Doppler power spectrum modeling method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a Doppler power spectrum modeling method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly building a power spectrum model which comprises a bilateral truncated asymmetric power spectrum model; for different scenes, the model parameters in the power spectrum model are fitted by using the measurement data corresponding to the scenes to obtain the parameter values corresponding to the simulation parameters in the scenes, and the values of the simulation parameters in the Doppler power spectrum model corresponding to the scenes are the parameter values, so that Doppler power spectrum modeling for the low-orbit satellite channel is realized.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a Doppler power spectrum modeling method, device, electronic equipment and storage medium. Background Art

[0002] Doppler power spectrum is one of the important statistical parameters in the channel. It can reflect the Doppler effect of the channel and the fading performance of multipath propagation signals in the frequency domain. It has a direct impact on the fading dynamics and fading depth of the system and is a key parameter in system planning and design. The establishment of the channel model of the space information network depends on the accurate establishment of its Doppler power spectrum model. At present, the research on the Doppler power spectrum model of the low-orbit constellation transmission channel is still incomplete. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a Doppler power spectrum modeling method, device, electronic device and storage medium to achieve Doppler power spectrum modeling for low-orbit satellite channels. The specific technical solution is as follows:

[0004] In a first aspect, an embodiment of the present invention provides a Doppler power spectrum modeling method, comprising:

[0005] Establishing a power spectrum model, wherein the power spectrum model includes a bilateral truncated asymmetric power spectrum model;

[0006] For different scenarios, the model parameters in the power spectrum model are fitted using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenario are the parameter values.

[0007] Optionally, before fitting the model parameters in the power spectrum model using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario, the method further includes:

[0008] For any one or more scenes, reference data corresponding to the scene is obtained, where the reference data is measurement data corresponding to the scene in multiple groups of measurement data.

[0009] Optionally, for different scenarios, fitting the model parameters in the power spectrum model using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario includes:

[0010] The model parameters in the power spectrum model are fitted using the reference data corresponding to the scene, and when the objective function obtains a minimum value, the parameter values ​​of the model parameters corresponding to the scene are obtained.

[0011] Optionally, before fitting the model parameters in the power spectrum model using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario, the method further includes:

[0012] Obtain multiple sets of measurement data;

[0013] Extract fingerprint features corresponding to each set of measurement data;

[0014] Cluster the fingerprint features corresponding to multiple groups of measurement data to obtain clusters corresponding to different scenarios;

[0015] For each scene, a fingerprint feature near a cluster center of a cluster corresponding to the scene is selected as a reference fingerprint corresponding to the scene, and measurement data corresponding to the reference fingerprint is used as reference data corresponding to the scene.

[0016] Optionally, clustering fingerprint features corresponding to the multiple groups of measurement data to obtain clusters corresponding to different scenarios includes:

[0017] The fingerprint features corresponding to each group of measurement data are standardized and reduced in dimension to obtain multiple two-dimensional fingerprint features;

[0018] Based on the K-Means clustering method, multiple two-dimensional fingerprint features are clustered to obtain clusters corresponding to different scenarios.

[0019] Optionally, clustering multiple two-dimensional fingerprint features based on the K-Means clustering method to obtain clusters corresponding to different scenarios includes:

[0020] Map multiple two-dimensional fingerprint features onto a two-dimensional plane to form a point set, where one two-dimensional fingerprint feature corresponds to one point in the point set;

[0021] The cluster center is repeatedly selected, the Euclidean distance from each point to the cluster center is calculated, and the Euclidean distance from each point to the cluster center is used to cluster multiple points to obtain multiple clusters, until the cluster center of each cluster no longer changes, and multiple clusters are obtained.

[0022] Optionally, the fingerprint feature includes a spectral fingerprint;

[0023] The extracting fingerprint features corresponding to each set of measurement data includes:

[0024] Each set of measurement data is plotted separately to obtain a spectrum corresponding to each set of measurement data;

[0025] For each set of measurement data, the shape features of the atlas corresponding to the set of measurement data are extracted as the atlas fingerprint corresponding to the set of measurement data.

[0026] Optionally, the bilaterally truncated asymmetric power spectrum model is used to represent the limitation and asymmetry of the direct component, the refracted component and the Doppler power spectrum broadening in the low-orbit satellite channel.

[0027] Optionally, the bilateral truncated asymmetric power spectrum model is expressed as:

[0028]

[0029] Where S(f) represents the Doppler power spectrum of the received signal, sgn(.) represents the sign function, f represents the received signal frequency, and f dev represents the frequency deviation adjustment factor, α represents the correlation factor, · represents the dot multiplication operation, and f max represents the maximum Doppler shift, S LOS (f) represents the Doppler power spectrum of the diameter signal, S NLOS (f) represents the Doppler power spectrum of the multipath component, k∈[0,1].

[0030] In a second aspect, an embodiment of the present invention provides a Doppler power spectrum modeling device, including:

[0031] An establishment module is used to establish a power spectrum model, and the power spectrum model includes a bilateral truncated asymmetric power spectrum model; a fitting module is used to fit the model parameters in the power spectrum model according to different scenarios using the measurement data corresponding to the scenarios to obtain the parameter values ​​corresponding to the simulation parameters under the scenarios, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenarios are the parameter values.

[0032] Optionally, the device also includes: a first acquisition module, which is used to obtain reference data corresponding to the scene for any one or more scenes before fitting the model parameters in the power spectrum model using the measurement data corresponding to the scene to obtain parameter values ​​corresponding to the simulation parameters under the scene, wherein the reference data is the measurement data corresponding to the scene in multiple groups of measurement data.

