Photovoltaic DC fault arc signal noise reduction method and device

Abstract

The invention provides a photovoltaic direct current fault arc signal noise reduction method and device, and relates to the technical field of photovoltaic direct current fault arc diagnosis. The method comprises the following steps: decomposing a photovoltaic DC fault arc signal based on an IMGO-ICEEMDAN decomposition method to obtain a photovoltaic DC fault arc intrinsic mode component; calculating a multivariate intrinsic mode index, and dividing the photovoltaic DC fault arc intrinsic mode component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic mode index; performing noise reduction on the noisy signal component through the improved wavelet threshold to obtain an improved wavelet threshold noise reduction signal; and reconstructing the low-frequency signal component and the improved wavelet threshold noise reduction signal to obtain a noise reduction signal. According to the noise reduction method, the noise reduction effect of the fault arc signal is effectively improved while the key characteristics of the photovoltaic direct current fault arc signal are reserved, and the fault arc recognition accuracy can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic DC fault arc diagnosis, and in particular to a photovoltaic DC fault arc signal noise reduction method and device. Background Art

[0002] In recent years, the country has vigorously supported the development of new energy, and my country's photovoltaic power generation installed capacity has grown rapidly. By the end of June 2024, the national photovoltaic power generation was 391.4 billion kWh, a year-on-year increase of 47%, and the national photovoltaic power generation utilization rate reached 97%. With the popularization of photovoltaic systems, fire accidents caused by photovoltaic DC fault arcs have increased year by year, accounting for 52% of photovoltaic fires. Therefore, the effective detection and identification of photovoltaic DC fault arcs is crucial to preventing photovoltaic system fires.

[0003] However, in real photovoltaic scenarios, the current output will be affected by the environment and produce oscillations. The increase in cable length and current will also increase the noise and weaken the fault arc signal characteristics, making it difficult to accurately identify the photovoltaic DC fault arc signal. Therefore, it is necessary to perform noise reduction on the photovoltaic DC fault arc signal. Existing signal noise reduction methods: EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition), VMD (Variational Mode Decomposition), etc. all have certain defects, such as mode aliasing, unstable number of IMF (Intrinsic Mode Functions) components, and the selection of empirical parameters mostly depends on experience. In addition, there is no scientific screening index for the decomposed IMF components to divide and define the noise components and signal components. The IMF components containing effective information may be eliminated or the noisy IMF components may be retained, and the noise reduction effect is not satisfactory. Summary of the invention

[0004] In order to solve the technical problem that the existing signal noise reduction method has an unsatisfactory noise reduction effect, the embodiment of the present invention provides a photovoltaic DC fault arc signal noise reduction method and device. The technical solution is as follows:

[0005] On the one hand, a photovoltaic DC fault arc signal noise reduction method is provided, which is implemented by a photovoltaic DC fault arc signal noise reduction device, and the method includes:

[0006] S1. Obtain photovoltaic DC fault arc signal.

[0007] S2. Based on the IMGO-ICEEMDAN decomposition method, the photovoltaic DC fault arc signal is decomposed to obtain the photovoltaic DC fault arc intrinsic mode components.

[0008] S3. Calculate the multivariate intrinsic modal index of the PV DC fault arc intrinsic modal component, and divide the PV DC fault arc intrinsic modal component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic modal index.

[0009] S4. For the noisy signal component smaller than the multivariate intrinsic mode index threshold, denoising is performed by using an improved wavelet threshold to obtain an improved wavelet threshold denoised signal.

[0010] S5. Reconstruct the low-frequency signal component and the improved wavelet threshold denoising signal to obtain the denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indicators.

[0011] Optionally, the IMGO-ICEEMDAN decomposition method in S2 is used to decompose the photovoltaic DC fault arc signal to obtain the photovoltaic DC fault arc intrinsic modal components, including:

[0012] S21. Determine a white noise amplitude weight and a noise adding number according to an improved moss growth algorithm IMGO, add white noise to the photovoltaic DC fault arc signal according to the white noise amplitude weight and the noise adding number, and obtain the added fault arc signal.

[0013] S22. Calculate and obtain the first-order residual component r1 according to the added fault arc signal.

[0014] S23. Calculate the first-order modal component c1 based on the first-order residual component r1.

[0015] S24, adding white noise to the first-order residual component r1 and taking the average value, and calculating the second-order modal component c2.

[0016] S25. According to the first-order residual component r1, the second-order residual component r2 is calculated; according to the second-order residual component r2, the third-order modal component c3 is calculated; and then the intrinsic modal component of the photovoltaic DC fault arc is obtained.

[0017] Optionally, determining the white noise amplitude weight and the number of noise additions according to the improved moss growth algorithm IMGO in S21 includes:

[0018] According to the moss growth algorithm, wind direction determination mechanism algorithm, dynamic weight optimization and particle swarm optimization algorithm, spore dispersal search algorithm and double propagation search algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight and the number of noise additions to determine the optimal white noise amplitude weight and the number of noise additions.

