CN111622896A

CN111622896A - Wind power plant wind driven generator load optimization control method and system

Info

Publication number: CN111622896A
Application number: CN202010500123.7A
Authority: CN
Inventors: 吕占鳌; 赵浩然
Original assignee: Shandong University
Current assignee: Shandong University
Priority date: 2020-06-04
Filing date: 2020-06-04
Publication date: 2020-09-04
Anticipated expiration: 2040-06-04
Also published as: CN111622896B

Abstract

The invention discloses a load optimization control method and a load optimization control system for wind power plants, which are used for acquiring the state quantity of each wind power generator of a wind power plant at the current moment; calculating the state quantity of each wind driven generator at the current moment and in a plurality of prediction time periods according to the state quantity of each wind driven generator in the wind power plant at the current moment; deducing output quantity in the prediction time period according to the state quantity of each wind driven generator in the prediction time period, and extracting a plurality of characteristic quantities; inputting the calculated characteristic quantities into a model predictive control cost function based on fast Fourier transform, solving a convex optimization or quasi-convex optimization problem under the cooperation of a single controller or a master controller and a slave controller, and outputting an optimal control quantity; the optimal control quantity is transmitted to the actuator, so that the fluctuation degree of the output quantity is reduced, and the service life of the system is further prolonged.

Description

Wind power plant wind driven generator load optimization control method and system

Technical Field

The disclosure relates to the technical field of wind power plant power control, in particular to a wind power plant wind driven generator load optimization control method and system.

Background

The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

In a traditional active power control strategy of a wind power plant, simple centralized proportion distribution is executed according to the current active power margin of each fan. This method of distribution does not take into account the mechanical fatigue of each fan due to variations in the distributed power. In actual operation, changes in fan power may cause variations such as T_sAnd F_tVariations, and the like. When the rate of change of torque or force is too great, mechanical fatigue of the equipment will be exacerbated and lead to a reduction in the life of the unit. In order to solve the problem, many scholars have solutions, but in the conventional Model Predictive Control (MPC) control of the cost function, weighing needs to be carried out among a plurality of objective function items when a weight coefficient is designed, and it is difficult to achieve overall optimization of a plurality of indexes.

Disclosure of Invention

In order to solve the defects of the prior art, the present disclosure provides a wind power plant wind power generator load optimization control method and system; by quantifying the amplitude of the fluctuation, the quick overshoot-free control of a plurality of control objects and indexes is achieved.

In a first aspect, the present disclosure provides a wind power plant wind generator load optimization control method;

the wind power plant wind driven generator load optimization control method comprises the following steps:

acquiring the state quantity of each wind driven generator of the wind power plant at the current moment;

calculating the optimal control quantity of the wind power plant according to the state quantity;

and reducing the fluctuation degree of the output quantity of the wind power plant based on the optimal control quantity of the wind power plant.

In a second aspect, the present disclosure provides a wind farm wind generator load optimization control system;

wind-powered electricity generation machine load optimal control system of wind-powered electricity generation field includes:

an acquisition module configured to: acquiring the state quantity of each wind driven generator of the wind power plant at the current moment;

an optimal control amount calculation module configured to: calculating the optimal control quantity of the wind power plant according to the state quantity;

a wind farm power optimization module configured to: and reducing the fluctuation degree of the output quantity of the wind power plant based on the optimal control quantity of the wind power plant.

In a third aspect, the present disclosure also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.

In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.

In a fifth aspect, the present disclosure also provides a computer program (product) comprising a computer program for implementing the method of any one of the preceding first aspects when run on one or more processors.

Compared with the prior art, the beneficial effect of this disclosure is:

by quantifying the amplitude of the fluctuation, the quick overshoot-free control of a plurality of control objects and indexes is achieved. The active power is optimized through the wind driven generator controller, so that the load fluctuation of the low-speed shaft torque of the wind driven generator is reduced, the tower thrust fluctuation is reduced, and the service life of the wind driven generator is further prolonged.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.

