Harmonic detection method and device based on moving average filtering

Abstract

The invention provides a harmonic detection method and device based on moving average filtering. The actual voltage and the actual current of a node in a power grid can be converted to obtain first harmonic spectrum data. Wherein the first harmonic frequency spectrum data comprises harmonic voltage and harmonic current. And performing moving average filtering on the first harmonic spectrum data to obtain second harmonic spectrum data. And grouping the second harmonic spectrum data, and detecting the center frequency of the harmonic according to a plurality of grouping results. The moving average filtering is carried out on the first harmonic spectrum data, so that the problem of discontinuous spectrum groups in groups caused by low amplitude of partial frequencies is avoided, the reliability of a grouping result is improved, and the accuracy of harmonic detection is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system analysis, and in particular to a harmonic detection method and device based on moving average filtering. Background Art

[0002] High-frequency harmonics are primarily generated by power electronic components such as high-frequency switches. They can easily cause power loss and shorten the lifespan of power transmission and distribution equipment (such as transformers, cables, and capacitors), as well as abnormal operation or failure of electrical equipment (such as switching power supplies and inverters in household appliances). Therefore, it is necessary to detect the center frequency of each spectral cluster in the harmonics.

[0003] Harmonic detection methods proposed in related technologies typically group harmonic spectra using the switching frequency of the harmonic source as a known condition. However, this is not suitable for applications where the switching frequency is unknown. Related technologies can also group harmonic spectra based on a preset fixed threshold. However, because each grouping result may contain lower frequency amplitudes, the spectrum groups near integer multiples of the switching frequency are discontinuous, resulting in unreliable grouping results and, in turn, low detection accuracy of the harmonic center frequency. Summary of the Invention

[0004] In order to solve the problem of low detection accuracy in the prior art, the present application provides a harmonic detection method and device based on moving average filtering.

[0005] In a first aspect, the present application provides a harmonic detection method based on moving average filtering, which may include:

[0006] The actual voltage and actual current of the nodes in the power grid are transformed to obtain first harmonic spectrum data, wherein the first harmonic spectrum data includes harmonic voltage and harmonic current.

[0007] Perform moving average filtering on the first harmonic spectrum data to obtain the second harmonic spectrum data.

[0008] The second harmonic spectrum data is grouped, and the center frequency of the harmonic is detected based on a plurality of grouping results.

[0009] In some possible implementations, transforming the actual voltage and actual current of a node in a power grid to obtain first harmonic spectrum data includes:

[0010] The actual voltage and current of the node are obtained according to the preset sampling frequency.

[0011] Perform a discrete Fourier transform on the actual voltage and current of the node to obtain the first harmonic spectrum data, where the frequency resolution of the first harmonic spectrum data is the power frequency.

[0012] In some other possible implementations, performing moving average filtering on the first harmonic spectrum data to obtain the second harmonic spectrum data includes:

[0013] Get the harmonic amplitudes at different frequencies in the first harmonic spectrum data.

[0014] The average value of each harmonic amplitude of the preset frequency band width adjacent to each frequency is calculated to obtain the second harmonic spectrum data.

[0015] In some further possible implementations, the second harmonic spectrum data is grouped, including:

[0016] The cumulative distribution function of the second harmonic spectrum data is calculated according to a preset value interval of the second harmonic spectrum data.

[0017] According to the preset quantile probability value and in combination with the corresponding relationship between the cumulative distribution function and the preset quantile probability value, the quantile grouping amplitude of the second harmonic spectrum data is determined.

[0018] The second harmonic spectrum data is grouped according to the quantile grouping amplitude to obtain multiple grouping results.

[0019] In some further possible implementations, detecting the center frequency of the harmonics according to the multiple grouping results includes:

[0020] The bandwidth of each group result in the plurality of group results is calculated, and the average bandwidth of each group result is calculated according to the bandwidth of each group result.

[0021] If the average width of each grouping result is within the preset bandwidth, each grouping result is determined to meet the grouping requirements, and the center frequency of the bandwidth of each grouping result is used as the center frequency of the harmonic. Otherwise, the preset quantile probability value and quantile grouping amplitude are adjusted, the second harmonic spectrum data is regrouped, and the center frequency of the harmonic is re-detected based on the multiple grouping results.

