Railway switch fault analysis method and system

Abstract

The present invention discloses a railway switch fault analysis method and system. The method includes: converting a three-phase operating current into a three-phase operating current digital signal according to a preset digital conversion strategy; extracting cyclic frequency information from the three-phase operating current digital signal and generating an enhanced envelope spectrum; quantizing and accumulating the relative characteristics of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identifying the equal-frequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain the cyclic frequency; feeding the cyclic frequency into a preset maximum cyclostationary blind deconvolution, the maximum cyclostationary blind deconvolution outputs a repetitive transient pulse caused by a local defect, and determining the railway switch fault signal based on the repetitive transient pulse. The method improves the accuracy of railway switch fault identification, enables good identification of fault-related signals even in the presence of strong external noise interference, and is beneficial to the troubleshooting of railway switch faults.

Description

Technical Field

[0001] The present invention belongs to the technical field of rail transit monitoring, and in particular relates to a railway switch fault analysis method and system. Background Art

[0002] Railway switches are crucial equipment in the railway transportation system, primarily used to change the direction of railroad switches to ensure trains stay on their intended tracks. The reliability of switch machines is crucial to the safety and efficiency of railway transportation, making the timely identification and repair of switch machine faults crucial. With the continuous expansion of railway transportation, switch machine failures are gaining increasing attention. Switch machine failures can arise from a variety of factors, including electrical, mechanical, and environmental factors. Common failures include improper opening and closing of the switch machine, inaccurate positioning, failure of the electronic control system, and sensor malfunctions. These failures can not only pose danger to train operations but also cause delays and increased maintenance costs. Currently, traditional methods for detecting switch machine faults rely primarily on manual inspections and periodic inspections. However, manual inspections are labor-intensive, inefficient, and difficult to detect potential faults in a timely manner. Especially in the vast railway network, manual inspections are limited in coverage and accuracy. The adoption of automated and intelligent fault detection technologies has become a crucial means of improving railway transportation safety and operational efficiency.

[0003] Existing fault analysis for switch machines relies heavily on the operator's experience and intuition. Fault symptoms often require operators to make a preliminary assessment through visual observation and testing, followed by manual inspection. This approach is not only prone to omissions or misjudgments, but also limits the efficiency and accuracy of manual judgment when faced with complex faults. Summary of the Invention

[0004] The present invention provides a railway switch fault analysis method and system, which are used to solve the technical problem of limited efficiency and accuracy of manual judgment when facing complex faults.

[0005] In a first aspect, the present invention provides a railway switch fault analysis method, comprising:

[0006] Obtaining the three-phase operating current of the railway switch machine, and converting the three-phase operating current into a three-phase operating current digital signal according to a preset digital conversion strategy;

[0007] Extracting cyclic frequency information from the three-phase action current digital signal and generating an enhanced envelope spectrum;

[0008] quantifying and accumulating fault features of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identifying an equal-frequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain a cyclic frequency;

[0009] The cyclic frequency is fed into a preset maximum cyclostationary blind deconvolution, the maximum cyclostationary blind deconvolution outputs a repetitive transient pulse caused by a local defect, and a fault signal of a railway switch is determined according to the repetitive transient pulse.

[0010] In a second aspect, the present invention provides a railway switch fault analysis system, comprising:

[0011] an acquisition module configured to acquire the three-phase operating current of the railway switch machine and convert the three-phase operating current into a three-phase operating current digital signal according to a preset digital conversion strategy;

[0012] A generating module configured to extract cyclostationarity information from the three-phase action current digital signal and generate an enhanced envelope spectrum;

[0013] an identification module configured to quantify and accumulate fault features of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identify an equifrequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain cyclic frequencies;

[0014] The analysis module is configured to feed the cyclic frequency into a preset maximum cyclostationary blind deconvolution, the maximum cyclostationary blind deconvolution outputs a repetitive transient pulse caused by a local defect, and the fault signal of the railway switch is determined based on the repetitive transient pulse.

[0015] In a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the railway switch fault analysis method of any embodiment of the present invention.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the railway switch fault analysis method of any embodiment of the present invention.

