A pipeline leakage signal denoising method and system based on improved adaptive VMD-SSD, a storage medium and a product
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HARBIN ENG UNIV
- Filing Date
- 2026-03-03
- Publication Date
- 2026-06-09
Smart Images
Figure CN122173778A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of signal processing technology, specifically relating to a method, system, storage medium, and product for denoising pipeline leakage signals based on an improved adaptive VMD-SSD. Background Technology
[0002] Sound waves are longitudinal waves formed by the propagation of mechanical vibrations in elastic media (such as air, liquids, and solids). Their core physical parameters include frequency, amplitude, wavelength, and velocity. Frequency determines pitch, while amplitude reflects sound intensity or loudness. Sound wave propagation depends on the density and elasticity of the medium, exhibiting significant velocity differences in different media. Propagation is accompanied by phenomena such as reflection, refraction, diffraction, and attenuation, with energy diffusing with distance and interacting with the medium. In engineering, sound wave analysis often involves time-domain waveforms, frequency-domain spectral characteristics, and energy distribution, serving as an important information carrier for condition monitoring and fault diagnosis.
[0003] The working environment of shipboard fire-fighting pipelines differs from that of long, straight oil and gas pipelines and buried water supply pipelines in cities. Shipboard interiors are often filled with various environmental noises, inevitably leading to noise interference in the collected signals. For example, vibration noise from ship navigation, noise from on-site personnel, and equipment operation noise all reduce the signal-to-noise ratio (SNR) of leakage signals. Furthermore, these noise signals are characterized by instability and randomness, resulting in unclear time-frequency characteristics of leakage signals, thus complicating pipeline leak detection and location. When a pipeline ruptures, the medium at the leak point is propelled outwards by the pressure difference between the inside and outside of the pipeline. The interaction between the medium and the surrounding environment generates impacts and diffusion, producing sound waves. Common methods to improve the SNR include frequency domain filtering and wavelet thresholding, but these are often based on fixed parameters or global assumptions, making it difficult to adapt to the non-stationarity and complexity of shipboard noise. This can easily lead to distortion of effective signals or noise residue, resulting in missed detections and misjudgments of leakage characteristics. Therefore, a highly robust denoising method that can adaptively separate noise while preserving weak leakage characteristics is urgently needed.
[0004] Variational Mode Decomposition (VMD) is an adaptive signal decomposition method based on a variational framework. Its core principle is to decompose a signal into multiple eigenmode functions with specific sparsity and center frequencies. By constraining the variational model, it achieves accurate signal separation in the frequency domain, effectively overcoming the mode aliasing problem in traditional recursive decomposition. However, VMD performance heavily relies on two key preset parameters: the number of modes *k* and the penalty factor *α*. *k* determines the number of decomposed modes; if set too small, different components will be mixed in the same mode; if set too large, spurious components or over-decomposition will occur. The penalty factor *α* controls the bandwidth of each mode, affecting the concentration of frequency distribution. Currently, these two parameters are mostly set based on user experience or multiple trial and error, lacking an objective and universally applicable adaptive determination mechanism. This results in the decomposition results being significantly influenced by subjectivity, and insufficient robustness in complex noisy environments and weak characteristic signals, limiting the reliable application of this method in practical engineering, especially in highly interference scenarios such as leak detection in ship fire-fighting pipelines.
[0005] Singular Spectral Decomposition (SSD) is an adaptive signal analysis method based on trajectory matrices and singular value decomposition. It constructs a one-dimensional time series as a Hankel matrix and performs singular value decomposition in a high-dimensional space. The magnitude of the singular values characterizes the energy of each component, thereby achieving effective separation of signal components and noise suppression. This method does not require pre-defined basis functions, can extract the main oscillation modes based on the signal's inherent structure, and has good adaptability to non-stationary and nonlinear signals, especially suitable for feature extraction in complex backgrounds. However, its performance is significantly affected by the choice of embedding window length and grouping threshold, and it involves a large computational load. In practical applications, a trade-off must be struck between decomposition accuracy and computational efficiency. Summary of the Invention
[0006] In order to overcome the shortcomings of the prior art, the present invention aims to provide a method, system, storage medium and product for denoising pipeline leakage signals based on an improved adaptive VMD-SSD, which is superior to single denoising technology in terms of improving noise suppression and feature preservation capabilities, and has the practical advantages of high precision and high computational efficiency in engineering applications.