[0033] Optionally, the fitting module is specifically used to fit the model parameters in the power spectrum model using the reference data corresponding to the scene, and when the objective function reaches a minimum value, obtain the parameter values ​​of the model parameters corresponding to the scene.

[0034] Optionally, the device further comprises:

[0035] A second acquisition module is used to acquire multiple groups of measurement data;

[0036] An extraction module, used to extract fingerprint features corresponding to each set of measurement data;

[0037] A clustering module is used to cluster the fingerprint features corresponding to multiple groups of measurement data to obtain clusters corresponding to different scenarios;

[0038] The selection module is used to select, for each scene, a fingerprint feature near a cluster center of a cluster corresponding to the scene as a reference fingerprint corresponding to the scene, and use measurement data corresponding to the reference fingerprint as reference data corresponding to the scene.

[0039] Optionally, the clustering module is specifically used to perform standardization and dimensionality reduction processing on the fingerprint features corresponding to each group of measurement data to obtain multiple two-dimensional fingerprint features; based on the K-Means clustering method, the multiple two-dimensional fingerprint features are clustered to obtain clusters corresponding to different scenarios.

[0040] Optionally, a clustering module is specifically used to map multiple two-dimensional fingerprint features onto a two-dimensional plane to form a point set, where one two-dimensional fingerprint feature corresponds to one point in the point set; repeatedly select cluster centers, calculate the Euclidean distance from each point to the cluster center, and use the Euclidean distance from each point to the cluster center to cluster multiple points to obtain multiple clusters, until the cluster center of each cluster no longer changes, thereby obtaining multiple clusters.

[0041] Optionally, the fingerprint feature includes a spectral fingerprint;

[0042] The extraction module is specifically used to draw each set of measurement data respectively to obtain the spectrum corresponding to each set of measurement data; for each set of measurement data, the shape features of the spectrum corresponding to the set of measurement data are extracted as the spectrum fingerprint corresponding to the set of measurement data.

[0043] Optionally, a bilaterally truncated asymmetric power spectrum model is used to represent the limited broadening and asymmetry of the direct component, the refracted component and the Doppler power spectrum in the low-orbit satellite channel.

[0044] Optionally, the bilateral truncated asymmetric power spectrum model is expressed as:

[0045]

[0046] Where S(f) represents the Doppler power spectrum of the received signal, sgn(.) represents the sign function, f represents the received signal frequency, and f dev represents the frequency deviation adjustment factor, α represents the correlation factor, · represents the dot multiplication operation, and f max represents the maximum Doppler shift, S LOS (f) represents the Doppler power spectrum of the diameter signal, S NLOS (f) represents the Doppler power spectrum of the multipath component, k∈[0,1].

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0048] Memory, used to store computer programs;

[0049] The processor is used to implement any method step described in the first aspect when executing a program stored in the memory.

[0050] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.

[0051] An embodiment of the present invention further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any of the above-described methods.

[0052] Beneficial effects of the embodiments of the present invention:

[0053] In an embodiment of the present invention, a power spectrum model is first established, that is, an initial model is first established, and then for different scenarios, the model parameters in the power spectrum model are fitted using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters under the scenario, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenario are the parameter values, thereby realizing Doppler power spectrum modeling for low-orbit satellite channels.

[0054] Of course, it is not necessary to achieve all of the advantages described above at the same time to implement any product or method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0056] Figure 1 A flow chart of a Doppler power spectrum modeling method provided by an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of the modeling principle of the bilaterally truncated asymmetric Jakes power spectrum model;

[0058] Figure 3 A schematic diagram of a Doppler power spectrum model established in an embodiment of the present invention;

[0059] Figure 4A flowchart of establishing reference data corresponding to different scenarios in an embodiment of the present invention;

[0060] Figure 5 Schematic diagram of the spectral fingerprint and the corresponding channel characteristics in an embodiment of the present invention;

[0061] Figure 6 Schematic diagram of the mapping relationship among reference data, reference fingerprints and scenes in an embodiment of the present invention;

[0062] Figure 7 A schematic diagram of a Doppler power spectrum modeling method provided by an embodiment of the present invention;

[0063] Figure 8 A schematic diagram of a specific embodiment of establishing reference data corresponding to different scenarios in an embodiment of the present invention;

[0064] Fig. 9 A schematic diagram of the structure of a Doppler power spectrum modeling device provided by an embodiment of the present invention;

[0065] Fig.10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field based on this application belong to the scope of protection of the present invention.

[0067] The embodiment of the present invention provides a Doppler power spectrum modeling method, such as Figure 1 As shown, it may include:

[0068] S101, establishing a power spectrum model, the power spectrum model including a bilateral truncated asymmetric power spectrum model;

[0069] S102, for different scenarios, using the measurement data corresponding to the scenario to fit the model parameters in the power spectrum model, to obtain parameter values ​​corresponding to the simulation parameters in the scenario, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenario are the parameter values.

[0070] In an embodiment of the present invention, a power spectrum model is first established, that is, an initial model is first established, and then for different scenarios, the model parameters in the power spectrum model are fitted using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters under the scenario, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenario are the parameter values, thereby realizing Doppler power spectrum modeling for low-orbit satellite channels.

[0071] Compared with the traditional low-orbit satellite Doppler power spectrum modeling method, in the embodiments of the present invention, for different scenarios, the measurement data corresponding to the scenario are used to fit the model parameters in the power spectrum model to obtain the parameter values ​​corresponding to the simulation parameters under the scenario. This can reduce the impact between scenes, reduce the error of the fitted model parameters, and further improve the accuracy of the Doppler power spectrum model.

[0072] In S101, the power spectrum model may be any Doppler power spectrum model, for example, may include a bilaterally truncated asymmetric power spectrum model.