[0019] Optionally, dynamic weight optimization and particle swarm optimization algorithm are as shown in the following formula (1) (2):

[0020] v i =w·v i-1 +c1r1(pbest i -M i )+c2r2(gbest i -M i ) (1)

[0021]

[0022] In the formula, v i represents the speed of particle i, w represents the inertia weight, c1 represents the personal experience factor, r1 represents the random number, gbest i represents the best historical position of particle i, M i Indicates the position where particles are randomly generated, c2 indicates the global experience factor, r2 indicates the random number, gbest i represents the best historical position of all particles, w max Indicates the maximum value of the inertia weight, w min It represents the minimum value of the inertia weight, it represents the current number of iterations, and Maxits represents the maximum number of iterations.

[0023] Optionally, the multivariate intrinsic modal index of the intrinsic modal component of the photovoltaic DC fault arc is calculated in S3, including:

[0024] The correlation coefficient, kurtosis and variance contribution rate of each component in the eigenmode component of the photovoltaic DC fault arc are calculated respectively, and the correlation coefficient, kurtosis and variance contribution rate are normalized. The normalized data are weighted using the coefficient of variation weighting method, and the multivariate intrinsic mode index is obtained according to the weighted data.

[0025] Optionally, the improved wavelet threshold in S4 includes:

[0026] The db4 wavelet basis function is selected as the discrete wavelet transform wavelet basis function, and the low-frequency signal component is decomposed by three layers of wavelets to obtain the high-frequency wavelet coefficient W j,k , according to the high-frequency wavelet coefficient W j,k The threshold t is calculated.

[0027] According to the high-frequency wavelet coefficient W j,k And the threshold t, through the improved wavelet threshold function processing, get the low-frequency wavelet coefficient

[0028] Optionally, the improved wavelet threshold function is as shown in the following formula (3):

[0029]

[0030] In the formula, represents the low-frequency wavelet coefficient, sign(·) represents the sign function, t represents the threshold, and α, β, and γ represent the adjustment factors of the threshold function.

[0031] On the other hand, a photovoltaic DC fault arc signal noise reduction device is provided, which is applied to a photovoltaic DC fault arc signal noise reduction method, and the device includes:

[0032] The acquisition module is used to acquire photovoltaic DC fault arc signals.

[0033] The decomposition module is used to decompose the photovoltaic DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the photovoltaic DC fault arc intrinsic mode components.

[0034] The calculation module is used to calculate the multivariate intrinsic modal index of the photovoltaic DC fault arc intrinsic modal component, and divide the photovoltaic DC fault arc intrinsic modal component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic modal index.

[0035] The denoising module is used to denoise the noisy signal component less than the multivariate intrinsic mode index threshold through an improved wavelet threshold to obtain an improved wavelet threshold denoised signal.

[0036] The output module is used to reconstruct the low-frequency signal component and the improved wavelet threshold denoising signal to obtain the denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indicators.

[0037] On the other hand, a photovoltaic DC fault arc signal noise reduction device is provided, and the photovoltaic DC fault arc signal noise reduction device includes: a processor; a memory, and the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, any one of the above-mentioned photovoltaic DC fault arc signal noise reduction methods is implemented.

[0038] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned photovoltaic DC fault arc signal noise reduction methods.

[0039] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0040] In an embodiment of the present invention, a photovoltaic DC fault arc signal noise reduction method based on IMGO-ICEEMDAN and multivariate intrinsic modal indicators is disclosed. Since the current output of the photovoltaic DC power supply constantly oscillates with environmental changes, and the current signal noise increases with the increase of line length and current, it is difficult to identify the fault characteristics when the fault arc occurs. The traditional improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) is used to decompose the fault arc signal into k intrinsic mode components (IMFs) from high frequency to low frequency, but the two key parameters (white noise amplitude weight (Nstd) and noise addition times (ME)) in the decomposition process of this method are difficult to accurately determine based on experience. Therefore, the present invention uses an improved moss growth algorithm (IMGO) to achieve intelligent optimization of the above two key parameters. The IMGO algorithm improves the global search capability and overall optimization effect, and solves the problems of high computational complexity, slow convergence speed, and easy to fall into local optimal solutions of the MGO algorithm. In addition, IMGO-ICEEMDAN also solves the problems of modal aliasing, endpoint effect, and difficulty in parameter selection in existing methods such as EMD, EEMD, and VMD. On this basis, the IMF components obtained by decomposition are divided into high-frequency noise components and low-frequency signal components through multivariate intrinsic mode indicators, and the low-frequency signal noise components less than the threshold are subjected to denoising using the wavelet denoising method with an improved threshold, and finally the processed IMF components are reconstructed to obtain the denoised signal. Compared with the existing methods, the denoising method disclosed in the present invention effectively improves the denoising effect of the fault arc signal while retaining the key features of the photovoltaic DC fault arc signal, which helps to significantly improve the accuracy of fault arc identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a flow chart of a photovoltaic DC fault arc signal noise reduction method provided by an embodiment of the present invention;

