Data learning server and method for generating and using learning model thereof
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Solution Overview
Problem
Existing systems lack an efficient method for dynamically adjusting air conditioner temperatures using artificial intelligence, relying on manual settings rather than adaptive learning models.
Innovation Solution
A network system comprising an air conditioner, a user terminal, and a cloud server, where a data learning server generates a learning model based on air conditioner status and external environment data to provide recommended temperatures, enabling AI-driven temperature adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual temperature settings are used in existing air conditioner systems, then device complexity is reduced and ease of operation is maintained, but adaptability to environmental conditions and energy optimization capability deteriorate
Solution Approach 1:
The patent introduces a cloud server as an intermediary between the air conditioner and the user/environment. The cloud server receives environmental data (temperature, humidity, weather forecasts) and user preferences, processes this information using AI algorithms, and returns optimized temperature settings to the air conditioner. This mediator approach enables advanced adaptability without increasing the complexity of the air conditioner itself.
Solution Approach 2:
The system enables the air conditioner to automatically adjust temperatures based on environmental conditions and learned user preferences without requiring manual intervention. The AI model continuously learns from user behavior patterns and environmental data, allowing the system to self-optimize temperature settings, thereby improving adaptability while maintaining ease of operation.
2Use of energy by moving object
If AI-driven dynamic temperature adjustment is implemented, then energy optimization and user comfort are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing environmental data and user preferences in advance. The AI model pre-processes this data to predict optimal temperature settings before actual cooling or heating is needed, allowing the air conditioner to operate more efficiently and reduce energy consumption while maintaining comfort levels.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors user responses to temperature adjustments and environmental conditions. This feedback is fed back into the AI model to refine and update temperature recommendations, enabling continuous energy optimization. The feedback loop allows the system to learn from past performance and improve energy efficiency over time without requiring complex hardware changes.
3Measurement precision
If learning models are generated using historical temperature settings and environmental data, then temperature recommendation accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant features from historical data and environmental information. Rather than analyzing every possible parameter, the AI model focuses on key factors such as outdoor temperature, humidity, weather forecasts, and dominant user preferences, thereby achieving high recommendation accuracy while minimizing data processing time and computational resource requirements.
Data Source
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AI summary
An apparatus and a method for a data learning server is provided. The apparatus of the disclosure includes a communicator configured to communicate with an external device, at least one processor configured to acquire a set temperature set in an air conditioner and a current temperature of the air conditioner at the time of setting the temperature via the communicator, and a generate or renew a learning model using the set temperature and the current temperature, and a storage configured to store the generated or renewed learning model to provide a recommended temperature to be set in the air conditioner as a result of generating or renewing the learning model. For example, the data learning server of the disclosure may generate a learned learning model to provide a recommended temperature using a neural network algorithm, a deep learning algorithm, a linear regression algorithm, or the like as an artificial intelligence algorithm.