[0017] The railway switch fault analysis method and system of the present application use a centralized signal monitoring system to collect the three-phase operating current of the switch through a current sensor, extract the cyclic frequency information of the current digital signal and generate an enhanced envelope spectrum (EES), and derive a diagnostic feature spectrum (DFS) by quantifying and accumulating the relative significance of multiple frequencies. The method can identify the equi-interval harmonic structure (EIHS) in the EES and provide an accurate cyclic frequency. The estimated cyclic frequency is fed into a maximum cyclostationary blind deconvolution (CYCBD) to retrieve repetitive transient pulses caused by local defects, thereby realizing adaptive diagnosis of railway switch faults, improving the accuracy of railway switch fault identification, and making it possible to well identify fault-related signals even in the presence of strong external noise interference, which is beneficial to the troubleshooting of railway switch faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A flow chart of a railway switch fault analysis method provided by one embodiment of the present invention;

[0020] Figure 2 This is a structural block diagram of a railway switch fault analysis system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] See also Figure 1 , which shows a flow chart of a railway switch fault analysis method of the present application.

[0023] like Figure 1 As shown, the railway switch machine fault analysis method specifically includes the following steps:

[0024] Step S101: acquiring a three-phase operating current of a railway switch machine, and converting the three-phase operating current into a three-phase operating current digital signal according to a preset digital conversion strategy.

[0025] In this step, the digital transformation strategy is expressed as:

[0026] ,

[0027] ,

[0028] ,

[0029] ,

[0030] Where, is the digital conversion function of the characteristic factor of the lightweight current signal, is the partial discharge signal amplitude coefficient, is a partial discharge signal with unit amplitude, is a pure partial discharge signal, For delay, To detect signal noise, is the acquisition length, is the reference signal, is the effective value of the electrical signal, is the peak value of the electrical signal, is the electrical signal pulse width, is the signal duty cycle, is the main frequency component of the signal, is the signal harmonic distribution, is the signal spectrum energy entropy, For short-time overcurrent signal, is a glitch signal, is the transient waveform distortion, is the signal activity, To reduce the sampling rate when the signal is stable, Trigger high sampling rate when a mutation occurs. is the similarity of adjacent sampling points, is the effective value error, For high temperature drift, For low temperature drift, is the environmental noise, It is a weak current signal. is the adaptive threshold, is the differential noise, For noise suppression, is the noise suppression weight, is the signal time information, is the partial discharge signal weight, is the electrical signal weight.

[0031] In this embodiment, a digital conversion strategy is adopted to compress and simplify the characteristic factors of the current signal so that it can be efficiently processed with fewer computing and storage resources after digitization, helping the system to reduce the computing burden, lower power consumption, and optimize performance while maintaining accuracy. The appropriate conversion strategy can be flexibly selected according to the system requirements, resource limitations, and processing goals.

[0032] Step S102: extracting cyclic frequency information from the three-phase action current digital signal and generating an enhanced envelope spectrum.

[0033] In this step, the three-phase action current digital signal is subjected to short-time Fourier transform. The STFT can be expressed as:

[0034] ,

[0035] in, , is the discrete frequency, is the sampling frequency, is the frequency resolution, is the window length, For time shift, is a window function, is a discrete frequency period, is the signal time-frequency, For time, is the frequency bandwidth, is the window interval;

[0036] Scanning spectrum It can be defined as:

[0037] ,

[0038] in, The corresponding frequency range is , is the frequency weight, For framing, Scan the spectrum for the previous frequency, For timeline information, is the sampling weight;

[0039] The transformed three-phase action current digital signal is subjected to noise reduction according to a preset noise reduction processing strategy to obtain a target three-phase action current digital signal;

[0040] Spectral correlation analysis is performed on the target three-phase operating current digital signal to obtain an enhanced envelope spectrum. Spectral correlation analysis involves decomposing the signal into different frequency components in the time domain. Correlating the spectra of different time windows quantifies the temporal spectral consistency of the signal. Fast spectral correlation is then used to extract cyclic frequencies and generate the corresponding enhanced envelope spectrum.

[0041] It should be noted that the expression of the noise reduction processing strategy is:

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] Where, is a stationary target signal, is a non-stationary target signal, is additive noise, is a multi-component FM signal, is a non-convex regularization term, is the L0 norm, is the signal sparsity, For time shift, is the frequency shift, To cover the signal, To modulate the frequency space, is the quantity of the portion, For frequency modulation rules, is the dictionary redundancy, is the variable component, is super-Gaussian sparsity, To over-penalize large coefficients, is iterative reweighting, is the Lp norm, is the hard threshold, is the half threshold, is the pre-denoising value, To separate high frequency noise, is a low-frequency signal, is the wavelet threshold, is the residual noise, To suppress the amount of impulse noise, is the noise standard deviation, is the number of dictionary atoms, are atomic parameters, is the proximal operator, is the initialization coefficient, is the time-frequency distribution, is additive Gaussian noise, For the impact component, is a transient non-stationary signal, For the wave group, For the ST segment, is the signal-to-noise ratio of the characteristic component, is a coarse-grained dictionary, is the compression coefficient vector, To update the weight incrementally, is the online calculation coefficient, For non-convex optimization of local minima, For high computational complexity, is the noise sensitivity, Automatic search for hyperparameters for Bayesian optimization, is a non-stationary target echo, is a high-frequency transient signal, For high-precision time-frequency representation, is the optimization coefficient, It is a low-precision time-frequency representation.

