Singular perturbation order reduction method matched with net-forming type wind power plant

Abstract

The invention discloses a singular perturbation order reduction method matched with a network construction type wind power plant. The method comprises the following steps: firstly, establishing a small signal model matched with each module of a network construction type wind power plant grid-connected system; constructing a full-order small signal model, solving a broadband oscillation mode, and calculating damping ratios of all modes to obtain an extremely weak damping mode; dividing fast and slow state variables based on the modal participation degree and Hankel singular value weight, and establishing a criterion to supplement the slow state variables based on a singular perturbation method to form a slow state variable set; and determining the order after order reduction, and establishing an order reduction model of the matching network construction type wind power plant grid-connected system in a singular perturbation form. According to the method, the problem that a matched network construction type wind power plant order reduction system of an existing order reduction scheme cannot replace a full-order system to efficiently analyze broadband oscillation can be solved, the order number of the matched network construction type wind power plant can be reduced, and the method has efficient and powerful broadband oscillation analysis capacity while the dynamic characteristics are reserved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a singular perturbation order reduction method for a matching grid-type wind farm. Background Art

[0002] Renewable energy sources such as wind power and photovoltaics are developing rapidly, and power systems are characterized by a high proportion of renewable energy and power electronics. Currently, wind power is integrated into the grid through grid-following control. To address the inertia issues associated with this high proportion, grid-matching control is gaining attention. However, grid-connected wind farms inherently present a significant risk of broadband oscillations. The integration of grid-matching wind farms increases the system size and complexity, presenting new challenges for studying broadband oscillations. To address these issues, reduced-order methods for analyzing broadband oscillations in systems offer new insights and approaches.

[0003] Currently, analysis of broadband oscillations primarily relies on eigenvalue analysis and impedance analysis. Eigenvalue analysis is widely used because it can reveal information about the frequency and stability of a system's oscillatory modes. However, as the system scale increases, it is susceptible to the "curse of dimensionality," which limits this method. To address this issue, order reduction methods can be introduced to reduce dimensionality and improve the efficiency of broadband oscillation analysis. Singular perturbation order reduction methods rely on the dominance of state variables over the system, retaining slow state variables. However, this method struggles to accurately represent dynamic characteristics when the system is strongly coupled, a characteristic often found in systems with broadband oscillations. Balanced order reduction methods use Hankel singular values ​​to retain state variables that contribute significantly to the dynamic characteristics, but their ability to preserve broadband oscillation modes is limited. Therefore, there is an urgent need for a reasonable order reduction method that can efficiently and accurately analyze broadband oscillations while preserving the system's dynamic characteristics. Summary of the Invention

[0004] The purpose of the present invention is to provide a singular perturbation order reduction method for a matching grid-type wind farm. This method can solve the problem that the matching grid-type wind farm reduced-order system of the existing order reduction scheme cannot replace the full-order system to efficiently analyze broadband oscillations. It can reduce the order of the matching grid-type wind farm and, while retaining the dynamic characteristics, has efficient and powerful broadband oscillation analysis capabilities.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A singular perturbation order reduction method for matching grid-type wind farms, the method comprising:

[0007] Step 1: Establish a small signal model for each module of the grid-connected wind farm system;

[0008] Step 2: Build a full-order small signal model and solve the broadband oscillation mode, calculate the damping ratio of all modes, and obtain the extremely weakly damped mode;

[0009] Step 3: Based on the modal participation and Hankel singular value weights, the fast and slow state variables are divided, and the slow state variables are supplemented by the criterion based on the singular perturbation method to form a slow state variable set;

[0010] Variables with a participation rate greater than or equal to 15% are defined as state variables with strong correlation to the dominant broadband oscillation mode, while variables with a participation rate less than 15% are defined as state variables with weak correlation to the dominant broadband oscillation mode. The Hankel singular value weight indicates the influence of the corresponding state variable on the dynamic characteristics.

[0011] Step 4: Determine the order after reduction, and establish a reduced-order model of the matching grid-connected wind farm system in the form of a singular perturbation.