[0043] Figure 2 This is an IMGO-ICEEMDAN decomposition effect diagram provided by an embodiment of the present invention;

[0044] Figure 3 is a curve diagram of multivariate intrinsic mode index D of different IMF components provided by an embodiment of the present invention;

[0045] Figure 4 is a graph showing changes in hyperparameters versus number of iterations provided by an embodiment of the present invention;

[0046] Figure 5 is a graph showing changes in fitness value versus number of iterations provided by an embodiment of the present invention;

[0047] Figure 6 The soft threshold function, hard threshold function and improved wavelet threshold function provided by the embodiments of the present invention;

[0048] Figure 7 This is a diagram showing the effect of noise reduction after the noise reduction method of the present invention provided in an embodiment of the present invention;

[0049] Figure 8 is the original signal provided by the embodiment of the present invention;

[0050] Fig. 9 This is a diagram showing the effect of noise reduction by the EMD noise reduction method provided by an embodiment of the present invention;

[0051] Fig.10 This is a diagram showing the effect of noise reduction using the EEMD noise reduction method provided by an embodiment of the present invention;

[0052] Fig.11 This is a diagram showing the effect of noise reduction by the VMD noise reduction method provided by an embodiment of the present invention;

[0053] Fig.12 It is a block diagram of a photovoltaic DC fault arc signal noise reduction device provided by an embodiment of the present invention;

[0054] Fig.13 It is a structural schematic diagram of a photovoltaic DC fault arc signal noise reduction device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides a photovoltaic DC fault arc signal noise reduction method, which can be implemented by a photovoltaic DC fault arc signal noise reduction device, and the photovoltaic DC fault arc signal noise reduction device can be a terminal or a server. Figure 1 The process flow chart of the photovoltaic DC fault arc signal noise reduction method shown in the figure may include the following steps:

[0061] S1. Obtain photovoltaic DC fault arc signal.

[0062] In a feasible implementation, a real photovoltaic scene DC fault arc experimental platform is built to obtain the real photovoltaic scene DC fault arc signal. The photovoltaic power supply consists of two strings of 32 photovoltaic power generation units, with a single string current output range of 3A-8A; a double string current output range of 8A-14A, and a voltage output range of 500V-510V; the load is a three-phase photovoltaic inverter model R3-50k, with a maximum input voltage of 1100V, a maximum input current of 50A, and an MPPT working voltage range of 180-1000V; the fault arc generator is controlled by a kh-01 stepper motor controller to control the stepper motor, thereby controlling the step distance when the fault arc occurs; the electrode material is a flat tungsten rod with a diameter of 6.4mm; the data acquisition equipment uses a Tektronix MSO4 4-channel oscilloscope with a maximum ADC sampling rate of 6.25GS / s, a maximum record length of 31.25M, and a maximum waveform capture rate of >500k wfms / s, which meets the experimental data acquisition requirements. In order to simulate the line environment, fault and normal signals of different time periods, different cable lengths, different current levels, different inverters, and different arc occurrence locations are collected.

[0063] S2. Based on the IMGO-ICEEMDAN decomposition method, the photovoltaic DC fault arc signal is decomposed to obtain the photovoltaic DC fault arc intrinsic mode components.

[0064] Optionally, the above step S2 may include the following steps S21-S25:

[0065] S21. Determine a white noise amplitude weight and a noise adding number according to an improved moss growth algorithm IMGO, add white noise to the photovoltaic DC fault arc signal according to the white noise amplitude weight and the noise adding number, and obtain the added fault arc signal.

[0066] Optionally, determining the white noise amplitude weight and the number of noise additions according to the improved moss growth algorithm IMGO in S21 includes:

[0067] According to the moss growth algorithm, wind direction determination mechanism algorithm, dynamic weight optimization and particle swarm optimization algorithm, spore dispersal search algorithm and double propagation search algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight and the number of noise additions to determine the optimal white noise amplitude weight and the number of noise additions.

[0068] In a feasible implementation, the ICEEMDAN decomposition method is as follows:

[0069] (1) Adding Gaussian white noise to the photovoltaic DC fault arc signal to be decomposed, we can obtain:

[0070] x (i) =x+e1E1(ω (i) )(i=1,2,…,N) (1)

[0071] where x (i) is the fault arc signal to be decomposed, x represents the original signal, e1 is the expected signal-to-noise ratio between the fault arc signal to be decomposed and the noise signal added during the first decomposition, and E1(.) is the first modal component after EMD decomposition; ω (i) is the first Gaussian white noise added, and N represents the number of data points of the original signal.

[0072] (2) Calculate the first residual component:

[0073]

[0074] Among them, M(.) is the local mean function, and N is the data length of the photovoltaic DC signal.