[0073] In one possible implementation, a bilaterally truncated asymmetric power spectrum model is used to represent the limited broadening and asymmetry of the direct component, the refracted component, and the Doppler power spectrum in the low-orbit satellite channel. For example, the bilaterally truncated asymmetric power spectrum model can be a bilaterally truncated asymmetric Rician power spectrum model, that is, the bilaterally truncated asymmetric Rician power spectrum model takes into account the existence of the direct component and the existence of the refracted component caused by the uneven distribution of scatterers, and the satellite channel Doppler power spectrum modeling should have asymmetry.

[0074] In S102, the established power spectrum model can be understood as an initial model that needs to fit parameters. The model parameters in the bilateral truncated asymmetric Rician power spectrum model are fitted using multiple sets of measurement data, that is, the model parameters are determined. When the model parameters are determined, the Doppler power spectrum model can be determined.

[0075] For example, in the case of the bilateral truncated asymmetric Rician power spectrum model established above, by determining the specific parameter set {f dev ,α,f max , P0, P1, f d}The Doppler power spectrum model in different scenarios can be obtained, such as Figure 3 As shown, the Doppler power spectrum models corresponding to different parameter data sets are shown, parameter data set 1: k = 0.25, f dev =0,α=0,f max =1, P0=1, P1=0.12, f d =0.01; parameter data set 2: k = 0.72, f dev =0.44,α=13.5,f max =1, P0 = 0.82, P1 = 0.086, f d =0.25; parameter data set 3: k = 0.72, f dev =0.28,α=18.5,f max =1, P0=0.76, P1=0.086, f d =0.2; parameter data set 4: k = 1, fdev =0,α=0,f max =1, P0=0.68, P1=0.048, f d =0.45; parameter data set 5: k=1, f dev =0.45,α=21.5,f max =1, P0=0, P1=0.064, f d =0, Figure 3 The horizontal and vertical axes are normalized time and energy respectively, and fdev, alpha, fmax, p0, p1, and fd are used to represent the parameters {f dev ,α,f max , P0, P1, f d}.

[0076] In an embodiment of the present invention, multiple sets of measurement data can be obtained, and the model parameters in the bilateral truncated asymmetric Rician power spectrum model can be directly fitted using the multiple sets of measurement data obtained to obtain a Doppler power spectrum model; or, for different scenarios, multiple sets of measurement data corresponding to the scenarios can be obtained respectively, and the model parameters in the bilateral truncated asymmetric Rician power spectrum model can be fitted using the multiple sets of measurement data corresponding to the scenarios, so that the Doppler power spectrum model corresponding to the scenario can be obtained.

[0077] The Doppler power spectrum model of low-orbit broadband satellites still uses the traditional ground models, such as the Jakes power spectrum model, the Rician power spectrum model, and the Gaussian power spectrum model. The most commonly used model is the bilateral truncated asymmetric Jakes power spectrum model, which is a derivative model of the Jakes power spectrum.

[0078] The bilaterally truncated asymmetric Jakes power spectrum model method is an improved implementation of the Jackes power spectrum simulation model based on the Rice sine sum (Sum of sinusoids, SOS). The main advantage of this Doppler power spectrum model is the asymmetry of the shape and the bilateral adjustable characteristics. It can be configured by separately configuring the Doppler frequency deviation cutoff value f at both ends of the spectrum. d1 、f d2 , it can realize bilateral truncation of the original classic Jackes Doppler spectrum, realize controllable obstruction angle, and can widely describe the receiving environment of multipath arrival angle range or various scatterer distribution situations. The principle block diagram of the algorithm is shown in the figure Figure 2 shown.

[0079] The White Gaussian Noise (WGN) function is used to generate Gaussian white noise. H0(f) represents the linear time-invariant filter of the SOS algorithm. v1(t) and v2(t) are two colored Gaussian noises generated by the SOS algorithm. Their power spectra are basically consistent with the Jackes power spectrum, but the power spectrum cutoff frequencies of v1(t) and v2(t) are different, that is, the Doppler spreads are f and f respectively. d1 and f d2 , then the Doppler power spectra of v1(t) and v2(t) are:

[0080]

[0081]

[0082] Among them, σ0 represents the variance of colored Gaussian noise, f represents the received signal frequency, represents the maximum Doppler frequency deviation, and rect represents the rectangular function.

[0083] According to the linear transformation characteristics of Hilbert transform, the Rayleigh expansion processes u1(t) and u2(t) after linear operation are still Gaussian random processes. is a Rayleigh random process, where

[0084]

[0085]

[0086] in, and They are the results of Hilbert transform of v1(t) and v2(t) respectively.

[0087] After a complex derivation process, the expression of the Doppler power spectrum of the fading process ξ(t) of the Jakes power spectrum with bilateral truncation property is as follows:

[0088]

[0089] in:

[0090]

[0091]

[0092] Here, sgn(f) represents the sign function.

[0093] This obtains the realization and theoretical expression of the bilaterally truncated asymmetric Jakes power spectrum model, providing theoretical support for the simulation of the Doppler power spectrum model of low-orbit satellites.

[0094] At present, when using the Jakes power spectrum, the description of the direct path in the low-orbit satellite transmission signal is not taken into account, and the distribution of scatterers in the satellite signal receiving environment is usually not dense and complex, which does not meet the assumptions of the Jakes power spectrum; when using the Guassian power spectrum, although the power spectrum takes the direct path into account, it does not accurately reflect the characteristics of the refraction component in the low-orbit satellite transmission signal as the absolute majority; when using the Rician power spectrum, it is also difficult to describe the Doppler expansion limitation caused by the severe obstruction of the signal and the uneven distribution of scatterers and the asymmetry of the Doppler power spectrum. In addition, these traditional Doppler power spectra have large errors in describing the low-orbit satellite channel, which can also be understood as the Doppler power spectrum established in the traditional technology has low accuracy in expressing the low-orbit satellite channel, that is, the reliability of the Doppler power spectrum established in the traditional technology is low.

