Power quality data compression method and system based on dimension transformation

Abstract

The invention discloses an electric energy quality data compression method and system based on dimension transformation, and the method comprises the steps: collecting electric energy quality data, calculating an electric energy period based on the time sequence autocorrelation of the electric energy quality data, carrying out the truncation and reorganization processing of the electric energy quality data based on the electric energy period, and obtaining a three-dimensional electric energy quality image. Performing color space conversion on the three-dimensional electric energy quality image to obtain a space conversion image, performing dimension conversion on the space conversion image by adopting a discrete cosine transform algorithm to obtain three-dimensional electric energy image energy distribution data, performing high-frequency energy removal processing on the three-dimensional electric energy image energy distribution data to obtain low-frequency energy distribution data, and performing high-frequency energy removal processing on the low-frequency energy distribution data to obtain low-frequency energy distribution data; and realizing primary compression, and performing secondary compression on the low-frequency energy distribution data by adopting a lossless compression algorithm to obtain power quality compression data. According to the electric energy quality data compression method and system based on dimension transformation provided by the embodiment of the invention, the compression effect on the electric energy quality data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for compressing power quality data based on dimensionality transformation. Background Art

[0002] Smart grid is a new type of power grid based on the physical power grid of various power generation equipment, transmission and distribution networks, power equipment and energy storage equipment. It highly integrates modern advanced sensor measurement technology, network technology, communication technology, computing technology, automation and intelligent control technology with the physical power grid. With the rapid development of digital technology, information and communication technology, artificial intelligence and other technologies, smart grid has begun to develop rapidly and has gradually become the development trend of future power systems.

[0003] Smart grids generate a large amount of power quality data of various types during operation, which takes up a lot of space and time resources in the storage and transmission process. Data compression technology can identify the structure in the data and represent the data information in a compact form, thereby effectively reducing the number of bits required to represent the data and reducing their storage and transmission costs.

[0004] However, existing data compression technologies are difficult to balance the compression ratio and the retention of effective information in the data, resulting in poor compression effect on power quality data.

[0005] It can be seen that how to improve the compression ratio of power quality data while retaining the effective information in the power quality data has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0006] The present invention provides a power quality data compression method and system based on dimensional transformation, so as to solve the problem that the existing data compression technology has a poor compression effect on power quality data and improve the compression effect on power quality data.

[0007] In order to solve the above technical problems, an embodiment of the present invention provides a power quality data compression method based on dimensionality transformation.

[0008] Collecting power quality data, and calculating the power cycle based on the time series autocorrelation of the power quality data;

[0009] Based on the power cycle, the power quality data is truncated and reorganized to obtain a three-dimensional power quality image; wherein the truncation and reorganization process is designed to: normalize the power quality data to obtain corresponding normalized power quality data; based on the power cycle, truncate the normalized power quality data to obtain power quality intercepted data; perform three-dimensional image conversion on all the power quality intercepted data to obtain a three-dimensional power quality image;

[0010] Performing color space conversion on the three-dimensional power quality image to obtain a space conversion image;

[0011] A discrete cosine transform algorithm is used to transform the dimension of the spatial conversion image to obtain energy distribution data of a three-dimensional electric energy image;

[0012] Performing high-frequency energy removal processing on the energy distribution data of the three-dimensional electric energy image to obtain low-frequency energy distribution data, thereby achieving first-level compression;

[0013] A lossless compression algorithm is used to perform secondary compression on the low-frequency energy distribution data to obtain power quality compressed data.

[0014] As one of the preferred solutions, the calculating of the power cycle based on the time series autocorrelation of the power quality data includes:

[0015] Calculating the autocorrelation function of the power quality data, and extracting all peak points in the autocorrelation function;

[0016] Taking the time interval between each peak point and the corresponding next peak point as the power quality peak interval of each peak point;

[0017] The average value of all the power quality peak intervals is taken as the power cycle.

