Refrigerator Freshness Model Data Screening Using Statistical Variance
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Solution Overview
Problem
Existing smart refrigerators face challenges with increasing database capacity and slower data processing speeds due to the accumulation of refrigerator user information, necessitating a method to maintain calculation accuracy while reducing server pressure.
Innovation Solution
A method for updating refrigerator freshness reservation model data by screening uploaded data using statistical means and variances to filter and maintain calculation accuracy, reducing server load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more refrigerator user information is stored in the database to improve statistical accuracy, then calculation accuracy is improved, but server processing pressure increases and data processing speed decreases
Solution Approach 1:
The patent extracts only the necessary and useful data from the uploaded refrigerator information, rather than storing all raw data. By selectively extracting relevant features and statistics, the system maintains calculation accuracy while reducing the volume of stored data, thereby improving data processing speed and reducing server pressure.
Solution Approach 2:
The patent applies different processing qualities to different parts of the data. Important data that contributes to calculation accuracy is preserved and processed with higher quality, while redundant or less important data is filtered out or processed with lower quality. This selective approach maintains necessary accuracy while reducing overall data volume.
2Measurement precision
If more refrigerator user information is stored in the database to improve statistical accuracy, then calculation accuracy is improved, but server storage pressure increases
Solution Approach 1:
The system extracts only the essential data elements needed for statistical calculations, discarding redundant information. This extraction process significantly reduces the quantity of stored data while preserving the information necessary for maintaining calculation accuracy in the freshness reservation model.
Solution Approach 2:
The patent transforms raw refrigerator information into processed statistical parameters and features. By changing the data from raw form to aggregated statistical form, the system reduces data volume while maintaining the information density needed for accurate calculations.
Data Source
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AI summary
Disclosed are a method for updating data of a refrigerator freshness reservation model, a device and a storage medium. The method includes: S1, collecting refrigerator information of a plurality of users, wherein the refrigerator information of each user includes an item identifier corresponding to each of stored items in a refrigerator, and a usage frequency; S2, performing statistical computation on the refrigerator information to acquire a mean and a variance of usage frequencies corresponding to each food identifier; and S3, screening the refrigerator information based on the acquired mean and variance. In the present invention, uploaded data are automatically screened in an optimizing manner by mutual cooperation between the means and the variances, such that the uploaded data can be filtered, thereby maintaining the calculation accuracy while reducing the pressure on a server.