[0095] In order to meet the needs of spatial information network channel modeling, the model can be improved on the basis of the classic Jakes power spectrum, so as to propose a Doppler power spectrum model suitable for LEO (low-orbit) satellite channel scenarios. In the actual low-orbit satellite channel transmission environment, the obstruction angle of the signal by obstacles may not always be symmetrical, especially when there are fewer multipath signals or the scatterers are unevenly distributed, which causes the extension of the Doppler power spectrum to be limited by the asymmetry of the shape. The original assumptions of the Jakes power spectrum are not applicable to this situation, or it can only belong to a special case at a certain moment. Therefore, the embodiment of the present invention proposes a double-ended truncated asymmetric Rician power spectrum, which is expressed as follows:

[0096]

[0097] Where S(f) represents the Doppler power spectrum of the received signal, sgn(.) represents the sign function, f represents the received signal frequency, and f dev represents the frequency deviation adjustment factor, α represents the correlation factor, · represents the dot multiplication operation, and f max represents the maximum Doppler shift, S LOS (f) represents the Doppler power spectrum of the diameter signal, which is the discrete impulse value, S NLOS (f) represents the Doppler power spectrum of the multipath component, k∈[0,1];

[0098] S LOS (f) = P0·δ(ff d )

[0099] Among them, P0 represents the power of the diameter signal, δ represents the Diklak function, also called the unit pulse function, and f d Indicates the Doppler frequency deviation.

[0100] SNLOS (f) is expressed as

[0101]

[0102] Where P1 represents the power of the multipath signal component, Indicates the maximum Doppler frequency deviation.

[0103] The bilaterally truncated asymmetric Rician power spectrum model is used to describe that the arrival angle distribution of multipath signals at the receiving end does not satisfy the uniform distribution on [0, 2π] due to the number of multipaths and shadow fading, resulting in the Doppler power spectrum having the characteristics of limited broadening and asymmetry.

[0104] The power spectrum model established in S101 in the embodiment of the present invention can be a bilaterally truncated asymmetric Rician power spectrum model, and can represent the limited broadening and asymmetry of the direct component, refracted component and Doppler power spectrum in the low-orbit satellite channel, which is more in line with the actual earth-orbit satellite channel. The bilaterally truncated asymmetric Rician power spectrum model can improve the accuracy of the Doppler power spectrum expression of the low-orbit satellite channel, that is, improve the reliability of the Doppler power spectrum.

[0105] In one possible implementation, before S102, the following may also be included:

[0106] For any one or more scenes, reference data corresponding to the scene is obtained, where the reference data is measurement data corresponding to the scene in the multiple groups of measurement data.

[0107] Since the amount of measured data in modeling is large and aliasing is prone to occur between different scenes, resulting in large numerical errors in model parameter fitting, in order to fit accurate model parameters and reduce errors, an embodiment of the present invention determines corresponding reference data for different scenes. In this way, in the process of fitting model parameters, reference data corresponding to the scene can be obtained; using the reference data corresponding to the scene, the model parameters in the bilateral truncated asymmetric Rician power spectrum model are fitted to obtain a Doppler power spectrum model corresponding to the scene, thereby reducing the impact between scenes, reducing the errors of the fitted model parameters, and further improving the accuracy of the Doppler power spectrum model.

[0108] Among them, the model parameters in the power spectrum model are fitted using the measurement data corresponding to the scene to obtain the parameter values ​​corresponding to the simulation parameters under the scene, including:

[0109] The model parameters in the bilateral truncated asymmetric Rician power spectrum model are fitted using the reference data corresponding to the scene. When the objective function reaches the minimum value, the parameter values ​​of the model parameters corresponding to the scene are obtained. In this way, the values ​​of the simulation parameters in the bilateral truncated asymmetric Rician power spectrum model are the fitted parameter values, that is, the Doppler power spectrum model corresponding to the scene is obtained.

[0110] The objective function is set using the L2-Norm method.

[0111]

[0112]

[0113] Among them, f min represents the minimum Doppler shift, S exp (f,t n ) indicates t n The measurement data obtained at all times, S(f dev ,α,k,f max , P0, P1) represents S exp (f,t n ) corresponds to the double-ended truncated asymmetric Rician power spectrum.

[0114] From the Doppler power spectrum expression, we can know that the parameter set to be fitted includes {f dev ,α,f max , P0, P1, f d}, when the objective function reaches the minimum value, the parameter set {f dev ,α,k,f max , P0, P1} values.

[0115] For example, reference data of different scenarios may be brought into the objective function so that the value of the reference data with the minimum objective function is the value of the acquired parameter set.

[0116] In order to obtain measurement data corresponding to different scenarios in the process of establishing the Doppler power spectrum model, the embodiment of the present invention establishes reference data corresponding to different scenarios before fitting the model parameters in the power spectrum model using multiple sets of measurement data, such as Figure 4 As shown, it may include:

[0117] S401, obtaining multiple groups of measurement data.

[0118] It may be multiple volumes of measurement data obtained by actual measurement of low-orbit satellite channels at different times.

[0119] S402, extracting fingerprint features corresponding to each set of measurement data.

[0120] Fingerprint features are features that can uniquely represent the set of measurement data.

