Method and system for estimating tolerance distance based on normal wave limiting mode under two sound tracks in Arctic ice region

Abstract

The invention provides a normal wave limiting mode-based tolerance distance estimation method and system under two sound tracks of an arctic ice region, and the method comprises the steps: carrying out the simulation prediction of the frequency domain sound pressure, phase velocity, group velocity and modal covariance matrix of a test sea area of the arctic ice region through a normal wave model; the method comprises the following steps: converting a time domain signal received by a vertical array of an ice sea area into frequency domain data by adopting fast Fourier transform; obtaining sound velocities and depths corresponding to the upper boundary, the sound channel axis and the lower boundary of the Port waveguide according to the double-sound-channel sound velocity profile of the test sea area; according to the phase velocity dispersion curve and the intersection point of the upper boundary of the Port waveguide and the axial sound velocity of the sound channel, obtaining an order index of a Port waveguide mode; carrying out modal separation by utilizing a matched filtering modal filtering method, and extracting a modal limited in the muffle waveguide; and constructing a distance estimation operator of the tolerance of the ice layer and the seabed based on the extracted modality in the muffle waveguide, and realizing tolerance estimation of the underwater target distance.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underwater acoustics, ocean engineering and sonar, and particularly relates to a wide tolerance distance estimation method and system based on normal mode limited mode in a double channel in the Arctic ice area. BACKGROUND

[0002] Environmental tolerance underwater target distance estimation is one of the hot and difficult problems in the research of underwater acoustic signal processing. The conventional matched field positioning has strong environmental sensitivity. When the parameters of sea ice / sea surface, sound speed structure, sea depth and seabed bottom are not sufficient, the real waveguide environment is mismatched, which causes the ambiguity of target distance and depth estimation and even the wrong positioning. In the Arctic sea area, the sound wave frequently interacts with the sea ice interface, causing scattering, absorption and dispersion of the sound signal. The complexity and diversity of the sea ice physical and acoustic parameters significantly enhance the ambiguity of the distance estimation caused by the mismatch of the sea ice parameters, which limits the application of the matched field method in the Arctic sea area.

[0003] At present, domestic and foreign scholars mainly carry out the mechanism research on the propagation characteristics and dispersion characteristics in the double channel in the Arctic, and there is no published article or patent on the wide tolerance distance estimation method in the double channel. SUMMARY

[0004] The purpose of the present application is to overcome the defect of high environmental sensitivity of the existing matched field technology in the distance estimation in the Arctic sea area, so as to solve the problem of wide tolerance distance estimation in the Arctic ice area.

[0005] In order to achieve the above purpose, the present application proposes a wide tolerance distance estimation method based on normal mode limited mode in the double channel in the Arctic ice area, which comprises the following steps: Step 1: using the normal mode model, simulating and predicting the frequency domain sound pressure, phase velocity, group velocity and modal covariance matrix of the test sea area in the Arctic ice area; Step 2: using fast Fourier transform, transforming the time domain signal received by the vertical array in the ice area into frequency domain data; Step 3: according to the sound speed profile of the test sea area in the double channel, obtaining the sound speed and depth corresponding to the upper boundary, channel axis and lower boundary of the Baffin waveguide; Step 4: according to the intersection of the phase velocity dispersion curve and the sound speed of the upper boundary and channel axis of the Baffin waveguide, obtaining the order index of the Baffin waveguide mode; Step 5: using the matched filter mode filtering method, carrying out mode separation and extracting the mode limited in the Baffin waveguide; Step 6: based on the extracted mode in the Baffin waveguide, constructing an ice layer and seabed wide tolerance distance estimation element, and realizing the wide tolerance estimation of the underwater target distance.

[0006] The frequency domain pressure of step 1 is preferably given by:

[0007] wherein, represents the distance to the sound source in meters, represents the receiving depth in meters is the discrete angular frequency; represents the imaginary unit; represents the modal depth function; represents the horizontal wave number of the m-th mode; represents the mode index; is the total number of modes; represents the depth of the target; represents the seawater density at the target depth; the phase velocity of the m-th mode and the group velocity of the m-th mode are given by:

[0008]

[0009] wherein, is the simulated angular frequency; the modal covariance matrix is given by:

[0010]

[0011] wherein, the superscript H denotes the conjugate transpose, represents the depth of the different vertical array elements; represents the modal depth function of the N-th element, is the modal depth function matrix.

