Methods, apparatus, computer equipment, readable storage media and program products for acquiring superharmonic signals

Abstract

This application relates to a method, apparatus, computer device, computer-readable storage medium, and computer program product for acquiring superharmonic signals. The method includes: acquiring a sampling frequency, a number of sampling points, and a frequency ratio of the sampling frequency; acquiring multiple data blocks from the power grid within a preset time period based on the sampling frequency and the number of sampling points, wherein each data block includes a superharmonic data sequence; acquiring a compressed sensing reconstruction model; reconstructing the data in the data blocks based on the compressed sensing reconstruction model; acquiring a superharmonic compressed sensing model of the reconstructed data blocks based on the number of sampling points and the frequency ratio; acquiring a global objective function; and acquiring the superharmonic signal from the reconstructed data blocks based on the superharmonic compressed sensing model and the global objective function, wherein the global objective function is used to acquire the frequency and amplitude of the superharmonic signal. The method provided in this application can accurately measure superharmonic signals in the power grid.

Description

Technical Field

[0001] This application relates to the field of signal and data analysis technology, and in particular to a method, apparatus, computer device, computer-readable storage medium, and computer program product for acquiring superharmonic signals. Background Technology

[0002] With the rapid development of modern power grids, especially the widespread adoption of renewable energy, electric vehicles, and high-efficiency household appliances, the penetration rate of power electronic converters is gradually increasing, bringing numerous power quality problems to the power grid. Among these, the problem of increased voltage and current distortion in the frequency range of 2 kHz to 150 kHz, namely superharmonic distortion, is particularly prominent. Superharmonic signals are characterized by a wide frequency range, diverse emission sources, and complex propagation interactions, which can pose a threat to the stable operation of the power grid. For example, superharmonic signals are prone to inducing frequency resonance, leading to equipment failure, and even affecting the normal operation of communication equipment; in severe cases, they may even cause power supply voltage interruptions. Therefore, accurate measurement of superharmonic signals is necessary. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for accurately measuring superharmonic signals, in order to address the aforementioned technical problems.

[0004] In a first aspect, this application provides a method for acquiring superharmonic signals, the method comprising:

[0005] The sampling frequency, the number of sampling points, and the frequency ratio of the sampling frequency are obtained. Based on the sampling frequency and the number of sampling points, multiple data blocks are obtained from the power grid within a preset time period. The data blocks include a superharmonic data sequence.

[0006] Obtain a compressed sensing reconstruction model, and reconstruct the data in the data block based on the compressed sensing reconstruction model;

[0007] Based on the number of sampling points and the frequency ratio, a superharmonic compressed sensing model of the reconstructed data block is obtained.

[0008] A global objective function is obtained. Based on the superharmonic compressed sensing model and the global objective function, the superharmonic signal is obtained from the reconstructed data block. The global objective function is used to obtain the frequency and amplitude of the superharmonic signal.

[0009] In one embodiment, acquiring multiple data blocks from the power grid within a preset time period based on the sampling frequency and the number of sampling points includes:

[0010] Based on the sampling frequency and the number of sampling points, the initial frequency resolution of the sampling frequency is obtained;

[0011] The data block is obtained based on the sampling frequency, the number of sampling points, and the initial frequency resolution.

[0012] In one embodiment, obtaining the superharmonic compressed sensing model of the reconstructed data block based on the number of sampling points and the frequency ratio includes:

[0013] Based on the number of sampling points, the corresponding initial signal length of the data block is obtained;

[0014] Obtain the interpolation factor, and based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio, obtain the superharmonic compressed sensing model.

[0015] In one embodiment, obtaining the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio includes:

[0016] The target frequency resolution is obtained based on the interpolation factor and the initial frequency resolution, and the target signal length is obtained based on the interpolation factor and the initial signal length.

[0017] Based on the target frequency resolution and the frequency ratio, obtain the corresponding signal frequency of the data block after data reconstruction;

[0018] The superharmonic compressed sensing model is obtained based on the target frequency resolution, the target signal length, and the signal frequency.