[0121] In one possible implementation, the fingerprint feature includes a spectral fingerprint, and extracting the fingerprint feature corresponding to each set of measurement data includes:

[0122] Each set of measurement data is plotted separately to obtain a spectrum corresponding to each set of measurement data; for each set of measurement data, the shape features of the spectrum corresponding to the set of measurement data are extracted as the spectrum fingerprint corresponding to the set of measurement data.

[0123] The spectrum form is as follows: (1) The width of the spectrum shape is the size of the Doppler power spectrum being broadened; (2) The height of the spectrum shape represents the size of the channel multipath fading; (3) The highest point of the spectrum shape represents the diameter power information; (4) The kurtosis of the spectrum shape is the fading dynamics and fading depth of the channel; (5) The skewness of the spectrum shape represents the degree of symmetry of the graph, which is used to describe the degree of asymmetry of the channel caused by the number of multipaths and shadow fading.

[0124] The construction of Doppler power spectrum fingerprint is based on the shape of Doppler power spectrum, which can reflect the Doppler effect of the channel and multipath fading information. Therefore, the constructed spectrum fingerprint can be composed of a set of vectors for the geometric features of the curve, including width mean, height mean, peak, kurtosis, skewness, etc. exp (f,t n ) is mathematically expressed as follows:

[0125]

[0126] The spectral fingerprint vectors corresponding to multiple sets of measurement data can constitute a spectral fingerprint library. The established spectral fingerprint library can be expressed as:

[0127]

[0128] K=K1,K2...K N , S=S1,S2...S N , N represents the number of measurement data, and T represents the transposition of the spectral fingerprint vector.

[0129] Among them, μ width The width mean of the spectrum shape can describe the extent to which the Doppler power spectrum in the channel is broadened. It can reflect the richness of multipath and the relative speed between transceivers; μ hight The height mean of the spectrum shape can describe the average multipath fading size, which can reflect the degree to which the signal is affected by shadow fading and multipath fading; H maxIndicates the highest point of the spectrum shape, indicating the LOS path (diameter) information, including power and Doppler frequency shift value, that is, the direct path frequency deviation; K represents the kurtosis of the spectrum, indicating the fast fading characteristics, which is a statistic of the steepness or smoothness of its curve graph, and can describe the fading dynamics and fading depth of the channel; S represents the skewness of the spectrum, which is a measure of the degree of symmetry of its curve graph, reflecting the number of multipaths in the channel or the influence of shadow fading caused by unevenly distributed obstructions. The corresponding description of the spectrum fingerprint and the channel characteristics (that is, the channel characteristics) is as follows: Figure 5 shown.

[0130] After feature extraction is completed, each spectral fingerprint is mapped to the Doppler power spectrum measured data corresponding to the fingerprint, that is, x n With S exp (f,t n ), so the spectral fingerprint can be used to characterize the characteristics of more comprehensive channel fading.

[0131] S403, clustering fingerprint features corresponding to the multiple groups of measurement data to obtain clusters corresponding to different scenarios.

[0132] The similarities between the fingerprint features corresponding to the multiple sets of measurement data can be calculated respectively, and the multiple fingerprint features can be clustered using the similarities to obtain clusters corresponding to different scenarios.

[0133] In one possible implementation, S403 includes standardizing and reducing the dimension of the fingerprint features corresponding to each group of measurement data to obtain multiple two-dimensional fingerprint features; clustering the multiple two-dimensional fingerprint features based on the K-Means clustering method to obtain clusters corresponding to different scenarios.

[0134] The fingerprint features corresponding to each group of measurement data can be first standardized, and then the standardized fingerprint features can be reduced to two dimensions. In this way, multiple two-dimensional fingerprint features can be obtained. The Euclidean distance between the fingerprint features can be used to represent the similarity between the fingerprint features. The K-Means clustering method can be used to cluster the multiple two-dimensional fingerprint features to obtain clusters corresponding to different scenarios.

[0135] Based on the K-Means clustering method, multiple two-dimensional fingerprint features are clustered to obtain clusters corresponding to different scenarios, including:

[0136] Map multiple two-dimensional fingerprint features onto a two-dimensional plane to form a point set, where one two-dimensional fingerprint feature corresponds to one point in the point set; repeatedly select cluster centers, calculate the Euclidean distance from each point to the cluster center, and use the Euclidean distance from each point to the cluster center to cluster multiple points to obtain multiple clusters, until the cluster center of each cluster no longer changes, and multiple clusters are obtained.

[0137] Randomly select multiple initial cluster centers; calculate the Euclidean distance from each point to the initial cluster center, and use the Euclidean distance from each point to the initial cluster center to cluster multiple points to obtain multiple clusters; re-determine the cluster center in each cluster. If multiple initial cluster centers in each cluster are the same, calculate the Euclidean distance from each point to the re-determined cluster center, and use the Euclidean distance from each point to the re-determined cluster center to re-cluster multiple points to obtain multiple clusters.

[0138] S404 , for each scene, selecting a fingerprint feature near a cluster center of a cluster corresponding to the scene as a reference fingerprint corresponding to the scene, and using measurement data corresponding to the reference fingerprint as reference data corresponding to the scene.

[0139] The fingerprint feature near the cluster center may be determined based on the distance from the cluster center. For example, a fingerprint feature whose distance from the cluster center is less than a preset threshold is determined as the fingerprint feature near the cluster center.

[0140] Since the number of points near the centroid (also known as the cluster center) of each type of fingerprint sample is the highest, its ability to characterize the Doppler power spectrum characteristics of the scene and its versatility are also the strongest, so the fingerprint at the centroid of each cluster can be selected as the reference fingerprint, and the corresponding measured data can be used as the reference data for fitting the Doppler power spectrum model of the scene. In this way, a mapping relationship between reference data, reference fingerprint and scene is established. Figure 6 As shown, scene 1 corresponds to reference fingerprint 1 and reference data 1; scene 2 corresponds to reference fingerprint 2 and reference data 2, ..., and scene K corresponds to reference fingerprint K and reference data K.