[0012] The frequency domain data of step 2 is preferably given by:

[0013]

[0014] wherein, represents the vertical receiving array complex pressure vector, represents the number of vertical array elements; represents the modal amplitude function vector; the superscript H denotes the conjugate transpose.​​​

[0015] Preferably, the step 3 comprises: According to the sound velocity profile of the experimental sea area, the upper boundary has positive gradient in shallow sound velocity and negative gradient in deep sound velocity, and the boundary has the maximum sound velocity , so as to obtain the upper boundary depth of the Baffin Bay waveguide and the corresponding sound velocity , the sound channel axis depth and the sound velocity , and the lower boundary depth and the corresponding sound velocity .

[0016] Preferably, the step 4 comprises: According to the intersection of the phase velocity dispersion curve and and , the modal order index limited in the Baffin Bay waveguide is obtained and :

[0017] .

[0018] Preferably, the step 5 extracts the modal limited in the Baffin Bay waveguide :

[0019] Wherein, , is the modal depth function matrix, is the frequency domain data of step 2.

[0020] Preferably, the step 6 constructs the ice layer and seabed parameter tolerant distance estimation algorithm :

[0021] Wherein, and are the modal order indexes limited in the Baffin Bay waveguide, is the modal limited in the Baffin Bay waveguide with the modal order index extracted from the vertical array receiving data in step 5; When the searched distance is consistent with the real distance of the target, a peak value will appear, and the target distance estimation can be obtained by searching the peak value.

[0022] In another aspect, the present application provides a polar ice region double-channel based on normal wave restriction mode of tolerant distance estimation system, based on the above method, the system comprises: Normal wave model calculation module, for using normal wave model, simulation forecast polar ice region test sea area frequency domain sound pressure, phase velocity, group velocity and modal covariance matrix; Fourier transform module, for using fast Fourier transform, the time domain signal received by the vertical array in the ice region sea area is transformed into frequency domain data; Sound velocity profile calculation module, for obtaining the sound velocity and depth corresponding to the upper boundary of waveguide, sound channel axis and lower boundary according to the double-channel sound velocity profile of test sea area; Order index acquisition module, for obtaining the order index of waveguide mode according to the intersection of phase velocity dispersion curve and waveguide upper boundary and sound channel axis sound velocity; Modal separation module, for using matched filter mode filtering method, modal separation is carried out, and the mode limited in waveguide is extracted; Distance estimation module, for constructing ice layer and seabed tolerant distance estimation sub-module based on the extracted mode in waveguide, realizing the tolerant estimation of underwater target distance Compared with the prior art, the sound channel axis has the advantages of the present application: Based on the dispersion characteristics of double-channel waveguide, the present application extracts the starting order index of normal wave limited in waveguide, carries out modal separation on the measured data of vertical array through modal separation method, constructs distance estimation sub-module based on the mode limited in waveguide, overcomes the environmental mismatch problem caused by the uncertainty of sea ice and seabed parameters, realizes the tolerant distance estimation under ice. Compared with the traditional matched field and matched mode method, the calculation amount is small, the robustness and environmental tolerance are high, the environmental mismatch problem caused by the uncertainty of sea ice and seabed parameters is avoided, and the present application is easy to popularize in actual sonar platform. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The polar ice region double-channel based on normal wave restriction mode of tolerant distance estimation method flow chart of the present application is shown; Figure 2 The measured double-channel sound velocity structure of ice region sea area is shown; Figure 3 The vertical array layout position schematic diagram is shown; Figure 4 The depth function distribution condition diagram of 2 / 10 / 60 and 80 order normal wave mode is shown; Figure 5 The phase velocity and group velocity dispersion curve is shown; Figure 6 The vertical array modal covariance matrix is shown; Figure 7 The intersection of phase velocity and waveguide characteristic sound speed is shown; Figure 8 The measured propagation loss of the vertical array and the calculated propagation loss of the waveguide limited mode are shown; Figure 9 The distance estimation results of 6.4km are shown; Figure 10 The comparison curve of distance estimation of 4~8.5km and GPS measured distance is shown. DETAILED DESCRIPTION

[0024] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings.

[0025] The present application proposes a normal distance estimation method and system based on normal mode limited mode in the Arctic ice area double channel, to solve the distance estimation environment mismatch problem caused by the uncertainty of Arctic sea ice and seabed, which can be used for normal distance estimation of underwater targets in the Arctic Chukchi Sea platform and Baffin Bay double channel sea area.

[0026] The application scope of the method / system is: the sound speed structure is a double channel sound speed structure, the sea ice density range is 0~1, the sea depth range is 300~6000m; the horizontal distance between the target sound source and the vertical linear array is 0~200km; the depth range of the target sound source and the vertical array is 50~300m.

