Compression method for partial discharge on-line monitoring data
A technology for monitoring data and compression methods, applied to electrical components, transmission systems, etc., can solve problems such as lossy compression, large information loss or distortion, and inability to obtain ideal use effects, etc., to achieve efficient transmission effects
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0022] Embodiment 1: the compression method of partial discharge on-line monitoring data of the present invention, at first collect monitoring data (step 1) from partial discharge monitoring device, then according to predetermined row width and row height, the monitoring data collected is stored (step 2 ); According to the row width and the row height of the stored monitoring data, judge the correlation of the row of the monitoring data and the correlation (step 3); according to the judgment result, use the XOR algorithm to correlate the monitoring data of the row Processing (step 4); storing the compressed data after correlation processing (step 5).
[0023] Since the row width or row height of the monitoring data is determined, the correlation between adjacent rows or columns can be determined. Because the size of the data source of the total monitoring data is known, determining the row height also determines the row width, and similarly, determining the row width also dete...
Embodiment 2
[0034] Embodiment 2: This embodiment has made further improvement on the basis of embodiment 1, further carries out further to the data after correlation processing by RL-B (Run Length To Binar, run length binary) algorithm after performing step 4 compression.
[0035] As a preferred embodiment of this embodiment, in order to make the characters with high occurrence probability in the original monitoring data collected in step 1 and the 0 appearing after XOR, one RL-B compression can be performed uniformly. Before step 3, the collected original monitoring data is counted, and when the character occurrence probability exceeds M (the value of M can be set according to the different needs of the user, for example, M=40% can be taken), and 0 is interacted with Change. For example, assuming that the character with high occurrence probability is 0xff, let 0xff and 0 be interchanged (expressed as 0xff0).
[0036] In order to show the compression effect of this embodiment, see the t...
Embodiment 3
[0045] Embodiment 3: As a further improvement to Embodiment 2, this embodiment further compresses the data through the LZW (Lempel-Ziv-Welch Encoding) algorithm after the RL-B compression processing.
[0046] Similarly, in order to show the compression effect of this embodiment, see the table below:
[0047] For example, if the PD spectrogram data (this data is 512*160 points) is transformed, the metadata is 10240 bytes, assuming that the character with high occurrence probability is 0xff, the results can be seen in Table 2.
[0048] Table 2 Processing results of single data source files according to different compression schemes
[0049]
[0050]RL-B+LZW: Indicates that the monitoring data after XOR processing is compressed using the RL-B algorithm, and then the monitoring data compressed by the RL-B algorithm is compressed using the LZW algorithm.
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 