Data learning server and method for generating and using learning model thereof
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Solution Overview
Problem
Conventional air conditioner temperature control methods lack the ability to provide personalized and optimal temperature settings based on user preferences and environmental conditions, leading to inefficient energy use and user dissatisfaction.
Innovation Solution
A data learning server system that generates a learning model using set and current air conditioner temperatures, along with external environmental data, to recommend optimal temperature settings, which can be continuously updated based on user behavior, improving the accuracy and comfort of temperature control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional air conditioner temperature control methods are used, then the system is simple and easy to operate, but it cannot provide personalized and optimal temperature settings based on user preferences and environmental conditions
Solution Approach 1:
A data learning server is introduced as an intermediary between the air conditioner and the user. The server receives temperature data from multiple air conditioners, generates learning models based on this data, and provides recommended temperatures back to the air conditioners. This intermediary handles the complexity of data analysis and model generation, allowing individual air conditioners to remain relatively simple while achieving personalized temperature control capabilities.
Solution Approach 2:
The system implements a feedback loop where air conditioners transmit their set temperatures and current temperatures to the data learning server. The server analyzes this feedback data, updates learning models accordingly, and generates recommended temperatures that are sent back to the air conditioners. This continuous feedback mechanism enables the system to adapt and improve temperature recommendations over time based on actual operating conditions and user preferences.
2Reliability
If a data learning server with learning models is implemented, then optimal and personalized temperature recommendations are provided, but the system complexity and data processing requirements increase
Solution Approach 1:
The system divides the data processing functionality into segments: air conditioners collect and transmit local temperature data, the data learning server aggregates data from multiple sources and performs model generation, and individual air conditioners apply received recommendations. This segmentation distributes the computational burden and allows each component to focus on specific tasks, improving overall reliability without requiring every device to handle complex data processing.
Solution Approach 2:
The data learning server serves multiple air conditioners simultaneously, generating universal learning models that can be applied across different devices. By consolidating the model generation function in a single multi-functional server rather than implementing separate learning systems in each air conditioner, the overall system complexity is reduced while maintaining high temperature control accuracy through shared intelligence.
3Measurement precision
If learning models are continuously updated based on user behavior, then the accuracy of temperature recommendations improves over time, but the data processing load and system resource consumption increase
Solution Approach 1:
Instead of continuously updating learning models in real-time, the system employs periodic updates where the data learning server processes accumulated temperature data at intervals and generates updated recommendations. This periodic approach allows the system to maintain high accuracy by regularly incorporating new user behavior patterns while significantly reducing instantaneous data processing loads and energy consumption compared to continuous real-time updates.
Data Source
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.