[0075] (3) Calculate the first modal component:

[0076] c1=x-r1 (3)

[0077] (4) Add a series of white noise to the residual component and take the average value to obtain the second-order modal component, as shown in the equation:

[0078]

[0079] (5) According to equations (2) and (3), calculate the k-order (k = 3, 4, 5, ..., N) residual component r k and the kth order modal component c k :

[0080]

[0081] c k =r k-1 -r k (6)

[0082] (6) Repeat (5) until all modal components are obtained to complete the signal decomposition.

[0083] Furthermore, the mathematical model used in the IMGO intelligent parameter optimization algorithm is as follows:

[0084] (1) Mechanism for determining wind direction:

[0085] IMGO has developed a creative mechanism for determining wind direction based on the following assumptions:

[0086] 1) The wind direction remains constant throughout the iteration process.

[0087] 2) Assuming that the moss individual represents the position in the solution space, the current best candidate position corresponds to the current moss individual in the optimal solution.

[0088] 3) The direction of wind always blows from areas with more mosses to individual mosses in the most favorable growing environment.

[0089] The most special individual in the population is called Mbest. The j-dimensional value of Mbest is used as the threshold and compared with the j-dimensional values ​​of all individuals:

[0090]

[0091] The function count(.) counts the number of moss individuals in a given set. After multiple calculations, the following set is obtained:

[0092]

[0093] In the formula, dn is the number of times to be divided, and the value of dn is set to [dim / 4] and not less than 1, p j Represents the jth random number, satisfying the range (1,2,…,dim).

[0094] Since the wind always blows from the area divX to the special individual Mbest, the wind direction calculation formula is as follows:

[0095]

[0096] Where D_wind represents the calculated wind direction and has the same dimension as the individual. The variable num represents the total number of individuals in dirX. The calculation of dirX can be obtained from equation (11). By calculating the average distance between the main individual and Mbest, the path of the individual approaching Mbest can be smoothed, thereby enhancing the optimization ability of IMGO.

[0097] dirX={M best -M i |M i ∈divX} (11)

[0098] (2) Dynamic weight optimization and particle swarm optimization:

[0099] The global optimal solution guidance and individual optimal solution guidance in the particle swarm optimization algorithm are used to improve the global search capability. By dynamically adjusting the inertia weight, a wide search is performed in the early stage of the algorithm, and a refined local search is performed in the later stage, thereby avoiding falling into the local optimal solution. The specific formula is as follows:

[0100] v i =w·v i-1 +c1r1(pbest i -M i )+c2r2(gbest i -M i ) (12)

[0101]

[0102] In the formula, v i represents the speed of particle i, w represents the inertia weight, c1 represents the personal experience factor, r1 represents a random number in the range of [0,1], pbest i represents the best historical position of particle i, M i Indicates the position where particles are randomly generated, c2 indicates the global experience factor, r2 indicates a random number in the range of [0,1], gbest i represents the best historical position of all particles, w max Indicates the maximum value of the inertia weight, w min It represents the minimum value of the inertia weight, it represents the current number of iterations, and Maxits represents the maximum number of iterations.

[0103] (3) Spore dispersal search:

[0104] Most spores disperse under steady wind conditions, while a small number disperse under turbulent conditions. Eventually, as the wind weakens, spores begin to settle closer to the moss. The location of the spores is determined by equation (13). The size of the two steps is significantly different. This allows individuals to make random choices to prevent a fixed step size from leading to slow convergence in the early stages and failure to converge in the later stages, thereby ensuring population diversity.

[0105]

[0106] In the formula, represents a new moss obtained by spreading spores from i moss individuals mi. r1 is a random number in the range (0,1). When r1>0.2, spores disperse under stable wind conditions and disperse under turbulent conditions. step1 is the spore propagation distance under stable wind conditions, and step2 is the spore propagation distance under turbulent wind conditions, as shown in the following formula:

[0107] step1=2·(r2-0.5)·E (15)

[0108]

[0109] r2 is a random vector in the range (0,1) with the same dimension as D_wind. E is the wind strength, which decreases as the iteration proceeds. r3 is a random vector in the range (0,1) with the same dimension as D_wind, see the following formula:

[0110]

[0111] Where FEs represents the current number of calculations, MaxFEs represents the maximum number of iterations, and β represents the ratio of the total number in divX to the total number in X.

[0112] (4) Double propagation search:

[0113] The double propagation search simulates both sexual and vegetative reproduction of mosses. Unlike the traditional Mas search, it increases the proportion of methods that only change a single dimension, thus enhancing the overall local exploration capability.

[0114]

[0115] In the formula, represents the i-th new individual, express The jth particle in M, j is a random number that does not exceed the maximum dimension of the individual. best Represents the current optimal individual. Where j represents M best The jth particle in D_wind jis the jth particle in D_wind. r4 is a random number in the range of (0,1). When r4>0.5, double reproduction search is simulated in the sexual reproduction stage, while different calculation methods are used to simulate in the vegetative reproduction stage.

[0116]

[0117] Where r5 is a random vector in the range (0,1) and M best They have the same dimension. r6 is a random number in the range of (0,1), and E is the wind force.