[0012] It can be seen from the technical solution provided by the present invention that the above method can solve the problem that the reduced-order system of the matching grid-type wind farm of the existing reduced-order solution cannot replace the full-order system to efficiently analyze broadband oscillations. It can reduce the order of the matching grid-type wind farm and have efficient and powerful broadband oscillation analysis capabilities while retaining the dynamic characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A schematic diagram of a singular perturbation order reduction method for a matching grid-type wind farm provided by an embodiment of the present invention;

[0015] Figure 2 Schematic diagram of the participation factor of the extremely weak damping mode in the grid-connected wind farm system of the matching grid configuration according to an embodiment of the present invention;

[0016] Figure 3 In the grid-connected wind farm system, the DC bus capacitor voltage set value disturbance Δu is DC0 = 0.1pu, the small signal model reduced by this method and the full-order small signal model, electromagnetic transient model, DC bus capacitor voltage u DC Dynamic response comparison chart;

[0017] Figure 4 In the grid-connected wind farm system, the DC bus capacitor voltage set value disturbance Δu is DC0= 0.1pu, the small signal model reduced by this method and the full-order small signal model, electromagnetic transient model, the grid-side voltage d-axis component u gd Dynamic response comparison chart;

[0018] Figure 5 In the grid-connected wind farm system, the DC bus capacitor voltage set value disturbance Δu is DC0 = 0.1pu, the small signal model reduced by this method and the full-order small signal model, electromagnetic transient model, the grid-side current d-axis component i gd Dynamic response comparison chart;

[0019] Figure 6 To match the C of the reduced-order and full-order systems in the grid-connected wind farm system DC Root locus diagram of dominant oscillation mode with parameter changes;

[0020] Figure 7 In the grid-connected wind farm system, the DC bus capacitor C DC After the change, the DC bus capacitor voltage u DC The dominant oscillation waveform diagram on . DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments, and do not constitute a limitation of the present invention. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] like Figure 1 FIG. 1 is a schematic diagram of a singular perturbation order reduction method for a matching grid-type wind farm provided by an embodiment of the present invention, the method comprising:

[0023] Step 1: Establish a small signal model for each module of the grid-connected wind farm system;

[0024] In this step, the grid-connected wind farm system with a matching grid is divided into a wind turbine shaft module, a wind turbine generator module, a machine-side converter module, a DC bus capacitor module, a grid-side converter module, a matching control module, a filter module, and an AC line module. Then, the linearization equations of each module are derived to obtain a modular small-signal model of the grid-connected wind farm system with a matching grid. The linearization equations of each module are based on the Taylor formula:

[0025]

[0026] Where R nis a high-order infinitesimal quantity; in order to obtain a modular small-signal model of the grid-connected wind farm system that matches the grid, the linearized equation is retained to the first order according to Taylor's formula.

[0027] Step 2: Build a full-order small signal model and solve the broadband oscillation mode, calculate the damping ratio of all modes, and obtain the extremely weakly damped mode;

[0028] In this step, the full-order small signal model of the grid-connected wind farm system is matched as follows:

[0029]

[0030] Where n and X are the number of state quantities and the corresponding matrix respectively; p and U are the number of input quantities and the corresponding matrix respectively; q and Y are the number of output quantities and the corresponding matrix respectively; A, B, C, and D are the state matrix, input matrix, output matrix, and direct transfer matrix of the system respectively;

[0031] The input quantity is the DC bus voltage given value u DC0 ; The output quantity is selected as DC bus capacitor voltage u DC ; Grid-side current i g The current in the dq coordinate is i gd 、i gq ; Grid side voltage u f The voltage in the dq coordinate is u fd 、u fq ;

[0032] Solving the state matrix in equation (2) yields all broadband oscillation modes of the grid-connected wind farm system. To further identify the extremely weakly damped modes, the damping ratio ξ of each mode is calculated:

[0033]

[0034] Where, σ and ω are the real and imaginary parts of the broadband oscillation mode respectively;

[0035] The damping ratio ξ is between ±1. When the system can operate stably, the damping ratio ξ of all broadband oscillation modes is positive. At this time, the broadband oscillation mode with a damping ratio close to 0 is selected as the extremely weakly damped mode.