[0018] As one of the preferred solutions, the truncation processing of the normalized power quality data based on the power cycle to obtain the power quality intercepted data includes:

[0019] If the normalized power quality data is continuously collected data, three times the square value of the product of a preset interception cycle number and the power cycle is used as a first interception length, and the normalized power quality data is truncated with the first interception length to obtain power quality intercepted data;

[0020] If the normalized power quality data is data of finite length, the second interception length is calculated based on the power cycle and the length of the normalized power quality data by the following calculation formula:

[0021] N2=(K×L) 2 ×3

[0022] K=argmin{N2≥N0}

[0023] Wherein, N2 is the second interception length, N0 is the length of the normalized power quality data, K is the interception period, K=1, 2, ..., L is the power period, argmin{} represents the parameter value of a function that obtains the minimum value in the corresponding definition domain;

[0024] The normalized power quality data is truncated with the second truncation length, and missing value processing is performed on the normalized power quality data to obtain power quality truncation data, so that the length of the power quality truncation data is equal to the second truncation length.

[0025] As one of the preferred solutions, the three-dimensional image conversion of all the power quality intercepted data to obtain a three-dimensional power quality image includes:

[0026] The product of the interception period and the power cycle is respectively the number of rows and the number of columns, all the power quality interception data are arranged in rows to obtain each single-channel power quality matrix;

[0027] constructing a three-dimensional power quality matrix based on all the single-channel power quality images;

[0028] Each element in the three-dimensional power quality matrix is ​​multiplied by a preset coefficient to obtain a non-integer pixel value of each element, and all the non-integer pixel values ​​in the three-dimensional power quality matrix are rounded to obtain a three-dimensional power quality image.

[0029] As one of the preferred solutions, the three-dimensional electric energy image energy distribution data includes a Y channel, a Cb channel and a Cr channel;

[0030] The high frequency energy removal process is designed to:

[0031] The data values ​​in the Y channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Y channel threshold are set to 0, the data values ​​in the Cb channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cb channel threshold are set to 0, and the data values ​​in the Cr channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cr channel threshold are set to 0.

[0032] Another embodiment of the present invention provides a power quality data compression system based on dimensional transformation, comprising:

[0033] An electric energy data cycle analysis module, used for collecting electric energy quality data and calculating the electric energy cycle based on the time series autocorrelation of the electric energy quality data;

[0034] A power quality imaging module, which performs truncation and reorganization processing on the power quality data based on the power cycle to obtain a three-dimensional power quality image; wherein the truncation and reorganization processing is designed to: perform normalization processing on the power quality data to obtain corresponding normalized power quality data; based on the power cycle, perform truncation processing on the normalized power quality data to obtain power quality intercepted data; perform three-dimensional imaging conversion on all the power quality intercepted data to obtain a three-dimensional power quality image;

[0035] Performing color space conversion on the three-dimensional power quality image to obtain a space conversion image;

[0036] The electric energy data dual compression module is used to use a discrete cosine transform algorithm to perform dimensional transformation on the spatial conversion image to obtain three-dimensional electric energy image energy distribution data; perform high-frequency energy removal processing on the three-dimensional electric energy image energy distribution data to obtain low-frequency energy distribution data to achieve primary compression; and use a lossless compression algorithm to perform secondary compression on the low-frequency energy distribution data to obtain power quality compressed data.

[0037] As one of the preferred solutions, the calculating of the power cycle based on the time series autocorrelation of the power quality data includes:

[0038] Calculating the autocorrelation function of the power quality data, and extracting all peak points in the autocorrelation function;

[0039] Taking the time interval between each peak point and the corresponding next peak point as the power quality peak interval of each peak point;

[0040] The average value of all the power quality peak intervals is taken as the power cycle.

[0041] As one of the preferred solutions, the truncation processing of the normalized power quality data based on the power cycle to obtain the power quality intercepted data includes:

[0042] If the normalized power quality data is continuously collected data, three times the square value of the product of a preset interception cycle number and the power cycle is used as a first interception length, and the normalized power quality data is truncated with the first interception length to obtain power quality intercepted data;

[0043] If the normalized power quality data is data of finite length, the second interception length is calculated based on the power cycle and the length of the normalized power quality data by the following calculation formula:

[0044] N2=(K×L) 2 ×3

[0045] K=argmin{N2≥N0}

[0046] Wherein, N2 is the second interception length, N0 is the length of the normalized power quality data, K is the interception period, K=1, 2, ..., L is the power period, argmin{} represents the parameter value of a function that obtains the minimum value in the corresponding definition domain;

[0047] The normalized power quality data is truncated with the second truncation length, and missing value processing is performed on the normalized power quality data to obtain power quality truncation data, so that the length of the power quality truncation data is equal to the second truncation length.