[0022] In a second aspect, the present application provides a harmonic detection device based on moving average filtering, which may include:

[0023] The conversion module is used to convert the actual voltage and actual current of the nodes in the power grid to obtain first harmonic spectrum data. The first harmonic spectrum data includes harmonic voltage and harmonic current.

[0024] The filtering module is used to perform moving average filtering on the first harmonic spectrum data to obtain second harmonic spectrum data.

[0025] The detection module is used to group the second harmonic spectrum data and detect the center frequency of the harmonic according to multiple grouping results.

[0026] In some possible implementations, the transformation module is specifically configured to:

[0027] The actual voltage and current of the node are obtained according to the preset sampling frequency.

[0028] Perform a discrete Fourier transform on the actual voltage and current of the node to obtain the first harmonic spectrum data, where the frequency resolution of the first harmonic spectrum data is the power frequency.

[0029] In some other possible implementations, the filtering module is specifically configured to:

[0030] Get the harmonic amplitudes at different frequencies in the first harmonic spectrum data.

[0031] The average value of each harmonic amplitude of the preset frequency band width adjacent to each frequency is calculated to obtain the second harmonic spectrum data.

[0032] In some further possible implementations, the detection module is specifically configured to:

[0033] The cumulative distribution function of the second harmonic spectrum data is calculated according to a preset value interval of the second harmonic spectrum data.

[0034] According to the preset quantile probability value and in combination with the corresponding relationship between the cumulative distribution function and the preset quantile probability value, the quantile grouping amplitude of the second harmonic spectrum data is determined.

[0035] The second harmonic spectrum data is grouped according to the quantile grouping amplitude to obtain multiple grouping results.

[0036] In some further possible implementations, the detection module is specifically configured to:

[0037] The bandwidth of each group result in the plurality of group results is calculated, and the average bandwidth of each group result is calculated according to the bandwidth of each group result.

[0038] If the average width of each grouping result is within the preset bandwidth, each grouping result is determined to meet the grouping requirements, and the center frequency of the bandwidth of each grouping result is used as the center frequency of the harmonic. Otherwise, the preset quantile probability value and quantile grouping amplitude are adjusted, the second harmonic spectrum data is regrouped, and the center frequency of the harmonic is re-detected based on the multiple grouping results.

[0039] On the other hand, the present application also provides a computer device, including: one or more processors.

[0040] A processor is used to execute one or more programs.

[0041] When one or more programs are executed by one or more processors, the above-mentioned detection method is implemented.

[0042] In another aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-mentioned detection method.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] In the detection method provided by the present application, the actual voltage and actual current of the nodes in the power grid are transformed to obtain the first harmonic spectrum data. The first harmonic spectrum data includes harmonic voltage and harmonic current. The first harmonic spectrum data is subjected to moving average filtering to obtain the second harmonic spectrum data. The second harmonic spectrum data is grouped, and the center frequency of the harmonic is detected based on the results of multiple groupings. By performing moving average filtering on the first harmonic spectrum data, the present application avoids the problem of discontinuity of spectrum groups in the grouping due to the low amplitude of some frequencies, thereby improving the reliability of the grouping results and thereby improving the accuracy of harmonic detection.

[0045] The detection method provided in the present application can obtain the first harmonic spectrum data based on the actual voltage and actual current of the node when the switching frequency is unknown, and obtain the second harmonic spectrum data through filtering. It can adaptively group the second harmonic spectrum data and detect the harmonic center frequency based on the grouping results, thereby improving the detection accuracy.

[0046] The detection method provided in this application only involves the calculation of the cumulative distribution function of the second harmonic spectrum data and the average width of each grouping result. It has low computational complexity and high computational efficiency, thereby improving the efficiency of harmonic detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 This is a schematic flow chart of a harmonic detection method based on moving average filtering in an embodiment of the present application;

[0049] Figure 2 This is a schematic structural diagram of multiple grouping results when the preset quantile probability value is 92% in the embodiment of the present application;

[0050] Figure 3This is a schematic structural diagram of multiple grouping results when the preset quantile probability value is 95% in the embodiment of the present application;

[0051] Figure 4 This is a schematic structural diagram of a harmonic detection device based on moving average filtering in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solution in this application will be described below with reference to the accompanying drawings.

