Product Data Statistics Storage for Time-Interval Memory Control
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and storing data associated with products, particularly in determining and saving statistical parameters over time intervals, while managing memory storage effectively.
Innovation Solution
A computer-implemented procedure that determines first data from at least one data source associated with a product, calculates second data representing statistical parameters, and saves these data if determined within a specified time interval or for a specified number of data values, with options for periodic determination and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is continuously collected and stored from multiple sensors, then data completeness and accuracy are improved, but memory storage capacity is rapidly consumed
Solution Approach 1:
The patent extracts only the essential statistical parameters (minimum, maximum, mean values) from the continuous sensor data stream, storing only these aggregated values rather than the complete raw data. This extraction principle reduces storage requirements while preserving the most important information about the measured quantities over time intervals.
Solution Approach 2:
The patent transforms the raw sensor data into different parameter representations by calculating statistical parameters (minimum, maximum, mean) over time intervals. This parameter transformation converts large volumes of continuous data into compact summary statistics, resolving the storage capacity issue while maintaining data accuracy for analysis purposes.
2Loss of information
If statistical parameters are determined and stored for every time interval, then data completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements periodic determination of statistical parameters at predefined time intervals rather than continuous processing. The system evaluates whether a predefined time interval has elapsed before calculating and storing new statistical parameters, which reduces processing frequency while ensuring data completeness at meaningful time scales.
Solution Approach 2:
The patent applies partial action by selectively processing data only when necessary - specifically when predefined time intervals are completed or when storage capacity thresholds are approached. This partial processing approach maintains data completeness for critical intervals while avoiding unnecessary computational overhead during intermediate periods.
3Productivity
If memory storage is optimized by storing only statistical parameters, then storage efficiency is improved, but data detail and resolution are reduced
Solution Approach 1:
The patent segments the data storage into two distinct layers: raw sensor data is stored temporarily for immediate high-resolution analysis, while aggregated statistical parameters are stored long-term for historical analysis. This segmentation allows the system to maintain high data resolution when needed while achieving storage efficiency for historical records, resolving the contradiction between storage efficiency and data detail.
Data Source
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AI summary
A method, for example a computer-implemented method, for processing data associated with a product, comprising: determining first data from at least one data source associated with the product, determining, based on the first data, second data that characterize at least one statistical parameter of the first data, for example, representing, checking whether the determination of the first data and/or the determination of the second data has been carried out for a predefinable first time interval, and, if the check shows that the determination of the first data and/or the determination of the second data has been carried out for the predefinable first time interval, storing at least the second data.