A method for collecting power data of a power box

Abstract

The present application relates to the field of data processing technology, and specifically to a method for collecting electric quantity data of an electric power box. The method includes: obtaining an original signal and a voltage signal; obtaining a sparse factor in the original signal through the energy of the spectrum in the voltage signal; obtaining the envelope of the voltage signal and the corresponding envelope value, in the time domain, obtaining the time domain variation degree according to the amplitude of the sampling point and the fluctuation of the envelope value, in the frequency domain, obtaining the frequency domain variation degree according to the energy of the frequency and the number of frequencies, and obtaining the variation factor according to the time domain variation degree and the frequency domain variation degree; obtaining a measurement matrix according to the variation factor and the sparse factor of the original signal combined with the length of the original signal; compressing and reconstructing the original signal according to the measurement matrix, and collecting the electric quantity data. The compression effect of the present application is good, and the complete collection of data can be guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a method for collecting power data of a power box. Background Art

[0002] The power box is an equipment box used for distribution, control and protection of circuits in the power system, in which a variety of control elements are installed, including circuit breakers, switches, fuses, relays, etc. Electricity data refers to various measurement data related to electric energy, including current, voltage, power, etc. Electricity data detection is crucial to ensure the safety and stability of the power system to prevent equipment damage or safety accidents. In order to achieve accurate recording and analysis of the power box electricity data, real-time collection is required. However, the large amount of data obtained by real-time collection puts great pressure on storage and transmission, especially the small memory of the equipment in the power box and the narrow transmission channel. Therefore, it is necessary to compress the collected data. The traditional method is usually to collect data first and then store it, while the method based on compressed sensing can compress it while collecting data, which greatly reduces the pressure on the memory. The size of the compression amount is related to the dimension of the measurement matrix. However, the dimension of the existing compressed sensing measurement matrix is ​​often selected based on experience, which is not necessarily suitable for the collection of all power box electricity data, resulting in unsatisfactory compression effect and even inability to reconstruct the original signal. Summary of the invention

[0003] In order to solve the technical problem of unsatisfactory data compression effect, the present application provides a method for collecting power data of a power box, and the technical solution adopted is as follows:

[0004] The present application proposes a method for collecting power data of a power box, the method comprising the following steps:

[0005] Collecting the original signal within a preset period, and dividing the original signal into a number of voltage signals;

[0006] Obtain the spectrum of the voltage signal, obtain the energy of each frequency according to the amplitude of each frequency in the voltage signal, set the energy threshold according to the total energy of the voltage signal, compare the energy with the energy threshold and divide it into important frequencies and unimportant frequencies, and obtain the sparse factor of the original signal according to the number of important frequencies in all voltage signals;

[0007] Obtain the envelope of the voltage signal and the envelope value of the sampling point, obtain the time domain variation of the original signal according to the difference between the amplitude of each sampling point in all voltage signals and the average amplitude of all sampling points of the voltage signal, the number of voltage signals and the fluctuation characteristics of the envelope value of all sampling points; obtain the frequency domain variation of the original signal according to the difference between the energy of each frequency in all voltage signals and the total energy, the number of voltage signals and the number of frequencies in the voltage signal; obtain the variation factor of the original signal according to the ratio of the frequency domain variation to the time domain variation of the original signal;

[0008] According to the variation factor of the original signal and the sparse factor of the original signal, a measurement matrix is ​​obtained in combination with the length of the original signal;

[0009] The original signal is compressed and reconstructed according to the measurement matrix, and the electrical quantity data is collected.

[0010] In the above scheme, the present application obtains the sparsity factor by collecting the energy threshold of the signal; obtains the time domain variation degree by collecting the change of the signal in the time domain, and obtains the frequency domain variation degree by combining the distribution of the frequency energy in the frequency domain of the collected signal, and further calculates the variability factor; finally, the dimension of the measurement matrix is ​​calculated, and the measurement matrix is ​​generated; the dimension of the measurement matrix is ​​determined by analyzing the characteristics of the power box voltage data in the time domain and the frequency domain, and the distribution of the signal and the number of signals are combined to obtain a better measurement matrix, avoiding the measurement matrix dimension being too large, resulting in poor compression effect, and avoiding the compression matrix being too small, resulting in the loss of important information, and thus the original signal cannot be reconstructed.

[0011] In one embodiment, the energy of each frequency is the square of the amplitude modulus corresponding to each frequency.