[0019] In one embodiment, obtaining the global objective function includes:

[0020] Obtain the Gaussian noise matrix and compressed sensing input parameters, and obtain a preset similarity matrix based on a preset clustering model;

[0021] A preset low-rank matrix is ​​obtained based on the Gaussian noise matrix and the preset similarity matrix;

[0022] The global objective function is obtained based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

[0023] In one embodiment, obtaining the superharmonic signal from the reconstructed data block based on the superharmonic compressed sensing model and the global objective function includes:

[0024] Obtain a regularization factor, and perform regularization on the preset low-rank matrix based on the regularization factor;

[0025] The global objective function is optimized based on the regularized low-rank matrix;

[0026] The superharmonic signal is obtained based on the superharmonic compressed sensing model and the optimized global objective function.

[0027] Secondly, this application also provides a superharmonic signal acquisition device, the device comprising:

[0028] The first acquisition module is used to acquire the sampling frequency, the number of sampling points and the frequency ratio of the sampling frequency, and based on the sampling frequency and the number of sampling points, acquire multiple data blocks from the power grid within a preset time period, wherein the data blocks include a superharmonic data sequence.

[0029] The second acquisition module is used to acquire the compressed sensing reconstruction model and reconstruct the data in the data block based on the compressed sensing reconstruction model.

[0030] The third acquisition module is used to acquire the superharmonic compressed sensing model of the data block after data reconstruction based on the number of sampling points and the frequency ratio.

[0031] The fourth acquisition module is used to acquire a global objective function. Based on the superharmonic compressed sensing model and the global objective function, it acquires the superharmonic signal from the reconstructed data block. The global objective function is used to acquire the frequency and amplitude of the superharmonic signal.

[0032] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the methods in any of the above embodiments.

[0033] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0034] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0035] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for acquiring superharmonic signals obtain sampling frequency, number of sampling points, and frequency ratio of the sampling frequency. Based on the sampling frequency and number of sampling points, multiple data blocks are acquired from the power grid within a preset time period. Each data block includes a superharmonic data sequence. A compressed sensing reconstruction model is acquired, and the data in the data blocks is reconstructed based on the compressed sensing reconstruction model. A superharmonic compressed sensing model of the reconstructed data blocks is acquired based on the number of sampling points and the frequency ratio. A global objective function is acquired, and the superharmonic signal is acquired from the reconstructed data blocks based on the superharmonic compressed sensing model and the global objective function. The global objective function is used to acquire the frequency and amplitude of the superharmonic signal. The method provided in this application can accurately measure superharmonic signals in the power grid. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a method for acquiring superharmonic signals in one embodiment;

[0038] Figure 2 This is a flowchart illustrating a data block acquisition method in one embodiment;

[0039] Figure 3 This is a schematic diagram of the measurement results in another embodiment;

[0040] Figure 4 This is a flowchart illustrating a method for acquiring superharmonic signals in another embodiment;

[0041] Figure 5 This is a structural block diagram of a superharmonic signal acquisition device in one embodiment;

[0042] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] In one embodiment, such as Figure 1As shown, a method for acquiring superharmonic signals is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0045] S102. Obtain the sampling frequency, the number of sampling points, and the frequency ratio of the sampling frequency. Based on the sampling frequency and the number of sampling points, obtain multiple data blocks from the power grid within a preset time period. The data blocks include the data sequence of superharmonic waves.

[0046] Frequency ratio refers to the proportional relationship between the sampling frequency and the highest frequency component of the signal during signal acquisition. A data block refers to a set of data organized according to a certain format or structure over a period of time. Data blocks are usually divided based on time and represent the sampling results for a specific time period. Each data block contains data from all sampling points acquired within the sampling window. Superharmonics refer to harmonics in power systems whose frequencies are higher than the fundamental frequency, generated by nonlinear loads or the switching action of power electronic equipment.

[0047] Optionally, the sampling period is first calculated based on the sampling frequency, and then data blocks are periodically collected at the sampling points based on the sampling period. For example, the preset time period can be 200ms, the corresponding time period for each data block is 0.5ms, and the number of data blocks is 32.

[0048] S104. Obtain the compressed sensing reconstruction model, and reconstruct the data in the data block based on the compressed sensing reconstruction model.

[0049] Among them, the compressed sensing reconstruction model refers to a mathematical model that uses compressed sensing theory to reconstruct signals or images. Compressed sensing is a signal processing technique that allows sparse or compressible signals to be sampled at a sampling rate much lower than that required by the Nyquist sampling theorem, and the original signal can be accurately recovered from these samples through optimization algorithms.