[0007] The objective of this invention is achieved through the following technical solution:
[0008] A method for denoising pipe leakage signals based on an improved adaptive VMD-SSD includes the following steps:
[0009] Step 1: Collect the acoustic signal of the pipeline leak as the raw signal data;
[0010] Step 2: Using the penalty factor and mode decomposition number of the VMD algorithm as optimization objectives, the original signal data is optimized using an adaptive optimization algorithm to obtain the optimal decomposition parameters;
[0011] Step 3: Perform VMD decomposition on the original signal data according to the decomposition parameters to obtain N IMF components;
[0012] Step 4: Calculate the correlation coefficients between the N IMF components and the original signal, and select the effective IMF components based on the correlation coefficients;
[0013] Step 5: Perform secondary decomposition on each effective IMF component using the SSD algorithm, select the principal component through the energy threshold for reconstruction, and obtain the denoised effective IMF component; synthesize the denoised effective IMF component to obtain the denoised pipeline leakage acoustic signal.
[0014] Furthermore, the optimization process in step two includes:
[0015] Initialize the number of mode decompositions k and the penalty factor α, and perform VMD decomposition on the original signal data to obtain multiple IMF components;
[0016] After performing a fast Fourier transform on each IMF component, the frequency with a frequency amplitude greater than the core threshold is the core frequency of each IMF component.
[0017] Based on the core frequencies of all IMF components, calculate the total number of normalized core frequencies b and the total number of normalized frequency repetitions c.
[0018] If the total number of normalized core frequencies b is lower than the first preset threshold, then return to increase the number of initial modal decompositions k; if the total number of normalized frequency repetitions c is higher than the second preset threshold, then return to increase the initialization penalty factor α; until b is greater than the first preset threshold and c is less than the second preset threshold, then the current number of modal decompositions k and penalty factor α are determined as the optimal decomposition parameters.
[0019] Furthermore, the core threshold is ,in, is the maximum peak value after the Fast Fourier Transform, and m is a set ratio.
[0020] Furthermore, the total number of normalized core frequencies b and the total number of normalized frequency repetitions c are:
[0021]
[0022]
[0023] in, is the number of core frequencies of the i-th IMF component; F is the maximum frequency after the fast Fourier transform, which is half the sampling frequency; denoted as , where is the number of core frequency repetitions for each IMF component; and is the total number of IMF components.
[0024] Furthermore, in step four, IMF components with correlation coefficients greater than or equal to the effective threshold are considered effective IMF components; the effective threshold... ,in To maintain a fixed proportion, This represents the calculated maximum correlation coefficient.
[0025] Furthermore, step five, which involves selecting principal components for reconstruction using an energy threshold, includes:
[0026] Calculate the energy corresponding to each singular value after SSD decomposition, and determine that the cumulative energy percentage is greater than or equal to a preset energy threshold. Minimum number of singular values r required;
[0027] The components corresponding to the first r singular values are selected for reconstruction to obtain the denoised effective IMF components.
[0028] Furthermore, the preset energy threshold ,
[0029]
[0030] in, The total energy of all singular values; The cumulative energy of the first r singular values;
[0031] The denoised effective IMF component ,
[0032]
[0033] in, To reconstruct the matrix The OK Column elements; L is the window length; K is the number of columns in the trajectory matrix; N is the length of the signal; t is the time.
