Method and device for identifying double star fault, electronic equipment and storage medium

Abstract

The application provides a method and device for double-star fault identification, electronic equipment and a storage medium, applied to the technical field of satellite navigation, and singular value space vectors and singular value space matrices are obtained by singular value decomposition of an observation coefficient matrix in pseudorange observation. Then, a singular value fault plane is constructed by using the singular value space matrix under double-star fault. Considering the influence of observation noise, whether there is double-star fault can be determined by the angle between the singular value space vector and the singular value fault plane. Through this geometric determination algorithm, the problem of being affected by the identification threshold in double-star fault identification is effectively solved, thereby avoiding the occurrence of missed detection and false alarm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation technology, in particular to a method and device for double-satellite fault identification, an electronic device and a storage medium. BACKGROUND

[0002] In a satellite navigation system, a receiver autonomous integrity monitoring (RAIM) method is usually used to detect and identify faulty satellites. At present, most RAIM algorithms are based on single-satellite and single-constellation fault assumptions. With the full networking of the four major navigation systems, more satellites can be observed by the receiver, which increases the probability of satellite failure. Therefore, RAIM monitoring algorithms sensitive to double-satellite or multi-satellite faults are needed. The existing RAIM method for traditional receiver autonomous integrity monitoring usually uses a threshold to identify double-satellite faults. In this identification process, there may be missed detection and false detection under the mapping of the parity change matrix.

[0003] Therefore, the satellite fault identification in the prior art is limited by the identification threshold, and the identification rate is low. SUMMARY

[0004] In view of the above shortcomings of the prior art, the present application provides a method and device for double-satellite fault identification, an electronic device and a storage medium, which are applied to the field of satellite navigation technology. Specifically, the observation coefficient matrix in the pseudorange observation is singular value decomposed to obtain a singular value space vector and a singular value space matrix. Then, a singular value fault plane is constructed using the singular value space matrix under double-satellite fault. Considering the influence of observation noise, whether there is a double-satellite fault can be determined by the angle between the singular value space vector and the singular value fault plane. Through this geometric judgment algorithm, the problem of being affected by the identification threshold in double-satellite fault identification is effectively solved, thereby avoiding missed detection and false alarms.

[0005] In a first aspect, the present application provides a method for double-satellite fault identification, which comprises the following steps:

[0006] Obtaining an observation coefficient matrix in a pseudorange observation;

[0007] Singular value decomposing the observation coefficient matrix to obtain a singular value space vector and a singular value space matrix;

[0008] Constructing a singular value fault plane according to the singular value space matrix;

[0009] Calculating the angle between the singular value space vector and the singular value fault plane;

[0010] Identifying a double-satellite fault according to the angle.

[0011] By the method for identifying double-star fault, the observation coefficient matrix in the pseudo-range observation is acquired first, then singular value decomposition is performed on the observation coefficient matrix, and a singular value space vector and a singular value space matrix are obtained, then a singular value fault plane is constructed by using the singular value space matrix under double-star fault, and considering the influence of observation noise, whether the double-star has fault is judged according to the included angle between the singular value space vector and the singular value fault plane. By the geometric judgment algorithm, the problem of being affected by the identification threshold in the double-star fault identification is effectively solved, so that the occurrence of missed detection and false alarm is avoided.

[0012] Preferably, in the method for identifying double-star fault provided by the application, the step of acquiring the observation coefficient matrix in the pseudo-range observation comprises:

[0013] The pseudo-range observation equation is acquired: ; wherein, is an n×1-dimensional residual error vector of the pseudo-range observation value, and n is the number of visible satellites; is the observation coefficient matrix; is a 4×1-dimensional user state vector, which is a four-dimensional vector composed of a receiver position vector and a clock correction amount; is an n×1-dimensional pseudo-range observation noise vector, and , σ is the standard deviation of the pseudo-range observation vector of the receiver pointing to the satellite; is an n×1-dimensional satellite fault vector;

[0014] The observation coefficient matrix is acquired according to the pseudo-range observation equation.

[0015] By the method for identifying double-star fault, the observation coefficient matrix can be quickly obtained according to the pseudo-range observation equation, which is convenient for subsequent calculation and improves the efficiency of identifying and determining double-star fault.

[0016] Preferably, in the method for identifying double-star fault provided by the application, the step of performing singular value decomposition on the observation coefficient matrix and obtaining a singular value space vector and a singular value space matrix comprises:

[0017] The singular value space vector is obtained according to the formula , and the singular value space matrix is ; wherein, is an orthogonal matrix, D is a diagonal singular value matrix, is an eigenvector, and T is a transpose symbol.