[0012] In one embodiment, the energy threshold is set by the total energy of the voltage signal, and the method of comparing the energy with the energy threshold and dividing the energy into important frequencies and unimportant frequencies is:

[0013] The energy of all frequencies in the voltage signal is added as the total energy of the voltage signal;

[0014] using a preset portion of the total energy of the voltage signal as an energy threshold of the voltage signal;

[0015] If the energy of the frequency is greater than the energy threshold, the frequency is an important frequency; if the energy of the frequency is less than or equal to the energy threshold, the frequency is an unimportant frequency.

[0016] In one embodiment, the method for obtaining the sparse factor of the original signal according to the number of important frequencies in all voltage signals is:

[0017] Counting the number of all important frequencies in the voltage signal is used as the sparsity of the voltage signal;

[0018] The accumulated sum of the sparsity of the original signal in the original signal is taken as the sparsity factor of the original signal.

[0019] In one embodiment, the method for obtaining the time domain variation of the original signal according to the difference between the amplitude of each sampling point in all voltage signals and the amplitude mean of all sampling points of the voltage signals, the number of voltage signals, and the fluctuation characteristics of the envelope values ​​of all sampling points is:

[0020] , where represents the standard deviation of the envelope value of the i-th voltage signal, represents the amplitude of the jth sampling point in the ith voltage signal, represents the amplitude mean of all sampling points of the i-th voltage signal, Indicates the number of voltage signals, represents the number of sampling points in the voltage signal, Indicates the time domain variation of the original signal.

[0021] In one embodiment, the method for obtaining the frequency domain variation of the original signal according to the difference between the energy of each frequency in all voltage signals and the total energy, the number of voltage signals, and the number of frequencies in the voltage signals is:

[0022] , represents the energy of the cth frequency in the ith voltage signal, represents the total energy of the i-th voltage signal, Indicates the number of voltage signals, represents the number of frequencies in the voltage signal frequency, Indicates the frequency domain variation of the original signal.

[0023] In one embodiment, the method for obtaining the change factor of the original signal according to the ratio of the frequency domain change degree to the time domain change degree of the original signal is:

[0024] , Represents the time domain variation of the original signal, represents the frequency domain variation of the original signal, represents the linear normalization function, Represents the change factor of the original signal.

[0025] In one embodiment, the method of obtaining the measurement matrix according to the variation factor of the original signal and the sparse factor of the original signal in combination with the length of the original signal is:

[0026] , B represents the change factor of the original signal, represents the ceiling function, represents the logarithmic function with base 10, represents the length of the original signal, M represents the dimension of the measurement matrix, and K represents the sparsity factor of the original signal;

[0027] The measurement matrix is ​​randomly generated with N rows and M columns as the size of the measurement matrix.

[0028] In one embodiment, the method of compressing and reconstructing the original signal according to the measurement matrix and collecting the electric quantity data is as follows:

[0029] An original signal of length N is collected, and the original signal is multiplied by the measurement matrix to obtain a measurement signal of length M. The measurement signal is reconstructed through a reconstruction algorithm to obtain the original signal.

[0030] In one embodiment, the envelope of the voltage signal is obtained through Hilbert transformation, and the envelope value of each sampling point in the voltage signal is obtained.

[0031] The beneficial effects of the present application are as follows: the present application obtains a sparsity factor by collecting an energy threshold of a signal; obtains a time domain variability by collecting changes in the signal in the time domain, and obtains a frequency domain variability by combining the distribution of frequency energy in the frequency domain of the collected signal, and further calculates the variability factor; finally, the dimension of the measurement matrix is ​​calculated, and a measurement matrix is ​​generated; the dimension of the measurement matrix is ​​determined by analyzing the characteristics of the power box voltage data in both the time domain and the frequency domain, and the distribution of the signal and the number of signals are combined to obtain a better measurement matrix, thereby avoiding the measurement matrix dimension being too large, resulting in poor compression effect, and also avoiding the compression matrix being too small, resulting in loss of important information, and thus being unable to reconstruct the original signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A flow chart of a method for collecting power data of a power box provided in one embodiment of the present application;

[0034] Figure 2 is the original signal image;

[0035] Figure 3 To compress the signal image;

[0036] Figure 4 To reconstruct the signal image. DETAILED DESCRIPTION

[0037] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the power data collection method of a power box proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0039] An embodiment of a method for collecting power data of a power box:

[0040] The specific scheme of the method for collecting power data of a power box provided by the present application is described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 , which shows a flow chart of a method for collecting power data of a power box provided by an embodiment of the present application, the method comprising the following steps:

[0042] Step S001, collecting an original signal within a preset period, and dividing the original signal into a plurality of voltage signals.