[0141] In the embodiment of the present invention, reference data corresponding to different scenes are established so that in the process of modeling the Doppler power spectrum model, the measurement data corresponding to the scenes can be obtained for different scenes to fit the model parameters. Figure 4 The embodiment is based on extracting the spectral fingerprint corresponding to each group of measurement data, clustering the spectral fingerprints, obtaining clusters corresponding to different scenes, and for each scene, selecting the spectral fingerprint near the cluster center of the cluster corresponding to the scene as the reference fingerprint corresponding to the scene, and using the measurement data corresponding to the reference fingerprint as the reference data corresponding to the scene. This method can also be understood as a LEO satellite multi-scene Doppler power spectrum modeling method of the spectral fingerprint method. Specifically Figure 7 As shown, including:

[0142] Step 1: Obtain Doppler power spectrum measurement data with a large sample capacity, plot each set of data, and store them in the form of a spectrum, that is, plot and obtain a Doppler power spectrum spectrum.

[0143] Step 2: Extract the shape features of each atlas as a corresponding atlas fingerprint and establish an atlas fingerprint library.

[0144] As mentioned above, the graph fingerprint can be composed of a set of vectors for the geometric features of the curve, including the mean width, mean height, peak, kurtosis, skewness, etc.

[0145] Step 3: Classify multiple scenes through fingerprint clustering and establish a mapping relationship between reference data and scenes.

[0146] Based on the above graph features, the K-Means clustering algorithm based on the minimum Euclidean distance is used to cluster large-scale graph fingerprints to achieve the classification of multiple scenes of low-orbit satellite channels. This process includes fingerprint standardization, principal component analysis (PCA) dimensionality reduction, K-Means clustering, scene labeling, etc. Figure 8 shown.

[0147] (1) Fingerprint standardization:

[0148] That is, the measurement data is standardized as mentioned above.

[0149] First, the deviation standardization method is used to make the fingerprint dimensionless and eliminate the influence of too large or too small values, so that the range of each feature value in all fingerprints is between [0, 1], which is convenient for subsequent PCA dimensionality reduction and clustering. Each column in the spectral fingerprint library X (that is, the value of each type of feature) is standardized, μ width Normalization can be expressed as:

[0150]

[0151] Among them, max{·} and min{·} are the maximum and minimum value functions.

[0152] Similarly, μ can be easily hight , H max , K, S are used to standardize the deviation, and the obtained standardized spectral fingerprint library for:

[0153]

[0154] Where Dst(·) represents the deviation normalization function.

[0155] Then, due to the dynamics and diversity of signal transmission scenarios, fingerprint samples between different scenarios may appear similar or aliased, which will seriously affect the convergence speed and accuracy of subsequent clustering processing. After a square operation, deviation normalization is performed to increase the distinction between fingerprint samples.

[0156]

[0157] (2) PCA Dimensionality Reduction

[0158] The PCA method is used to reduce the high-dimensional spectral fingerprint to two dimensions, and the similarity between fingerprint samples can be evaluated using the Euclidean distance, which facilitates the subsequent clustering processing in the two-dimensional plane space. The fingerprint library after dimensionality reduction is recorded as:

[0159]

[0160] Where v1 = [v 11 , v 12 , ..., v 1N ],v2=[v 21 , v 22 , ..., v 2N ], the fingerprint sample after dimension reduction is recorded as x n =[v 1n , v 2n ].

[0161] (3) K-Means clustering based on minimum Euclidean distance

[0162] By clustering the atlas fingerprints, we can achieve detailed classification of multiple scenes of measurement data, and through two-dimensional visualization analysis, we can select the best reference data to correctly fit the model parameters. The clustering method is as follows:

[0163] First, the atlas fingerprint is mapped to a two-dimensional plane according to the eigenvalues ​​in two dimensions to form a point set. Then, according to the measured K different scenes, an initial centroid is randomly selected in each scene as the cluster center. The Euclidean distance from each point to the center is calculated and divided into K clusters. The centroid of each cluster is recalculated, and then the clustering and updating of the centroid are iterated until the centroid no longer changes. Here, the recalculated centroid of each cluster is obtained according to the objective function. Considering the fingerprint data of the Euclidean distance, the sum of squared errors (Sum of Squared Error, SSE) is used as the objective function of clustering. After performing clustering iterations to generate clusters, the sample point with the smallest SSE is used as the centroid of each update. The objective function and its optimization are as follows:

[0164]

[0165] Among them, K represents the number of classes, c i Represents the i-th cluster center. By iSolve to minimize SSE, that is, take the derivative of SSE, set the derivative function equal to 0, and solve c i , as follows:

[0166]

[0167] Here, arg represents a function that seeks a set of parameters for a function.

[0168] Therefore, the atlas fingerprint clustering is completed, and each type of fingerprint sample can be labeled by recording the prior information during the early measurement to achieve scene classification.

[0169] In this way, in the process of building the Doppler power spectrum model, for different scenarios, the measurement data corresponding to the scenarios can be obtained respectively to perform model parameter fitting on the model parameters.

[0170] Step 4: Select the reference data corresponding to the eigenvector with the strongest representation ability in each category to fit the model parameters, so as to obtain the specific Doppler power spectrum model in each scenario.