[0018] By the method for identifying double-star fault, the singular value decomposition is performed on the observation coefficient matrix by using the formula, so that the singular value space vector and the singular value space matrix are obtained. improve the computational efficiency.

[0019] Preferably, the application provides a method for double-satellite fault identification, and the step of constructing a singular value fault plane according to the singular value space matrix comprises:

[0020] calculating a fault characteristic vector according to the singular value space matrix;

[0021] constructing a singular value fault plane according to the fault characteristic vector.

[0022] Through the above method for double-satellite fault identification, it can be defined in advance that the i-th satellite has a fault, so as to calculate a fault characteristic vector according to the singular value space matrix. Since the fault characteristic vector should be located in the singular value fault plane when double-satellite fault occurs, a singular value fault plane can be constructed according to the known fault characteristic vector.

[0023] Preferably, the application provides a method for double-satellite fault identification, and the step of calculating a fault characteristic vector according to the singular value space matrix comprises:

[0024] calculating the fault characteristic vector according to the formula: wherein, is the fault characteristic vector of the i-th satellite, represents the i-th column of the singular value space matrix .

[0025] The step of constructing a singular value fault plane according to the fault characteristic vector comprises:

[0026] constructing the singular value fault plane according to the formula wherein, is the singular value fault plane formed by the i-th satellite and the j-th satellite, is the fault characteristic vector of the j-th satellite, and Plane() is a plane construction function.

[0027] Preferably, the application provides a method for double-satellite fault identification, and the step of calculating the included angle between the singular value space vector and the singular value fault plane comprises:

[0028] calculating the included angle according to the formula wherein, is the included angle between the i-th satellite and the j-th satellite, is the singular value fault plane formed by the i-th satellite and the j-th satellite, and i and j represent the i-th satellite and the j-th satellite, both of which are natural numbers.

[0029] Preferably, the application provides a method for double-satellite fault identification, and the step of identifying the double-satellite fault according to the included angle comprises:

[0030] ​When the included angle is 0, a fault feature vector of a singular value fault plane is obtained;

[0031] A satellite corresponding to the fault feature vector is obtained, and it is determined that the satellite has a fault.

[0032] In a second aspect, the present application provides a device for double-satellite fault identification, which comprises

[0033] An obtaining module is configured to obtain an observation coefficient matrix in pseudo-range observation;

[0034] A decomposition module is configured to perform singular value decomposition on the observation coefficient matrix, and obtain a singular value space vector and a singular value space matrix;

[0035] A construction module is configured to construct a singular value fault plane according to the singular value space matrix;

[0036] A calculation module is configured to calculate an included angle between the singular value space vector and the singular value fault plane;

[0037] An identification module is configured to identify a double-satellite fault according to the included angle.

[0038] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the steps in the method provided in the first aspect are executed.

[0039] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are executed.

[0040] Advantages: The method, device, electronic device and storage medium for double-satellite fault identification provided by the present application perform singular value decomposition on an observation coefficient matrix in pseudo-range observation, obtain a singular value space vector and a singular value space matrix, and then construct a singular value fault plane using the singular value space matrix under a double-satellite fault. Considering the influence of observation noise, whether there is a double-satellite fault can be determined by the included angle between the singular value space vector and the singular value fault plane. Through this geometric judgment algorithm, the problem of being affected by the identification threshold in double-satellite fault identification is effectively solved, thereby avoiding the occurrence of missed detection and false alarms. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A flowchart of the method for double-satellite fault identification provided by the present application.

[0042] Figure 2 A structural schematic diagram of the device for double-satellite fault identification provided by the present application.

[0043] Figure 3A structural schematic diagram of an electronic device provided in the present application.

[0044] Label description: 201, acquisition module; 202, decomposition module; 203, construction module; 204, calculation module; 205, identification module; 301, processor; 302, memory; 303, communication bus; 3, electronic device. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and indicated in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0046] It should be noted that: similar labels and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first, second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0047] The following disclosure provides many different embodiments or examples for achieving the purposes of the present application, solving the problems existing in the prior art. In view of the low recognition rate in the current double-star fault threshold recognition, the present application provides a double-star fault recognition method, device, electronic device and storage medium, specifically:

[0048] Please refer to Figure 1 , in a first aspect, the double-star fault recognition method of the embodiments of the present application comprises the following steps:

[0049] A1: obtaining an observation coefficient matrix in pseudorange observation;

[0050] A2: singular value decomposition is performed on the observation coefficient matrix, and a singular value space vector and a singular value space matrix are obtained;

[0051] A3: constructing a singular value fault plane according to the singular value space matrix;

[0052] A4: calculating the included angle between the singular value space vector and the singular value fault plane;

[0053] A5: identifying the double-star fault according to the included angle.