FIG. 1 is a flow chart of a method of the first embodiment;

FIG. 2 is a model of a spring mass on a flat surface with a viscous material according to a first embodiment;

FIG. 3 is the coordinates of the mass in steps of 1s according to the first embodiment;

FIG. 4 is the 0.1s step mass coordinates of the first embodiment;

FIG. 5 is the coordinates of the mass at 1s step after the algorithm is used according to the first embodiment;

FIG. 6 is the coordinates of the mass at 0.1s steps after the algorithm is used according to the first embodiment;

FIG. 7 is the variation of the external force 1s step after the algorithm is used according to the first embodiment;

FIG. 8 is the change in step size of 0.1s after the algorithm is used for the first embodiment;

FIG. 9 shows the input wind speeds of 5 of the 10 fans of the first embodiment;

FIG. 10 shows P of 10 fans of the first embodiment_ref；

FIG. 11 shows a first embodiment of a T with 5 fans_s；

FIG. 12 shows a first embodiment in which 5 fans are arranged F_t。

Detailed Description

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.

Example one

The embodiment provides a load optimization control method for a wind power plant wind driven generator;

s101: acquiring the state quantity of each wind driven generator of the wind power plant at the current moment;

s102: calculating the optimal control quantity of the wind power plant according to the state quantity;

s103: and reducing the fluctuation degree of the output quantity of the wind power plant based on the optimal control quantity of the wind power plant.

As one or more embodiments, the obtaining the state quantity of each wind driven generator of the wind farm at the current time includes:

acquiring the state quantity of each wind driven generator of the wind power plant at the current moment; and calculating the state quantity of each wind driven generator at the current time and in a plurality of prediction time periods according to the state quantity of each wind driven generator in the wind power plant at the current time.

According to one or more embodiments, the optimal control quantity of the wind power plant is calculated according to the state quantity; the method comprises the following specific steps:

deducing output quantity in the prediction time period according to the state quantity of each wind driven generator in the prediction time period, and extracting a plurality of characteristic quantities; and calculating the optimal control quantity of the wind power plant based on the characteristic quantity.

As one or more embodiments, deriving an output quantity in the prediction time period according to the state quantity of each wind turbine in the prediction time period, and extracting a plurality of feature quantities, specifically: the coefficient matrix is extracted as a feature quantity.

As one or more embodiments, the wind farm optimal control amount is calculated based on the characteristic amount; the method comprises the following specific steps:

and inputting the calculated characteristic quantities into a model predictive control cost function based on fast Fourier transform, solving a convex optimization or quasi-convex optimization problem under the cooperation of a single controller or a master controller and a slave controller, and outputting an optimal control quantity.

As one or more embodiments, the model based on fast fourier transform predicts a control cost function as: a control quantity is calculated that minimizes a cost function, wherein the cost function is designed based on the predicted fluctuation width.

As one or more embodiments, the fluctuation degree of the wind farm output quantity is reduced based on the wind farm optimal control quantity; the method comprises the following specific steps: the optimal control quantity is transmitted to the actuator, so that the fluctuation degree of the output quantity is reduced, and the service life of the system is further prolonged.

As one or more embodiments, acquiring a state quantity of each wind driven generator of a wind power plant at the current moment; calculating the state quantity of each wind driven generator at the current moment and in a plurality of prediction time periods according to the state quantity of each wind driven generator in the wind power plant at the current moment; the method comprises the following specific steps:

x(k+1)＝A_dx(k)+B_du(k)+E_dd(k)+F_d；

y(k)＝C_dx(k)+D_du(k)+G_dd(k)+H_d；

wherein x (k +1) represents the state quantity of the wind turbine generator in the (k +1) th step, A_dRepresenting the coefficient matrix, x (k) representing the state quantity of the wind turbine in step k, B_dRepresenting a matrix of coefficients, E_dRepresents a coefficient matrix, u (k) represents a control quantity of the (k +1) th step, d (k) represents a disturbance quantity of the (k +1) th step, F_dRepresenting a constant matrix, y (k) representing the output of step k, C_dRepresenting a matrix of coefficients, D_dRepresenting a matrix of coefficients, G_dRepresenting a matrix of coefficients, H_dA matrix of constants is represented.

Illustratively, the state quantity includes: rotational speed, pitch angle, wind speed, etc.

The output quantity comprises: transmission shaft torque T of each wind driven generator_sAnd tower thrust F_tAnd the like.

The characteristic quantity refers to a coefficient matrix.

The single controller refers to a controller of a single wind driven generator.

The master controller and the slave controllers refer to a wind power plant controller and a wind power generator controller.

The convex optimization or quasi-convex optimization problem is a quadratic programming problem with constraints.