[0048] As one of the preferred solutions, the three-dimensional image conversion of all the power quality intercepted data to obtain a three-dimensional power quality image includes:

[0049] The product of the interception period and the power cycle is respectively the number of rows and the number of columns, all the power quality interception data are arranged in rows to obtain each single-channel power quality matrix;

[0050] constructing a three-dimensional power quality matrix based on all the single-channel power quality images;

[0051] Each element in the three-dimensional power quality matrix is ​​multiplied by a preset coefficient to obtain a non-integer pixel value of each element, and all the non-integer pixel values ​​in the three-dimensional power quality matrix are rounded to obtain a three-dimensional power quality image.

[0052] As one of the preferred solutions, the three-dimensional electric energy image energy distribution data includes a Y channel, a Cb channel and a Cr channel;

[0053] The high frequency energy removal process is designed to:

[0054] The data values ​​in the Y channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Y channel threshold are set to 0, the data values ​​in the Cb channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cb channel threshold are set to 0, and the data values ​​in the Cr channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cr channel threshold are set to 0.

[0055] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0056] (1) Collect power quality data, analyze the time series autocorrelation of the power quality data, and calculate the power cycle. Considering that power quality data usually contains a large number of sampling points and monitoring time, after being converted into an image, it can be efficiently compressed using image compression technology to significantly reduce the file size. First, the power quality data is truncated and reorganized based on the power cycle to obtain a three-dimensional power quality image, and then the three-dimensional power quality image is converted into a color space to obtain a spatial conversion image. Through the periodicity estimation of the power quality data collected by power grid monitoring, the entire cycle power quality data is converted into three-dimensional matrix data and mapped into a color image. The color image data is then converted into a color space, and the spatial conversion image is subsequently compressed, thereby improving the compression effect of the power quality data;

[0057] (2) For the spatial conversion image, discrete cosine transform is first used for dimensional transformation to obtain the three-dimensional power image energy distribution data. Then, based on different preset thresholds, high-frequency energy removal processing is performed on different channels of the three-dimensional power image energy distribution data to obtain low-frequency energy distribution data, and first-level compression is achieved. Then, a lossless compression algorithm is used to perform secondary compression on the low-frequency energy distribution data to obtain power quality compressed data. Independent thresholds are used for different channels to remove smaller data. The image-decomposed data is compressed twice using a lossless compression algorithm to remove redundancy in the data matrix after the image-decomposition processing, significantly reducing the amount of data. While retaining the effective information in the power quality data, the compression ratio is improved, solving the problem that the existing data compression technology has a poor compression effect on power quality data. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a method for compressing power quality data based on dimensional transformation in one embodiment of the present invention;

[0059] Figure 2 is a time domain waveform diagram of power quality data in one embodiment of the present invention;

[0060] Figure 3 is a schematic diagram of obtaining a three-dimensional power quality matrix in one embodiment of the present invention;

[0061] Figure 4 It is a structural block diagram of a power quality data compression system based on dimensional transformation in one embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0064] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0065] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0066] An embodiment of the present invention provides a method for compressing power quality data based on dimensional transformation. For details, see Figure 1 , Figure 1 The figure shows a flow chart of a method for compressing power quality data based on dimensional transformation in one embodiment of the present invention.

[0067] Step S1: Collect power quality data, and calculate the power cycle based on the time series autocorrelation of the power quality data.

[0068] It should be noted that the power quality data collected by the power grid in real time contains fundamental waves and harmonics, and the power quality data has a certain periodicity. Therefore, by compressing the power quality data of multiple cycles as a whole, it is possible to avoid destroying the effective information in the power quality data.