[0054] wherein, in practical application, an observation coefficient matrix in the pseudo-range observation is obtained, and then singular value decomposition is performed on the observation coefficient matrix to obtain a singular value space vector and a singular value space matrix, and then a singular value fault plane is constructed by using the singular value space matrix under double satellite fault, and considering the influence of observation noise, whether the double satellite has fault can be judged by calculating the included angle between the singular value space vector and the singular value fault plane. The specific principle is that when double satellite fault occurs, the singular value space vector corresponding to the double satellite should be located on the singular value fault plane, at this time the included angle is 0, so the principle can be deduced reversely, first assume that any two satellites in the satellite have fault, and establish the singular value fault plane, and then verify whether the included angle between the singular value space vector corresponding to the double satellite and the singular value fault plane is 0, when it is 0, it means that the singular value space vector corresponding to the double satellite falls on the singular value fault plane, and it can be judged that the double satellite has fault. Through this geometric judgment algorithm, the problem of being affected by the identification threshold in double satellite fault identification is effectively solved, so as to avoid the occurrence of missed detection and false alarm.

[0055] In further embodiments, the step A1 comprises:

[0056] The pseudo-range observation equation is obtained: wherein, is a residual vector of n x 1 dimension pseudo-range observation value, n is the number of visible satellites; is an observation coefficient matrix; is a 4 x 1 dimension user state vector, which includes a four-dimensional vector composed of a receiver position vector and a clock correction amount; is an n x 1 dimension pseudo-range observation noise vector, and , σ is the standard deviation of the pseudo-range observation vector of the receiver pointing to the satellite; is an n x 1 dimension satellite fault vector;

[0057] The observation coefficient matrix is obtained according to the pseudo-range observation equation.

[0058] wherein, in practical application, the pseudo-range observation equation is obtained according to the satellite navigation positioning principle.

[0059] In further embodiments, the step A2 comprises:

[0060] The singular value decomposition is performed on the observation coefficient matrix according to the formula to obtain a singular value space vector , and a singular value space matrix ; wherein, is an orthogonal matrix, D is a diagonal singular value matrix, is an eigenvector, and T is a transpose symbol.

[0061] In practical applications, for Equation (1) can be decomposed to obtain: Equation (2) can be further derived based on the properties of unitary matrices: Equation (3), where, It represents the identity matrix (in matrix multiplication, there is a type of matrix that plays a special role, just like 1 in number multiplication; this type of matrix is ​​called the identity matrix).

[0062] make , .in, for The first 4 lines, for The rest OK, for The first 4 lines, This represents the product of the diagonal singular value matrix and the conjugate eigenvector. Substituting into equation (2), we get: (4)

[0063] make , By combining equation (3), we can obtain (where, Q , P (Custom parameters defined to simplify the formula)

[0064] Equation (5) can be transformed to obtain: (6)

[0065] make The least squares solution can be obtained: (7) Equation;

[0066] Therefore, the singular value space vector is calculated. : (8) Equation.

[0067] Among them, the definition Let be the singular value space matrix. From equation (8), it can be seen that the singular value space vector constructed in this application... Unlike the odd-even vectors obtained by traditional QR decomposition, although the two algorithms are equivalent in monitoring satellite faults, they differ in performance in satellite fault identification.

[0068] In a further embodiment, step A3 includes:

[0069] Calculate the fault feature vector based on the singular value space matrix;

[0070] Construct a singular value fault plane based on the fault feature vector.

[0071] wherein, in practical applications, it can be assumed that the ith and jth satellites are faulty, and the corresponding fault vectors are and , and and are not 0, and and are substituted into the formula , and after being combined, the singular value space vector is obtained. As can be seen from the formula, when double satellite faults exist, the satellite fault deviation is mapped to by each column of the singular value space matrix, and therefore, the geometric relationship between the singular value space vector and the singular value space matrix can be used to identify satellite faults, that is, when double satellite faults exist, the singular value space vector should be located on the plane formed by the vectors and , and the plane formed by the vectors and is the singular value fault plane. Considering the influence of observation noise, if the angle between the singular value space vector and the singular value fault plane is 0, it can be determined that double satellite faults exist. According to the geometric determination algorithm, the problem of being affected by the identification threshold in double satellite fault identification can be effectively solved, and the occurrence of missed detection and false alarms can be avoided.

