A wind storage system pre-adjustment method, system and device considering wind resource uncertainty

Abstract

The application provides a wind storage system pre-adjustment method, system and device considering wind resource uncertainty, comprising: dividing obtained historical wind resource data and historical wind power prediction data respectively to obtain different wind conditions and prediction wind condition power data; obtaining power generation power data of a target wind turbine under normal power generation conditions corresponding to each wind condition; dividing the power generation power data corresponding to each wind condition to obtain a plurality of wind speed buckets, and counting power generation power variance corresponding to each wind speed bucket; calculating the Euclidean distance between the obtained power generation power data and the obtained prediction power generation power data; establishing a relationship matrix of the prediction wind condition and the real wind condition; and adjusting the charge and discharge capacity of the energy storage device in the wind storage system by using the obtained relationship matrix of the prediction wind condition and the real wind condition; the application can reasonably pre-allocate energy storage reserves under different wind condition power prediction deviation conditions, so that the energy storage system has the ability to participate in wind power prediction compensation.

Description

Technical Field

[0001] The invention belongs to the field of new energy power, and in particular relates to a method, system and equipment for pre-adjusting a wind storage system that takes into account the uncertainty of wind resources. Background Technology

[0002] Against the backdrop of promoting low-carbon energy transition and addressing climate change, wind power, as a clean and renewable energy source, is playing an increasingly important role in the new energy structure. However, one of the challenges of wind power lies in the instability of its output, mainly due to the variability and unpredictability of wind energy. To address the problem of unstable output, introducing energy storage systems is an effective solution. Energy storage systems can not only stabilize the output of wind farms but also improve the overall reliability of wind power systems.

[0003] The integration of energy storage systems with wind farms can more effectively achieve functions such as frequency regulation, active power dispatch, and wind power prediction compensation. Wind power prediction is now a standard feature in wind farm construction, transmitting short- and long-term wind power prediction data to the power grid dispatch system as a reference for the grid to formulate power generation plans. However, due to the limited prediction scale, the accuracy of wind power prediction is difficult to guarantee, and the prediction effect is often worse for wind farms in complex terrain. Wind power prediction compensation, on the other hand, involves coordinating wind farms with energy storage systems and designing energy storage charging and discharging strategies to match the wind farm's output power with the reported prediction, reducing the deviation between the wind farm's output and the reported value, and reducing assessment risks. Specifically, when the wind farm's output is lower than the prediction curve, the energy storage system replenishes the power to bring the wind power energy storage system's output back to near the prediction curve; when the wind farm's output is higher than the prediction curve, the energy storage system absorbs the power to bring the wind power energy storage system's output back to near the prediction curve. Summary of the Invention

[0004] The purpose of this invention is to provide a wind storage system pre-adjustment method, system, and device that considers the uncertainty of wind resources, thereby solving the problem of the impact of the uncertainty of existing wind resources on the pre-adjustment strategy of energy storage system. Wind resources are a manifestation of a stochastic process. If the uncertainty of current wind resources and predicted wind power is not assessed, the energy storage system may be unable to achieve a reasonable charging and discharging strategy in response to the wind power prediction curve.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] This invention provides a pre-adjustment method for a wind-storage system considering the uncertainty of wind resources, comprising the following steps:

[0007] Step 1: Obtain historical wind resource data and historical wind power prediction data;

[0008] Step 2: Divide the obtained historical wind resource data and historical wind power prediction data into different categories to obtain various wind conditions and predicted wind power data.

[0009] Step 3: Obtain the power generation data of the target wind turbine under normal power generation conditions for each wind condition.

[0010] Step 4: Divide the power generation data corresponding to each wind condition into multiple wind speed buckets, and calculate the variance of power generation corresponding to each wind speed bucket.

[0011] Step 5: Calculate the Euclidean distance between the obtained power generation data and the obtained predicted power generation data;

[0012] Step 6: Establish a relationship matrix between predicted wind conditions and actual wind conditions based on the variances of all power generation and all Euclidean distances obtained.

[0013] Step 7: Adjust the energy storage capacity of the energy storage devices in the wind-storage system using the obtained relationship matrix between predicted and actual wind conditions.

[0014] Preferably, after step 1 and before step 2, the obtained historical wind resource data and historical wind power prediction data are cleaned to remove outliers, resulting in cleaned historical wind resource data and historical wind power prediction data.

[0015] Preferably, the cleaned historical wind resource data is divided into several different wind conditions. The specific method is as follows:

[0016] The cleaned historical wind resource data is divided according to macro-meteorological conditions to obtain m types of historical wind resource sub-data.

[0017] Each type of historical wind resource sub-data is divided according to atmospheric stability, resulting in n types of historical wind resource sub-data.

[0018] The time-series wind data in each type of historical wind resource subdata is abstracted into a wind spectrum to obtain various different wind conditions.

[0019] Preferably, the cleaned historical wind power prediction data is divided into multiple different predicted wind power data. The specific method is as follows:

[0020] The cleaned historical wind power prediction data is divided according to macro meteorological conditions to obtain m types of historical wind power prediction sub-data.