[0043] The voltage data collected by the sensor in real time is compressed and collected by the compressed sensing method. Since the voltage fluctuation in the area under the responsibility of a power box is small at different times, the voltage data in one cycle is collected for analysis and calculation to obtain the dimension of the measurement matrix. The measurement matrix is ​​then used to compress and sample the data collected in real time by the power box.

[0044] In order to avoid noise interference in the transmission process, analog signals are generally converted into digital signals through sampling. When the sampling frequency is small and does not meet the Nyquist sampling theorem, aliasing will occur, and a large sampling frequency will cause excessive data volume. Therefore, the original voltage analog signal can be collected for analysis, and the sampling frequency can be reduced through compressed sensing to reduce the data volume.

[0045] An analog voltage sensor is used to continuously collect the voltage signal of the power box to obtain an analog signal; a day is used as a cycle, and the signal is collected in the first 2 seconds of each hour of each day to obtain the original signal, and the original signal is divided according to the data collected every hour to obtain 24 voltage signals. In this embodiment, the analog signal obtained is sampled by an analog-to-digital converter, and the sampling frequency is 1000 Hz, that is, a signal sequence of 2000 sampling points is collected every hour.

[0046] So far, several voltage signals within one cycle have been obtained.

[0047] Step S002, obtain the spectrum of the voltage signal, obtain the energy of each frequency according to the amplitude of each frequency in the voltage signal, set the energy threshold according to the total energy of the voltage signal, compare the energy with the energy threshold and divide it into important frequencies and unimportant frequencies, and obtain the sparse factor of the original signal according to the number of important frequencies in all voltage signals.

[0048] The output of the analog voltage sensor in the power box is usually an analog signal, which is usually sampled and converted into a digital signal using an analog-to-digital converter for computer processing. Due to the presence of harmonics in the power grid, a larger sampling frequency is usually required to meet the Nyquist sampling theorem and avoid aliasing, which results in a large amount of data. These data need to be stored before compression, which puts pressure on the storage and transmission of the equipment in the power box.

[0049] The compressed sensing method can break through the limitations of the Nyquist sampling theorem. Even if the voltage signal is sampled at a lower sampling frequency, the original signal can be reconstructed. The amount of data can be reduced during sampling, thus achieving compression of the voltage signal.

[0050] The premise of applying compressed sensing theory is that the signal is sparse. Ideally, the voltage signal of the power system is a sine wave, but in reality, the voltage signal of the power system is disturbed, so it is not sparse in the time domain; therefore, it is necessary to use a sparse representation basis to transform the voltage signal to obtain a sparse signal. In this embodiment, the sparse representation basis used is a Fourier basis.

[0051] The dimension of the measurement matrix is ​​related to the sparsity of the signal. Sparsity refers to the number of non-zero elements in the signal. However, in actual situations, after discrete Fourier transform, there are harmonics or other frequencies in the frequency domain in addition to the fundamental frequency. Although these frequencies are not as important as the fundamental frequency, they cannot be ignored directly. This embodiment distinguishes the importance of frequencies through energy thresholds, where frequencies with energy greater than the threshold are important frequencies, and frequencies less than the threshold are unimportant frequencies. Important frequencies are regarded as non-zero elements in sparse signals, and unimportant frequencies are regarded as zero elements in sparse signals, thereby calculating the sparsity of the signal.

[0052] Since the working hours of various loads in the power system are different, the sparsity of signals collected at different times is also different. The working hours of the loads are basically fixed every day. In this embodiment, each voltage signal is analyzed to calculate the sparsity factor of the power box voltage data.

[0053] For a voltage signal, a discrete Fourier transform is performed on the voltage signal to obtain its spectrum, and the energy of each frequency of the voltage signal is calculated, where the energy of each frequency is the square of the amplitude modulus corresponding to each frequency. The energies of all frequencies are summed to obtain the total energy of the voltage signal, and 5% of the total energy of the voltage signal is used as the energy threshold of the voltage signal.