[0072] In further embodiments, calculating the fault feature vector according to the singular value space matrix comprises:

[0073] calculating the fault feature vector according to the formula: , wherein is the fault feature vector of the ith satellite, represents the ith column of the singular value space matrix ; constructing the singular value fault plane according to the fault feature vector comprises:

[0074] constructing the singular value fault plane according to the formula

[0075] , wherein is the singular value fault plane formed by the ith satellite and the jth satellite, is the fault feature vector of the jth satellite, and Plane() is a plane construction function. wherein, in specific embodiments, the normalized fault feature vector of the ith satellite can be defined as

[0076] , and the singular value fault plane constructed from the fault feature vector is defined as , and it is assumed that the ith and jth satellites are faulty, and . , record The projection of the singular value fault plane is , then The angle between is The angle between and .

[0077] Therefore, the A4 step includes:

[0078] The angle is calculated according to the formula , where is the angle between the ith satellite and the jth satellite, is the singular value fault plane formed by the ith satellite and the jth satellite, i and j represent the ith satellite and the jth satellite, and both are natural numbers.

[0079] In practical applications, , where represents the inner product operation, and From the above formula, we know that:

[0080] ,

[0081] If and only if and are completely coincident, that is, . Thus, when the singular value space vector is located on the singular value fault plane , it can be determined that the satellite corresponding to the fault feature vector that forms the singular value fault plane has failed. Therefore, the A5 step includes:

[0082] The satellite corresponding to the fault feature vector has failed. Therefore, the A5 step includes:

[0083] When the angle is 0, the fault feature vector that forms the singular value fault plane is obtained;

[0084] The satellite corresponding to the fault feature vector is obtained, and it is determined that the satellite has failed.

[0085] From the above, the application provides a method for double-star fault identification, singular value space vectors and singular value space matrices are obtained by singular value decomposition on the observation coefficient matrix in the pseudo-range observation, and then the singular value fault plane is constructed by using the singular value space matrices under double-star fault. Considering the influence of observation noise, whether there is double-star fault can be determined by the angle between the singular value space vector and the singular value fault plane. Through this geometric judgment algorithm, the problem of being affected by the identification threshold in double-star fault identification is effectively solved, so as to avoid the occurrence of missed detection and false alarm.

[0086] Please refer to Figure 2 The application provides a device for double-star fault identification, which comprises:

[0087] The acquisition module 201 is used for acquiring the observation coefficient matrix in the pseudo-range observation.

[0088] The decomposition module 202 is used for singular value decomposition on the observation coefficient matrix, and singular value space vectors and singular value space matrices are obtained.

[0089] The construction module 203 is used for constructing the singular value fault plane according to the singular value space matrix.

[0090] The calculation module 204 is used for calculating the angle between the singular value space vector and the singular value fault plane.

[0091] The identification module 205 is used for identifying the double-star fault according to the angle.

[0092] In actual application, the acquisition module 201 acquires the observation coefficient matrix according to the pseudo-range observation equation: The decomposition module 202 performs singular value decomposition on the observation coefficient matrix according to the formula to obtain singular value space vectors , and singular value space matrices The construction module 203 calculates the fault characteristic vector according to the formula: and constructs the singular value fault plane according to the formula The calculation module 204 calculates the angle between the singular value space vector and the singular value fault plane according to , and The identification module 205 identifies the double-star fault according to the formula When and are completely coincident, that is, , it is determined that the satellite corresponding to the fault characteristic vector constituting the singular value fault plane has a fault.

[0093] From the above, the application provides a device for double-star fault identification, singular value decomposition is performed on the observation coefficient matrix in the pseudo-range observation to obtain a singular value space vector and a singular value space matrix, and then a singular value fault plane is constructed by using the singular value space matrix under double-star fault. Considering the influence of observation noise, whether there is double-star fault can be determined by the angle between the singular value space vector and the singular value fault plane. By using this geometric determination algorithm, the problem of being affected by the identification threshold in double-star fault identification is effectively solved, so that the occurrence of missed detection and false alarm is avoided.

[0094] Please refer to Figure 3 , Figure 3 A structural schematic diagram of an electronic device provided by the embodiment of the application, the application provides an electronic device 3, comprising: a processor 301 and a memory 302, the processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanism (not marked), the memory 302 stores computer readable instructions executable by the processor 301, when the electronic device runs, the processor 301 executes the computer readable instructions to execute the method in any optional implementation manner of the above-mentioned embodiment, to realize the following functions: obtaining the observation coefficient matrix in the pseudo-range observation; singular value decomposition is performed on the observation coefficient matrix, and a singular value space vector and a singular value space matrix are obtained; a singular value fault plane is constructed according to the singular value space matrix; the angle between the singular value space vector and the singular value fault plane is calculated; and the double-star fault is identified according to the angle.