[0050] Optionally, the signal is reconstructed using a standard compressed sensing reconstruction algorithm to obtain the initial estimated phasor d:

[0051]

[0052] In the formula, s represents the sampled signal, and D represents the random measurement matrix.

[0053] S106. Based on the number of sampling points and the frequency ratio, obtain the superharmonic compressed sensing model of the data block after data reconstruction.

[0054] Among them, the superharmonic compressed sensing model is a specific type of compressed sensing model designed for superharmonic signal processing.

[0055] S108. Obtain the global objective function. Based on the superharmonic compressed sensing model and the global objective function, obtain the superharmonic signal from the data block after data reconstruction. The global objective function is used to obtain the frequency and amplitude of the superharmonic signal.

[0056] Optionally, solve for the superharmonic frequency estimate. and amplitude estimate Solving the simplified global objective function yields the final reconstructed vector. Estimated frequencies of superharmonic components and amplitude estimate It can be obtained by the following formula:

[0057]

[0058] in, For vectors The estimate of the 0th derivative, For vectors The first derivative estimate.

[0059] Once the frequency and amplitude of the superharmonic signal are obtained, the superharmonic signal can be identified.

[0060] The above-described method for acquiring superharmonic signals involves obtaining the sampling frequency, the number of sampling points, and the frequency ratio of the sampling frequency. Based on the sampling frequency and the number of sampling points, multiple data blocks are acquired from the power grid within a preset time period. Each data block includes a superharmonic data sequence. A compressed sensing reconstruction model is acquired, and the data in the data blocks is reconstructed based on this model. A superharmonic compressed sensing model for the reconstructed data blocks is obtained based on the number of sampling points and the frequency ratio. A global objective function is obtained, and the superharmonic signal is acquired from the reconstructed data blocks based on the superharmonic compressed sensing model and the global objective function. The global objective function is used to obtain the frequency and amplitude of the superharmonic signal. The method provided in this application can accurately measure superharmonic signals in the power grid.

[0061] In some embodiments, such as Figure 2 As shown, based on the sampling frequency and the number of sampling points, multiple data blocks are acquired from the power grid within a preset time period, including:

[0062] S202. Based on the sampling frequency and the number of sampling points, obtain the initial frequency resolution of the sampling frequency.

[0063] S204. Obtain data blocks based on sampling frequency, number of sampling points, and initial frequency resolution.

[0064] Frequency resolution refers to the minimum frequency difference that a spectrum analyzer or signal processing system can distinguish between two adjacent frequency components in spectrum analysis.

[0065] Optionally, the signal model of the corresponding sampled signal s(t) of the data block is shown in the following equation:

[0066]

[0067] In the formula, , , Let V represent the amplitude (V), frequency (Hz), and initial phase (°) of the h-th harmonic component, respectively, and Y represent the highest harmonic order. The sampling period is Sampling frequency, Here, N is the frequency resolution, N is the total length of the sample sequence, and n is the number of sampling points. , where n is an integer.

[0068] In this embodiment, data blocks are acquired based on the sampling frequency, the number of sampling points, and the initial frequency resolution, making the acquired data blocks more accurate, thereby making the subsequently acquired superharmonic signals more accurate.

[0069] In some embodiments, obtaining a superharmonic compressed sensing model of a data block after data reconstruction based on the number of sampling points and frequency ratio includes: obtaining the initial signal length of the data block based on the number of sampling points; obtaining an interpolation factor; and obtaining a superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio.

[0070] The signal length refers to the time range occupied by the signal on the time axis, that is, the time span from the beginning to the end of the signal; the interpolation factor is a parameter introduced in the reconstruction process, which is used to improve the frequency resolution of the reconstructed signal and improve the signal quality.

[0071] In this embodiment, a superharmonic compressed sensing model is obtained based on the interpolation factor, initial frequency resolution, initial signal length, and frequency ratio, making the obtained superharmonic compressed sensing model more accurate, thereby making the subsequently obtained superharmonic signals more accurate.

[0072] In some embodiments, obtaining a superharmonic compressed sensing model based on interpolation factors, initial frequency resolution, initial signal length, and frequency ratio includes: obtaining a target frequency resolution based on interpolation factors and initial frequency resolution, and obtaining a target signal length based on interpolation factors and initial signal length; obtaining the corresponding signal frequency of the data block after data reconstruction based on the target frequency resolution and frequency ratio; and obtaining a superharmonic compressed sensing model based on the target frequency resolution, target signal length, and signal frequency.