[0069] For details, see Figure 2 , Figure 2 Shown is a time domain waveform diagram of power quality data in one embodiment of the present invention.

[0070] Step S2: performing truncation and reorganization processing on the power quality data based on the power cycle to obtain a three-dimensional power quality image; wherein the truncation and reorganization processing is designed as follows: performing normalization processing on the power quality data to obtain corresponding normalized power quality data; performing truncation processing on the normalized power quality data based on the power cycle to obtain power quality intercepted data; performing three-dimensional image conversion on all power quality intercepted data to obtain a three-dimensional power quality image;

[0071] The color space conversion is performed on the three-dimensional power quality image to obtain a space conversion image.

[0072] In this embodiment, the difference between the maximum value and the minimum value of all power quality data is taken as the extreme power quality, the difference between each power quality data and the minimum value is taken as the quality deviation of each power quality data, and the ratio of the quality deviation to the extreme power quality is taken as the corresponding normalized power quality data.

[0073] Step S3: Use a discrete cosine transform algorithm to perform dimensional transformation on the spatial conversion image to obtain three-dimensional power image energy distribution data; perform high-frequency energy removal processing on the three-dimensional power image energy distribution data to obtain low-frequency energy distribution data and achieve primary compression; use a lossless compression algorithm to perform secondary compression on the low-frequency energy distribution data to obtain power quality compressed data.

[0074] It should be noted that the lossless compression algorithm is a well-known technology. This embodiment selects the lzma algorithm to perform secondary compression on the low-frequency energy distribution data. The selection of the lossless compression algorithm is other implementation methods that the implementer can choose at his own discretion, and this application does not impose any special restrictions on this.

[0075] Furthermore, in the above embodiment, the calculation of the power cycle based on the time series autocorrelation of the power quality data includes:

[0076] Calculate the autocorrelation function of the power quality data and extract all peak points in the autocorrelation function;

[0077] The time interval between each peak point and the corresponding next peak point is taken as the power quality peak interval of each peak point;

[0078] The average value of all power quality peak intervals is taken as the power cycle.

[0079] Furthermore, in the above embodiment, the truncation processing of the normalized power quality data based on the power cycle to obtain the power quality intercepted data includes:

[0080] If the normalized power quality data is continuously collected data, three times the square value of the product of the preset interception cycle number and the power cycle is used as the first interception length, and the normalized power quality data is truncated with the first interception length to obtain power quality intercepted data;

[0081] If the normalized power quality data is data of finite length, the second interception length is calculated by the following calculation formula based on the power cycle and the length of the normalized power quality data:

[0082] N2=(K×L) 2 ×3

[0083] K=argmin{N2≥N0}

[0084] Wherein, N2 is the second truncation length, N0 is the length of the normalized power quality data, K is the truncation period, K=1, 2, ..., L is the power period, and argmin{} represents the parameter value of a function that obtains the minimum value in the corresponding domain.

[0085] For example, when the function f(x) reaches its minimum value at x=3, argmin{f(x)} is equal to 3. This means that when x=3, the value of the function f(x) is the minimum.

[0086] Furthermore, the normalized power quality data is truncated with a second truncation length, and missing value processing is performed on the normalized power quality data to obtain power quality truncation data, so that the length of the power quality truncation data is equal to the second truncation length.

[0087] Furthermore, in the above embodiment, the three-dimensional image conversion of all power quality intercepted data to obtain a three-dimensional power quality image includes:

[0088] The product of the interception period and the power period is used as the number of rows and the number of columns respectively, and all the power quality interception data are arranged in rows to obtain each single-channel power quality matrix;

[0089] Construct a three-dimensional power quality matrix based on all single-channel power quality images;

[0090] Each element in the three-dimensional power quality matrix is ​​multiplied by a preset coefficient to obtain a non-integer pixel value of each element, and all non-integer pixel values ​​in the three-dimensional power quality matrix are rounded to obtain a three-dimensional power quality image.

[0091] It should be noted that the preset coefficient in this embodiment is 255. By multiplying each element in the three-dimensional power quality matrix by the preset coefficient and performing rounding processing, the power quality data is converted into pixel points in the three-dimensional power quality image.