The optimal control quantity refers to an active power reference value of each wind driven generator.

The actuator is characterized in that: a generator set torque controller.

The service life of the system is prolonged, wherein the system refers to a wind driven generator and a wind power plant.

The disturbance amount refers to wind speed.

As one or more embodiments, according to the state quantity of each wind driven generator in the prediction time period, deriving an output quantity in the prediction time period, and extracting a plurality of characteristic quantities; the method comprises the following specific steps:

where, phi denotes a first matrix of coefficients,_ua second matrix of coefficients is represented which is,_da third matrix of coefficients is represented which,_fa fourth matrix of coefficients is represented and y represents the output of all wind turbines during the time period including the prediction.

As one or more embodiments, the fast fourier transform based model predictive control cost function is derived from the formula:

A＝y(1)+y(2)·i-y(3)-y(4)·i。

as one or more embodiments, the model predictive control cost function based on the fast fourier transform is constructed as:

Gu＝b

wherein, min_uJ denotes a control amount u when the cost function J is found to be minimum,

represents n_tCost function addition of wind turbines, J_iRepresenting the cost function, u, of the ith wind turbine_iIndicates the control quantity of the ith wind turbine, H_iCoefficient of quadratic term, g, in cost function representing ith wind turbine with respect to control amount_i' coefficient of first order term regarding control amount in cost function of ith wind turbine generator, g_i' represents g_iB represents a constant;

for a total power reference value, u, of the entire wind farm_i' means u_iG denotes the coefficient vector of the image data,

representing a coefficient vector, G_iCoefficient vector, n_pThe predicted number of steps is indicated.

In the field of communications, Fast Fourier Transform (FFT) is a common signal processing method, and FFT is divided into forward transform (DFT) and inverse transform (IDFT), where DFT requires certain sample data, and after processing a set of sample data on a time sequence, the amplitude and phase of a signal at multiple frequencies can be obtained. Assume a sampling frequency of F_sThe number of sampling points is N, and after DFT processing, the result is a complex number of N points, each point corresponds to a frequency point, and the modulus of the point is the amplitude characteristic of the frequency value. Suppose the amplitude of the corresponding frequency of the original signal at the ith point is A_iThe first point corresponds to the DC component, its modulus is N times of the DC component, and the modulus of the ith point from the 2 nd point in the FFT result is A_iIs/are as follows

The phase of each point is the phase of the signal at that frequency. The signal component information of N points is shared from the unit signal frequency represented by the 2 nd point to the last point, and the frequency of each point increases in sequence. Sampling frequency F_sAnd unit signal frequency F_nThe relationship of (a) to (b) is as follows:

the operation of DFT can also be completed through the matrix, the form is simple and more efficient, and the design and the realization of the high-performance parallel FFT processor:

y_out＝F*x_in(2)

wherein y is_outIs a processing result of N +1 rows and 1 column containing 1 DC component and N different frequency components, F is an N-point type FFT matrix, x_inData is sampled for N rows and 1 column. A four-point FFT matrix can be obtained by butterfly operation, etc., as follows:

by analogy, an FFT matrix of any positive integer point can be obtained, and the signal processing operation is completed. Meanwhile, the observation of the matrix shows that only one row needs to be operated to obtain a processing result of a certain frequency. And the more the number of points of FFT processing, the larger the scale of the FFT matrix, the longer the required calculation time, if only the signal expression of a certain frequency is concerned, only one line needs to be processed, so that the calculation complexity can be reduced, and the calculation efficiency can be improved.

MPC has the following characteristics:

1) controlled systems are described using state space models (e.g., LTI)

2) Constructing a cost function J in the case of a rolling field

3) Minimizing the cost function J by continuously updating the control quantity u using an optimization algorithm

The above features are explained one by one below:

MPC is also called Receding Horizon Control (RHC), i.e. rolling domain Control, and in the case of a system state space equation, the system state and output over a period of time can be estimated according to the current system state. Assuming a discrete model, the state space equation is as follows:

x(k+1)＝A_dx(k)+B_du (4)

y＝C_dx(k) (5)

by continuously substituting formula (4) for formula (5), it is possible to obtain

Namely:

y＝Φx(0)+u

wherein y comprises starting from the first step of prediction to the nth step_pA plurality of outputs of the steps.