[0073] Signal frequency refers to the number of times a signal repeats itself per unit of time.

[0074] Optionally, by introducing an interpolation factor F, the frequency resolution of the reconstructed signal is increased. Signal length Signal frequency ,in, This represents the frequency ratio. The 32 data blocks are reconstructed into a matrix S using vector rearrangement. Considering the impact of noise, the superharmonic compressed sensing model is constructed as follows:

[0075]

[0076] Where S(c) represents the c-th data block in the sampling sequence, d(c) represents the c-th high-resolution signal to be reconstructed, e(c) represents the noise vector, and D represents a dimensionless vector. The random measurement matrix.

[0077] In this embodiment, a superharmonic compressed sensing model is obtained based on the target frequency resolution, target signal length, and signal frequency, making the obtained superharmonic compressed sensing model more accurate, thereby making the subsequently obtained superharmonic signals more accurate.

[0078] In some embodiments, obtaining the global objective function includes: obtaining a Gaussian noise matrix and compressed sensing input parameters, and obtaining a preset similarity matrix based on a preset clustering model; obtaining a preset low-rank matrix based on the Gaussian noise matrix and the preset similarity matrix; and obtaining the global objective function based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

[0079] Among them, the Gaussian noise matrix refers to Gaussian noise represented in matrix form, which is usually used to simulate or introduce randomness into data; compressed sensing input parameters refer to several key parameters that need to be determined when applying compressed sensing algorithms. For example, compressed sensing input parameters may include signal sampling rate, sensing matrix and signal processing parameters; low-rank matrix refers to a matrix with a small rank.

[0080] Optionally, the k-nearest neighbor clustering algorithm is used to construct the similarity matrix. Following the principle of minimizing Euclidean distance, let w be the search radius, and find M points within the search range that are parallel to each other. Nearest points are identified, and similar points are grouped into a similarity matrix. , The Euclidean distance principle states that the distance between any x and y is minimized by finding the minimum distance between x and y. Minimum, of which express Norm, at this time, for Norm and The dataset with the smallest distance.

[0081] Considering the interference of noise in actual testing, let... ,in and Let represent the low-rank matrix and the Gaussian noise matrix to be determined, respectively.

[0082] In this embodiment, a global objective function is obtained based on a preset similarity matrix, a preset low-rank matrix, and compressed sensing input parameters, making the obtained global objective function more accurate, thereby making the subsequently obtained superharmonic signals more accurate.

[0083] In some embodiments, obtaining superharmonic signals from reconstructed data blocks based on a superharmonic compressed sensing model and a global objective function includes: obtaining a regularization factor; regularizing a preset low-rank matrix based on the regularization factor; optimizing the global objective function based on the regularized low-rank matrix; and obtaining the superharmonic signals based on the superharmonic compressed sensing model and the optimized global objective function.

[0084] The regularization factor is used to control the degree of influence of the regularization term on the objective function or cost function.

[0085] Alternatively, to avoid directly calculating The NP-hard problem introduced when minimizing the rank of the solution requires constructing an auxiliary matrix. As shown in the following formula:

[0086]

[0087] in, It is a diagonal matrix with diagonal elements as follows: singular values, Let represent a small constant value, and I represent the identity matrix. Then, the Lagrange form of the constrained minimization problem and the global objective function for compressed sensing recovery are as follows:

[0088]

[0089]

[0090] in, Represents a given regularization factor. This represents a matrix composed of similar block matrices. This represents the input parameters for compressed sensing.

[0091] In this embodiment, the superharmonic signal is obtained based on the superharmonic compressed sensing model and the optimized global objective function, making the obtained superharmonic signal more accurate.

[0092] In one embodiment, another method for acquiring superharmonic signals is provided, the method comprising the following:

[0093] To highlight the detection performance of this invention, this embodiment selects the Multi-Measurement Vector Compressed Sensing-Orthogonal Matching Pursuit (MCS-OMP), the Compressed Sensing-Multi-Frequency Taylor Fourier Transform (CS-TFM) algorithm, and the equal-interval sampling method proposed in IEC 61000-4-30 as comparison algorithms (IEC). Frequency relative error (FE) and amplitude relative error (ME) are used to compare the estimation effects of different methods, expressed as a percentage (%). and Initialize as a matrix of all zeros, parameters The search radius is w=0.06 and the number of similar points is M=300.

