Data Compression Region Resetting for Lower SDT Error
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Solution Overview
Problem
Existing data compression methods, such as the Swing Door Trending (SDT) algorithm, face challenges in minimizing compression errors during data processing and transmission in applications like Industry 4.0 and IoT, where large amounts of data are generated in real-time.
Innovation Solution
A data processing system and method that sets multiple adjacent regions in a two-dimensional spatial representation of data, expands one region to overlap the other, calculates compression errors, and resets the regions according to these errors to minimize compression errors, using the SDT algorithm for efficient data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the SDT algorithm is used for data compression, then data transmission efficiency is improved, but compression errors increase
Solution Approach 1:
The data sequence is divided into multiple sub-sequences, and each sub-sequence is compressed independently using the SDT algorithm. This segmentation allows for better error control within each segment while maintaining overall compression efficiency, resolving the contradiction between transmission efficiency and compression accuracy.
Solution Approach 2:
The invention dynamically adjusts the compression threshold parameter based on the characteristics of different data segments. By changing the parameter adaptively, the system can achieve high compression ratios for suitable segments while maintaining lower error rates for segments requiring higher precision, thus balancing transmission efficiency and compression error.
2Speed
If compression is applied to reduce data size, then transmission speed is improved, but data accuracy deteriorates
Solution Approach 1:
By segmenting the data into multiple sub-sequences, the invention enables differential compression strategies for different segments. Critical data segments can be compressed with lower thresholds to maintain accuracy, while less critical segments can use higher compression ratios, thus improving transmission speed without uniformly sacrificing data accuracy.
Solution Approach 2:
The invention applies different compression quality levels to different local segments of the data based on their importance and characteristics. This local quality approach ensures that data accuracy is maintained where needed while achieving high transmission speeds in other areas, resolving the contradiction between speed and accuracy.
Data Source
AI summary
A data processing system and method are provided. The data processing system includes: a data acquisition unit, configured to acquire a plurality pieces of data related to a target object; and a data processing unit, configured to receive the plurality pieces of data and set a plurality of adjacent regions in a two-dimensional spatial representation of the plurality pieces of data according to a tolerable compression error. The plurality of regions include an adjacent first region and second region, respectively covering a plurality pieces of data. The data processing unit is configured to forwardly expand the second region to obtain the expanded second region overlapping the first region, calculate a compression error of data covered by the expanded second region, reset the first region and compress the data covered by the reset first region. The data processing system can reduce or minimize the data compression error.