[0027] Embodiment 1 As Figure 1 shown, the embodiment 1 of the present application proposes a normal distance estimation method based on normal mode limited mode in the Arctic ice area double channel, which comprises: Step 1) using normal mode model, simulating and predicting the normal mode depth function, phase velocity and group velocity dispersion curve, and mode covariance matrix of the test sea area; and the distance between the sound source and the vertical array meters, the frequency domain sound pressure of the receiving depth meters can be expressed as

[0028] wherein, represents the frequency domain receiving data; is the discrete angular frequency; represents the imaginary unit; represents the mode depth function; represents the horizontal wave number of the mode; represents the mode index; is the total mode order; represents the depth where the target is located; represents the density of seawater at the target depth. modal phase velocity and group velocity can be expressed as:

[0029]

[0030] modal covariance matrix can be expressed as:

[0031]

[0032] where, represents the depth of the different vertical array array elements; the depth of the vertical array involves the typical 40~200m depth range of the Baffin Bay duct, represents the modal depth function of the Nth array element, is the modal depth function matrix.

[0033] Step 2) Through fast Fourier transform, the frequency domain receiving data of the vertical array can be expressed as:

[0034]

[0035] where, represents the vertical receiving array complex pressure vector, represents the number of vertical array elements; represents the modal amplitude function vector; the superscript H represents the conjugate transpose.

[0036] Step 3) According to the sound speed structure of the test sea area, the upper boundary has a positive gradient of shallow sound speed and a negative gradient of deep sound speed, and the boundary is the maximum value of sound speed , so as to obtain the upper boundary depth and the sound speed , the sound channel axis depth and the sound speed , and the lower boundary depth and the sound speed ; Step 4) According to the intersection of the phase velocity dispersion curve and and , the modal order index and restricted in the Baffin Bay duct are obtained:

[0037]

[0038] Step 5) Extract the modal amplitudes that can be distinguished from the measured data using the matched filter modal separation method, denoted as:

[0039] where, represents the estimation of the modal amplitude function; the modal depth function matrix is calculated by the normal mode model.

[0040] Step 6) Select the modes that are limited in the waveguide from the normal mode modal amplitudes , and construct the ice layer and seabed parameter tolerant distance estimator:

[0041] where, and are the modal order index limited in the waveguide. When the searched distance is consistent with the true distance of the target, a peak value will appear. The target distance estimation can be obtained by peak searching.

[0042] Example 2 Figure 2 The two-channel sound speed structure measured during the Chinese Arctic Scientific Expedition in the Canada Basin is shown. The sea depth at the test location is 792 m, and there is a seamount. The sea ice density is 70%, the average thickness of sea ice is 2 m, and the roughness is uneven. Figure 3 The deployment position of the vertical array at the test time is shown. The number of array elements is 15, the array element spacing is 10 m, and the water depth covered is 40~180 m. The target frequency is 700 Hz, and the tolerant distance estimation is carried out by the following steps: Step 1: Use the normal mode KRAKEN model to simulate and predict the normal mode modal depth function Figure 2 , horizontal wave number , phase velocity , group velocity and modal covariance matrix of the ice-free, flat-bottom sea area with the sound speed structure, sea depth, etc. of the test sea area as the model input. The 2nd, 10th, 60th and 80th modal depth function distributions at 700 Hz are shown in Figure 4 , which correspond to (a), (b), (c), (d) respectively. It can be seen that the 10th modal is completely limited in the waveguide and does not interact with the sea ice and seabed. Figure 5 The phase velocity and group velocity dispersion curves are shown in Figure 6The modal covariance matrix is shown. It can be seen that the vertical array used in the experiment can distinguish modes.

[0043] Step 2: The time-domain data of each depth element of the vertical receiving array is , sampled at a sampling rate of 10 kHz, and the data length is 12 s. Fourier transform is performed to convert the time-domain data into frequency-domain data . The 700 Hz frequency band is selected to generate the measured field sound pressure frequency-domain matrix .

[0044] Step 3: According to the sound velocity structure of the experimental sea area shown in Figure 2 , the upper and lower boundaries and the sound channel axis of the Baffin Bay waveguide are obtained, as shown by the red dots in Figure 2 . is 1447.52 m / s, is 1441.6 m / s, is 114 m, and are 29 m and 224 m; Step 4: According to the intersection of the 700 Hz phase velocity curve shown in Figure 7 with the intersection of the 700 Hz phase velocity curve shown in Figure 7 and (blue dashed line) and (red electrical line), that is, the mode between 1441.6~1447.52 m / s, the modal order index confined in the Baffin Bay waveguide is obtained, where , ; Step 5: The modal depth function calculated by the KRAKEN model is . The normal mode amplitude that can be distinguished by the measured data of the vertical array is extracted by the matched filter mode separation method .