[0171] For any one or more scenes, reference data corresponding to the scene is obtained; using the reference data corresponding to the scene, model parameters in the bilateral truncated asymmetric Rician power spectrum model containing unknown parameters are fitted to obtain a Doppler power spectrum model corresponding to the scene.

[0172] The measured data are used to fit the Doppler power spectrum model parameters of each type of scene in the LEO satellite channel. Therefore, in order to ensure the accuracy of the model parameters, the selection of measured data for parameter fitting in each type of scene is crucial. Since the number of points near the centroid of each type of fingerprint sample is the highest, its ability to characterize the Doppler power spectrum characteristics of the scene and its versatility are also the strongest. Therefore, the fingerprint at the centroid of each cluster is selected as the reference fingerprint, and the corresponding measured data is used as the reference data for fitting the Doppler power spectrum model of the scene.

[0173] For example, from the Doppler power spectrum expression, we know that the parameter set to be fitted includes {f dev ,α,k,f max , P0, P1}, the L2-Norm method can be used to set the objective function

[0174]

[0175] When the objective function reaches the minimum value, the parameter set {f dev ,α,k,f max , P0, P1}, the model in each scenario is determined by a unique set of parameters. Thus, the Doppler power spectrum modeling of LEO satellite channels in multiple scenarios is completed.

[0176] In the channel modeling of the embodiment of the present invention, a new satellite channel Doppler power spectrum model, a bilaterally truncated asymmetric Rician power spectrum model, is first established as an initial model that needs to fit parameters, and then a LEO satellite Doppler power spectrum modeling method based on the spectral fingerprint method is used to fit the parameters of the initial model to complete the modeling. The bilaterally truncated asymmetric Rician power spectrum model takes into account the existence of direct components and the existence of refraction components caused by the uneven distribution of scatterers, and uses the bilaterally truncated asymmetric Rician power spectrum model to replace the traditional bilaterally truncated asymmetric Jakes power spectrum model; at the same time, due to the large amount of measured data in the modeling, and the aliasing between different scenes, the numerical error of the model parameter fitting is large. In order to fit accurate model parameters and reduce errors, a LEO satellite Doppler power spectrum modeling method based on the spectral fingerprint method is proposed to obtain the expected results.

[0177] Compared with the traditional LEO satellite Doppler power spectrum modeling method, the bilateral truncated asymmetric Rician power spectrum model in the embodiment of the present invention takes into account the diameter effect, multipath effect and channel asymmetry, making it more reliable. At the same time, a machine learning method is adopted to propose a LEO satellite Doppler power spectrum modeling method based on the graph fingerprint method, which makes the channel simulation model error smaller.

[0178] The embodiment of the present invention provides a Doppler power spectrum modeling device, such as Fig. 9 As shown, including:

[0179] Establishing module 901, used for establishing a power spectrum model, the power spectrum model includes a bilateral truncated asymmetric power spectrum model;

[0180] The fitting module 902 is used to fit the model parameters in the power spectrum model according to different scenarios using the measurement data corresponding to the scenarios to obtain the parameter values ​​corresponding to the simulation parameters under the scenarios. The values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenarios are the parameter values.

[0181] Optionally, the device also includes: a first acquisition module, which is used to obtain reference data corresponding to the scene for any one or more scenes before fitting the model parameters in the power spectrum model using the measurement data corresponding to the scene to obtain parameter values ​​corresponding to the simulation parameters under the scene, where the reference data is the measurement data corresponding to the scene in multiple groups of measurement data.

[0182] Optionally, the fitting module 902 is specifically used to fit the model parameters in the power spectrum model using the reference data corresponding to the scene, and when the objective function obtains the minimum value, obtain the parameter value of the model parameter corresponding to the scene.

[0183] Optionally, the device further comprises:

[0184] A second acquisition module is used to acquire multiple groups of measurement data;

[0185] An extraction module, used to extract fingerprint features corresponding to each set of measurement data;

[0186] A clustering module is used to cluster the fingerprint features corresponding to multiple groups of measurement data to obtain clusters corresponding to different scenarios;

[0187] The selection module is used to select, for each scene, a fingerprint feature near the cluster center of the cluster corresponding to the scene as a reference fingerprint corresponding to the scene, and use the measurement data corresponding to the reference fingerprint as reference data corresponding to the scene.

[0188] Optionally, the clustering module is specifically used to perform standardization and dimensionality reduction processing on the fingerprint features corresponding to each group of measurement data to obtain multiple two-dimensional fingerprint features; based on the K-Means clustering method, the multiple two-dimensional fingerprint features are clustered to obtain clusters corresponding to different scenarios.

[0189] Optionally, a clustering module is specifically used to map multiple two-dimensional fingerprint features onto a two-dimensional plane to form a point set, where one two-dimensional fingerprint feature corresponds to one point in the point set; repeatedly select cluster centers, calculate the Euclidean distance from each point to the cluster center, and use the Euclidean distance from each point to the cluster center to cluster multiple points to obtain multiple clusters, until the cluster center of each cluster no longer changes, thereby obtaining multiple clusters.

[0190] Optionally, the fingerprint feature includes a spectral fingerprint;

[0191] The extraction module is specifically used to draw each set of measurement data respectively to obtain the spectrum corresponding to each set of measurement data; for each set of measurement data, the shape features of the spectrum corresponding to the set of measurement data are extracted as the spectrum fingerprint corresponding to the set of measurement data.

[0192] Optionally, a bilaterally truncated asymmetric power spectrum model is used to represent the limited broadening and asymmetry of the direct component, the refracted component and the Doppler power spectrum in the low-orbit satellite channel.