[0053] The terms "first," "second," and the like in the description, embodiments, claims, and drawings of this application are used solely for descriptive purposes and are not to be construed as indicating or implying relative importance or order. Furthermore, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, inclusion of a series of steps or units. A method, system, product, or apparatus is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0055] Example 1:

[0056] The embodiment of the present application provides a harmonic detection method based on moving average filtering. Figure 1 As shown, the detection method 100 includes the following steps:

[0057] Step S1: transforming the actual voltage and actual current of a node in the power grid to obtain first harmonic spectrum data, wherein the first harmonic spectrum data includes harmonic voltage and harmonic current.

[0058] Step S2: performing moving average filtering on the first harmonic spectrum data to obtain second harmonic spectrum data.

[0059] Step S3: grouping the second harmonic spectrum data, and detecting the center frequency of the harmonics according to the plurality of grouping results.

[0060] In some possible implementations, the step S1 of transforming the actual voltage and the actual current of the node in the power grid to obtain the first harmonic spectrum data includes:

[0061] The actual voltage and current of the node are obtained according to the preset sampling frequency.

[0062] Perform a discrete Fourier transform (DFT) on the actual voltage and current of the node to obtain first harmonic spectrum data, where the frequency resolution of the first harmonic spectrum data is the power frequency.

[0063] In some other possible implementations, performing moving average filtering on the first harmonic spectrum data to obtain second harmonic spectrum data in step S2 includes:

[0064] Get the harmonic amplitudes at different frequencies in the first harmonic spectrum data.

[0065] The average value of each harmonic amplitude of the preset frequency band width adjacent to each frequency is calculated to obtain the second harmonic spectrum data.

[0066] In some further possible implementations, grouping the second harmonic spectrum data in step S3 includes:

[0067] The cumulative distribution function of the second harmonic spectrum data is calculated according to a preset value interval of the second harmonic spectrum data.

[0068] According to the preset quantile probability value and in combination with the corresponding relationship between the cumulative distribution function and the preset quantile probability value, the quantile grouping amplitude of the second harmonic spectrum data is determined.

[0069] The second harmonic spectrum data is grouped according to the quantile grouping amplitude to obtain multiple grouping results.

[0070] In some further possible implementations, detecting the center frequencies of the harmonics according to the multiple grouping results in step S3 includes:

[0071] The bandwidth of each group result in the plurality of group results is calculated, and the average bandwidth of each group result is calculated according to the bandwidth of each group result.

[0072] If the average width of each grouping result is within the preset bandwidth, each grouping result is determined to meet the grouping requirements, and the center frequency of the bandwidth of each grouping result is used as the center frequency of the harmonic. Otherwise, the preset quantile probability value and quantile grouping amplitude are adjusted, the second harmonic spectrum data is regrouped, and the center frequency of the harmonic is re-detected based on the multiple grouping results.

[0073] In the embodiment of the present application, a time domain simulation of a photovoltaic inverter with dual closed-loop control is performed to obtain the actual current on the inverter side (i.e., the node) at a sampling frequency of 2 MHz. The actual current on the inverter side is processed by discrete Fourier transform (i.e., DFT) to obtain the first harmonic spectrum data with a frequency resolution of 50 Hz. Then, a moving average filter is performed on the first harmonic spectrum data to obtain the second harmonic spectrum data. The second harmonic spectrum data is then grouped, and the center frequency of the harmonic is detected based on the multiple grouping results.

[0074] When the preset quantile probability value is 92%, the multiple grouping results can be obtained as follows Figure 2 shown. Figure 2 In the figure, the yellow area is used to represent the grouping situation and bandwidth, the vertical axis represents the harmonic content in the harmonic spectrum of the grouping result, and the horizontal axis represents the frequency. When the preset quantile probability value is 95%, the multiple grouping results can be obtained as follows Figure 3 shown. Figure 3 In the figure, the green area represents the grouping and bandwidth. The vertical axis represents the harmonic content in the harmonic spectrum of the grouping result, and the horizontal axis represents the frequency. The grouping result reflects the distribution characteristics of the harmonic spectrum itself, that is, the presence of multiple local harmonic groups.