[0034] A computer system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a pipe leakage signal denoising method based on an improved adaptive VMD-SSD.
[0035] A computer program product includes a computer program that, when executed by a processor, implements the steps of a pipe leakage signal denoising method based on an improved adaptive VMD-SSD.
[0036] A readable storage medium having a computer program stored thereon, which, when executed, implements the steps of a pipe leak signal denoising method based on an improved adaptive VMD-SSD.
[0037] The beneficial effects of this invention are as follows:
[0038] (1) By constructing an adaptive optimization algorithm, the present invention enables the number of modes and the penalty factor of VMD to be determined autonomously without relying on empirical presets, thereby enhancing the adaptability and engineering universality of the method.
[0039] (2) The present invention performs deep noise stripping on the IMF after VMD decomposition by SSD, which significantly improves the signal-to-noise ratio and preserves effective signal details.
[0040] (3) Compared with the traditional parameter optimization algorithm, the present invention has a fast convergence speed, low computational resource consumption, and is suitable for real-time engineering processing.
[0041] (4) This invention integrates the advantages of VMD and SSD to form a complementary processing framework, which is suitable for different leakage conditions in complex pipeline networks. Attached Figure Description
[0042] Figure 1 This is a flowchart of a pipe leakage signal denoising method based on an improved adaptive VMD-SSD according to the present invention.
[0043] Figure 2 The images shown are time-domain plots and spectrum plots of each IMF component obtained from VMD decomposition in the example.
[0044] Figure 3 The examples show the time-domain plots and spectrum diagrams of each IMF component obtained from the adaptive VMD decomposition in this embodiment.
[0045] Figure 4 The images shown are the time-domain and frequency-domain plots of the pipe leakage acoustic signal obtained by VMD decomposition and denoising in the embodiment.
[0046] Figure 5 The images shown are the time-domain and frequency-domain plots of the pipe leakage acoustic signal obtained by adaptive VMD-SSD decomposition and denoising in the embodiment. Detailed Implementation
[0047] The present invention will now be further described with reference to the accompanying drawings.
[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0049] Depend on Figure 1As shown, a method for denoising pipe leakage signals based on an improved adaptive VMD-SSD is described below, with the specific implementation steps as follows:
[0050] Step 1: Use a capacitive hydrophone to acquire the acoustic signal of the pipeline leak as raw data.
[0051] Step two involves performing VMD decomposition on the original signal using an initialized number of modes k and a penalty factor α. In this step, the signal is decomposed into multiple IMF components, each IMF representing a specific set of frequency components that reveal the dynamic characteristics of the original signal.
[0052] A Fast Fourier Transform (FFT) is performed on each IMF obtained from the decomposition to analyze its frequency characteristics. After the FFT, the spectrum is further analyzed to identify the core frequency of each IMF. The core frequency is the frequency at which the frequency amplitude is greater than a core threshold after performing the FFT on each IMF. ,in The maximum peak value after the Fast Fourier Transform is denoted by m, which is a set ratio and can be chosen according to the required precision. In this embodiment, m is set to 0.1. All frequencies with amplitudes higher than the core threshold are defined as the core frequencies of this IMF component. These core frequencies are considered to be the key regions that best represent the frequency domain information distribution of the IMF.
[0053] The number of frequencies for each IMF core is defined as F. i The number of core frequencies spliced in each IMF is calculated and normalized to obtain the normalized total number of core frequencies, b. The calculation formula is as follows:
[0054]
[0055] Where F is the maximum frequency after the Fast Fourier Transform, and its value is half of the sampling frequency.
