Measurement Data Compression by Priority and Prediction Error
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Solution Overview
Problem
Existing data compression technologies are ineffective for high-speed, bandlimited analog signals, and conventional methods fail to handle data volume reduction flexibly for measurement data with varying recording priorities.
Innovation Solution
A data recording apparatus and method that determines the reducibility of data volume for each set of measurement data, selectively applying lossless or lossy compression based on priority, time, and prediction error, and triggers data volume reduction processing as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is performed on measurement data to reduce data volume, then storage capacity is optimized, but measurement precision is degraded due to lossy compression
Solution Approach 1:
The patent applies different compression strategies to different measurement data based on their specific characteristics. High-priority measurement data undergoes lossless compression to maintain full precision, while low-priority measurement data undergoes lossy compression to achieve greater reduction. This localized application of quality levels resolves the contradiction by ensuring precision is only degraded where acceptable.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on measurement data characteristics such as priority levels, time intervals, and predicted changes. By changing the compression parameter (lossless vs. lossy) according to the specific measurement data being processed, the system optimizes the balance between data volume reduction and precision maintenance for each data set.
2Device complexity
If all measurement data is compressed using the same method, then processing is simplified, but compression efficiency is reduced due to inability to differentiate between high and low priority data
Solution Approach 1:
The patent implements differentiated compression processing where high-priority measurement data receives lossless compression and low-priority measurement data receives lossy compression. This local quality approach targets compression efficiency improvements specifically where precision degradation is acceptable, without complicating the overall system beyond what is needed for the differentiation.
3Quantity of substance
If lossy compression is applied to reduce data volume, then storage capacity is optimized, but information loss occurs in the measurement data
Solution Approach 1:
The patent segments measurement data into different priority categories (high-priority and low-priority) and applies appropriate compression methods to each segment. This segmentation ensures that information loss through lossy compression only occurs in data segments where such loss is acceptable, while preserving full fidelity in critical data segments.
Solution Approach 2:
The patent changes the compression parameter (lossless vs. lossy) based on the priority level of the measurement data. By dynamically selecting the appropriate compression parameter for each data set, the system minimizes information loss while achieving optimal data volume reduction for each category.
Data Source
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AI summary
A data recording apparatus is described which includes a data obtaining section for obtaining multiple sets of measurement data obtained by measuring a measurement target; a determining section for determining for every measurement data of the multiple sets of measurement data whether a data volume is reducible; a data volume reducing section for reducing, in response to a result of the determining, a data volume for measurement data of which data volume has been determined as being reducible; a data compressing section for compressing multiple sets of measurement data including the measurement data of which data volume has been reduced; and a data recording section for recording the multiple sets of measurement data which have been compressed.