[0092] For details, see Figure 3 , Figure 3 FIG. 1 is a schematic diagram of obtaining a three-dimensional power quality matrix in one embodiment of the present invention.

[0093] Furthermore, in the above embodiment, the three-dimensional electric energy image energy distribution data includes a Y channel, a Cb channel and a Cr channel; the high-frequency energy removal process is designed as follows:

[0094] The data values ​​in the Y channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Y channel threshold are set to 0, the data values ​​in the Cb channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cb channel threshold are set to 0, and the data values ​​in the Cr channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cr channel threshold are set to 0.

[0095] It should be noted that the preset Y channel threshold, the preset Cb channel threshold and the preset Cr channel threshold are all manually preset values. In this embodiment, the preset Y channel threshold is 5.5, and the preset Cb channel threshold and the preset Cr channel threshold are both 11. For the values ​​of the preset Y channel threshold, the preset Cb channel threshold and the preset Cr channel threshold, as other implementation methods, the implementer can select them at his own discretion, and this application does not impose any special restrictions on this.

[0096] Another embodiment of the present invention provides a power quality data compression system based on dimensional transformation. For details, see Figure 4 , Figure 4 Shown is a structural block diagram of a power quality data compression system based on dimensional transformation in one embodiment of the present invention.

[0097] Power data cycle analysis module 11: used to collect power quality data and calculate the power cycle based on the autocorrelation characteristics of the power quality data;

[0098] The power quality imaging module 12: performs truncation and reorganization processing on the power quality data based on the power cycle to obtain a three-dimensional power quality image; wherein the truncation and reorganization processing is designed to: perform normalization processing on the power quality data to obtain corresponding normalized power quality data, perform truncation processing on the normalized power quality data based on the power cycle to obtain power quality intercepted data, and perform three-dimensional imaging conversion on all power quality intercepted data to obtain a three-dimensional power quality image;

[0099] Performing color space conversion on the three-dimensional power quality image to obtain a space conversion image;

[0100] The electric energy data dual compression module 13 is used to perform dimensional transformation on the spatial conversion image using a discrete cosine transform algorithm to obtain three-dimensional electric energy image energy distribution data; perform high-frequency energy removal processing on the three-dimensional electric energy image energy distribution data to obtain low-frequency energy distribution data to achieve primary compression; and perform secondary compression on the low-frequency energy distribution data using a lossless compression algorithm to obtain power quality compressed data.

[0101] Furthermore, in the above embodiment, the calculation of the power cycle based on the time series autocorrelation of the power quality data includes:

[0102] Calculate the autocorrelation function of the power quality data and extract all peak points in the autocorrelation function;

[0103] The time interval between each peak point and the corresponding next peak point is taken as the power quality peak-to-peak interval of each peak point; the average value of all power quality peak-to-peak intervals is taken as the power cycle.

[0104] Furthermore, in the above embodiment, the truncation processing of the normalized power quality data based on the power cycle to obtain the power quality intercepted data includes:

[0105] If the normalized power quality data is continuously collected data, three times the square value of the product of the preset interception cycle number and the power cycle is used as the first interception length, and the normalized power quality data is truncated with the first interception length to obtain power quality intercepted data;

[0106] If the normalized power quality data is data of finite length, the second interception length is calculated by the following calculation formula based on the power cycle and the length of the normalized power quality data:

[0107] N2=(K×L) 2 ×3

[0108] K=argmin{N2≥N0}

[0109] Wherein, N2 is the second interception length, N0 is the length of the normalized power quality data, K is the interception period, K=1, 2, ..., L is the power period, argmin{} represents the parameter value of a function that obtains the minimum value in the corresponding definition domain;

[0110] The normalized power quality data is truncated with a second truncation length, and missing value processing is performed on the normalized power quality data to obtain power quality truncation data, so that the length of the power quality truncation data is equal to the second truncation length.