[0094] In this implementation, the test signal is selected as shown in the following formula:

[0095]

[0096] baseband Take 50.1Hz. , These are the fundamental frequency phase angle and the superharmonic phase angle, respectively, and their ranges are as follows: The high-frequency harmonic orders k are taken as 195, 197, 199, 201, 203, and 205, respectively. Considering the influence of interference and uncertainty in actual measurements, a white noise signal nosie with a signal-to-noise ratio of 20 dB is superimposed on the test signal.

[0097] Measurement results as follows Figure 3 As shown. Figure 3 (a) represents the frequency estimation error FE between the present invention and the comparison algorithm. Figure 3 (b) The amplitude estimation error ME of the present invention and the comparison algorithm. As can be seen from the figure, when using the method of the present invention, the values ​​of frequency error FE and amplitude estimation error ME are both within 1%, which is smaller than the estimation error of the comparison algorithm.

[0098] See Figure 4 The specific implementation method includes the following steps:

[0099] S1. Obtain the sampled signal s(t) and the initial phasor estimate d: Based on the equal interval sampling technique proposed in the IEC 61000-4-30 standard, the sampling signal s(t) containing 32 0.5 ms data blocks is obtained through the sampling module of the power quality intelligent sensing and collaborative control platform. The signal is reconstructed using the standard compressed sensing reconstruction algorithm to obtain the initial estimated phasor d.

[0100] S2. Interpolation and reconstruction of the superharmonic compressed sensing model: An interpolation factor F is introduced, and 32 data blocks are reconstructed into a matrix S through vector recombination. Considering the influence of noise, a superharmonic compressed sensing model is constructed.

[0101] S3. Construct a similarity matrix using the k-nearest neighbor clustering algorithm. Following the principle of minimizing Euclidean distance, let w be the search radius, and find M points within the search range that are parallel to each other. Nearest points are identified, and similar points are grouped into a similarity matrix. .set up ,in and Let represent the low-rank matrix and the Gaussian noise matrix to be determined, respectively.

[0102] S4. Constructing the auxiliary matrix ,Will Low-rank regularization yields a simplified constraint minimization problem and a global objective function for compressed sensing recovery.

[0103] S5. Reconstructed vector obtained from solving the global objective function. Solve for the superharmonic frequency estimate. and amplitude estimate .

[0104] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0105] Based on the same inventive concept, this application also provides a superharmonic signal acquisition device for implementing the superharmonic signal acquisition method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the superharmonic signal acquisition device provided below can be found in the limitations of the superharmonic signal acquisition method described above, and will not be repeated here.

[0106] In one exemplary embodiment, such as Figure 5 As shown, a superharmonic signal acquisition device 500 is provided, including: a first acquisition module 501, a second acquisition module 502, a third acquisition module 503, and a fourth acquisition module 504, wherein:

[0107] The first acquisition module 501 is used to acquire the sampling frequency, the number of sampling points, and the frequency ratio of the sampling frequency. Based on the sampling frequency and the number of sampling points, it acquires multiple data blocks from the power grid within a preset time period. The data blocks include a superharmonic data sequence.

[0108] The second acquisition module 502 is used to acquire a compressed sensing reconstruction model and reconstruct the data in the data block based on the compressed sensing reconstruction model.

[0109] The third acquisition module 503 is used to acquire the superharmonic compressed sensing model of the data block after data reconstruction based on the number of sampling points and the frequency ratio.

[0110] The fourth acquisition module 504 is used to acquire a global objective function, and to acquire a superharmonic signal from the reconstructed data block based on the superharmonic compressed sensing model and the global objective function. The global objective function is used to acquire the frequency and amplitude of the superharmonic signal.

[0111] In some embodiments, the first acquisition module 501 is further configured to acquire the initial frequency resolution of the sampling frequency based on the sampling frequency and the number of sampling points; and acquire the data block based on the sampling frequency, the number of sampling points, and the initial frequency resolution.

[0112] In some embodiments, the third acquisition module 503 includes:

[0113] The first acquisition unit is used to acquire the corresponding initial signal length of the data block based on the number of sampling points.

[0114] The second acquisition unit is used to acquire the interpolation factor and, based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio, acquire the superharmonic compressed sensing model.