A typical cost function for MPC is as follows:

where ω is the weight coefficient of each term, r_iFor the reference variable, Δ u is the increment of the control quantity u. The cost function design of the type generally has a difference punishment item for setting a reference variable and a punishment item for controlling the increment square, and a good effect can be obtained when a weight coefficient is proper, but the weight coefficient and the whole cost function are difficult to quantify, and the difficulty is large when a satisfactory control effect is obtained when a plurality of targets are processed.

MPC cost function designed according to DFT matrix characteristics:

by combining the analytical effect of DFT on the signal and the control effect of MPC on the system in the prediction field of view, we can predict and optimize the signal with specific frequency.

Processing the signal of the unit frequency by using DFT, referring to the second row of the DFT matrix, and the processing result at the frequency is as follows:

A＝y(1)+y(2)·i-y(3)-y(4)·i (8)

the result is in complex form, containing amplitude and phase information. This patent relates to the prediction and optimization of amplitude, independent of signal phase, so removing complex interference:

writing the cost function into a quadratic expression with the controlled variable u as an argument, and writing the cost function meeting the specification of the MPC cost function as follows:

wherein, H is the coefficient of the quadratic term of the control quantity in the cost function, and g is the coefficient of the first order term of the control quantity in the cost function.

Example of the method of use: in the MATLAB simulation process, H and g are used as input quantities to carry out real-time solution in a quadratic programming function, a solution result u is a vector containing four values, wherein the Nth value represents the optimal solution result of u in the Nth step, and the first result can be taken as the value of u in the next step.

A simple and popular scene is introduced to demonstrate the control effect of the optimization algorithm: as shown in fig. 2, a mass M with a mass M is assumed and is regarded as a mass point, which is placed on a plane with certain viscosity, the viscosity coefficient of the plane is b, and the mass point is linked with a spring at a coordinate origin, and the spring elastic coefficient is k. It is subject to two forces, the spring force of the spring and the resistance of the viscous substance. Because the spring force and the spring length are in a linear relationship, the resistance of the viscous substance is in a linear relationship with the motion speed of the mass block, and according to Newton's second law, the stress equation is listed as follows:

ma(t)＝u(t)-bv(t)-ky(t)

wherein: y (t) is position; v (t) is velocity; a (t) is acceleration; u (t) is the applied force; b is the coefficient of viscous friction; k is the spring constant; m is the mass of the object.

The state space equation is written from the above equation:

in the state space equation:

x₁(t) represents the position of the object;

is the speed of movement of the object;

is the acceleration of the object; the output y (t) is the position of the mass.

And (3) carrying out controllability test on the model after the modeling is completed:

the upper matrix is a full rank matrix, and the model is controllable.

It is tested for observability:

as above, the model is considerable.

Discretizing the model to obtain A_d、B_d、C_dThree matrices. Using 1s and 0.1s as model discrete step length, respectively, setting mass of the mass block to be 10kg, initial position to be 10m, initial speed to be 10m/s, direction to the right, k to be 2.5, b to be 0.1, and changing coordinates of the slide block as shown in fig. 3 and 4;

as can be seen from fig. 3 and 4, the mass is in a damped oscillation state. Since the discrete model satisfies the forms of the formula (4) and the formula (5), the scene initial condition is unchanged, and the cost function design in the formula (9) is adopted to stabilize the position of the mass block. Taking H and g as the input of a quadratic programming function, solving out the optimal external force control quantity and intervening, limiting the value range of the control quantity acting force under the condition that the discrete step length of the model is 1 second and 0.1 second, respectively simulating by using an MATLAB platform, wherein the coordinate fluctuation of the mass block is shown in figures 5 and 6. FIG. 5 coordinates of the proof mass at 1s step length after using the algorithm; FIG. 6 coordinates of the mass at 0.1s step after the algorithm.

As can be seen from simulation results, the algorithm has good effect in a spring-mass block scene under the simulation step lengths of 1s and 0.1s, so that the mass block can quickly reach a stable state without overshoot.

The change in the control amount is shown in fig. 7 and 8. From the results, in the simulation of the spring mass block scene on the viscous plane, the fluctuation stabilizing algorithm of the model predictive control cost function based on the FFT has good performance in the step length of 1s and the step length of smaller 0.1s, and the coordinates of the mass block can be quickly stabilized within a reasonable external force constraint range.