[0111] Furthermore, in the above embodiment, the three-dimensional image conversion of all power quality intercepted data to obtain a three-dimensional power quality image includes:

[0112] The product of the interception period and the power period is used as the number of rows and the number of columns respectively, and all the power quality interception data are arranged in rows to obtain each single-channel power quality matrix;

[0113] Construct a three-dimensional power quality matrix based on all single-channel power quality images;

[0114] Each element in the three-dimensional power quality matrix is ​​multiplied by a preset coefficient to obtain a non-integer pixel value of each element, and all non-integer pixel values ​​in the three-dimensional power quality matrix are rounded to obtain a three-dimensional power quality image.

[0115] Further, in the above embodiment, the three-dimensional electric energy image energy distribution data includes a Y channel, a Cb channel and a Cr channel;

[0116] The high frequency energy removal process is designed to:

[0117] The data values ​​in the Y channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Y channel threshold are set to 0, the data values ​​in the Cb channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cb channel threshold are set to 0, and the data values ​​in the Cr channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cr channel threshold are set to 0.

[0118] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A power quality data compression method based on dimensional transformation, characterized in that: The method comprises: Collecting power quality data, and calculating the power cycle based on the time series autocorrelation of the power quality data; Based on the power cycle, the power quality data is truncated and reorganized to obtain a three-dimensional power quality image; wherein the truncation and reorganization process is designed to: normalize the power quality data to obtain corresponding normalized power quality data; based on the power cycle, truncate the normalized power quality data to obtain power quality intercepted data; perform three-dimensional image conversion on all the power quality intercepted data to obtain a three-dimensional power quality image; Performing color space conversion on the three-dimensional power quality image to obtain a space conversion image; A discrete cosine transform algorithm is used to transform the dimension of the spatial conversion image to obtain energy distribution data of a three-dimensional electric energy image; Performing high-frequency energy removal processing on the energy distribution data of the three-dimensional electric energy image to obtain low-frequency energy distribution data, thereby achieving first-level compression; A lossless compression algorithm is used to perform secondary compression on the low-frequency energy distribution data to obtain power quality compressed data.

2. The method for compressing power quality data based on dimensional transformation according to claim 1, characterized in that: The calculating of the electric energy cycle based on the time series autocorrelation of the electric energy quality data comprises: Calculating the autocorrelation function of the power quality data, and extracting all peak points in the autocorrelation function; Taking the time interval between each peak point and the corresponding next peak point as the power quality peak interval of each peak point; The average value of all the power quality peak intervals is taken as the power cycle.

3. The method for compressing power quality data based on dimensional transformation according to claim 1, characterized in that: The method of performing truncation processing on the normalized power quality data based on the power cycle to obtain power quality intercepted data includes: If the normalized power quality data is continuously collected data, three times the square value of the product of a preset interception cycle number and the power cycle is used as a first interception length, and the normalized power quality data is truncated with the first interception length to obtain power quality intercepted data; If the normalized power quality data is data of finite length, the second interception length is calculated based on the power cycle and the length of the normalized power quality data by the following calculation formula: N2=(K×L) 2 ×3 K=argmin{N2≥N0} Wherein, N2 is the second interception length, N0 is the length of the normalized power quality data, K is the interception period, K=1, 2, ..., L is the power period, argmin{} represents the parameter value of a function that obtains the minimum value in the corresponding definition domain; The normalized power quality data is truncated with the second truncation length, and missing value processing is performed on the normalized power quality data to obtain power quality truncation data, so that the length of the power quality truncation data is equal to the second truncation length.

4. The method for compressing power quality data based on dimensional transformation according to claim 3, characterized in that: The three-dimensional image conversion of all the power quality intercepted data to obtain a three-dimensional power quality image includes: The product of the interception period and the power cycle is respectively the number of rows and the number of columns, all the power quality interception data are arranged in rows to obtain each single-channel power quality matrix; constructing a three-dimensional power quality matrix based on all the single-channel power quality images; Each element in the three-dimensional power quality matrix is ​​multiplied by a preset coefficient to obtain a non-integer pixel value of each element, and all the non-integer pixel values ​​in the three-dimensional power quality matrix are rounded to obtain a three-dimensional power quality image.