[0115] In some embodiments, the second acquisition unit is further configured to acquire a target frequency resolution based on the interpolation factor and the initial frequency resolution, and acquire a target signal length based on the interpolation factor and the initial signal length; acquire the signal frequency corresponding to the data block after data reconstruction based on the target frequency resolution and the frequency ratio; and acquire the superharmonic compressed sensing model based on the target frequency resolution, the target signal length, and the signal frequency.

[0116] In some embodiments, the fourth acquisition module 504 is further configured to acquire a Gaussian noise matrix and compressed sensing input parameters, and acquire a preset similarity matrix based on a preset clustering model; acquire a preset low-rank matrix based on the Gaussian noise matrix and the preset similarity matrix; and acquire the global objective function based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

[0117] In some embodiments, the fourth acquisition module 504 is further configured to acquire a regularization factor, regularize the preset low-rank matrix based on the regularization factor, optimize the global objective function based on the regularized low-rank matrix, and acquire the superharmonic signal based on the superharmonic compressed sensing model and the optimized global objective function.

[0118] Each module in the aforementioned superharmonic signal acquisition device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0119] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for acquiring superharmonic signals.

[0120] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0121] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: obtaining a sampling frequency, a number of sampling points, and a frequency ratio of the sampling frequency; based on the sampling frequency and the number of sampling points, obtaining multiple data blocks from a power grid within a preset time period, the data blocks including a superharmonic data sequence; obtaining a compressed sensing reconstruction model; reconstructing the data in the data blocks based on the compressed sensing reconstruction model; obtaining a superharmonic compressed sensing model of the reconstructed data blocks based on the number of sampling points and the frequency ratio; obtaining a global objective function; and obtaining a superharmonic signal from the reconstructed data blocks based on the superharmonic compressed sensing model and the global objective function, the global objective function being used to obtain the frequency and amplitude of the superharmonic signal.

[0122] In one embodiment, the processor, when executing a computer program, acquires multiple data blocks from the power grid within a preset time period based on the sampling frequency and the number of sampling points, including: acquiring an initial frequency resolution of the sampling frequency based on the sampling frequency and the number of sampling points; and acquiring the data blocks based on the sampling frequency, the number of sampling points, and the initial frequency resolution.

[0123] In one embodiment, the superharmonic compressed sensing model of a reconstructed data block, implemented by the processor executing a computer program based on the number of sampling points and the frequency ratio, includes: obtaining the initial signal length of the data block based on the number of sampling points; obtaining an interpolation factor; and obtaining the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio.

[0124] In one embodiment, the superharmonic compressed sensing model obtained by the processor executing a computer program based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio includes: obtaining a target frequency resolution based on the interpolation factor and the initial frequency resolution, and obtaining a target signal length based on the interpolation factor and the initial signal length; obtaining the signal frequency corresponding to the data block after data reconstruction based on the target frequency resolution and the frequency ratio; and obtaining the superharmonic compressed sensing model based on the target frequency resolution, the target signal length, and the signal frequency.

[0125] In one embodiment, the process of obtaining a global objective function implemented by the processor when executing a computer program includes: obtaining a Gaussian noise matrix and compressed sensing input parameters, and obtaining a preset similarity matrix based on a preset clustering model; obtaining a preset low-rank matrix based on the Gaussian noise matrix and the preset similarity matrix; and obtaining the global objective function based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

[0126] In one embodiment, the superharmonic signal obtained from the reconstructed data block based on the superharmonic compressed sensing model and the global objective function, implemented by the processor executing a computer program, includes: obtaining a regularization factor; regularizing the preset low-rank matrix based on the regularization factor; optimizing the global objective function based on the regularized low-rank matrix; and obtaining the superharmonic signal based on the superharmonic compressed sensing model and the optimized global objective function.

[0127] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon. When executed by a processor, the computer program performs the following steps: obtaining a sampling frequency, a number of sampling points, and a frequency ratio of the sampling frequency; acquiring multiple data blocks from a power grid within a preset time period based on the sampling frequency and the number of sampling points, wherein the data blocks include a superharmonic data sequence; acquiring a compressed sensing reconstruction model; reconstructing the data in the data blocks based on the compressed sensing reconstruction model; acquiring a superharmonic compressed sensing model of the reconstructed data blocks based on the number of sampling points and the frequency ratio; and acquiring a global objective function; acquiring a superharmonic signal from the reconstructed data blocks based on the superharmonic compressed sensing model and the global objective function, wherein the global objective function is used to acquire the frequency and amplitude of the superharmonic signal.