[0118] Using the above intelligent optimization algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight (Nstd) and the number of noise additions (ME) of ICEEMDAN to determine the optimal parameter combination.

[0119] S3. Calculate the multivariate intrinsic modal index of the PV DC fault arc intrinsic modal component, and divide the PV DC fault arc intrinsic modal component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic modal index.

[0120] In a feasible implementation, the existing intrinsic mode screening methods are mostly based on a single evaluation index and experience. The multivariate intrinsic mode screening index proposed in the present invention integrates three indicators that can reflect the difference between noise and signal, namely correlation coefficient ρ, kurtosis γ, and variance contribution rate λ, to distinguish high-frequency noise from low-frequency signals.

[0121] Furthermore, based on the IMF components obtained by IMGO-ICEEMDAN decomposition, the correlation coefficient ρ, kurtosis γ, and variance contribution rate λ of each IMF component are calculated. The correlation coefficient ρ reflects the linear correlation between the intrinsic mode component and the signal, the kurtosis γ reflects the sharpness of the signal and is sensitive to high-frequency noise, and the variance contribution rate λ can effectively identify the noise in the low-frequency signal. The weight of each indicator is determined by the coefficient of variation weighting method, and the multivariate intrinsic mode index is obtained to screen the high-frequency noise and low-frequency signal in the IMF component.

[0122] The specific calculation method is as follows:

[0123] 1. The correlation coefficient ρ, kurtosis γ, and variance contribution rate λ are calculated as follows:

[0124]

[0125] Among them, X and Y are the signal to be calculated and the original signal respectively; μ is the mean of signal X, σX and σY are the standard deviations of signal X and signal Y respectively. After normalizing the correlation coefficient ρ, kurtosis γ, and variance contribution rate λ, ρ′, γ′, and λ′ are obtained. The three normalized indicators are weighted using the coefficient of variation weighting method:

[0126]

[0127] The multivariate intrinsic mode index D can be expressed as:

[0128] D=W ρ ×ρ′+W γ ×γ′+W λ ×λ′ (25)

[0129] The correlation coefficient ρ, kurtosis γ, and variance contribution rate λ are positively correlated, negatively correlated, and positively correlated with the change trend of the signal, respectively. In order to unify the change trend, the kurtosis γ takes a negative value. The multivariate intrinsic mode index D will have a minimum point in the process of increasing the number of reconstruction layers, which determines the boundary between noise information and signal information.

[0130] First, the IMF components before the IMF component corresponding to the first minimum value of the multivariate intrinsic mode index D are judged as pure noise components and should be discarded; then, for the other retained IMF components, the threshold of the multivariate intrinsic mode index D is selected as 0.2. If the multivariate intrinsic mode index D≥0.2, it is considered that the IMF component contains valid information and should be retained; if the multivariate intrinsic mode index D<0.2, it is considered that the IMF component contains some noise and needs further processing.

[0131] S4. For the noisy signal component smaller than the multivariate intrinsic mode index threshold, denoising is performed by using an improved wavelet threshold to obtain an improved wavelet threshold denoised signal.

[0132] Optionally, the improved wavelet threshold in S4 includes:

[0133] The db4 wavelet basis function is selected as the discrete wavelet transform wavelet basis function, and the IMF component with multivariate intrinsic mode index D < 0.2 is decomposed by three layers of wavelet to obtain the high-frequency wavelet coefficient W j,k , W j,k Represents the kth wavelet coefficient in the detail coefficient group of the jth layer of wavelet decomposition.

[0134] Furthermore, the universal threshold VisuShrink threshold is used to calculate the threshold t:

[0135]

[0136] Where σ is the standard deviation of the noise, N is the signal length, media(abs(W j,k )) is the median of the amplitude of the high-frequency sub-band wavelet coefficients of the j-layer wavelet decomposition, and 0.6745 is the adjustment coefficient of the noise standard deviation.

[0137] Furthermore, the wavelet coefficient W obtained based on the j-layer wavelet decomposition is j,k , processed by the improved wavelet threshold function, the low-frequency wavelet coefficients are obtained The improved wavelet threshold function calculation formula is:

[0138]

[0139] Among them, α, β, and γ are adjustment factors of the threshold function, α, β>0, 0<γ<1. By adjusting the adjustment factors, the problems of the original soft and hard threshold functions in noise processing and signal distortion are solved, thereby retaining more effective signals and improving the wavelet threshold denoising performance.

[0140] S5. Reconstruct the low-frequency signal component and the improved wavelet threshold denoising signal to obtain the denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indicators.