[0054] If the energy of the frequency of the voltage signal is greater than the energy threshold, its frequency is considered to be an important frequency; if the energy of the frequency of the voltage signal is less than or equal to the energy threshold, its frequency is considered to be an unimportant frequency; the number of all important frequencies is counted and recorded as the sparsity of the voltage signal; the sparsity of all voltage signals in one period is averaged to obtain the sparse factor of the original signal.

[0055] The sparsity factor is the average value of the sparsity of all collected voltage signals. The sparsity of the voltage signal is the number of frequencies in the frequency domain whose energy is greater than the energy threshold, so the smaller the sparsity, the sparser the signal. For the collected original signal, the smaller the calculated sparsity factor, the sparser the signal of the power box voltage data, and the smaller the dimension of the measurement matrix should be. Because the sparser the signal, the less information it contains, the greater the degree of compression can be, and the smaller the dimension of the measurement matrix, the greater the degree of compression of the signal.

[0056] At this point, the sparse factor of the original signal is obtained.

[0057] Step S003, obtaining the envelope of the voltage signal and the envelope value of the sampling point, obtaining the time domain variation of the original signal according to the difference between the amplitude of each sampling point in all voltage signals and the mean amplitude of all sampling points of the voltage signal, the number of voltage signals and the fluctuation characteristics of the envelope value of all sampling points; obtaining the frequency domain variation of the original signal according to the difference between the energy of each frequency in all voltage signals and the total energy, the number of voltage signals and the number of frequencies in the voltage signal; obtaining the variation factor of the original signal according to the ratio of the frequency domain variation to the time domain variation of the original signal.

[0058] Since only considering the sparsity of the signal is not enough to reflect the amount of information contained in the signal, even if the signal is sparse, it may still contain a lot of information if the signal amplitude varies in a large range. Therefore, the amplitude variation of the signal should also be considered. The sparser the signal, the less information it contains and the greater the degree of compression. Conversely, the more and more complex the signal amplitude variations are, the more information it contains, and the compression degree should be reduced. Otherwise, important information may be lost, making it impossible to reconstruct the original signal from the compressed signal.

[0059] The variability of the signal is analyzed from the time domain and frequency domain respectively. For one of the voltage signals, the envelope of the voltage signal is obtained through the Hilbert change, thereby obtaining the envelope value of each sampling point in each voltage signal. The envelope value represents the magnitude of the change of the voltage signal amplitude over time. The change of the envelope can reflect the amplitude change of the signal, and then the time domain variability is obtained by combining the amplitude change of the signal in the time domain.

[0060] The time domain variation is obtained based on the number of voltage signals, the fluctuation of the envelope value in the voltage signal, and the amplitude difference. The expression is:

[0061] , where represents the standard deviation of the envelope value of the i-th voltage signal, represents the amplitude of the jth sampling point in the ith voltage signal, represents the amplitude mean of all sampling points of the i-th voltage signal, Indicates the number of voltage signals, represents the number of sampling points in the voltage signal, Indicates the time domain variation of the original signal.

[0062] The greater the change in the amplitude of each sampling point in the time domain of the voltage signal relative to the average value, the greater the change in the voltage signal. Combined with the standard deviation of the voltage signal envelope value, it further reflects the degree of change of the voltage signal in the time domain. The larger the standard deviation of the voltage signal envelope value, the greater the degree of change of the voltage signal. Finally, the average value of all collected voltage signals is taken as the time domain change degree. The greater the time domain change degree, the more the original signal collected changes in the time domain, that is, the more information it contains, so the dimension of the measurement matrix should be larger to avoid information loss during compression. On the contrary, the sparser the original signal collected, the smaller the dimension of the measurement matrix should be.

[0063] The fluctuation of the amplitude of the voltage signal in the time domain can reflect the amount of information contained in the voltage signal, but it cannot reflect the distribution of each frequency in the original signal. The distribution of the frequency of the voltage signal in the frequency domain can also reflect the amount of information contained. The frequency domain distribution degree is calculated based on the distribution of each frequency in the voltage signal in the frequency domain.

[0064] The frequency domain variation of the original signal is obtained according to the ratio of the frequency energy to the total energy of the voltage signal, the number of frequencies and the number of voltage signals.

[0065] The frequency domain variation is proportional to the energy of the frequency and inversely proportional to the total energy of the voltage signal, the number of voltage signals and the number of frequencies.

[0066] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by actual application and this application does not impose any special restrictions.

[0067] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by actual application and this application does not impose any special restrictions.