[0075] from Figure 2 and Figure 3 It can be seen that at each multiple frequency of 10kHz, there is an obvious local peak, and the adjacent harmonic spectra also show a relatively high amplitude, that is, multiple harmonic spectrum groups are formed (i.e., multiple grouping results). When selecting the grouping thresholds corresponding to different quantile probability values, the detection method provided in the embodiment of the present application can more accurately capture the frequency range of each harmonic spectrum group, and the center frequency is located in the center of the extracted harmonic spectrum group band. Figure 2 and Figure 3 It can be seen that as the quantile probability value increases, the number of harmonic spectrum groups detected is relatively small, and harmonic spectrum groups with smaller amplitudes are ignored. However, the center frequencies of each detected harmonic spectrum group are all located at the center of each harmonic spectrum group, and the center frequency values ​​do not vary much. It can be seen that the detection method provided by this application can improve the accuracy and precision of harmonic detection.

[0076] Example 2:

[0077] Based on the same inventive concept, the embodiment of the present application also provides a harmonic detection device based on moving average filtering. Figure 4 As shown, the detection device 200 includes:

[0078] The conversion module 201 is used to convert the actual voltage and actual current of the nodes in the power grid to obtain first harmonic spectrum data, wherein the first harmonic spectrum data includes harmonic voltage and harmonic current.

[0079] The filtering module 202 is configured to perform moving average filtering on the first harmonic spectrum data to obtain second harmonic spectrum data.

[0080] The detection module 203 is configured to group the second harmonic spectrum data and detect the center frequency of the harmonic according to a plurality of grouping results.

[0081] In some possible implementations, the transformation module 201 is specifically configured to:

[0082] The actual voltage and current of the node are obtained according to the preset sampling frequency.

[0083] Perform a discrete Fourier transform on the actual voltage and current of the node to obtain the first harmonic spectrum data, where the frequency resolution of the first harmonic spectrum data is the power frequency.

[0084] In some other possible implementations, the filtering module 202 is specifically configured to:

[0085] Get the harmonic amplitudes at different frequencies in the first harmonic spectrum data.

[0086] The average value of each harmonic amplitude of the preset frequency band width adjacent to each frequency is calculated to obtain the second harmonic spectrum data.

[0087] In some further possible implementations, the detection module 203 is specifically configured to:

[0088] The cumulative distribution function of the second harmonic spectrum data is calculated according to a preset value interval of the second harmonic spectrum data.

[0089] According to the preset quantile probability value and in combination with the corresponding relationship between the cumulative distribution function and the preset quantile probability value, the quantile grouping amplitude of the second harmonic spectrum data is determined.

[0090] The second harmonic spectrum data is grouped according to the quantile grouping amplitude to obtain multiple grouping results.

[0091] It can be seen that the detection module 203 can implement grouping of the second harmonic spectrum data through the above process.

[0092] In some further possible implementations, the detection module 203 is specifically configured to:

[0093] The bandwidth of each group result in the plurality of group results is calculated, and the average bandwidth of each group result is calculated according to the bandwidth of each group result.

[0094] If the average width of each grouping result is within the preset bandwidth, each grouping result is determined to meet the grouping requirements, and the center frequency of the bandwidth of each grouping result is used as the center frequency of the harmonic. Otherwise, the preset quantile probability value and quantile grouping amplitude are adjusted, the second harmonic spectrum data is regrouped, and the center frequency of the harmonic is re-detected based on the multiple grouping results.

[0095] It can be seen that the detection module 203 can detect the center frequency of the harmonics according to the multiple grouping results through the above process.

[0096] Example 3:

[0097] Based on the same inventive concept, an embodiment of the present application further provides a computer device, the computer device including a processor and a memory, the memory being used to store a computer program, the computer program including program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of the detection method provided in the above embodiment.

[0098] Example 4:

[0099] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium herein may include both a built-in storage medium in a computer device and, of course, an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. Furthermore, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the detection method provided in the above embodiment.

[0100] Those skilled in the art will appreciate that embodiments of the application may be provided as methods, systems, or computer program products. Thus, the application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] The above are merely embodiments of the application and are not intended to limit the application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the application are included in the scope of the claims of the pending application.

Claims

1. A harmonic detection method based on moving average filtering, characterized in that: include: Transforming the actual voltage and actual current of the nodes in the power grid to obtain first harmonic spectrum data; wherein the first harmonic spectrum data includes harmonic voltage and harmonic current; Performing a moving average filter on the first harmonic spectrum data to obtain second harmonic spectrum data; The second harmonic spectrum data is grouped, and the center frequency of the harmonic is detected according to a plurality of grouping results.