[0021] The obtained historical wind power prediction sub-data for each category is divided according to atmospheric stability, resulting in n categories of historical wind power prediction sub-data.

[0022] The time-series wind data in each type of historical wind power prediction subdata is abstracted into a wind spectrum to obtain various different predicted wind power data.

[0023] A wind storage system pre-adjustment system considering wind resource uncertainty includes:

[0024] The data acquisition unit is used to acquire historical wind resource data and historical wind power prediction data.

[0025] The data partitioning unit is used to divide the obtained historical wind resource data and historical wind power prediction data into different types of wind conditions and predicted wind power data.

[0026] The power generation data acquisition unit is used to acquire the power generation data of the target wind turbine under normal power generation conditions for each wind condition.

[0027] The power generation variance calculation unit is used to divide the power generation data corresponding to each wind condition, obtain multiple wind speed buckets, and calculate the power generation variance corresponding to each wind speed bucket.

[0028] The Euclidean distance calculation unit is used to calculate the Euclidean distance between the obtained power generation data and the obtained predicted power generation data.

[0029] The relation matrix construction unit is used to establish a relation matrix between predicted wind conditions and actual wind conditions based on the variances of all power generation and all Euclidean distances obtained.

[0030] The adjustment unit is used to adjust the energy storage capacity of the energy storage devices in the wind-storage system by using the relationship matrix between the predicted wind conditions and the actual wind conditions.

[0031] Preferably, it further includes a data cleaning unit for cleaning the obtained historical wind resource data and historical wind power prediction data to remove outliers and obtain cleaned historical wind resource data and historical wind power prediction data.

[0032] Preferably, the data partitioning unit includes a first data partitioning subunit, the first data partitioning subunit comprising:

[0033] The first sub-module of data partitioning is used to divide the cleaned historical wind resource data according to macro meteorological conditions, resulting in m types of historical wind resource sub-data.

[0034] The second sub-module of data partitioning is used to divide each type of historical wind resource sub-data according to atmospheric stability, resulting in n types of historical wind resource sub-data.

[0035] The third module of the data partitioning sub-data is used to abstract the time-series wind data in each type of historical wind resource sub-data into a wind spectrum, resulting in a variety of different wind conditions.

[0036] Preferably, the data partitioning unit includes a second data partitioning subunit, the second data partitioning subunit comprising:

[0037] The first sub-module of data partitioning is used to divide the cleaned historical wind power prediction data according to macro meteorological conditions to obtain m types of historical wind power prediction sub-data.

[0038] The second sub-module of data partitioning is used to divide each type of historical wind power prediction sub-data according to atmospheric stability, resulting in n types of historical wind power prediction sub-data.

[0039] The third sub-module of data partitioning is used to abstract the time-series wind data in each type of historical wind power prediction sub-data into wind spectrum, thereby obtaining a variety of different predicted wind power data.

[0040] A processing apparatus comprising at least a processor and a memory, wherein a computer program is stored in the memory, and the processor executes steps to implement the method when running the computer program.

[0041] A computer storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the steps of the method.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This invention provides a pre-adjustment method for wind power storage systems that considers the uncertainty of wind resources. Quantifying the uncertainty of wind resources enables wind power storage systems to more effectively predict and compensate for wind power. Combining meteorology and statistics, historical wind resources are classified into several wind conditions. The power performance of actual wind farms under different wind conditions is analyzed. Based on the deviation of power performance under different wind conditions and combined with historical wind power prediction data, the error of wind power prediction under different wind conditions is obtained. This serves as the basis for the pre-charge and pre-discharge adjustment strategy of the energy storage system. The aim is to reasonably pre-allocate energy storage reserves under the power prediction deviation of different wind conditions, enabling the energy storage system to participate in wind power prediction compensation. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0045] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0046] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0047] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0048] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0049] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0051] Example 1

[0052] This embodiment provides a pre-adjustment method for a wind-storage system that considers the uncertainty of wind resources. Based on historical wind resource data, historical wind power prediction data, and wind turbine output power data, the method abstracts the wind resource uncertainty corresponding to different wind conditions. Combining historical prediction deviations, it simulates the uncertainty of current wind resources, which serves as the basis for the pre-charge and pre-discharge adjustment strategy of the energy storage system, thus satisfying the wind power prediction compensation response capability.

[0053] like Figure 1 As shown, the specific steps include:

[0054] Step 1: Obtain historical wind resource data and historical wind power prediction data;

[0055] Step 2: Clean the obtained historical wind resource data and historical wind power prediction data to remove outliers, and obtain cleaned historical wind resource data and historical wind power prediction data.

[0056] Step 3: Divide the cleaned historical wind resource data into different types of wind conditions;

[0057] Step 4: Calculate the power generation data of the target wind turbine under normal power generation conditions for each wind condition.

[0058] Step 5: Divide the power generation data corresponding to each wind condition into multiple wind speed buckets; calculate the power generation variance corresponding to each wind speed bucket, and use the obtained power generation variance as a measure of the uncertainty of historical wind condition power performance.