[0056] When the core frequencies of different IMFs overlap, the number of times the core frequencies of different IMFs repeat is denoted as G. j The number of frequency repetitions for all IMF core frequencies is calculated and normalized to obtain the normalized total number of frequency repetitions, c. The calculation formula is as follows:
[0057]
[0058] The normalized values b and c are used to adaptively adjust the number of modes k and the penalty factor α. If b is lower than the set threshold, it indicates that the number of modes obtained from the decomposition is insufficient, and the number of modes k needs to be increased. If c is higher than the set threshold, it indicates that there is too much frequency band overlap, and the value of the penalty factor α should be increased to optimize the independence of modes. The penalty factor α and the number of modes k corresponding to the condition that the total number of core frequencies b is greater than the set threshold and the total number of core frequency repetitions c is less than the set threshold are identified as the optimal penalty factor α and the optimal number of modes k. Through this iterative adjustment, the number of modes k and the penalty factor α can be adaptively obtained, effectively balancing the number of modes and the decomposition accuracy.
[0059] Step 3: Based on the number of modes k and penalty factor α output in Step 2, set the parameters of the VMD algorithm, and import the leakage acoustic signal from Step 1 into the VMD algorithm for VMD decomposition to obtain N IMF components.
[0060] Step four: From the N IMF components obtained in step three, use the correlation coefficient to filter out the most correlated effective IMF components. The higher the correlation coefficient, the more effective information from the leaked acoustic signal is contained in this IMF; the lower the correlation coefficient, the more noise information is contained in this IMF. Define an effective threshold. Where v sets the ratio, To calculate the maximum correlation coefficient, v is set to 0.02 in this embodiment. IMF components with correlation coefficients less than the effective threshold are considered noise and discarded, while IMF components with correlation coefficients greater than or equal to the effective threshold are considered valid IMF components.
[0061] The specific formula for calculating the correlation coefficient is as follows:
[0062]
[0063]
[0064]
[0065]
[0066]
[0067]
[0068] Where IMFi(t) represents the signal value of the i-th IMF component at time t; X(t) represents the signal value of the pipeline leakage acoustic signal at time t; t represents time, t=1,2,…,N, and N represents the signal length; This represents the average signal value of the i-th IMF component; This represents the average signal value of the acoustic signal from a pipeline leak. This represents the standard deviation of the acoustic signal from a pipeline leak. This represents the standard deviation of the i-th IMF component; This represents the correlation coefficient between the i-th IMF component and the acoustic signal of pipeline leakage.
[0069] Step 5: Perform SSD decomposition on the effective IMF components selected in Step 4, set an energy threshold, and select appropriate signal components based on the energy of the signal components to reconstruct each effective IMF component. Finally, synthesize the reconstructed effective IMF components to obtain the denoised pipeline leakage acoustic signal. The details are as follows:
[0070] Choose an embedding dimension (window length) L that satisfies: ; Calculate the number of columns in the trajectory matrix .
[0071] Will Constructed as Hankel matrix Y:
[0072]
[0073] Perform singular value decomposition on the Hankel matrix Y:
[0074]
[0075] Where U is The left singular vector matrix satisfies Its column vector is denoted as V is The right singular vector matrix satisfies Its column vector is denoted as ; for The singular value matrix is of the form: , for The singular values of a matrix satisfy the following conditions: .
[0076] The number of groups r is determined by calculating the energy corresponding to each singular value:
[0077]
[0078]
[0079]
[0080] in, The total energy of all singular values; The cumulative energy of the first r singular values; To set an energy threshold.
[0081] Select the smallest positive integer r that satisfies the condition.
[0082] Divide the first r components into signal components and construct a reconstruction matrix. :
[0083]
[0084] in, Two-dimensional matrix The denoised matrix.
[0085] Reconstructing the matrix using diagonal averaging Converting back to a one-dimensional time series yields the denoised effective IMF components. :
[0086]
[0087] in, To reconstruct the matrix The OK Column elements.
[0088] The effective IMF components after SSD decomposition and denoising are synthesized to obtain the denoised pipeline leakage acoustic signal.