[0047] In an embodiment, a noise reduction processing strategy extracts useful information from a complex signal, especially when the signal contains noise and has non-stationary characteristics (i.e., the statistical characteristics of the signal change over time). By selecting an appropriate time-frequency analysis method based on the signal characteristics and combining it with noise suppression technology, the accuracy and robustness of the signal analysis can be significantly improved.

[0048] Step S103 , quantifying and accumulating fault features of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identifying an equi-frequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain cyclic frequencies.

[0049] In this step, the diagnostic features of each cyclic frequency in the enhanced envelope spectrum are calculated according to the amplitude of the enhanced envelope spectrum. The expression is:

[0050] ,

[0051] Where, For diagnostic features, To enhance the amplitude of the envelope spectrum, is the frequency axis vector, is the background noise band considered, is the frequency;

[0052] The diagnostic characteristic spectrum is constructed based on the diagnostic characteristics of each cycle frequency, and the expression is:

[0053] ,

[0054] Where, For diagnostic characteristics, is the first cycle frequency, is the second cycle frequency, is the frequency of the nth cycle, is the amplitude of the n-fold enhanced envelope spectrum, is the harmonic amplitude, is the number of frequency cycles.

[0055] For a certain fundamental frequency of the equal frequency interval harmonic structure ,when hour, Only affected by noise, making it very small, when and When overlapped, middle The harmonic amplitude at the location is significantly higher than the local background noise. A sharp component will appear at the position where the predicted periodic frequency may be incorrect under strong noise interference, which will eventually affect the accuracy of ACYCBD. On the other hand, The amplitude of represents the significant level of non-interval harmonics above the background noise level. The diagnostic characteristic spectrum is achieved by quantifying the relatively prominent features at multiple frequencies and accumulating them.

[0056] It should be noted that the expression for calculating the cycle frequency is:

[0057] ,

[0058] Where, is the cycle frequency, is the first cycle frequency, is the first cycle frequency, is the H-th cycle frequency, is the cycle frequency of the nth period, is the frequency, is the harmonic amplitude, is the number of frequency cycles.

[0059] Step S104: feeding the cyclic frequency into a preset maximum cyclostationary blind deconvolution, outputting a repetitive transient pulse caused by a local defect from the maximum cyclostationary blind deconvolution, and determining a fault signal of a railway switch according to the repetitive transient pulse.

[0060] In this step, the filter is constructed and the maximum cyclostationary blind deconvolution uses ICS2 as the objective function to determine the optimal filter and seeks maximization through the iterative eigenvalue decomposition algorithm. In order to determine the ideal inverse filter , the evaluation metrics can be defined as:

[0061] ,

[0062] In fact, the basic principle of all blind convolution algorithms is to reduce the influence of transmission and Retrieve the fault pulse from is an ideal inverse filter, is the objective function, is an inverse FIR filter, as shown below:

[0063] ,

[0064] in, is the target signal, For signal, is the unknown impulse response, Fault pulse, Represents the convolution operator. The matrix expression of this convolution operation is:

[0065] ,

[0066] Among them, the cycle frequency can be described as:

[0067] ,

[0068] in, is the cyclic frequency of the discrete-time signal, is the pulse correlation period, is the sample index, which is used as an indicator to characterize the periodicity of signal energy. The definition of can be expressed as:

[0069] ,

[0070] ,

[0071] ,

[0072] ,

[0073] ,

[0074] ,

[0075] ,

[0076] in, is an indicator that characterizes the periodicity of signal energy. represents the period-dependent component, is the sample index, is the sample period, is a k-order sample, is an o-order sample, is the conjugate transpose operation of the matrix, is the correlation matrix, is the sample attenuation coefficient, is the length of the signal, is the sample norm, is the number of samples, is the sample weight;

[0077] Signals covering all periodic frequencies of interest Described as:

