A wind turbine gearbox elastic support failure monitoring method, device, equipment and medium

Abstract

The present application relates to the field of wind power generation equipment maintenance, and discloses a wind power gear box elastic support failure monitoring method, device, equipment and medium. The method comprises the following steps: acquiring a monitoring image of the wind power gear box elastic support; extracting features of the monitoring image to obtain load features, surface morphology features and gear friction features of the elastic support; inputting the load features, surface morphology features, gear friction features and main shaft endpoint displacement of the elastic support into a time sequence neural network to obtain a load prediction value of the elastic support; and determining whether the elastic support is failed according to the load prediction value of the elastic support. The present application can accurately monitor whether the wind power gear box elastic support is failed through multiple features.

Description

Technical Field

[0001] This invention relates to the field of wind power equipment maintenance, and in particular to a method, device, equipment and medium for monitoring the failure of elastic supports in wind turbine gearboxes. Background Technology

[0002] The gearbox of a wind turbine generator set is typically connected to the main bearing or nacelle base via a flexible support structure to buffer dynamic loads and suppress vibration transmission. These flexible supports often employ a rubber-metal composite structure, which is prone to stiffness degradation or even failure during long-term operation due to material aging, fatigue cracks, or environmental corrosion. Failure of this structure will cause abnormal vibration of the main shaft, subsequently triggering a chain reaction of failures such as poor gear meshing and bearing wear.

[0003] Current monitoring methods primarily rely on vibration sensors to collect acceleration signals from the elastic supports. Anomalies are then identified through spectral analysis, and the system is boarded to investigate whether the elastic supports have failed. However, a single vibration signal is insufficient to distinguish between interference and the true signs of elastic support failure, leading to false alarms or missed alarms. Furthermore, fixed threshold alarm strategies do not consider the differences in vibration baselines under different wind speeds and power conditions, limiting their applicability. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for monitoring the failure of elastic supports in wind turbine gearboxes, which can accurately monitor whether the elastic supports of wind turbine gearboxes have failed through multiple features.

[0005] The present invention also proposes an apparatus, equipment, and medium having the above-mentioned method for monitoring the failure of elastic supports in wind turbine gearboxes.

[0006] A method for monitoring the failure of an elastic support in a wind turbine gearbox according to a first aspect of the present invention includes: Acquire monitoring images of the elastic support of the wind turbine gearbox; The features of the monitoring image are extracted to obtain the load features, surface morphology features, and gear friction features of the elastic support; The load characteristics, surface morphology characteristics, gear friction characteristics, and spindle end displacement of the elastic support are input into a time-series neural network to obtain the load prediction value of the elastic support. Based on the predicted load value of the elastic support, determine whether the elastic support has failed.

[0007] A method for monitoring the failure of elastic supports in wind turbine gearboxes according to an embodiment of the present invention has at least the following beneficial effects: The present invention extracts image features to obtain characteristics of spindle vibration disturbances caused by load imbalance of the elastic support, sudden changes in aerodynamic load, and changes in gear friction state. These disturbance factors are then incorporated as input variables into a time-series neural network, improving the accuracy of failure determination and reducing misjudgments caused by aerodynamic imbalance, sudden changes in wind speed, or lubrication fluctuations. The present invention can accurately monitor whether the elastic supports of wind turbine gearboxes have failed through multiple features.

[0008] According to some embodiments of the present invention, the extraction of features from the monitoring image includes: Each frame of the monitoring image is divided into several unit partitions along the radial direction of the elastic support; The pre-trained semantic segmentation network is used to classify each unit partition of each frame of the monitoring image, resulting in partitions with intact coating and partitions with peeling coating.

[0009] According to some embodiments of the present invention, the load characteristics of the elastic support are obtained in the following ways: At the position corresponding to the preset load measurement angle of the elastic support, for any unit partition, the displacement of the pixel cluster area in two consecutive monitoring images is determined, and this displacement is used as the load feature of the elastic support.

[0010] According to some embodiments of the present invention, the surface morphological features of the elastic support are obtained in the following ways: Surface morphology parameters are determined based on the features extracted from the monitoring images; The ratio of the Euclidean distance between the surface morphology parameters and the historical average morphology parameters to the current rate of change of wind speed is used as the surface morphology feature of the elastic support.

[0011] According to some embodiments of the present invention, the surface morphological features of the elastic support are obtained in the following ways: The ratio of the number of unit zones identified as coating peeling zones to the total number of unit zones is used as a surface morphology parameter.