[0193] Alternatively, the bilaterally truncated asymmetric power spectrum model is expressed as:

[0194]

[0195] Where S(f) represents the Doppler power spectrum of the received signal, sgn(.) represents the sign function, f represents the received signal frequency, and f dev represents the frequency deviation adjustment factor, α represents the correlation factor, · represents the dot multiplication operation, and f max represents the maximum Doppler shift, S LOS (f) represents the Doppler power spectrum of the diameter signal, SNLOS (f) represents the Doppler power spectrum of the multipath component, k∈[0,1].

[0196] The embodiment of the present invention further provides an electronic device, such as Fig.10 As shown, it includes a processor 1001, a communication interface 1002, a memory 1003 and a communication bus 1004, wherein the processor 1001, the communication interface 1002, and the memory 1003 communicate with each other through the communication bus 1004.

[0197] Memory 1003, used for storing computer programs;

[0198] The processor 1001 is used to implement the method steps of the above-mentioned Doppler power spectrum modeling method when executing the program stored in the memory 1003.

[0199] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0200] The communication interface is used for communication between the above electronic device and other devices.

[0201] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0202] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0203] In another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned Doppler power spectrum modeling methods are implemented.

[0204] In another embodiment of the present invention, a computer program product including instructions is provided. When the computer program product is run on a computer, the computer executes any Doppler power spectrum modeling method in the above embodiments.

[0205] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0206] It should be noted that, in this article, relational terms such as first and second, etc. 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 these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0207] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, electronic device, computer-readable storage medium, and computer program product embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0208] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A Doppler power spectrum modeling method, characterized in that: include: Establishing a power spectrum model, wherein the power spectrum model includes a bilateral truncated asymmetric power spectrum model; For different scenarios, the model parameters in the power spectrum model are fitted using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenario are the parameter values.

2. The method according to claim 1, characterized in that Before fitting the model parameters in the power spectrum model using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario, the method further includes: For any one or more scenes, reference data corresponding to the scene is obtained, where the reference data is measurement data corresponding to the scene in multiple groups of measurement data.

3. The method according to claim 2, characterized in that The method of fitting the model parameters in the power spectrum model using the measurement data corresponding to the different scenarios to obtain parameter values ​​corresponding to the simulation parameters in the scenarios includes: The model parameters in the power spectrum model are fitted using the reference data corresponding to the scene, and when the objective function obtains a minimum value, the parameter values ​​of the model parameters corresponding to the scene are obtained.

4. The method according to claim 2, characterized in that: Before fitting the model parameters in the power spectrum model using the measurement data corresponding to the scenario to obtain parameter values ​​corresponding to the simulation parameters in the scenario, the method further includes: Obtain multiple sets of measurement data; Extract fingerprint features corresponding to each set of measurement data; Cluster the fingerprint features corresponding to multiple groups of measurement data to obtain clusters corresponding to different scenarios; For each scene, a fingerprint feature near a cluster center of a cluster corresponding to the scene is selected as a reference fingerprint corresponding to the scene, and measurement data corresponding to the reference fingerprint is used as reference data corresponding to the scene.

5. The method according to claim 4, characterized in that The fingerprint features corresponding to the multiple groups of measurement data are clustered to obtain clusters corresponding to different scenarios, including: The fingerprint features corresponding to each group of measurement data are standardized and reduced in dimension to obtain multiple two-dimensional fingerprint features; Based on the K-Means clustering method, multiple two-dimensional fingerprint features are clustered to obtain clusters corresponding to different scenarios.

6. The method according to claim 5, characterized in that The K-Means clustering method is used to cluster multiple two-dimensional fingerprint features to obtain clusters corresponding to different scenarios, including: Map multiple two-dimensional fingerprint features onto a two-dimensional plane to form a point set, where one two-dimensional fingerprint feature corresponds to one point in the point set; The cluster center is repeatedly selected, the Euclidean distance from each point to the cluster center is calculated, and the Euclidean distance from each point to the cluster center is used to cluster multiple points to obtain multiple clusters, until the cluster center of each cluster no longer changes, and multiple clusters are obtained.

7. The method according to claim 4, characterized in that The fingerprint features include spectral fingerprints; The extracting fingerprint features corresponding to each set of measurement data includes: Each set of measurement data is plotted separately to obtain a spectrum corresponding to each set of measurement data; For each set of measurement data, the shape features of the atlas corresponding to the set of measurement data are extracted as the atlas fingerprint corresponding to the set of measurement data.

8. The method according to any one of claims 1 to 7, characterized in that: The bilaterally truncated asymmetric power spectrum model is used to represent the limitation and asymmetry of the broadening of the direct component, the refracted component and the Doppler power spectrum in the low-orbit satellite channel.

9. The method according to claim 8, characterized in that The bilaterally truncated asymmetric power spectrum model is expressed as: Where S(f) represents the Doppler power spectrum of the received signal, sgn(.) represents the sign function, f represents the received signal frequency, and f dev represents the frequency deviation adjustment factor, α represents the correlation factor, · represents the dot multiplication operation, and f max represents the maximum Doppler shift, S LOS (f) represents the Doppler power spectrum of the diameter signal, S NLOS (f) represents the Doppler power spectrum of the multipath component, k∈[0,1].

10. A Doppler power spectrum modeling device, characterized in that: include: An establishing module, used for establishing a power spectrum model, wherein the power spectrum model comprises a bilateral truncated asymmetric power spectrum model; The fitting module is used to fit the model parameters in the power spectrum model according to different scenarios using the measurement data corresponding to the scenarios to obtain the parameter values ​​corresponding to the simulation parameters under the scenarios, and the values ​​of the simulation parameters in the Doppler power spectrum model corresponding to the scenarios are the parameter values.

11. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 9 when executing a program stored in a memory.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 9 are implemented.

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