[0141] In a feasible implementation, the photovoltaic DC fault arc signal is decomposed by IMGO-ICEEMDAN, and the retained signal obtained by the multivariate intrinsic mode index is reconstructed into IMF s , reconstruct the signal obtained by wavelet denoising with improved threshold function into IMF p , the IMF s 、IMF p And the residual R is reconstructed to obtain the final denoised signal S:

[0142] S=IMF s +IMF p +R (29)

[0143] In order to measure the noise reduction effect, three indicators, mean square error MSE, signal-to-noise ratio SNR, and waveform similarity coefficient NCC, are introduced. The smaller the MSE, the larger the SNR, and the closer the NCC is to 1, the better the noise reduction effect. The calculation formula is as follows:

[0144]

[0145] Among them, x i is the original signal, x′ i The signal after noise reduction, N is the signal length.

[0146] The effectiveness of this method is verified through an example below, and the noise reduction is verified using the fault arc signal of a real photovoltaic scene.

[0147] The photovoltaic DC fault arc signal collected in step S1 is input into IMGO-ICEEMDAN with 2ms as samples, and each sample has 1000 points. According to step S2, the optimization range of the noise amplitude weight is set to [0.15, 0.6], the optimization range of the number of noise additions is set to [50, 600], the maximum number of iterations is 10, the population size is 4, and the photovoltaic DC fault arc signal is decomposed by IMGO-ICEEMDAN. The decomposition effect is as follows: Figure 2 shown.

[0148] It can be clearly seen that this method decomposes the photovoltaic DC fault signal into 8 IMF components and a residual R, the high-frequency component and the low-frequency component are clearly divided, and there is no modal aliasing phenomenon; according to step S3, the multivariate intrinsic modal index D of each IMF is calculated, and the calculation results are as follows: Figure 3 , Figure 4 As shown in the figure, the hyperparameters and fitness values ​​of the algorithm optimization change with the number of iterations. Figure 5 shown.

[0149] According to the screening criteria, IMF1-2 are regarded as pure noise components and should be eliminated. For the remaining IMF components, IMF4 with D≥0.2 is retained, and IMF3 and IMF5-8 with D<0.2 are subjected to improved wavelet threshold denoising according to step S4. The function diagram based on the improved threshold method in this method is shown in FIG. Figure 6 As shown, the evaluation indicators of different IMF component denoising are shown in Table 1:

[0150] Table 1

[0151]

[0152]

[0153] According to step S5, IMF s 、IMF p And the residual R is reconstructed to obtain Figure 7 , Figure 8 is the original signal, Fig. 9 , Fig.10 , Fig.11 The following are the effects of using EMD, EEMD, and VMD methods for noise reduction. The noise reduction evaluation indicators of photovoltaic DC fault arc signals based on different noise reduction methods are shown in Table 2:

[0154] Table 2

[0155] Noise reduction method SNR MSE NCC EMD 42.70 0.001296 1 EEMD 51.61 0.00016 1 VMD 52.57 0.000134 1 The present invention 53.33 0.000112 1

[0156] In an embodiment of the present invention, a photovoltaic DC fault arc signal noise reduction method based on IMGO-ICEEMDAN and multivariate intrinsic modal indicators is disclosed. Since the current output of the photovoltaic DC power supply constantly oscillates with environmental changes, and the current signal noise increases with the increase of line length and current, it is difficult to identify the fault characteristics when the fault arc occurs. The traditional improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) is used to decompose the fault arc signal into k intrinsic mode components (IMFs) from high frequency to low frequency, but the two key parameters (white noise amplitude weight (Nstd) and noise addition times (ME)) in the decomposition process of this method are difficult to accurately determine based on experience. Therefore, the present invention uses an improved moss growth algorithm (IMGO) to achieve intelligent optimization of the above two key parameters. The IMGO algorithm improves the global search capability and overall optimization effect, and solves the problems of high computational complexity, slow convergence speed, and easy to fall into local optimal solutions of the MGO algorithm. In addition, IMGO-ICEEMDAN also solves the problems of modal aliasing, endpoint effect, and difficulty in parameter selection in existing methods such as EMD, EEMD, and VMD. On this basis, the IMF components obtained by decomposition are divided into high-frequency noise components and low-frequency signal components through multivariate intrinsic mode indicators, and the low-frequency signal noise components less than the threshold are subjected to denoising using the wavelet denoising method with an improved threshold, and finally the processed IMF components are reconstructed to obtain the denoised signal. Compared with the existing methods, the denoising method disclosed in the present invention effectively improves the denoising effect of the fault arc signal while retaining the key features of the photovoltaic DC fault arc signal, which helps to significantly improve the accuracy of fault arc identification.

[0157] Fig.12 The block diagram of a photovoltaic DC fault arc signal noise reduction device according to an exemplary embodiment is shown, and the device is used in a photovoltaic DC fault arc signal noise reduction method. Fig.12 The device includes an acquisition module 310, a decomposition module 320, a calculation module 330, a noise reduction module 340 and an output module 350.

[0158] in:

[0159] The acquisition module 310 is used to acquire a photovoltaic DC fault arc signal.

[0160] The decomposition module 320 is used to decompose the photovoltaic DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the photovoltaic DC fault arc intrinsic mode components.