[0128] In one embodiment, when a computer program is executed by a processor, it acquires multiple data blocks from a power grid within a preset time period based on the sampling frequency and the number of sampling points, including: acquiring an initial frequency resolution of the sampling frequency based on the sampling frequency and the number of sampling points; and acquiring the data blocks based on the sampling frequency, the number of sampling points, and the initial frequency resolution.

[0129] In one embodiment, when a computer program is executed by a processor, it acquires a superharmonic compressed sensing model of a reconstructed data block based on the number of sampling points and the frequency ratio, including: acquiring the initial signal length of the data block based on the number of sampling points; acquiring an interpolation factor; and acquiring the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio.

[0130] In one embodiment, the computer program, when executed by a processor, implements the acquisition of the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio, comprising: acquiring a target frequency resolution based on the interpolation factor and the initial frequency resolution, and acquiring a target signal length based on the interpolation factor and the initial signal length; acquiring the signal frequency corresponding to the data block after data reconstruction based on the target frequency resolution and the frequency ratio; and acquiring the superharmonic compressed sensing model based on the target frequency resolution, the target signal length, and the signal frequency.

[0131] In one embodiment, the acquisition of a global objective function implemented by a computer program when executed by a processor includes: acquiring a Gaussian noise matrix and compressed sensing input parameters, and acquiring a preset similarity matrix based on a preset clustering model; acquiring a preset low-rank matrix based on the Gaussian noise matrix and the preset similarity matrix; and acquiring the global objective function based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

[0132] In one embodiment, when a computer program is executed by a processor, it implements the acquisition of superharmonic signals from the reconstructed data block based on the superharmonic compressed sensing model and the global objective function, including: acquiring a regularization factor; regularizing the preset low-rank matrix based on the regularization factor; optimizing the global objective function based on the regularized low-rank matrix; and acquiring the superharmonic signal based on the superharmonic compressed sensing model and the optimized global objective function.

[0133] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: acquiring a sampling frequency, a number of sampling points, and a frequency ratio of the sampling frequency; acquiring multiple data blocks from a power grid within a preset time period based on the sampling frequency and the number of sampling points, the data blocks including a superharmonic data sequence; acquiring a compressed sensing reconstruction model; reconstructing the data in the data blocks based on the compressed sensing reconstruction model; acquiring a superharmonic compressed sensing model of the reconstructed data blocks based on the number of sampling points and the frequency ratio; and acquiring a global objective function; acquiring a superharmonic signal from the reconstructed data blocks based on the superharmonic compressed sensing model and the global objective function, the global objective function being used to acquire the frequency and amplitude of the superharmonic signal.

[0134] In one embodiment, when a computer program is executed by a processor, it acquires multiple data blocks from a power grid within a preset time period based on the sampling frequency and the number of sampling points, including: acquiring an initial frequency resolution of the sampling frequency based on the sampling frequency and the number of sampling points; and acquiring the data blocks based on the sampling frequency, the number of sampling points, and the initial frequency resolution.

[0135] In one embodiment, when a computer program is executed by a processor, it acquires a superharmonic compressed sensing model of a reconstructed data block based on the number of sampling points and the frequency ratio, including: acquiring the initial signal length of the data block based on the number of sampling points; acquiring an interpolation factor; and acquiring the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio.

[0136] In one embodiment, the computer program, when executed by a processor, implements the acquisition of the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio, comprising: acquiring a target frequency resolution based on the interpolation factor and the initial frequency resolution, and acquiring a target signal length based on the interpolation factor and the initial signal length; acquiring the signal frequency corresponding to the data block after data reconstruction based on the target frequency resolution and the frequency ratio; and acquiring the superharmonic compressed sensing model based on the target frequency resolution, the target signal length, and the signal frequency.

