Method for updating data of refrigerator freshness reservation model, device and storage medium
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Solution Overview
Problem
Existing smart refrigerators face challenges in managing increasing data volumes, leading to slower data processing speeds and the need for continuous database capacity expansion.
Innovation Solution
A method for updating data of a refrigerator freshness reservation model that involves collecting refrigerator information, performing statistical computations to acquire mean and variance of usage frequencies, and screening the data based on these metrics to maintain calculation accuracy while reducing server pressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more refrigerator data is continuously stored in the database to improve statistical accuracy, then the accuracy of statistical results is improved, but the data processing speed deteriorates due to increased database capacity requirements
Solution Approach 1:
The patent extracts only the necessary and valuable data from the collected refrigerator information using screening criteria based on variance thresholds. By taking out only the essential data points that contribute meaningfully to statistical accuracy, the system avoids storing redundant information, thereby maintaining statistical precision while reducing database size and improving processing speed.
Solution Approach 2:
The patent applies different quality standards to different data points by using variance-based screening. Data with variance below a threshold is retained for statistical analysis, while data exceeding the threshold is filtered out. This local quality approach ensures that only high-quality, relevant data is stored, optimizing both accuracy and processing efficiency.
2Quantity of substance
If the database capacity is continuously expanded to store more refrigerator data, then the data storage capacity is improved, but the system complexity and processing overhead worsen
Solution Approach 1:
The system extracts and retains only the essential data points that meet the variance screening criteria. By removing redundant and low-value data, the database maintains adequate storage capacity for meaningful information without requiring continuous expansion, thereby reducing system complexity and processing overhead.
Solution Approach 2:
The patent changes the parameter of data selection by introducing variance-based screening thresholds. This parameter change enables the system to dynamically determine which data points to retain, optimizing the balance between storage capacity and system complexity without requiring continuous database expansion.
3Loss of information
If all collected refrigerator data is retained without screening, then the data completeness is improved, but the server processing pressure worsens
Solution Approach 1:
The system extracts and retains only the essential data points that meet the variance screening criteria. By removing redundant and low-value data, the database maintains adequate storage capacity for meaningful information without requiring continuous expansion, thereby reducing system complexity and processing overhead.
Solution Approach 2:
The patent applies different quality standards to different data points by using variance-based screening. Data with variance below a threshold is retained for statistical analysis, while data exceeding the threshold is filtered out. This local quality approach ensures that only high-quality, relevant data is stored, optimizing both accuracy and processing efficiency.
Data Source
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.