[0161] The calculation module 330 is used to calculate the multivariate intrinsic modal index of the photovoltaic DC fault arc intrinsic modal component, and divide the photovoltaic DC fault arc intrinsic modal component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic modal index.

[0162] The denoising module 340 is used to denoise the noisy signal component smaller than the multivariate intrinsic mode index threshold value by using an improved wavelet threshold value to obtain an improved wavelet threshold denoised signal.

[0163] The output module 350 is used to reconstruct the low-frequency signal component and the improved wavelet threshold denoised signal to obtain a denoised signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indicators.

[0164] In an embodiment of the present invention, a photovoltaic DC fault arc signal noise reduction method based on IMGO-ICEEMDAN and multivariate intrinsic modal indicators is disclosed. Since the current output of the photovoltaic DC power supply constantly oscillates with environmental changes, and the current signal noise increases with the increase of line length and current, it is difficult to identify the fault characteristics when the fault arc occurs. The traditional improved fully adaptive noise ensemble empirical mode decomposition method (ICEEMDAN) is used to decompose the fault arc signal into k intrinsic mode components (IMFs) from high frequency to low frequency, but the two key parameters (white noise amplitude weight (Nstd) and noise addition times (ME)) in the decomposition process of this method are difficult to accurately determine based on experience. Therefore, the present invention uses an improved moss growth algorithm (IMGO) to achieve intelligent optimization of the above two key parameters. The IMGO algorithm improves the global search capability and overall optimization effect, and solves the problems of high computational complexity, slow convergence speed, and easy to fall into local optimal solutions of the MGO algorithm. In addition, IMGO-ICEEMDAN also solves the problems of modal aliasing, endpoint effect, and difficulty in parameter selection in existing methods such as EMD, EEMD, and VMD. On this basis, the IMF components obtained by decomposition are divided into high-frequency noise components and low-frequency signal components through multivariate intrinsic mode indicators, and the low-frequency signal noise components less than the threshold are subjected to denoising using the wavelet denoising method with an improved threshold, and finally the processed IMF components are reconstructed to obtain the denoised signal. Compared with the existing methods, the denoising method disclosed in the present invention effectively improves the denoising effect of the fault arc signal while retaining the key features of the photovoltaic DC fault arc signal, which helps to significantly improve the accuracy of fault arc identification.

[0165] Fig.13 is a schematic diagram of the structure of a photovoltaic DC fault arc signal noise reduction device provided by an embodiment of the present invention, such as Fig.13 As shown, the photovoltaic DC fault arc signal noise reduction device may include the above Fig.12 The photovoltaic DC fault arc signal noise reduction device shown. Optionally, the photovoltaic DC fault arc signal noise reduction device 410 may include a first processor 2001 .

[0166] Optionally, the photovoltaic DC fault arc signal noise reduction device 410 may further include a memory 2002 and a transceiver 2003 .

[0167] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0168] Combine the following Fig.13 The various components of the photovoltaic DC fault arc signal noise reduction device 410 are specifically introduced:

[0169] The first processor 2001 is the control center of the photovoltaic DC fault arc signal noise reduction device 410, which can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0170] Optionally, the first processor 2001 can perform various functions of the photovoltaic DC fault arc signal noise reduction device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0171] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Fig.13 CPU0 and CPU1 are shown in FIG.

[0172] In a specific implementation, as an embodiment, the photovoltaic DC fault arc signal noise reduction device 410 may also include multiple processors, such as Fig.13 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0173] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0174] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be accessed through the interface circuit ( Fig.13 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0175] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0176] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Fig.13 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0177] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Fig.13 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0178] It should be noted that Fig.13 The structure of the photovoltaic DC fault arc signal noise reduction device 410 shown in the figure does not constitute a limitation on the router, and the actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0179] In addition, the technical effects of the photovoltaic DC fault arc signal noise reduction device 410 can refer to the technical effects of the photovoltaic DC fault arc signal noise reduction method described in the above method embodiment, and will not be repeated here.

[0180] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also 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 gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0181] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0182] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0183] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0184] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0185] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0186] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0187] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0188] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0189] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0191] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0192] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A photovoltaic DC fault arc signal noise reduction method, characterized in that: The method comprises: S1, obtaining photovoltaic DC fault arc signal; S2. Based on the IMGO-ICEEMDAN decomposition method, the photovoltaic DC fault arc signal is decomposed to obtain the photovoltaic DC fault arc intrinsic modal component; S3, calculating the multivariate intrinsic modal index of the photovoltaic DC fault arc intrinsic modal component, and dividing the photovoltaic DC fault arc intrinsic modal component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic modal index; S4, denoising the noisy signal component that is less than the multivariate intrinsic mode index threshold value by using an improved wavelet threshold value to obtain an improved wavelet threshold denoised signal; S5. Reconstruct the low-frequency signal component and the improved wavelet threshold denoising signal to obtain a denoising signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indicators.