5. The method for compressing power quality data based on dimensional transformation according to claim 1, characterized in that: The three-dimensional electric energy image energy distribution data includes a Y channel, a Cb channel and a Cr channel; The high frequency energy removal process is designed to: The data values ​​in the Y channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Y channel threshold are set to 0, the data values ​​in the Cb channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cb channel threshold are set to 0, and the data values ​​in the Cr channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cr channel threshold are set to 0.

6. A power quality data compression system based on dimensional transformation, characterized in that: include: An electric energy data cycle analysis module, used for collecting electric energy quality data and calculating the electric energy cycle based on the time series autocorrelation of the electric energy quality data; A power quality imaging module, which performs truncation and reorganization processing on the power quality data based on the power cycle to obtain a three-dimensional power quality image; wherein the truncation and reorganization processing is designed to: perform normalization processing on the power quality data to obtain corresponding normalized power quality data; based on the power cycle, perform truncation processing on the normalized power quality data to obtain power quality intercepted data; perform three-dimensional imaging conversion on all the power quality intercepted data to obtain a three-dimensional power quality image; Performing color space conversion on the three-dimensional power quality image to obtain a space conversion image; The electric energy data dual compression module is used to use a discrete cosine transform algorithm to perform dimensional transformation on the spatial conversion image to obtain three-dimensional electric energy image energy distribution data; perform high-frequency energy removal processing on the three-dimensional electric energy image energy distribution data to obtain low-frequency energy distribution data to achieve primary compression; and use a lossless compression algorithm to perform secondary compression on the low-frequency energy distribution data to obtain power quality compressed data.

7. The power quality data compression system based on dimensional transformation according to claim 6, characterized in that: The calculating of the electric energy cycle based on the time series autocorrelation of the electric energy quality data comprises: Calculating the autocorrelation function of the power quality data, and extracting all peak points in the autocorrelation function; Taking the time interval between each peak point and the corresponding next peak point as the power quality peak interval of each peak point; The average value of all the power quality peak intervals is taken as the power cycle.

8. The power quality data compression system based on dimensional transformation according to claim 6, characterized in that: The method of performing truncation processing on the normalized power quality data based on the power cycle to obtain power quality intercepted data includes: If the normalized power quality data is continuously collected data, three times the square value of the product of a preset interception cycle number and the power cycle is used as a first interception length, and the normalized power quality data is truncated with the first interception length to obtain power quality intercepted data; If the normalized power quality data is data of finite length, the second interception length is calculated based on the power cycle and the length of the normalized power quality data by the following calculation formula: N2=(K×L) 2 ×3 K=argmin{N2≥N0} Wherein, N2 is the second interception length, N0 is the length of the normalized power quality data, K is the interception period, K=1, 2, ..., L is the power period, argmin{} represents the parameter value of a function that obtains the minimum value in the corresponding definition domain; The normalized power quality data is truncated with the second truncation length, and missing value processing is performed on the normalized power quality data to obtain power quality truncation data, so that the length of the power quality truncation data is equal to the second truncation length.

9. The power quality data compression system based on dimensional transformation according to claim 8, characterized in that: The three-dimensional image conversion of all the power quality intercepted data to obtain a three-dimensional power quality image includes: The product of the interception period and the power cycle is respectively the number of rows and the number of columns, all the power quality interception data are arranged in rows to obtain each single-channel power quality matrix; constructing a three-dimensional power quality matrix based on all the single-channel power quality images; Each element in the three-dimensional power quality matrix is ​​multiplied by a preset coefficient to obtain a non-integer pixel value of each element, and all the non-integer pixel values ​​in the three-dimensional power quality matrix are rounded to obtain a three-dimensional power quality image.

10. The power quality data compression system based on dimensional transformation according to claim 6, characterized in that: The three-dimensional electric energy image energy distribution data includes a Y channel, a Cb channel and a Cr channel; The high frequency energy removal process is designed to: The data values ​​in the Y channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Y channel threshold are set to 0, the data values ​​in the Cb channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cb channel threshold are set to 0, and the data values ​​in the Cr channel of the three-dimensional electric energy image energy distribution data that are lower than the preset Cr channel threshold are set to 0.

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