2. The detection method according to claim 1, wherein The step of transforming the actual voltage and the actual current of the nodes in the power grid to obtain the first harmonic spectrum data includes: Acquire the actual voltage and actual current of the node according to a preset sampling frequency; Performing a discrete Fourier transform on the actual voltage and actual current of the node to obtain the first harmonic spectrum data; wherein the frequency resolution of the first harmonic spectrum data is the power frequency.

3. The detection method according to claim 1, wherein The performing moving average filtering on the first harmonic spectrum data to obtain second harmonic spectrum data includes: Obtaining harmonic amplitudes at different frequencies in the first harmonic spectrum data; The average value of each harmonic amplitude of a preset frequency band width adjacent to each frequency is calculated to obtain the second harmonic spectrum data.

4. The detection method according to claim 1, wherein The grouping of the second harmonic spectrum data includes: Calculating a cumulative distribution function of the second harmonic spectrum data according to a preset value interval of the second harmonic spectrum data; Determining a quantile grouping amplitude of the second harmonic spectrum data according to a preset quantile probability value and in combination with a correspondence between the cumulative distribution function and the preset quantile probability value; The second harmonic spectrum data is grouped according to the quantile grouping amplitude to obtain multiple grouping results.

5. The detection method according to claim 1, wherein The detecting the center frequency of the harmonics according to the multiple grouping results includes: Calculating the bandwidth of each grouping result in the plurality of grouping results, and calculating the average bandwidth of each grouping result according to the bandwidth of each grouping result; If the average width of each grouping result is within the preset bandwidth range, it is determined that each grouping result meets the grouping requirements, and the center frequency of the bandwidth of each grouping result is used as the center frequency of the harmonic; otherwise, the preset quantile probability value and the quantile grouping amplitude are adjusted, the second harmonic spectrum data is regrouped, and the center frequency of the harmonic is redetected based on the multiple grouping results.

6. A harmonic detection device based on moving average filtering, characterized in that: include: A conversion module, configured to convert the actual voltage and actual current of a node in the power grid to obtain first harmonic spectrum data; wherein the first harmonic spectrum data includes harmonic voltage and harmonic current; a filtering module, configured to perform a moving average filter on the first harmonic spectrum data to obtain second harmonic spectrum data; The detection module is configured to group the second harmonic spectrum data and detect the center frequency of the harmonics according to a plurality of grouping results.

7. The detection device according to claim 6, characterized in that The transformation module is specifically used for: Acquire the actual voltage and actual current of the node according to a preset sampling frequency; Performing a discrete Fourier transform on the actual voltage and actual current of the node to obtain the first harmonic spectrum data; wherein the frequency resolution of the first harmonic spectrum data is the power frequency.

8. The detection device according to claim 6, characterized in that The filtering module is specifically used for: Obtaining harmonic amplitudes at different frequencies in the first harmonic spectrum data; The average value of each harmonic amplitude of a preset frequency band width adjacent to each frequency is calculated to obtain the second harmonic spectrum data.

9. The detection device according to claim 6, characterized in that: The detection module is specifically used for: Calculating a cumulative distribution function of the second harmonic spectrum data according to a preset value interval of the second harmonic spectrum data; Determining a quantile grouping amplitude of the second harmonic spectrum data according to a preset quantile probability value and in combination with a correspondence between the cumulative distribution function and the preset quantile probability value; The second harmonic spectrum data is grouped according to the quantile grouping amplitude to obtain multiple grouping results.

10. The detection device according to claim 6, characterized in that: The detection module is specifically used for: Calculating the bandwidth of each grouping result in the plurality of grouping results, and calculating the average bandwidth of each grouping result according to the bandwidth of each grouping result; If the average width of each grouping result is within a preset frequency bandwidth range, determining that each grouping result meets the grouping requirement, and using the center frequency of the frequency bandwidth of each grouping result as the center frequency of the harmonic; Otherwise, the preset quantile probability value and the quantile grouping amplitude are adjusted, the second harmonic spectrum data is regrouped, and the center frequency of the harmonic is redetected according to the multiple grouping results.

11. A computer device, characterized in that: include: one or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the detection method according to any one of claims 1 to 5 is implemented.

12. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the detection method according to any one of claims 1 to 5 is implemented.

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