[0012] According to some embodiments of the present invention, the gear friction characteristics of the elastic support are obtained in the following ways: Obtain the estimated spindle torque value and the spindle rotation position; Based on the estimated spindle torque and the spindle's rotational position, combined with a simplified thermal balance relationship, the deviation between the actual oil temperature and the theoretical steady-state oil temperature is calculated, which serves as the gear friction characteristic of the elastic support.

[0013] According to some embodiments of the present invention, it further includes: The bending moment is obtained and corrected based on the principal axis azimuth angle; The displacement of the principal shaft endpoints is obtained based on the bending moments before and after the correction.

[0014] A wind turbine gearbox elastic support failure monitoring device according to a second aspect of the present invention includes: Industrial cameras are used to acquire monitoring images of the elastic support of wind turbine gearboxes; The processor is configured to receive the monitoring image; extract features from the monitoring image to obtain the load features, surface morphology features, and gear friction features of the elastic support; input the load features, surface morphology features, gear friction features, and spindle end displacement of the elastic support into a time-series neural network to obtain the load prediction value of the elastic support; and determine whether the elastic support has failed based on the load prediction value of the elastic support.

[0015] An electronic device according to a third aspect of the present invention includes: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of the first aspects.

[0016] According to a fourth aspect of the present invention, a storage medium stores computer-executable instructions for performing the method as described in any one of the first aspects.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0018] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0019] Figure 1 This is a flowchart of a method for monitoring the failure of elastic supports in wind turbine gearboxes, provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] It should be understood that in the description of the embodiments of the present invention, "multiple" (or "amounts") means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If "first," "second," etc., are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for monitoring the failure of elastic supports in wind turbine gearboxes, including: Step S100: Obtain monitoring images of the elastic support of the wind turbine gearbox; Step S200: Extract the features of the monitoring image to obtain the load features, surface morphology features, and gear friction features of the elastic support; Step S300: Input the load characteristics, surface morphology characteristics, gear friction characteristics, and spindle end displacement of the elastic support into a time-series neural network to obtain the load prediction value of the elastic support; Step S400: Determine whether the elastic support has failed based on the predicted load value of the elastic support.

[0023] This invention extracts image features to obtain spindle vibration disturbances caused by imbalance of elastic support load, sudden change of aerodynamic load, and change of gear friction state, and incorporates these disturbance factors as input variables into a time-series neural network, thereby improving the accuracy of failure determination and reducing misjudgments caused by aerodynamic imbalance, sudden change of wind speed, or lubrication fluctuation.

[0024] It should be noted that the failure of elastic supports is affected by a variety of factors, including the periodic changes in the bending moment of the main shaft due to gravity, wind speed and turbulence disturbances, and changes in the rotational position of the main shaft. Therefore, it is necessary to accurately reflect whether the elastic support has failed through a variety of characteristics.

[0025] In one embodiment, in step S200, extracting the features of the monitoring image includes: Each frame of the monitoring image is divided into several unit partitions along the radial direction of the elastic support; The pre-trained semantic segmentation network is used to classify each unit partition of each frame of the monitoring image, resulting in partitions with intact coating and partitions with peeling coating.

[0026] It should be noted that the wind turbine gearbox used in this embodiment includes several elastic supports, which surround the main shaft of the wind turbine gearbox. The direction in which each elastic support surrounds the main shaft is the radial direction of the elastic support.

[0027] In one embodiment, the method for obtaining the load characteristics of the elastic support in step S200 includes: At the position corresponding to the preset load measurement angle of the elastic support, for any unit partition, the displacement of the pixel cluster area in two consecutive monitoring images is determined, and this displacement is used as the load feature of the elastic support.

[0028] It should be noted that when determining displacement using the optical flow method, the pixel clustering region refers to a local pixel area, that is, a small area of ​​pixels that have the same motion attributes and are spatially clustered together in two consecutive frames of images. The wind turbine is a horizontal axis type, with the gearbox main shaft arranged horizontally (parallel to the ground and extending laterally along the nacelle tower). The elastic support connects the gearbox to the nacelle base / main bearing and is distributed around the main shaft circumferentially. Taking the vertical diameter of the main shaft as a reference, rotating 90° clockwise / counterclockwise along the circumference of the main shaft gives the 90° angle position of the elastic support (i.e., the load measurement angle). This position is located on the horizontal side of the main shaft and is the horizontal radial orientation of the main shaft circumference.