[0036] In the specific implementation, among the damping of all broadband oscillation modes of the grid-connected wind farm system, mode 4 has the lowest damping and is defined as an extremely weakly damped mode, corresponding to the greatest risk of broadband oscillation.

[0037] Step 3: Based on the modal participation and Hankel singular value weights, the fast and slow state variables are divided, and the slow state variables are supplemented by the criterion based on the singular perturbation method to form a slow state variable set;

[0038] In this step, if Figure 2 The figure shows a schematic diagram of the participation factor of the extremely weak damping mode in the grid-connected wind farm system of the matching grid configuration according to an embodiment of the present invention. Variables with a participation degree greater than or equal to 15% are defined as state variables that are strongly correlated with the dominant broadband oscillation mode, and variables with a participation degree less than 15% are defined as state variables that are weakly correlated with the dominant broadband oscillation mode. The Hankel singular value weight indicates the influence of the corresponding state variable on the dynamic characteristics.

[0039] In the specific implementation, the participation factor analysis is performed based on the extremely weak damping mode calculated in step 2 to obtain the contribution of each state variable to the dominant broadband oscillation. First, the left eigenvector of the i-th mode in the grid-connected wind farm system with matching grid configuration is solved:

[0040] A nn U i =λ i U i (4)

[0041] Where λ i is the i-th mode of the system; U i is the left eigenvector; the left eigenvector U of all modes i Construct the left eigenvector matrix U nn :

[0042] U nn =[U1…U i …U n ] (5)

[0043] Under normalization, the right eigenvector matrix V nn The left eigenvector matrix U nn Taking the inverse transposition we get:

[0044]

[0045] According to the elements of the left and right eigenvector matrices, the participation factors P of all broadband oscillation modes are obtained. i :

[0046] P i =[U 1i V 1i …U ki V ki …U ni V ni ] T (7)

[0047] Where i and k represent the mode number and state variable number respectively;

[0048] The participation degree is the per-unit form of the participation factor. Under normalization, the participation factor and the participation degree are equal. The number of the extremely weak damping mode is determined, and the number of the state variable is determined according to the level of participation. The state variable with an extremely weak damping mode participation greater than or equal to 15% is classified as a slow state variable.

[0049] Then, the weights of the Hankel singular values ​​are calculated, and the state variables whose Hankel singular values ​​accumulate to 95% are divided into the slow state variable set. Specifically:

[0050] According to the system shown in formula (2), the Gram matrix is ​​solved according to the Lyapunov equation:

[0051]

[0052] Where, P nn , Q nn are the Gram matrices in controllable and observable forms respectively;

[0053] Solve for the Hankel singular values ​​of state variable i:

[0054]

[0055] Where λ i () indicates the i-th eigenvalue of the matrix in the brackets;

[0056] Normalize the obtained Hankel singular value to obtain the normalized form of Hankel singular value i :

[0057]

[0058] To Han i The weights are sorted from large to small, and the top 95% of the state variables are selected as slow state variables;

[0059] Then, determine whether the singular perturbation method is valid. If not, add slow state variables based on the principle of minimizing the order of the reduced model to ensure that the state matrix of the state space model containing only fast state variables is non-singular.

[0060] The selected state variables successively meet the requirements of extremely weak damping mode participation contribution, Hankel weight, and singular perturbation method validity criterion to form a set of slow state variables.

[0061] Step 4: Determine the order after reduction, and establish a reduced-order model of the matching grid-connected wind farm system in the form of a singular perturbation.