[0089] Example 1
[0090] A data acquisition system was built using a DSP main control board, signal conversion circuit, sensors, and a 24V DC power supply to obtain the acoustic signal of pipeline leakage. The DSP main control board has 16 signal acquisition channels and 2 ADC conversion modules, using an 8-channel cascaded configuration. The ADC sampling frequency was set to 1280Hz, and the pipeline leakage acoustic signal acquired by the ADC was then transmitted to a host computer via serial port for noise reduction processing.
[0091] Specifically, the mV-level voltage signal output by the hydrophone is converted into a stable and clear current signal. An AD694 chip is selected to build the peripheral circuit for signal conversion. Meanwhile, since the ADC module in the main control chip's DSP can only receive 0~3V voltage signals, while the signals from the pressure transmitter and hydrophone after conversion by the AD694 are both 0~20mA current signals, an AD620 chip is used to implement the current-to-voltage signal conversion.
[0092] Using the number of modes K and the penalty factor α as optimization objectives, the pipe leakage acoustic signal acquired in step one was subjected to both traditional VMD decomposition and the adaptive VMD decomposition proposed in this invention. The traditional VMD parameters were preset to k=5 and α=2000; the adaptive VMD output k=7 and α=2200 after iterative optimization. The time-domain and frequency-domain plots obtained from the traditional VMD and the adaptive VMD decomposition of this invention are shown below. Figure 2 and Figure 3 As shown, the IMF components obtained by adaptive VMD are uniformly distributed in the frequency domain, and the information of different frequency bands are separated in sequence, without any mode aliasing.
[0093] Correlation coefficients were calculated for each IMF and the pipeline leakage acoustic signal acquired in step one. With an effective threshold set to 0.36, the correlation coefficients between the seven IMF components and the pipeline leakage acoustic signal were found to be 0.274, 0.376, 0.421, 0.452, 0.457, 0.358, and 0.292, respectively. Specifically, IMF2, IMF3, IMF4, and IMF5 were selected as effective IMF components, while IMF1, IMF6, and IMF7 were selected as noise components.
[0094] Four components with relatively high correlation coefficients, IMF4, IMF5, IMF6, and IMF7, were selected for SSD decomposition and denoising of the signal. The time-domain and frequency-domain plots obtained after VMD decomposition and the adaptive VMD-SSD decomposition of this invention are shown below. Figure 4 and Figure 5 As shown.
[0095] The spectral entropies of the original pipeline leakage acoustic signal, the pipeline leakage acoustic signal after VMD denoising, and the signal after adaptive VMD-SSD denoising are 5.807, 5.025, and 4.648, respectively. After applying the adaptive VMD-SSD method for denoising, the spectral entropy of the source signal decreased from 5.807 to 4.648, a reduction of 27.3%, indicating that the adaptive VMD-SSD denoising method effectively reduces the spectral complexity of the signal. Simultaneously, the reduction in spectral entropy indicates that after adaptive VMD-SSD denoising, the signal has a higher energy concentration at specific frequency components, meaning that the time-frequency characteristics of the signal become more pronounced and prominent.
[0096] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the pipe leakage signal denoising method based on the improved adaptive VMD-SSD described in any of the above embodiments.
[0097] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, wherein when the computer program is executed by a processor, the steps of the pipe leakage signal denoising method based on the improved adaptive VMD-SSD described in any of the above embodiments are implemented.
[0098] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the pipeline leakage signal denoising method based on improved adaptive VMD-SSD, which will not be repeated here.
[0099] Computer-readable storage media encompass a variety of types, including persistent and non-persistent, portable and fixed. These media store information using different technologies, and the content can be machine instructions, data structures, program modules, or other types of data. Some typical examples of computer storage media include: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), various types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory and other storage technologies, optical storage media such as CD-ROM and digital video disc (DVD), magnetic storage devices such as magnetic tape and disks, and other non-transferable media used to store information accessible to computing devices. It is important to note that the computer-readable media described herein do not include temporary storage media, such as modulated data signals and carrier waves.
