Air Conditioner Adapter Data Filtering to Reduce AI Training Traffic
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Solution Overview
Problem
The transmission of operation history data from air conditioners to server devices for AI learning generates significant communication traffic, and simply deleting data to reduce this traffic can lower the learning accuracy of AI models.
Innovation Solution
An air conditioner adapter that acquires operation history data, determines the amount of change between temporally continuous data, and transmits only the data with significant changes to the server, while deleting data with minimal impact on AI learning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all operation history data are transmitted to the server device, then the learning accuracy of AI is maintained, but the communication traffic becomes large
Solution Approach 1:
The patent extracts only the essential features from operation history data by calculating feature amounts that represent characteristic changes. Instead of transmitting all raw data, the system extracts and transmits only the extracted features that are necessary for AI learning, thereby reducing communication traffic while maintaining learning accuracy.
Solution Approach 2:
The patent transforms operation history data by changing its representation parameters. Raw operation data is converted into feature amounts that quantify characteristic changes. This parameter transformation reduces data dimensionality and size while preserving the essential information needed for AI model generation.
2Quantity of substance
If operation history data are deleted to reduce communication traffic, then the traffic is reduced, but the learning accuracy may be lowered
Solution Approach 1:
The patent extracts only the essential features from operation history data by calculating feature amounts that represent characteristic changes. Instead of transmitting all raw data, the system extracts and transmits only the extracted features that are necessary for AI learning, thereby reducing communication traffic while maintaining learning accuracy.
Solution Approach 2:
The patent applies different processing treatments to different portions of operation history data based on their importance. By identifying and extracting only the locally significant features that contribute to learning accuracy, the system selectively transmits data with the highest value while discarding redundant information.
3Quantity of substance
If feature amounts are calculated from operation history data, then the data size is reduced, but the calculation complexity increases
Solution Approach 1:
The patent segments the operation history data processing into distinct stages: data acquisition, feature extraction, and transmission. By dividing the processing task into modular segments performed by the adapter, the calculation complexity is distributed and managed systematically, making the overall process more efficient despite the added extraction step.
Solution Approach 2:
The patent performs feature extraction as a preliminary action before data transmission. By pre-calculating feature amounts and extracting essential characteristics in advance at the adapter, the system reduces the data size that needs to be transmitted, and the AI model can be generated more efficiently at the server.
Data Source
AI summary
An air conditioner includes an adapter including a control unit that controls the air conditioner using a learning model and a communication unit that communicates with a server device that generates the learning model on the basis of operation history data of the air conditioner. The adapter includes an acquisition unit, a determination unit, an erasing unit, and a transmission unit. The acquisition unit acquires the data every predetermined cycle from the air conditioner. The determination unit determines whether or not an amount of change between temporally continuous data of the data acquired by the acquisition unit is within a predetermined range. The erasing unit leaves at least one of the continuous data and deletes the other data in a case where the amount of change between the continuous data is within the predetermined range. The transmission unit transmits the data after being deleted to the server device.