[0062] In this step, the singular perturbation method obtains the time scale basis according to the research object, divides the system variables into fast state variables and slow state variables, and the singular perturbation form of the state space model is:

[0063]

[0064] Where n1 and n2 are the numbers of the slow and fast state variables; ε is the ignored factor;

[0065] When the factor ε is ignored and is equal to 0, the intermediate equation of the singular perturbation reduction method is obtained:

[0066]

[0067] In formula (12), A is required to n2n2 Non-singular; at this time, only the slow state variables of the grid-connected wind farm system matching the grid configuration are retained, which can fully reflect the steady-state characteristics;

[0068] Substituting equation (12) into equation (11) yields the reduced-order small signal model:

[0069]

[0070] Where A T 、B T 、C T 、D T The coefficient matrix is ​​expressed as:

[0071]

[0072] The key to order reduction using the singular perturbation method is the A formed by the fast state variables. n2n2 :

[0073] If A n2n2 If A is non-singular, the system is reduced in order; n2n2 If it is singular, you need to continue to add slow state variables until A n2n2 Non-singular, and then reduce the order of the system.

[0074] In the specific implementation, the small signal model of the grid-connected direct-drive wind farm system can be reduced from 24th order to 13th order.

[0075] Based on the above technical solution, since the participation factor can reflect the degree of participation of state variables in broadband oscillation, the participation factor of the weak broadband oscillation mode in the system is selected, and the set of slow state variables is preliminarily determined to ensure that the reduced-order system can predict the risk of broadband oscillation. Among them, the weak broadband oscillation mode is selected as the mode with relatively small damping, that is, the system has a greater risk of broadband oscillation in this mode. Hankel singular values ​​can evaluate the contribution of state variables in the balanced system to the dynamic characteristics, and the new energy grid-connected system is internally balanced under stable operating conditions. Therefore, Hankel singular values ​​can determine slow state variables to ensure that the reduced-order system retains sufficient dynamic characteristics.

[0076] It should be noted that the contents not described in detail in the embodiments of the present invention belong to the prior art known to those skilled in the art.

[0077] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method.

[0078] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method.

[0079] The following is a simulation verification of the process and effectiveness of the method described in the embodiment of the present invention using a specific example. Figure 3 The figure shows the disturbance Δu of the DC bus capacitor voltage given value in the grid-connected wind farm system. DC0 = 0.1pu, the small signal model reduced by this method and the full-order small signal model, electromagnetic transient model, DC bus capacitor voltage u DC Dynamic response comparison chart of Figure 4 The figure shows the disturbance Δu of the DC bus capacitor voltage given value in the grid-connected wind farm system. DC0 = 0.1pu, the small signal model reduced by this method and the full-order small signal model, electromagnetic transient model, the grid-side voltage d-axis component u gd Dynamic response comparison chart of Figure 5 The figure shows the disturbance Δu of the DC bus capacitor voltage given value in the grid-connected wind farm system. DC0 = 0.1pu, the small signal model reduced by this method and the full-order small signal model, electromagnetic transient model, the grid-side current d-axis component i gdThe dynamic response comparison chart shows that the steady-state performance of the small-signal model and the electromagnetic transient model is the same before and after the step. The oscillation trends of the transient response of the reduced-order small-signal model and the full-order small-signal model remain similar. This shows that the reduced-order small-signal model using the order reduction method of the present invention can maintain the dynamic performance of the grid-connected wind farm system with matching grid configuration.

[0080] Table 1 shows the broadband oscillation modes before and after order reduction using the order reduction method of the present invention in a grid-matching wind farm grid-connected system.

[0081] Table 1

[0082]

[0083] As shown in Table 1, the extremely weak damping mode is retained before and after the order reduction of the grid-connected wind farm system, and the reduced-order system has the basis for broadband oscillation analysis.

[0084] This extremely weak damping mode 4 is selected for broadband oscillation analysis. Figure 6 The figure shows the C of the reduced-order and full-order systems in the grid-connected wind farm system. DC The root loci of the dominant oscillation mode with parameter changes show that the root loci of the extremely weakly damped mode of the reduced-order small signal model and the full-order small signal model are highly consistent, which verifies the broadband oscillation retention capability of the reduced-order system. It can be analyzed that: with the change of C DC As the parameter increases, the root locus moves toward the origin, the damping of the broadband oscillation mode gradually increases, and the frequency gradually decreases.