[0078] ,

[0079] but The expression is:

[0080] ,

[0081] Calculate the weight matrix and obtain the maximum eigenvalue , find the ideal filter , the weighted matrix Defined as:

[0082] ,

[0083] in, and are weighted correlation matrix and correlation matrix respectively. The problem can be transformed into finding the maximum eigenvalue :

[0084] ,

[0085] From the above formula, we can get the maximum eigenvalue and its corresponding eigenvector, maximum eigenvalue Corresponding to the best , the eigenvector is used as the ideal filter of CYCBD ,

[0086] Determine whether the convergence criterion is met, obtain the final filtered signal y, obtain the envelope spectrum of signal y, analyze the envelope spectrum, and complete fault diagnosis.

[0087] In summary, the method of the present application uses a signal centralized monitoring system to collect the three-phase operating current of the switch through a current sensor; then, the cyclic frequency information of the current digital signal is extracted and an enhanced envelope spectrum (EES) is generated; secondly, a diagnostic feature spectrum (DFS) is obtained by quantifying and accumulating the relative significance of multiple frequencies, which can identify the equi-interval harmonic structure (EIHS) in the EES and provide an accurate cyclic frequency; finally, the estimated cyclic frequency is fed into the maximum cyclostationary blind deconvolution (CYCBD) to retrieve repetitive transient pulses caused by local defects, thereby realizing adaptive diagnosis of railway switch faults, improving the accuracy of railway switch fault identification, and making it possible to well identify fault-related signals even in the presence of strong external noise interference, which is beneficial to the troubleshooting of railway switch faults.

[0088] See also Figure 2 , which shows a structural block diagram of a railway switch fault analysis system of the present application.

[0089] like Figure 2 As shown, the railway switch fault analysis system 200 includes an acquisition module 210 , a generation module 220 , an identification module 230 and an analysis module 240 .

[0090] The acquisition module 210 is configured to acquire the three-phase operating current of the railway switch machine and convert the three-phase operating current into a three-phase operating current digital signal according to a preset digital conversion strategy;

[0091] A generating module 220 is configured to extract cyclostationarity information from the three-phase operating current digital signal and generate an enhanced envelope spectrum;

[0092] an identification module 230 configured to quantify and accumulate fault features of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identify an equi-frequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain cyclic frequencies;

[0093] The analysis module 240 is configured to feed the cyclic frequency into a preset maximum cyclostationary blind deconvolution, the maximum cyclostationary blind deconvolution outputs a repetitive transient pulse caused by a local defect, and determines a fault signal of a railway switch according to the repetitive transient pulse.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A railway switch fault analysis method, characterized in that: include: The three-phase operating current of the railway switch is obtained, and the three-phase operating current is converted into a three-phase operating current digital signal according to a preset digital conversion strategy, wherein the expression of the digital conversion strategy is: Where, is the digital conversion function of the characteristic factor of the lightweight current signal, u ν is the partial discharge signal amplitude coefficient, g ν is a partial discharge signal with unit amplitude, G ν is a pure partial discharge signal, a m is the time delay, τ ν To detect signal noise, v i+1 is the acquisition length, x is the reference signal, f α (x) is the effective value of the electrical signal, c α is the peak value of the electrical signal, τ α (x) is the pulse width of the electrical signal, f χ is the signal duty cycle, a i0 is the main frequency component of the signal, is the signal harmonic distribution, p0 is the signal spectrum energy entropy, a ij For short-time overcurrent signal, is the glitch signal, j χ is the transient waveform distortion, is the signal activity, To reduce the sampling rate when the signal is stable, When a mutation occurs, a high sampling rate is triggered, β v is the similarity of adjacent sampling points, is the effective value error, For high temperature drift, is low temperature drift, u0 is environmental noise, It is a weak current signal. is the adaptive threshold, is differential noise, sgn() is noise suppression, is the noise suppression weight, t is the signal time information, is the partial discharge signal weight, is the electrical signal weight; Extracting cyclic frequency information from the three-phase action current digital signal and generating an enhanced envelope spectrum; quantifying and accumulating fault features of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identifying an equal-frequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain a cyclic frequency; The cyclic frequency is fed into a preset maximum cyclostationary blind deconvolution, the maximum cyclostationary blind deconvolution outputs a repetitive transient pulse caused by a local defect, and a fault signal of a railway switch is determined according to the repetitive transient pulse.