[0029] In one embodiment, the method for obtaining the surface morphological features of the elastic support in step S200 includes: Surface morphology parameters are determined based on the features extracted from the monitoring images; The ratio of the Euclidean distance between the surface morphology parameters and the historical average morphology parameters to the current rate of change of wind speed is used as the surface morphology feature of the elastic support.

[0030] In one embodiment, the method for obtaining the surface morphological features of the elastic support in step S200 includes: The ratio of the number of unit zones identified as coating peeling zones to the total number of unit zones is used as a surface morphology parameter.

[0031] In one embodiment, the method for obtaining the gear friction characteristics of the elastic support in step S200 includes: Obtain the estimated spindle torque value and the spindle rotation position; Based on the estimated spindle torque and the spindle's rotational position, combined with a simplified thermal balance relationship, the deviation between the actual oil temperature and the theoretical steady-state oil temperature is calculated, which serves as the gear friction characteristic of the elastic support.

[0032] Specifically, the formula for the friction characteristics of an elastically supported gear is as follows:

[0033] in, The friction correction coefficient for the main shaft rotation position (calibrated using historical data from the same model of fan). Let be the known torque-friction heat conversion efficiency, n be the obtained real-time spindle speed, and T be the estimated spindle torque. The spindle rotation position. Given the known overall heat dissipation coefficient of the lubricating oil, To obtain the oil temperature at the previous sampling time, Where m is the ambient temperature, and m is the total mass of the lubricating oil in the wind turbine gearbox. The specific heat capacity of the lubricating oil in the wind turbine gearbox; The amount of heat generated by friction per unit time. This refers to the amount of heat dissipated by the lubricating oil per unit time.

[0034] In one embodiment, it further includes: The bending moment is obtained and corrected based on the principal axis azimuth angle; The displacement of the principal shaft endpoints is obtained based on the bending moments before and after the correction.

[0035] It should be noted that the spindle azimuth angle is based on the horizontal arrangement of the horizontal axis fan spindle, and θ is the rotation angle of the spindle around its own axis (measured by displacement sensor).

[0036] The bending moment of the main shaft is composed of the gravitational bending moment gravity (core, which varies periodically with θ) and the aerodynamic load bending moment air (auxiliary, generated by the aerodynamic load of the fan). This invention uses the gravitational bending moment as the core calculation term and the aerodynamic bending moment as the correction term. The formula is a simplified engineering form, suitable for real-time calculation. In one embodiment, the bending moment is obtained based on the principal axis azimuth angle, specifically including: Calculate gravity bending moment (Core bending moment varying with azimuth angle θ): The gravitational bending moment is generated by the equivalent gravity of the principal shaft itself. Its magnitude varies periodically with the azimuth angle θ of the principal shaft. The core principle is that when the principal shaft rotates to different azimuth angles, the lever arm of gravity on the elastic support changes, causing the bending moment to fluctuate in a sine or cosine pattern. The calculation formula is as follows:

[0037] Where m is the equivalent mass of the main shaft (gearbox design parameter); g is the gravitational acceleration; L is the effective span of the main shaft (gearbox design parameter, distance from the elastic support to the end point of the main shaft); and θ is the measured azimuth angle of the main shaft (collected by the displacement sensor). Calculate the bending moment of aerodynamic loads (Auxiliary correction item) The aerodynamic bending moment is generated by the aerodynamic load of the wind turbine blades transmitted to the main shaft. The magnitude of the load is estimated by the ambient wind speed and unit power collected by the SCADA system. The calculation formula is as follows:

[0038] in, The measured aerodynamic load on the main shaft (calculated from wind speed and power in the SCADA system); L is the effective span of the main shaft (calculated from wind speed and power in the SCADA system). Calculate the combined bending moment : Since both the gravitational bending moment and the aerodynamic bending moment act in the same direction along the principal axis (the direction of force on the elastic support), they are directly linearly superimposed (for engineering simplification, minor deviations in the bending moment direction are ignored) to obtain the combined bending moment. The combined bending moment is the obtained bending moment, which is a scalar. We only care about its magnitude and use it for subsequent stiffness correction and displacement calculation.

[0039] In one embodiment, the process of correcting the bending moment is as follows: first, the composite load is derived from the composite bending moment, and then the "reference linear stiffness" is corrected to "the actual nonlinear stiffness" through the Sigmoid function, and finally the corrected composite stiffness is obtained. Synthetic Load The formula is as follows: ; Corrected overall stiffness The formula is as follows:

[0040] in, Given the reference stiffness, α is the preset slope coefficient of the Sigmoid function. The load is the inflection point load of the preset Sigmoid function; Spindle end point displacement The formula is as follows: .

