Cloud server and air conditioner based on parameter learning using artificial intelligence, and method for driving and controlling air conditioner
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Solution Overview
Problem
Existing air conditioner control systems fail to dynamically adapt to changes in environmental conditions such as temperature, humidity, and occupancy, limiting their efficiency and comfort in operation modes.
Innovation Solution
An air conditioner system that utilizes parameter learning and a cloud server to generate and analyze operation parameters, transitioning from a rapid mode to a comfortable mode based on learned factors, optimizing energy use and maintaining target temperatures with reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the air conditioner operates in rapid mode with maximum cooling capacity to quickly cool indoor space, then the cooling speed is improved, but the energy consumption increases
Solution Approach 1:
The system dynamically switches between rapid mode and comfortable mode based on real-time temperature monitoring. When the indoor temperature reaches the setpoint, the system transitions from maximum cooling capacity to a lower, more energy-efficient capacity, optimizing the balance between cooling speed and energy consumption throughout the operation cycle
Solution Approach 2:
The air conditioner implements periodic operation by alternating between rapid cooling phase and comfortable maintenance phase. This periodic action allows the system to achieve quick cooling when needed while reducing energy consumption during temperature maintenance, creating an optimized operational rhythm
2Use of energy by moving object
If the air conditioner switches to comfortable mode with higher set temperature to save energy, then the energy consumption is reduced, but the adaptability to environmental changes deteriorates
Solution Approach 1:
The system continuously monitors indoor temperature and uses this feedback to determine when to switch between operational modes. This feedback mechanism ensures the system adapts to actual environmental conditions while maintaining energy efficiency, preventing both excessive energy consumption and poor adaptability
Solution Approach 2:
The air conditioner automatically adjusts its operational mode based on its own sensor data without requiring external intervention. The system self-regulates by transitioning between rapid and comfortable modes according to its internal temperature monitoring, achieving both energy efficiency and environmental adaptability
3Device complexity
If fixed operation modes are used without learning capabilities, then the device complexity is reduced, but the productivity in terms of operational efficiency deteriorates
Solution Approach 1:
The patent replaces complex mechanical control systems with software-based artificial intelligence algorithms. The learning unit uses machine learning models to optimize operational parameters, achieving high productivity through intelligent software control rather than complex hardware mechanisms
Data Source
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AI summary
The present invention provides technology relating to a cloud server and an air conditioner based on parameter learning using artificial intelligence, and a method for driving and controlling the air conditioner, the air conditioner according to one embodiment of the present invention comprises: a parameter generator for, in relation to parameters calculated in an operating period of the air conditioner, calculating at least one parameter in an operating period according to first air-conditioning capability matching a set temperature so that the air conditioner can efficiently operate in a period which is divided into at least two operating modes of the air conditioner; and a learning unit for receiving the parameter as a learning factor so as to, after the operating period according to the first air-conditioning capability, output operating mode information indicating an operating mode in which the air conditioner operates according to second air-conditioning capability different from the first air-conditioning capability.