[0068] Preferably, in this embodiment, the expression of frequency domain variation is:

[0069] , represents the energy of the cth frequency in the ith voltage signal, represents the total energy of the i-th voltage signal, Indicates the number of voltage signals, represents the number of frequencies in the voltage signal frequency, Indicates the frequency domain variation of the original signal.

[0070] If the energy of each frequency in the frequency domain of a voltage signal is evenly distributed, then the energy of each frequency should account for , the ratio of the energy of a frequency to the total energy is The larger the phase difference, the more uneven the energy distribution of the voltage signal in the frequency domain is, the larger the energy difference between the frequencies is, and the frequencies with larger energy are important frequencies. Finally, the average value of all collected voltage signals is taken as the frequency domain variation degree.

[0071] The greater the frequency domain variation, the more uneven the energy distribution of each frequency of the original signal in the frequency domain, and the greater the energy difference between each frequency, that is, there are important frequencies with larger energy and unimportant frequencies with smaller energy. At this time, the sparser the original signal is, the smaller the dimension of the measurement matrix should be used. On the contrary, the more uniform the energy distribution of each frequency in the frequency domain of the original signal is, the less sparse the original signal is, and the larger the dimension of the measurement matrix should be.

[0072] Through the above analysis, the information contained in the signal is obtained by analyzing the fluctuation of amplitude in the time domain and the energy distribution of frequency in the frequency domain from the time domain and frequency domain respectively. The variation factor is calculated by combining the variation degree in the time domain and the variation degree in the frequency domain. The expression of the variation factor is:

[0073] , Represents the time domain variation of the original signal, represents the frequency domain variation of the original signal, represents the linear normalization function, Represents the change factor of the original signal.

[0074] The larger the variation factor, the greater the variation of the original signal in the time domain and frequency domain, that is, the more information the original signal contains. A larger measurement matrix dimension should be used to avoid the loss of key information during compression, which makes it impossible to reconstruct the original signal from the compressed signal. On the contrary, the measurement matrix should choose a smaller dimension to make the compression effect more obvious.

[0075] At this point, the change factor of the original signal is obtained.

[0076] Step S004, obtaining a measurement matrix according to the variation factor of the original signal and the sparse factor of the original signal in combination with the length of the original signal.

[0077] The process of compressed sensing can be expressed as , where x represents the original signal with a length of N, and y represents the compressed signal with a length of M after compression. Indicates dimension N The measurement matrix of M.

[0078] The measurement matrix is ​​the key to compressed sensing, and its dimension is N M, where N is the length of the original signal, and M is determined according to the sparsity factor and the variability factor. In this embodiment, a random Gaussian measurement matrix is ​​selected, and the Gaussian matrix is ​​a matrix in which all elements have a mean of 0 and a variance of The matrix of .

[0079] When the measurement matrix is ​​a Gaussian matrix, in order to satisfy the constraint equidistance condition, , that is, M increases with The growth of is logarithmic, where represents the complexity calculation, Represents the sparse factor of the original signal, and the calculation expression for M is obtained as follows:

[0080] , B represents the change factor of the original signal, represents the ceiling function, represents the logarithmic function with base 10, represents the length of the original signal, M represents the dimension of the measurement matrix, and K represents the sparsity factor of the original signal.

[0081] The size of the measurement matrix is ​​taken as N rows and M columns. After the size of the measurement matrix is ​​obtained, a matrix is ​​randomly obtained through a random Gaussian measurement matrix as the measurement matrix.

[0082] At this point, the measurement matrix is ​​obtained.

[0083] Step S005: compress and reconstruct the original signal according to the measurement matrix, and collect the electric quantity data.

[0084] After obtaining the measurement matrix, the original signal with a length of N is collected. In this embodiment, the sampling frequency is 100 Hz. The original signal is multiplied by the measurement matrix to obtain a measurement signal with a length of M, thereby completing the compression. The compressed data is then transmitted to the dispatch center, which reconstructs the compressed data into the original voltage signal. The original signal is as follows: Figure 2 As shown, the compressed signal is Figure 3 As shown, the reconstructed signal is Figure 4 As shown, the reconstruction algorithm selected in this embodiment is the OMP reconstruction algorithm. So far, a method for collecting power box power data has been completed.