[0041] It should be noted that the training process of the temporal neural network is similar to steps S100-S400, but the input is historical data and the verification is performed by the failure results corresponding to the historical data.

[0042] This invention also provides a wind turbine gearbox elastic support failure monitoring device, comprising: Industrial cameras are used to acquire monitoring images of the elastic support of wind turbine gearboxes; The processor is configured to receive the monitoring image; extract features from the monitoring image to obtain the load features, surface morphology features, and gear friction features of the elastic support; input the load features, surface morphology features, gear friction features, and spindle end displacement of the elastic support into a time-series neural network to obtain the load prediction value of the elastic support; and determine whether the elastic support has failed based on the load prediction value of the elastic support.

[0043] It should be noted that the device provided in this embodiment is used to perform the above-described method for monitoring the failure of the elastic support of a wind turbine gearbox.

[0044] In one embodiment, the method of the present invention is implemented on a 16MW rated power three-bladed horizontal axis wind turbine generator. The generator is equipped with a SCADA system to acquire operating parameters such as main shaft speed, generator power, and ambient wind speed. An industrial camera with a resolution of 2448×2048 is installed inside the nacelle near the main shaft. The lens focal length is calibrated to cover the elastic support area within a 180° azimuth angle range, and the image acquisition frequency is set to one frame every 5 seconds. Simultaneously, a displacement sensor is installed in the gearbox lubrication oil circuit with a sampling frequency of 1Hz; an eddy current displacement sensor is installed at the main bearing housing to acquire the radial vibration displacement of the main shaft, with a sampling frequency of 100Hz.

[0045] During operation, the following steps are performed: For each frame of image, the elastic support is divided into 12 unit partitions radially. A pre-trained U-Net semantic segmentation network is used to classify pixels in each partition, and the proportion of the coating peeling area to the total area of ​​the partition is calculated as a surface morphology parameter. The local pixel displacement of the same partition in two consecutive frames is calculated using optical flow, and its modulus is used as a representation of the load state parameter. The first interference coefficient (i.e., load feature) is obtained for the load state parameter of the elastic support at a 90° angle position. The main shaft operating position to which the current partition will rotate in the next monitoring cycle is calculated based on the current blade rotation speed. The average surface morphology parameter of the elastic support at this position under the same wind speed range is retrieved from the historical database. The Euclidean distance between the current measured value and the historical average is calculated, and combined with the current wind speed change rate, a second interference coefficient (i.e., surface morphology feature) is generated. Specifically, there are n historical data points under the same wind speed range and the same position, and each sample contains m elastic support surface morphology parameters (such as strain, displacement, etc.). The j-th parameter of the i-th sample is denoted as... (i=1,2,...,n; j=1,2,...,m), then the historical mean of the j-th parameter The final historical parameter mean vector is: Assume the currently measured morphological parameter vector is The calculated Euclidean distance between the measured value and the historical mean is: The final second interference coefficient is: ,in The absolute value of the current wind speed change rate; the deviation between the actual oil temperature and the theoretical steady-state oil temperature is calculated by using the estimated spindle torque value provided by the SCADA system and the measured spindle rotation position, combined with a simplified thermal balance relationship, and is used as the third interference coefficient (i.e., gear friction characteristics). Establish a dynamic stiffness model for the main shaft: calculate the bending moment using the main shaft azimuth angle as input; the stiffness value decreases nonlinearly with the increase of the combined load, and the Sigmoid function is used for correction; calculate the static displacement of the main shaft endpoints accordingly.

[0046] The above four variables are input into a time-series neural network composed of three layers of LSTM units. The network is trained using historical data from the same type of wind turbine in the wind farm over the past two years, including normal operation data and data from three confirmed elastic support failure events. The model outputs the predicted value of the elastic support operating load in the next 5 seconds, which is compared with the measured value of the displacement sensor. If the absolute difference is greater than the dynamic threshold corresponding to the current wind speed and power, the elastic support failure status flag is triggered.

[0047] This invention also provides an electronic device, which includes, but is not limited to: Memory, used to store programs; The processor is used to execute the program stored in the memory. When the processor executes the program stored in the memory, the processor is used to execute the above-mentioned method for monitoring the failure of the elastic support of the wind turbine gearbox.