[0045] Step 6: The modes confined in the Baffin Bay waveguide are selected from the normal mode amplitude , and and are set to construct the ice layer and seabed parameter tolerant distance estimation algorithm:

[0046] When the search distance is consistent with the actual distance of the target sound source, there is a local maximum value, and the distance estimation can be obtained by peak value search. Figure 8 The upper graph in Figure 9 is the measured propagation loss of the vertical array, and the lower graph is the propagation loss calculated by the Baffin Bay waveguide mode. It can be seen that in the sound convergence zone, the energy proportion of the Baffin Bay waveguide is large.Figure 9 For a distance of 6.4 km Curve, Figure 10 The comparison of the 4~8.5km tolerant distance estimation and GPS measurement results is shown, and the maximum estimation error is less than 2.8%, which proves the tolerance of the method to the mismatch of the sea ice and seabed parameter environment.

[0047] Embodiment 3 The application also provides a polar ice area double-channel based on normal wave limited mode for tolerant distance estimation system, which is realized based on the above method, and the system comprises: A normal wave model calculation module is configured to simulate and predict the normal wave mode depth function, phase velocity and group velocity dispersion curve, and mode covariance matrix of the test sea area by using the normal wave model; A Fourier transform module is configured to transform the time domain signal received by the vertical array in the ice area into the frequency domain to obtain the frequency domain data of the array received signal; A sound velocity profile calculation module is configured to obtain the sound velocity and depth corresponding to the upper boundary of the Baffin waveguide, the sound channel axis and the lower boundary by the sound velocity profile of the test sea area; An order index acquisition module is configured to obtain the order index of the Baffin waveguide mode according to the intersection of the phase velocity dispersion curve and the sound velocity of the upper boundary of the Baffin waveguide and the sound channel axis; A mode separation module is configured to separate the modes by using the matched filter mode filtering method and extract the modes limited in the Baffin waveguide; A distance estimation module is configured to construct the ice layer and seabed tolerant distance estimation algorithm based on the extracted modes in the Baffin waveguide to realize the tolerant estimation of the underwater target distance.

[0048] It is worth noting that in the above embodiment of the system, each module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding function can be realized; in addition, the specific name of each functional module is only for the convenience of mutual differentiation, and does not limit the protection scope of the application.

[0049] Embodiment 4 The application also provides a computer device, which comprises at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It can be understood that the bus system is used to realize the connection and communication between the components. In addition to the data bus, the bus system also includes a power bus, a control bus and a state signal bus.

[0050] The user interface can include a display, a keyboard or a clicking device. For example, a mouse, a trackball, a touchpad or a touch screen, etc.

[0051] It is to be understood that the memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory described herein is intended to include, without being limited to, these and any other suitable types of memory.

[0052] In some embodiments, the memory stores elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and an application program.

[0053] Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program includes various application programs, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The program for implementing the method of the embodiments of the present application can be included in the application program.

[0054] In the above-mentioned embodiments, the processor can also be used to execute the steps of the above-mentioned method by invoking the programs or instructions stored in the memory, in particular, the programs or instructions stored in the application program. execute the steps of the above-mentioned method.

[0055] The method can be applied to a processor or implemented by the processor. The processor can be an integrated circuit chip having a signal processing capability. In implementation process, each step of the method can be completed by hardware integrated logic circuit or software form of instruction in the processor. The processor can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Each method, step and logic block disclosed above can be implemented or executed. The general processor can be a microprocessor or any conventional processor. The steps of the method disclosed above can be directly embodied as a hardware code processor to execute, or a combination of hardware and software modules in the code processor to execute. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in a memory, and the processor reads information in the memory and combines the hardware to complete the steps of the method.

[0056] It can be understood that the embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for executing functions of the present application or a combination thereof.

