CSV Data Compression via Bitmap Image Conversion
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Solution Overview
Problem
Industrial IoT devices generate vast amounts of control data that need to be stored for long periods, requiring an efficient and lossless compression method to reduce storage capacity and communication time, while ensuring data can be restored accurately.
Innovation Solution
A data compression device that converts CSV data into character string data composed of 0s and 1s, eliminates unnecessary items like commas and character codes, and uses image processing methods like PNG format for lossless compression, arranging the data in a two-dimensional array for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in original CSV format, then data restoration is simple, but storage capacity requirements are high and communication time is long
Solution Approach 1:
The patent changes the parameter representation by converting CSV data into a bitmap image format, where numerical values are transformed into visual pixel patterns. This parameter transformation enables significant compression ratios while maintaining lossless restoration capability, directly reducing storage capacity requirements without overly complicating the processing system.
Solution Approach 2:
The patent creates a visual copy of the data by generating a bitmap image that represents the numerical CSV data. This image copy can be stored and transmitted more efficiently, yet the original numerical data can be perfectly restored through image recognition and data extraction processes, thus reducing storage needs while maintaining data integrity.
2Quantity of substance
If data is compressed to reduce storage, then storage capacity is reduced, but data restoration accuracy may be compromised
Solution Approach 1:
The patent creates a visual copy of the data by generating a bitmap image that represents the numerical CSV data. This image copy can be stored and transmitted more efficiently, yet the original numerical data can be perfectly restored through image recognition and data extraction processes, thus reducing storage needs while maintaining data integrity.
Solution Approach 2:
The patent changes the parameter representation by converting CSV data into a bitmap image format, where numerical values are transformed into visual pixel patterns. This parameter transformation enables significant compression ratios while maintaining lossless restoration capability, directly reducing storage capacity requirements without overly complicating the processing system.
3Productivity
If image processing methods are used for compression, then compression ratio is improved, but processing time and complexity increase
Solution Approach 1:
The patent segments the data processing into distinct stages: CSV data conversion to bitmap, image compression, and image-to-data restoration. This segmentation allows each stage to be optimized independently, improving overall compression efficiency while managing processing time through systematic task division.
Data Source
AI summary
In a data compression method of compressing data including at least one of a character and a number, the data is converted into character string data composed of 0s and 1s. Each of the 0s and 1s included in the character string data is treated as 1-bit data, and the 1-bit data is arranged in a data region having a two-dimensional array. Lossless compression of the data arranged in the data region is performed by using an image processing method.