2. A railway switch fault analysis method according to claim 1, characterized in that: The extracting the cycle frequency information from the three-phase action current digital signal and generating an enhanced envelope spectrum includes: Performing short-time Fourier transform on the three-phase action current digital signal; The transformed three-phase action current digital signal is subjected to noise reduction according to a preset noise reduction processing strategy to obtain a target three-phase action current digital signal; A spectrum correlation analysis is performed on the target three-phase action current digital signal to obtain an enhanced envelope spectrum.

3. A railway switch fault analysis method according to claim 2, characterized in that: The expression of the noise reduction processing strategy is: Where, is a stationary target signal, x t is the non-stationary target signal, Ω is the additive noise, is a multi-component FM signal, is a non-convex regularization term, T φ is the L0 norm, T δ is the signal sparsity, E is the frequency shift, To cover the signal, To modulate the frequency space, is the quantity of the portion, For frequency modulation rules, is the dictionary redundancy, is the variation component, α t is super-Gaussian sparsity, is to over-penalize large coefficients, H1 is iterative reweighting, is the Lp norm, C1 is the hard threshold, is the half threshold, T ε is the pre-denoising value, C2 is for separating high-frequency noise, C6 is for low-frequency signal, γ ε is the wavelet threshold, C0 is the residual noise, C5 is the amount of suppressed impulse noise, C3 is the noise standard deviation, is the number of dictionary atoms, C4 is the atomic parameter, δ ε is the proximal operator, n r is the initialization coefficient, G ε is the time-frequency distribution, R ε is additive Gaussian noise, is the impact component, x t+1 is a transient non-stationary signal, T t is the wave group, x t is the ST segment, α t is the signal-to-noise ratio of the characteristic component, is a coarse-grained dictionary, e t is the compression coefficient vector, T t-1 To update the weight incrementally, T 0 is the online calculation coefficient, x 0 For non-convex optimization of local minima, T k is the noise sensitivity, α k-1 Automatic search for hyperparameters for Bayesian optimization, is the non-stationary target echo, e k-1 is a high-frequency transient signal, Φ t,0 For high-precision time-frequency representation, x 0 is the optimization coefficient, Φ t,k It is a low-precision time-frequency representation.

4. A railway switch fault analysis method according to claim 1, characterized in that: The quantifying and accumulating the fault characteristics of multiple cycle frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum includes: According to the amplitude of the enhanced envelope spectrum, the diagnostic characteristics of each cycle frequency in the enhanced envelope spectrum are calculated, and the expression is: Where DF(w) is the diagnostic feature of the first cycle frequency, E(w) is the amplitude of the enhanced envelope spectrum, E(α) is the frequency axis vector, and f b is the background noise band considered, w is the frequency; The diagnostic characteristic spectrum is constructed based on the diagnostic characteristics of each cycle frequency, and the expression is: Where DFS(w) is the diagnostic characteristic spectrum, DF(2w) is the diagnostic characteristic of the second cycle frequency, DF(nw) is the diagnostic characteristic of the n-th cycle frequency, E(nw) is the amplitude of the n-fold enhanced envelope spectrum, H is the harmonic amplitude, and n is the number of frequency cycles.

5. A railway switch fault analysis method according to claim 1, characterized in that: The expression for calculating the cycle frequency is: Where P(w) is the cycle frequency, A(w) is the cycle frequency of the first period, A(2w) is the cycle frequency of the second period, A(Hw) is the cycle frequency of the Hth period, A(nw) is the cycle frequency of the nth period, w is the frequency, H is the harmonic amplitude, and n is the number of frequency cycles.

6. A railway switch fault analysis system, using the method according to any one of claims 1 to 5, characterized in that: The system comprises: an acquisition module configured to acquire the three-phase operating current of the railway switch machine and convert the three-phase operating current into a three-phase operating current digital signal according to a preset digital conversion strategy; A generating module configured to extract cyclostationarity information from the three-phase action current digital signal and generate an enhanced envelope spectrum; an identification module configured to quantify and accumulate fault features of multiple cyclic frequencies in the enhanced envelope spectrum to obtain a diagnostic characteristic spectrum, and identify an equifrequency-interval harmonic structure in the enhanced envelope spectrum based on the diagnostic characteristic spectrum to obtain cyclic frequencies; The analysis module is configured to feed the cyclic frequency into a preset maximum cyclostationary blind deconvolution, the maximum cyclostationary blind deconvolution outputs a repetitive transient pulse caused by a local defect, and the fault signal of the railway switch is determined based on the repetitive transient pulse.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images