[0137] In one embodiment, the acquisition of a global objective function implemented by a computer program when executed by a processor includes: acquiring a Gaussian noise matrix and compressed sensing input parameters, and acquiring a preset similarity matrix based on a preset clustering model; acquiring a preset low-rank matrix based on the Gaussian noise matrix and the preset similarity matrix; and acquiring the global objective function based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

[0138] In one embodiment, when a computer program is executed by a processor, it implements the acquisition of superharmonic signals from the reconstructed data block based on the superharmonic compressed sensing model and the global objective function, including: acquiring a regularization factor; regularizing the preset low-rank matrix based on the regularization factor; optimizing the global objective function based on the regularized low-rank matrix; and acquiring the superharmonic signal based on the superharmonic compressed sensing model and the optimized global objective function.

[0139] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0140] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for acquiring superharmonic signals, characterized in that, The method includes: The sampling frequency, the number of sampling points, and the frequency ratio of the sampling frequency are obtained. Based on the sampling frequency and the number of sampling points, multiple data blocks are acquired from the power grid within a preset time period. The data blocks include a superharmonic data sequence. The frequency ratio refers to the proportional relationship between the sampling frequency and the highest frequency component of the signal during the signal acquisition process. Obtain a compressed sensing reconstruction model, and reconstruct the data in the data block based on the compressed sensing reconstruction model; Based on the number of sampling points and the frequency ratio, a superharmonic compressed sensing model of the reconstructed data block is obtained. A global objective function is obtained. Based on the superharmonic compressed sensing model and the global objective function, the superharmonic signal is obtained from the reconstructed data block. The global objective function is used to obtain the frequency and amplitude of the superharmonic signal.

2. The method according to claim 1, characterized in that, The method of acquiring multiple data blocks from the power grid within a preset time period based on the sampling frequency and the number of sampling points includes: Based on the sampling frequency and the number of sampling points, the initial frequency resolution of the sampling frequency is obtained; The data block is obtained based on the sampling frequency, the number of sampling points, and the initial frequency resolution.

3. The method according to claim 2, characterized in that, The process of obtaining the superharmonic compressed sensing model of the reconstructed data block based on the number of sampling points and the frequency ratio includes: Based on the number of sampling points, the corresponding initial signal length of the data block is obtained; Obtain the interpolation factor, and based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio, obtain the superharmonic compressed sensing model.

4. The method according to claim 3, characterized in that, The process of obtaining the superharmonic compressed sensing model based on the interpolation factor, the initial frequency resolution, the initial signal length, and the frequency ratio includes: The target frequency resolution is obtained based on the interpolation factor and the initial frequency resolution, and the target signal length is obtained based on the interpolation factor and the initial signal length. Based on the target frequency resolution and the frequency ratio, obtain the corresponding signal frequency of the data block after data reconstruction; The superharmonic compressed sensing model is obtained based on the target frequency resolution, the target signal length, and the signal frequency.

5. The method according to claim 1, characterized in that, The process of obtaining the global objective function includes: Obtain the Gaussian noise matrix and compressed sensing input parameters, and obtain a preset similarity matrix based on a preset clustering model; A preset low-rank matrix is ​​obtained based on the Gaussian noise matrix and the preset similarity matrix; The global objective function is obtained based on the preset similarity matrix, the preset low-rank matrix, and the compressed sensing input parameters.

6. The method according to claim 5, characterized in that, The step of obtaining the superharmonic signal from the reconstructed data block based on the superharmonic compressed sensing model and the global objective function includes: Obtain a regularization factor, and perform regularization on the preset low-rank matrix based on the regularization factor; The global objective function is optimized based on the regularized low-rank matrix; The superharmonic signal is obtained based on the superharmonic compressed sensing model and the optimized global objective function.

7. A superharmonic signal acquisition device, characterized in that, The device includes: The first acquisition module is used to acquire the sampling frequency, the number of sampling points, and the frequency ratio of the sampling frequency. Based on the sampling frequency and the number of sampling points, it acquires multiple data blocks from the power grid within a preset time period. The data blocks include a superharmonic data sequence. The frequency ratio refers to the proportional relationship between the sampling frequency and the highest frequency component of the signal during the signal acquisition process. The second acquisition module is used to acquire the compressed sensing reconstruction model and reconstruct the data in the data block based on the compressed sensing reconstruction model. The third acquisition module is used to acquire the superharmonic compressed sensing model of the data block after data reconstruction based on the number of sampling points and the frequency ratio. The fourth acquisition module is used to acquire a global objective function. Based on the superharmonic compressed sensing model and the global objective function, it acquires the superharmonic signal from the reconstructed data block. The global objective function is used to acquire the frequency and amplitude of the superharmonic signal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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