved, but the adaptability to varied installation settings and operating environments deteriorates
Solution Approach 1:
The air conditioning system performs self-diagnosis and self-adjustment by automatically capturing images of its installation environment, analyzing them through machine learning models to determine layout factors and object states, and then autonomously selecting and adjusting operation profiles without requiring manual user input or professional installation guidance
Solution Approach 2:
The system pre-processes installation environment images and determines layout factors and object states before the air conditioner begins operation, allowing it to pre-select appropriate operation profiles and control parameter ranges based on the detected environment, thereby achieving adaptability before actual cooling/heating starts
2Use of energy by moving object
If conventional air conditioners rely on preprogrammed operation profiles with fixed trigger conditions, then the control system remains simple, but the ability to optimize operation and reduce energy waste deteriorates
Solution Approach 1:
The system continuously monitors the installation environment by capturing images, compares detected layout factors and object states against the selected operation profile, and dynamically adjusts control parameters within predefined ranges to optimize energy efficiency based on real-time environmental conditions rather than relying on fixed preprogrammed schedules
Solution Approach 2:
The operation profiles include control parameters with predefined value ranges rather than fixed values, allowing the system to dynamically adjust parameters such as temperature setpoints, fan speeds, and compressor capacity within optimized ranges based on detected environmental factors like room layout, window positions, and heat-generating objects
3Ease of operation
If conventional air conditioners lack real-time environmental analysis, then the device complexity is reduced, but the user comfort deteriorates
Solution Approach 1:
The system replaces manual environmental assessment and manual control adjustment with automated computer vision and machine learning analysis, where cameras capture images and AI models automatically determine layout factors and object states to select optimal operation profiles, eliminating the need for manual user configuration while enhancing comfort
Data Source
AI summary
An air conditioning system includes one or more cameras, one or more air conditioning operation units, and an air conditioner control unit that obtains, via the one or more cameras, images of a surrounding environment of the air conditioning operation units, and determines a first set of factors, including a layout of the surrounding environment, based on analysis of the images through predefined machine learning models. The control unit, in accordance with the first set of factors that has been determined based on the analysis of the images through the predefined machine learning models, selects a first operation profile from a plurality of predefined operation profiles to control the air conditioning operation units.