[0085] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0086] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for collecting power data of a power box, characterized in that: The method comprises the following steps: Collecting the original signal within a preset period, and dividing the original signal into a number of voltage signals; Obtain the spectrum of the voltage signal, obtain the energy of each frequency according to the amplitude of each frequency in the voltage signal, set the energy threshold according to the total energy of the voltage signal, compare the energy with the energy threshold and divide it into important frequencies and unimportant frequencies, and obtain the sparse factor of the original signal according to the number of important frequencies in all voltage signals; Obtain the envelope of the voltage signal and the envelope value of the sampling point, obtain the time domain variation of the original signal according to the difference between the amplitude of each sampling point in all voltage signals and the average amplitude of all sampling points of the voltage signal, the number of voltage signals and the fluctuation characteristics of the envelope value of all sampling points; obtain the frequency domain variation of the original signal according to the difference between the energy of each frequency in all voltage signals and the total energy, the number of voltage signals and the number of frequencies in the voltage signal; obtain the variation factor of the original signal according to the ratio of the frequency domain variation to the time domain variation of the original signal; According to the variation factor of the original signal and the sparse factor of the original signal, a measurement matrix is ​​obtained in combination with the length of the original signal; The original signal is compressed and reconstructed according to the measurement matrix to collect the power data; The method for obtaining the sparse factor of the original signal according to the number of important frequencies in all voltage signals is: Counting the number of all important frequencies in the voltage signal is used as the sparsity of the voltage signal; The cumulative sum of the sparsity of all voltage signals in the original signal is taken as the sparsity factor of the original signal; The method for obtaining the time domain variation of the original signal according to the difference between the amplitude of each sampling point in all voltage signals and the amplitude mean of all sampling points of the voltage signals, the number of voltage signals and the fluctuation characteristics of the envelope values ​​of all sampling points is: , where represents the standard deviation of the envelope value of the i-th voltage signal, represents the amplitude of the jth sampling point in the ith voltage signal, represents the amplitude mean of all sampling points of the i-th voltage signal, Q represents the number of voltage signals, P represents the number of sampling points in the voltage signal, and S represents the time domain variation of the original signal; The method for obtaining the frequency domain variation of the original signal according to the difference between the energy of each frequency in all voltage signals and the total energy, the number of voltage signals, and the number of frequencies in the voltage signals is: , represents the energy of the cth frequency in the ith voltage signal, represents the total energy of the i-th voltage signal, Indicates the number of voltage signals, represents the number of frequencies in the voltage signal frequency, and Y represents the frequency domain variation of the original signal; The method for obtaining the change factor of the original signal according to the ratio of the frequency domain change degree and the time domain change degree of the original signal is: , S represents the time domain variation of the original signal, and Y represents the frequency domain variation of the original signal. represents the linear normalization function, and B represents the change factor of the original signal; The method of obtaining the measurement matrix according to the variation factor of the original signal and the sparse factor of the original signal in combination with the length of the original signal is: , B represents the change factor of the original signal, represents the ceiling function, represents the logarithmic function with base 10, N represents the length of the original signal, M represents the dimension of the measurement matrix, and K represents the sparsity factor of the original signal; The measurement matrix is ​​randomly generated with N rows and M columns as the size of the measurement matrix.

2. The method for collecting power data of a power box according to claim 1, characterized in that: The energy of each frequency is the square of the amplitude modulus corresponding to each frequency.

3. The method for collecting power data of a power box according to claim 1, characterized in that: The method of setting the energy threshold by the total energy of the voltage signal and comparing the energy with the energy threshold to divide the energy into important frequencies and unimportant frequencies is as follows: The energy of all frequencies in the voltage signal is added as the total energy of the voltage signal; using a preset portion of the total energy of the voltage signal as an energy threshold of the voltage signal; If the energy of the frequency is greater than the energy threshold, the frequency is an important frequency, and if the energy of the frequency is less than or equal to the energy threshold, the frequency is an unimportant frequency.

4. The method for collecting power data of a power box according to claim 1, characterized in that: The method of compressing and reconstructing the original signal according to the measurement matrix and collecting the electric quantity data is as follows: An original signal of length N is collected, and the original signal is multiplied by the measurement matrix to obtain a measurement signal of length M. The measurement signal is reconstructed through a reconstruction algorithm to obtain the original signal.

5. The method for collecting power data of a power box according to claim 1, characterized in that: The envelope of the voltage signal is obtained through Hilbert change, and the envelope value of each sampling point in the voltage signal is obtained.

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