2. The photovoltaic DC fault arc signal noise reduction method according to claim 1, characterized in that: The IMGO-ICEEMDAN decomposition method in S2 is used to decompose the photovoltaic DC fault arc signal to obtain the photovoltaic DC fault arc intrinsic modal components, including: S21, determining a white noise amplitude weight and a noise adding number according to an improved moss growth algorithm IMGO, and adding white noise to the photovoltaic DC fault arc signal according to the white noise amplitude weight and the noise adding number to obtain an added fault arc signal; S22, calculating and obtaining a first-order residual component r1 according to the added fault arc signal; S23, calculating and obtaining a first-order modal component c1 according to the first-order residual component r1; S24, adding white noise to the first-order residual component r1 and taking the average value, and calculating the second-order modal component c2; S25. According to the first-order residual component r1, the second-order residual component r2 is calculated; according to the second-order residual component r2, the third-order modal component c3 is calculated; and then the intrinsic modal component of the photovoltaic DC fault arc is obtained.

3. The photovoltaic DC fault arc signal noise reduction method according to claim 2, characterized in that: The step S21 of determining the white noise amplitude weight and the number of noise additions according to the improved moss growth algorithm IMGO includes: According to the moss growth algorithm, wind direction determination mechanism algorithm, dynamic weight optimization and particle swarm optimization algorithm, spore dispersal search algorithm and double propagation search algorithm, the minimum envelope entropy function is used as the fitness function to optimize and update the white noise amplitude weight and the number of noise additions to determine the optimal white noise amplitude weight and the number of noise additions.

4. The photovoltaic DC fault arc signal noise reduction method according to claim 3 is characterized in that: The dynamic weight optimization and particle swarm optimization algorithm are shown in the following formulas (1) and (2): v i =w·v i-1 +c1r1(pbest i -M i )+c2r2(gbest i -M i ) (1) In the formula, v i represents the speed of particle i, w represents the inertia weight, c1 represents the personal experience factor, r1 represents the random number, pbest i represents the best historical position of particle i, M i Indicates the position where particles are randomly generated, c2 indicates the global experience factor, r2 indicates the random number, gbest i represents the best historical position of all particles, w max Indicates the maximum value of the inertia weight, w min It represents the minimum value of the inertia weight, it represents the current number of iterations, and Maxits represents the maximum number of iterations.

5. The photovoltaic DC fault arc signal noise reduction method according to claim 1, characterized in that: The multivariate intrinsic modal index of the intrinsic modal component of the photovoltaic DC fault arc calculated in S3 includes: The correlation coefficient, kurtosis and variance contribution rate of each component in the eigenmodal component of the photovoltaic DC fault arc are calculated respectively, the correlation coefficient, kurtosis and variance contribution rate are normalized, the normalized data are weighted using the coefficient of variation weighting method, and the multivariate intrinsic modal index is obtained according to the weighted data.

6. The photovoltaic DC fault arc signal noise reduction method according to claim 1, characterized in that: The improved wavelet threshold in S4 includes: The db4 wavelet basis function is selected as the discrete wavelet transform wavelet basis function, and the low-frequency signal component is decomposed by three layers of wavelet to obtain the high-frequency wavelet coefficient W j,k , according to the high frequency wavelet coefficient W j,k Calculate the threshold t; According to the high-frequency wavelet coefficient W j,k And the threshold t, through the improved wavelet threshold function processing, get the low-frequency wavelet coefficient 7. The photovoltaic DC fault arc signal noise reduction method according to claim 6, characterized in that: The improved wavelet threshold function is shown in the following formula (3): In the formula, represents the low-frequency wavelet coefficient, sign(·) represents the sign function, t represents the threshold, and α, β, and γ represent the adjustment factors of the threshold function.

8. A photovoltaic DC fault arc signal noise reduction device, the photovoltaic DC fault arc signal noise reduction device is used to implement the photovoltaic DC fault arc signal noise reduction method according to any one of claims 1 to 7, characterized in that: The device comprises: An acquisition module, used for acquiring photovoltaic DC fault arc signals; A decomposition module, used for decomposing the photovoltaic DC fault arc signal based on the IMGO-ICEEMDAN decomposition method to obtain the photovoltaic DC fault arc intrinsic mode component; A calculation module, used for calculating a multivariate intrinsic modal index of a photovoltaic DC fault arc intrinsic modal component, and dividing the photovoltaic DC fault arc intrinsic modal component into a high-frequency noise component, a low-frequency signal component and a noisy signal component according to the multivariate intrinsic modal index; A denoising module, used for denoising the noisy signal component less than the multivariate intrinsic mode index threshold value through an improved wavelet threshold value to obtain an improved wavelet threshold denoised signal; The output module is used to reconstruct the low-frequency signal component and the improved wavelet threshold denoising signal to obtain a denoising signal based on IMGO-ICEEMDAN and multivariate intrinsic mode screening indicators.

9. A photovoltaic DC fault arc signal noise reduction device, characterized in that: The photovoltaic DC fault arc signal noise reduction device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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