[0100] Those skilled in the art will further recognize that the operation of the module can be achieved using existing technical protocols or programs, without relying on new computer programs themselves. The units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.
[0101] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, software modules executed by a processor, or a combination of both. The software modules can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the scope of protection of the invention.
[0102] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for denoising pipeline leakage signals based on improved adaptive VMD-SSD, characterized in that, Includes the following steps: Step 1: Collect the acoustic signal of the pipeline leak as the raw signal data; Step 2: Using the penalty factor and mode decomposition number of the VMD algorithm as optimization objectives, the original signal data is optimized using an adaptive optimization algorithm to obtain the optimal decomposition parameters; Step 3: Perform VMD decomposition on the original signal data according to the decomposition parameters to obtain N IMF components; Step 4: Calculate the correlation coefficients between the N IMF components and the original signal, and select the effective IMF components based on the correlation coefficients; Step 5: Perform secondary decomposition on each effective IMF component using the SSD algorithm, select the principal component through the energy threshold for reconstruction, and obtain the denoised effective IMF component; synthesize the denoised effective IMF component to obtain the denoised pipeline leakage acoustic signal.
2. The method for denoising pipeline leakage signals based on improved adaptive VMD-SSD according to claim 1, characterized in that, The optimization process in step two includes: Initialize the number of mode decompositions k and the penalty factor α, and perform VMD decomposition on the original signal data to obtain multiple IMF components; After performing a fast Fourier transform on each IMF component, the frequencies with frequency amplitudes greater than the core threshold are the core frequencies of each IMF component. Based on the core frequencies of all IMF components, calculate the total number of normalized core frequencies b and the total number of normalized frequency repetitions c. If the total number of normalized core frequencies b is lower than the first preset threshold, then return to increase the number of initial modal decompositions k; if the total number of normalized frequency repetitions c is higher than the second preset threshold, then return to increase the initialization penalty factor α; until b is greater than the first preset threshold and c is less than the second preset threshold, then the current number of modal decompositions k and penalty factor α are determined as the optimal decomposition parameters.
3. A method for denoising pipeline leakage signals based on an improved adaptive VMD-SSD according to claim 2, characterized in that, The core threshold is ,in, is the maximum peak value after the Fast Fourier Transform, and m is a set ratio.
4. A method for denoising pipeline leakage signals based on an improved adaptive VMD-SSD according to claim 2, characterized in that, The normalized total number of core frequencies b and the normalized total number of frequency repetitions c are: Among them, F i， Let be the core frequency of the i-th IMF component; F is the maximum frequency value after the Fast Fourier Transform, which is half the sampling frequency; G j denoted as , where is the number of core frequency repetitions for each IMF component; and is the total number of IMF components.
5. A method for denoising pipeline leakage signals based on improved adaptive VMD-SSD according to claim 1, characterized in that, In step four, IMF components with correlation coefficients greater than or equal to the effective threshold are considered effective IMF components; the effective threshold... ,in To maintain a fixed proportion, This represents the calculated maximum correlation coefficient.
6. A method for denoising pipeline leakage signals based on an improved adaptive VMD-SSD according to claim 1, characterized in that, Step five, which involves selecting principal components for reconstruction using an energy threshold, includes: Calculate the energy corresponding to each singular value after SSD decomposition, and determine that the cumulative energy percentage is greater than or equal to a preset energy threshold. Minimum number of singular values r required; The components corresponding to the first r singular values are selected for reconstruction to obtain the denoised effective IMF components.
7. A method for denoising pipeline leakage signals based on an improved adaptive VMD-SSD according to claim 6, characterized in that, The preset energy threshold , in, The total energy of all singular values; The cumulative energy of the first r singular values; The denoised effective IMF component , in, To reconstruct the matrix The OK Column elements; L is the window length; K is the number of columns in the trajectory matrix; N is the length of the signal; t is the time.
8. A computer system comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.
10. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.