[0085] like Figure 7 The figure shows the DC bus capacitor C in the grid-connected wind farm system. DC After the change, the DC bus capacitor voltage u DC The dominant oscillation waveform on the DC After the increase of the parameter, the amplitude and frequency of the dominant broadband oscillation decreased, which is consistent with the Figure 6 The conclusions remain consistent, verifying the broadband oscillation analysis capability of the reduced-order small signal model using the order reduction method proposed in this invention.

[0086] The simulation results above demonstrate that the reduced-order system constructed using the method described in this invention retains similar dynamic characteristics while maintaining consistent broadband oscillation analysis capabilities compared to the full-order system. These simulation results validate the effectiveness and feasibility of the method described in this invention.

[0087] In summary, the method described in the embodiment of the present invention ensures the dynamic consistency of the system before and after order reduction, while enabling the reduced-order system to have the main broadband oscillation analysis capability of the full-order system, and can more efficiently analyze broadband oscillation problems; according to the full and reduced-order small signal model simulation and electromagnetic transient simulation results of the grid-connected system of the matching grid-connected wind farm before and after order reduction, the effectiveness and accuracy of the method of the present invention are verified, providing theoretical support for the broadband oscillation problem of new energy sites such as large-scale matching grid-connected wind farms and grid-connected wind farms.

[0088] In addition, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing the relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0089] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or any form of implication that the information constitutes prior art already known to those skilled in the art.

Claims

1. A singular perturbation reduction method for matching grid-type wind farms, characterized in that: The method comprises: Step 1: Establish a small signal model for each module of the grid-connected wind farm system; Step 2: Build a full-order small signal model and solve the broadband oscillation mode, calculate the damping ratio of all modes, and obtain the extremely weakly damped mode; Step 3: Based on the modal participation and Hankel singular value weights, the fast and slow state variables are divided, and the slow state variables are supplemented by the criterion based on the singular perturbation method to form a slow state variable set; Variables with a participation rate greater than or equal to 15% are defined as state variables with strong correlation to the dominant broadband oscillation mode, while variables with a participation rate less than 15% are defined as state variables with weak correlation to the dominant broadband oscillation mode. The Hankel singular value weight indicates the influence of the corresponding state variable on the dynamic characteristics. Step 4: Determine the order after reduction, and establish a reduced-order model of the matching grid-connected wind farm system in the form of a singular perturbation.

2. The singular perturbation order reduction method for matching grid-type wind farms according to claim 1, characterized in that: In step 1, the grid-connected system of the matching grid-connected wind farm is divided into a wind turbine shaft module, a wind turbine generator module, a machine-side converter module, a DC bus capacitor module, a grid-side converter module, a matching control module, a filter module, and an AC line module. Then, the linearized equations of each module are derived to obtain a modular small-signal model of the grid-connected system of the matching grid-connected wind farm. Among them, the basis of the linearization equation of each module is Taylor's formula: Where R n is a high-order infinitesimal quantity; in order to obtain a modular small-signal model of the grid-connected wind farm system that matches the grid, the linearized equation is retained to the first order according to Taylor's formula.

3. The singular perturbation order reduction method for matching grid-type wind farms according to claim 1, characterized in that: The process of step 2 is specifically as follows: The full-order small signal model of the grid-connected wind farm system with matching grid configuration is: Where n and X are the number of state quantities and the corresponding matrix respectively; p and U are the number of input quantities and the corresponding matrix respectively; q and Y are the number of output quantities and the corresponding matrix respectively; A, B, C, and D are the state matrix, input matrix, output matrix, and direct transfer matrix of the system respectively; The input quantity is the DC bus voltage given value u DC0 ; The output quantity is selected as DC bus capacitor voltage u DC ; Grid-side current i g The current in the dq coordinate is i gd 、i gq ; Grid side voltage u f The voltage in the dq coordinate is u fd 、u fq ; Solving the state matrix in equation (2) yields all broadband oscillation modes of the grid-connected wind farm system. To further identify the extremely weakly damped modes, the damping ratio ξ of each mode is calculated: Where, σ and ω are the real and imaginary parts of the broadband oscillation mode respectively; The damping ratio ξ is between ±1. When the system can operate stably, the damping ratio ξ of all broadband oscillation modes is positive. At this time, the broadband oscillation mode with a damping ratio close to 0 is selected as the extremely weakly damped mode.