[0048] The processor and memory can be connected via a bus or other means.

[0049] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the method described in the embodiments of the present invention. The processor implements the above method by running the non-transitory software program and instructions stored in the memory.

[0050] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function; the data storage area may store data for executing the methods described above. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0051] The non-transitory software program and instructions required to implement the above terminal selection method are stored in memory and are executed by one or more processors.

[0052] This invention also provides a storage medium storing computer-executable instructions for performing the above-described methods.

[0053] In one embodiment, the storage medium stores computer-executable instructions that are executed by one or more control processors.

[0054] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0055] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0056] This document describes embodiments of the invention, including preferred embodiments known to the inventors for carrying out the invention. Variations of these embodiments will become apparent to those skilled in the art upon reading the foregoing description. The inventors encourage those skilled in the art to adopt such variations as appropriate, and the inventors intend to practice embodiments of the invention in ways other than those specifically described herein. Therefore, the scope of the invention includes all modifications and equivalents of the subject matter set forth in the appended claims, as permitted by applicable law. Furthermore, the scope of the invention covers any combination of the foregoing elements in all possible variations thereof, unless otherwise indicated herein or otherwise clearly contradicted by the context.

Claims

1. A method for monitoring the failure of elastic supports in wind turbine gearboxes, characterized in that, include: Acquire monitoring images of the elastic support of the wind turbine gearbox; The features of the monitoring image are extracted to obtain the load features, surface morphology features, and gear friction features of the elastic support; The load characteristics, surface morphology characteristics, gear friction characteristics, and spindle end displacement of the elastic support are input into a time-series neural network to obtain the load prediction value of the elastic support. Based on the predicted load value of the elastic support, determine whether the elastic support has failed.

2. The method for monitoring the failure of elastic supports in wind turbine gearboxes according to claim 1, characterized in that, The features extracted from the monitoring image include: Each frame of the monitoring image is divided into several unit partitions along the radial direction of the elastic support; The pre-trained semantic segmentation network is used to classify each unit partition of each frame of the monitoring image, resulting in partitions with intact coating and partitions with peeling coating.

3. The method for monitoring the failure of elastic supports in wind turbine gearboxes according to claim 2, characterized in that, The methods for obtaining the load characteristics of the elastic support include: At the position corresponding to the preset load measurement angle of the elastic support, for any unit partition, the displacement of the pixel cluster area in two consecutive monitoring images is determined, and this displacement is used as the load feature of the elastic support.

4. The method for monitoring the failure of elastic supports in wind turbine gearboxes according to claim 2, characterized in that, The methods for obtaining the surface morphological features of the elastic support include: Surface morphology parameters are determined based on the features extracted from the monitoring images; The ratio of the Euclidean distance between the surface morphology parameters and the historical average morphology parameters to the current rate of change of wind speed is used as the surface morphology feature of the elastic support.

5. The method for monitoring the failure of elastic supports in wind turbine gearboxes according to claim 4, characterized in that, The methods for obtaining the surface morphological features of the elastic support include: The ratio of the number of unit zones identified as coating peeling zones to the total number of unit zones is used as a surface morphology parameter.

6. The method for monitoring the failure of elastic supports in wind turbine gearboxes according to claim 2, characterized in that, The methods for obtaining the gear friction characteristics of the elastic support include: Obtain the estimated spindle torque value and the spindle rotation position; Based on the estimated spindle torque and the spindle's rotational position, combined with a simplified thermal balance relationship, the deviation between the actual oil temperature and the theoretical steady-state oil temperature is calculated, which serves as the gear friction characteristic of the elastic support.

7. The method for monitoring the failure of elastic supports in wind turbine gearboxes according to claim 2, characterized in that, Also includes: The bending moment is obtained and corrected based on the principal axis azimuth angle; The displacement of the principal shaft endpoints is obtained based on the bending moments before and after the correction.

8. A failure monitoring device for elastic supports in wind turbine gearboxes, characterized in that, include: Industrial cameras are used to acquire monitoring images of the elastic support of wind turbine gearboxes; The processor is configured to receive the monitoring image; extract features from the monitoring image to obtain the load features, surface morphology features, and gear friction features of the elastic support; input the load features, surface morphology features, gear friction features, and spindle end displacement of the elastic support into a time-series neural network to obtain the load prediction value of the elastic support; and determine whether the elastic support has failed based on the load prediction value of the elastic support.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 7.

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