[0057] For software implementation, the present application can be implemented by executing function modules (such as processes, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0058] Embodiment 5 The application further provides a nonvolatile storage medium for storing the computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in Arctic ice regions, the method comprising: Step 1: Using the normal mode model, simulate and predict the frequency domain sound pressure, phase velocity, group velocity, and modal covariance matrix of the experimental sea area in the Arctic ice zone; Step 2: Use Fast Fourier Transform to transform the time-domain signal received by the vertical array in the ice-covered sea area into frequency-domain data; Step 3: Based on the dual-channel sound velocity profile of the test sea area, obtain the sound velocity and depth corresponding to the upper boundary, channel axis and lower boundary of the Beaufort waveguide; Step 4: Based on the intersection of the phase velocity dispersion curve with the upper boundary of the Beaufort waveguide and the sound velocity of the channel axis, obtain the order index of the Beaufort waveguide modes; Step 5: Use matched filtering mode filtering method to perform mode separation and extract the modes confined in the Beaufort waveguide; Step 6: Based on the extracted modes in the Beaufort waveguide, construct a tolerance-based distance estimation operator for ice and seabed to achieve tolerance-based estimation of underwater target distances.

2. The tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in the Arctic ice region according to claim 1, characterized in that, The frequency domain sound pressure level in step 1 for: ； in, Indicates the distance from the sound source, in meters. Indicates the reception depth, in meters. Discrete angular frequencies; Represents the imaginary unit; Represents the modal depth function; represent The horizontal wavenumber of the first mode; Represents a modal index; This represents the total modal order; Represents the depth of the target; The density of seawater at the target depth; First mode phase velocity Group speed for: ； ； in, To simulate angular frequency; Modal covariance matrix for: ； ； Among them, superscript H This indicates the conjugate transpose. Representing different vertical arrays The depth of each array element; This represents the modal depth function of the Nth element. is the modal depth function matrix.

3. The tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in the Arctic ice region according to claim 2, characterized in that, The frequency domain data in step 2 for: ； ； in, This represents the complex sound pressure vector of the vertical receiving array. Indicates the number of elements in the vertical array; Represents the modal amplitude function vector; superscript H This indicates the conjugate transpose.

4. The tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in the Arctic ice region according to claim 3, characterized in that, Step 3 includes: Based on the sound speed profile of the test sea area, the upper boundary has a positive gradient of shallow sound speed and a negative gradient of deep sound speed, with the sound speed maxima at the boundary. Thus, the upper boundary depth of the Beaufort waveguide is obtained. and the corresponding speed of sound , channel axis depth and speed of sound and lower boundary depth and the corresponding speed of sound .

5. The tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in the Arctic ice region according to claim 4, characterized in that, Step 4 includes: Based on the dispersion curve of phase velocity and and The intersection points yield the mode order index confined in the Beaufort waveguide. and : ； 。 6. The tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in the Arctic ice region according to claim 5, characterized in that, The modes restricted in the Beaufort waveguide extracted in step 5 for: ； in, , The modal depth function matrix, This refers to the frequency domain data from step 2.

7. The tolerance distance estimation method based on normal mode-constrained modes in dual-channel audio in the Arctic ice region according to claim 6, characterized in that, The distance estimation operator with tolerance for ice layer and seabed parameters constructed in step 6 for: ； in, and It is a mode order index restricted in Beaufort waveguides. The mode index for the constraint extracted from the data received from the vertical array in step 5 is: The modality; When the search distance matches the actual target distance, A peak will appear, and the target distance estimate can be obtained by searching for the peak.

8. A robust distance estimation system based on normal mode-constrained modes in dual-channel audio in Arctic ice regions, implemented using the method described in any one of claims 1-7, characterized in that... The system includes: The normal mode model calculation module is used to simulate and predict the frequency domain sound pressure, phase velocity, group velocity, and modal covariance matrix of the experimental sea area in the Arctic ice zone using the normal mode model. The Fourier transform module is used to transform the time-domain signal received by the vertical array in the ice-covered sea area into frequency-domain data using fast Fourier transform. The sound velocity profile calculation module is used to obtain the sound velocity and depth corresponding to the upper boundary, channel axis and lower boundary of the Beaufort waveguide based on the dual-channel sound velocity profile of the test sea area. The order index acquisition module is used to obtain the order index of the Beaufort waveguide mode based on the intersection of the phase velocity dispersion curve with the upper boundary of the Beaufort waveguide and the sound velocity of the channel axis. The mode separation module is used to perform mode separation using matched filtering mode filtering methods, extracting modes confined in the Beaufort waveguide; and The distance estimation module is used to construct a tolerance-based distance estimation operator for ice and seabed based on the extracted modes in the Beaufort waveguide, thereby achieving tolerance-based estimation of the distance to underwater targets.

9. A computer device, 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 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1-7.

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