4. The singular perturbation order reduction method for matching grid-type wind farms according to claim 3, characterized in that: In step 3, a participation factor analysis is performed based on the extremely weak damping mode calculated in step 2 to obtain the contribution of each state variable to the dominant broadband oscillation. First, the left eigenvector of the i-th mode in the grid-connected wind farm system with matching grid configuration is solved: A nn U i =λ i U i (4) Where λ i is the i-th mode of the system; U i is the left eigenvector; the left eigenvector U of all modes i Construct the left eigenvector matrix U nn : U nn =[U1…U i …U n ] (5) Under normalization, the right eigenvector matrix V nn The left eigenvector matrix U nn Taking the inverse transposition we get: According to the elements of the left and right eigenvector matrices, the participation factors P of all broadband oscillation modes are obtained. i : P i =[U 1i V 1i …U ki V ki …U ni V ni ] T (7) Where i and k represent the mode number and state variable number respectively; The participation degree is the per-unit form of the participation factor. Under normalization, the participation factor and the participation degree are equal. The number of the extremely weak damping mode is determined, and the number of the state variable is determined according to the level of participation. The state variable with an extremely weak damping mode participation greater than or equal to 15% is classified as a slow state variable.

5. The singular perturbation order reduction method for matching grid-type wind farms according to claim 3, characterized in that: In step 3, the weights of the Hankel singular values ​​are calculated, and the state variables whose Hankel singular values ​​accumulate to 95% are divided into the slow state variable set. Specifically: According to the system shown in formula (2), the Gram matrix is ​​solved according to the Lyapunov equation: Where, P nn , Q nn are the Gram matrices in controllable and observable forms respectively; Solve for the Hankel singular values ​​of state variable i: Where λ i () indicates the i-th eigenvalue of the matrix in the brackets; Normalize the obtained Hankel singular value to obtain the normalized form of Hankel singular value i : To Han i The weights are sorted from large to small, and the top 95% of the state variables are selected as slow state variables; Then, determine whether the singular perturbation method is valid. If not, add slow state variables based on the principle of minimizing the order of the reduced model to ensure that the state matrix of the state space model containing only fast state variables is non-singular. The selected state variables successively meet the requirements of extremely weak damping mode participation contribution, Hankel weight, and singular perturbation method validity criterion to form a set of slow state variables.

6. The singular perturbation order reduction method for matching grid-type wind farms according to claim 3, characterized in that: The process of step 4 is specifically as follows: The singular perturbation method obtains the time scale basis according to the research object, divides the system variables into fast state variables and slow state variables, and the singular perturbation form of the state space model is: Where n1 and n2 are the numbers of the slow and fast state variables; ε is the ignored factor; When the factor ε is ignored and is equal to 0, the intermediate equation of the singular perturbation reduction method is obtained: In formula (12), A is required to n2n2 Non-singular; at this time, only the slow state variables of the grid-connected wind farm system matching the grid configuration are retained, which can fully reflect the steady-state characteristics; Substituting equation (12) into equation (11) yields the reduced-order small signal model: Where A T 、B T 、C T 、D T The coefficient matrix is ​​expressed as: The key to order reduction using the singular perturbation method is the A formed by the fast state variables. n2n2 : If A n2n2 If A is non-singular, the system is reduced in order; n2n2 If it is singular, you need to continue to add slow state variables until A n2n2 Non-singular, and then reduce the order of the system.

7. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 6.

8. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 6.

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