Wind farm case simulation

In a wind farm with 10 5MW wind generators, Distributed model predictive control of a wind farm for optimal active power control I, Clustering-based wind turbine model rectification J is used].IEEETransactions on Sustainable Energy,2015,6(3):831–839.DOI:10.1109/TSTE.2015.2418282.]In the 5MW wind driven generator piecewise affine (PWA) model based on parameter identification, the active power of each fan is solved and distributed by adopting distributed model predictive control, and the T of each fan before and after control is compared_sAnd F_tThe fluctuation situation specifically comprises the following steps:

PWA model of 5MW offshore wind turbine:

x(k+1)＝A_dx(k)+B_du(k)+E_dd(k)+F_d(14)

y(k)＝C_dx(k)+D_du(k)+G_dd(k)+H_d(15)

from the MPC characteristics, the output within the prediction step is found:

the service life characteristics of a fan transmission shaft and a tower are integrated to formulate a cost function, a coefficient H of a quadratic term and a coefficient g of a primary term in the cost function are calculated, and the cost function is constructed into the following form:

Gu＝b (18)

wherein:

is the total power reference value of the whole wind power plant.

Setting the step length of the model to be 0.1s, firstly, not using a control algorithm, fixing the power reference value of each fan, starting to use the control algorithm 50 seconds after the state initialization of the fans is completed, calculating the power reference value of each fan and transmitting the power reference value to a fan controller, and then setting the power reference value of each fan and the T of 5 fans in the power reference value_sAnd F_tAs shown in fig. 9, 10, 11 and 12.

The simulation result can be preliminarily judged, and after the MPC dynamic weight load reduction algorithm based on FFT is applied, the T of each fan in the wind power plant is dynamically updated through the dynamic update of the power reference value_sThe fluctuation is greatly improved, and F of each fan_tThe fluctuation situation is improved to a certain extent.

Calculate 10 fans T_s，F_tThe average values of the first fifty-second mean value and the last one hundred fifty-second mean value of the two indexes are respectively taken as the average values, and the statistical data are as follows:

TABLE 1T_sApplying a control algorithm pre-and post-variance

TABLE 2F_tApplying a control algorithm pre-and post-variance

According to variance statistics, after the MPC dynamic weight load shedding algorithm based on FFT is applied, each fan T of the wind power plant_sIs greatly reduced, F_tThe fluctuation condition is reduced to a large extent, the service life of the fan can be prolonged by the algorithm, and further the operation cost of the wind power plant is saved.

Example two

The embodiment provides a load optimization control system for wind power generators of a wind power plant;

It should be noted here that the acquiring module, the optimal control amount calculating module and the wind farm power optimizing module correspond to steps S101 to S103 in the first embodiment, and the modules are the same as the corresponding steps in the example and application scenarios, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.

In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.

EXAMPLE III

The present embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first embodiment.

It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.

The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.

Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

Example four

The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.

The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. The wind power plant wind driven generator load optimization control method is characterized by comprising the following steps:

2. The method as claimed in claim 1, wherein the obtaining of the state quantity of each wind driven generator of the wind farm at the current time comprises:

3. The method according to claim 1, characterized in that, based on the state quantity, the optimal control quantity of the wind power plant is calculated; the method comprises the following specific steps:

4. The method as claimed in claim 3, wherein the output quantity in the prediction time period is derived from the state quantity of each wind turbine in the prediction time period, and a plurality of characteristic quantities are extracted, specifically: the coefficient matrix is extracted as a feature quantity.

5. A method according to claim 3, characterized in that said optimal control quantity for the wind farm is calculated on the basis of the characteristic quantity; the method comprises the following specific steps:

6. The method of claim 5, wherein the fast fourier transform based model predictive control cost function is: a control quantity is calculated that minimizes a cost function, wherein the cost function is designed based on the predicted fluctuation width.

7. The method according to claim 1, wherein the fluctuation degree of the wind farm output quantity is reduced based on the wind farm optimum control quantity; the method comprises the following specific steps: the optimal control quantity is transmitted to the actuator, so that the fluctuation degree of the output quantity is reduced, and the service life of the system is further prolonged.

8. Wind-powered electricity generation field aerogenerator load optimal control system, characterized by includes:

9. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, the processor executing the one or more computer programs stored in the memory when the electronic device is running, to cause the electronic device to perform the method of any of the preceding claims 1-7.

10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.