[0095] The embodiment of the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to execute the method in any optional implementation manner of the above-mentioned embodiment, to realize the following functions: obtaining the observation coefficient matrix in the pseudo-range observation; singular value decomposition is performed on the observation coefficient matrix, and a singular value space vector and a singular value space matrix are obtained; a singular value fault plane is constructed according to the singular value space matrix; the angle between the singular value space vector and the singular value fault plane is calculated; and the double-star fault is identified according to the angle.

[0096] The computer readable storage medium can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0097] In the embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and another division mode can be used in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0098] In addition, the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiments.

[0099] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0100] In this paper, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations.

[0101] The above merely provides an example of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for identifying faults in a dual-satellite system, characterized in that, The method includes the following steps: Obtain the observation coefficient matrix from pseudorange observations; Singular value decomposition is performed on the observation coefficient matrix to obtain the singular value space vector and singular value space matrix; Construct a singular value fault plane based on the singular value space matrix; Calculate the angle between the singular value space vector and the singular value fault plane; The binary star fault is identified based on the included angle.

2. The method for identifying binary star faults according to claim 1, characterized in that, The steps for obtaining the observation coefficient matrix in pseudorange observations include: Obtain the pseudorange observation equation: ;in, is the residual vector of n×1 dimensional pseudorange observations, where n is the number of visible satellites; This is the observation coefficient matrix; It is a 4×1 dimensional user state vector, which is a four-dimensional vector consisting of the receiver position vector and the clock correction. Let be an n×1 dimensional pseudorange observation noise vector, and σ is the standard deviation of the pseudorange observation vector pointed at the satellite by the receiver; The satellite fault vector is an n×1 dimensional vector. The observation coefficient matrix is ​​obtained based on the pseudorange observation equation.

3. The method for identifying binary star faults according to claim 2, characterized in that, The step of performing singular value decomposition on the observation coefficient matrix to obtain the singular value space vector and singular value space matrix includes: According to the formula Singular value decomposition is performed on the observed coefficient matrix to obtain the singular value space vector. , The singular value space matrix is ;in, It is an orthogonal matrix. D It is a diagonal singular value matrix. It is the eigenvector, and T is the transpose symbol.

4. The method for identifying binary star faults according to claim 3, characterized in that, The step of constructing the singular value fault plane based on the singular value space matrix includes: Calculate the fault feature vector based on the singular value space matrix; Singular value fault planes are constructed based on the fault feature vectors.

5. The method for identifying binary star faults according to claim 4, characterized in that, The calculation of the fault feature vector based on the singular value space matrix includes: According to the formula: Calculate the fault feature vector, where, Let be the fault characteristic vector of the i-th satellite. Represents the singular value space matrix The List; The step of constructing the singular value fault plane based on the fault feature vector includes: According to the formula Construct the singular value fault plane, wherein, Let be the singularity fault plane formed by the i-th satellite and the j-th satellite. Let be the fault characteristic vector of the j-th satellite, and Plane() is the plane constructor function.

6. The method for identifying binary star faults according to claim 5, characterized in that, The step of calculating the angle between the singular value space vector and the singular value fault plane includes: According to the formula Calculate the included angle, where, Let be the angle between the i-th satellite and the j-th satellite. Let i be the singular value fault plane formed by the i-th satellite and the j-th satellite, where i and j represent the i-th satellite and the j-th satellite, respectively, and are both natural numbers.

7. The method for identifying binary star faults according to claim 6, characterized in that, The step of identifying binary star faults based on the included angle includes: When the included angle is 0, the fault feature vectors that make up the singular value fault plane are obtained; Obtain the satellite corresponding to the fault feature vector and determine that the satellite has malfunctioned.

8. A device for identifying dual-satellite faults, characterized in that, The device includes: Acquisition module: Used to acquire the observation coefficient matrix in pseudorange observations; Decomposition module: used to perform singular value decomposition on the observation coefficient matrix and obtain the singular value space vector and singular value space matrix; Construction module: used to construct a singular value fault plane based on the singular value space matrix; Calculation module: used to calculate the angle between the singular value space vector and the singular value fault plane; Identification module: used to identify binary star faults based on the included angle.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method as described in 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 performs the steps of the method as described in any one of claims 1-7.

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