[0059] Step 6: Divide the cleaned historical wind power prediction data to obtain various different predicted wind power data.

[0060] Step 7: Statistically analyze the predicted power generation data of the target wind turbine under normal power generation conditions for each type of predicted wind power data.

[0061] Step 8: Calculate the Euclidean distance between the power generation data obtained in Step 4 and the predicted power generation data obtained in Step 7, as a reference for the uncertainty of wind power prediction;

[0062] Step 10: Establish a relationship matrix between predicted wind conditions and actual wind conditions based on all power generation variances obtained in Step 5 and all Euclidean distances obtained in Step 8.

[0063] Step 11: Adjust the energy storage capacity of the energy storage device in the wind storage system using the obtained relationship matrix between the predicted wind conditions and the actual wind conditions.

[0064] Example 2

[0065] Based on Example 1, this example provides a pre-adjustment method for a wind storage system considering the uncertainty of wind resources. In step 3, the cleaned historical wind resource data is divided to obtain various different wind conditions. The specific method is as follows:

[0066] The cleaned historical wind resource data is divided according to macro-meteorological conditions to obtain m types of historical wind resource sub-data.

[0067] Each type of historical wind resource sub-data is divided according to atmospheric stability, resulting in n types of historical wind resource sub-data.

[0068] The time-series wind data in each type of historical wind resource subdata is abstracted into a wind spectrum to obtain various different wind conditions.

[0069] Example 3

[0070] Based on Example 1, this example provides a pre-adjustment method for a wind storage system considering wind resource uncertainty. In step 6, the cleaned historical wind power prediction data is divided to obtain various different predicted wind power data. The specific method is as follows:

[0071] The cleaned historical wind power prediction data is divided according to macro meteorological conditions to obtain m types of historical wind power prediction sub-data.

[0072] The obtained historical wind power prediction sub-data for each category is divided according to atmospheric stability, resulting in n categories of historical wind power prediction sub-data.

[0073] The time-series wind data in each type of historical wind power prediction subdata is abstracted into a wind spectrum to obtain various different predicted wind power data.

[0074] Example 4

[0075] To better illustrate the implementation of the present invention, specific embodiments will be given below to explain the specific application methods and effects of the present invention.

[0076] Suppose there is a wind farm located in a mountainous area, which is significantly affected by seasonal wind speed variations, especially in winter and summer. The wind farm is equipped with a wind power energy storage system based on meteorological variation characteristics. The specific implementation steps are as follows:

[0077] Data acquisition and preprocessing: First, historical wind resource data and wind power prediction data are collected and preprocessed, including data cleaning and outlier removal, to ensure the accuracy and reliability of the data.

[0078] Wind resource classification and wind power prediction error statistics: Historical wind resource data is abstracted into different wind conditions, and the normal power generation of wind turbines under each wind condition is statistically analyzed. The variance is calculated based on the wind resource data and the actual power generation, serving as a measure of the uncertainty of historical wind condition power performance. Simultaneously, historical wind power prediction data is processed using the same method to obtain a relationship matrix between predicted and actual wind conditions.

[0079] Simulate current wind resource uncertainty and pre-charge / discharge adjustment strategy: Based on historical data and relationship matrices, simulate the uncertainty of current wind resources, and combine the relationship matrix between predicted and actual wind conditions to establish relationship matrices between different predicted wind conditions, actual wind conditions, and the uncertainty of the wind conditions themselves. Based on current wind power prediction data, estimate the amount of electricity that the energy storage system needs to pre-store, and adjust the pre-charge / discharge strategy of the energy storage system.

[0080] Real-time monitoring and adjustment: Integrating advanced meteorological monitoring and forecasting technologies, the system monitors and analyzes changes in key meteorological parameters such as wind speed, wind direction, temperature, and humidity in real time, and continuously adjusts the pre-charge and discharge strategies of the energy storage system to ensure that the system can respond promptly to changes in wind power forecasts, thereby maximizing the stability and reliability of the wind power system.

[0081] Performance evaluation and optimization: Statistical analysis of data during implementation, optimization and adjustment of strategies to continuously improve system efficiency and stability.

[0082] Example 5

[0083] This embodiment provides a wind storage system pre-adjustment system that considers the uncertainty of wind resources, including:

[0084] The data acquisition unit is used to acquire historical wind resource data and historical wind power prediction data.

[0085] The data partitioning unit is used to divide the obtained historical wind resource data and historical wind power prediction data into different types of wind conditions and predicted wind power data.

[0086] The power generation data acquisition unit is used to acquire the power generation data of the target wind turbine under normal power generation conditions for each wind condition.

[0087] The power generation variance calculation unit is used to divide the power generation data corresponding to each wind condition, obtain multiple wind speed buckets, and calculate the power generation variance corresponding to each wind speed bucket.

[0088] The Euclidean distance calculation unit is used to calculate the Euclidean distance between the obtained power generation data and the obtained predicted power generation data.

[0089] The relation matrix construction unit is used to establish a relation matrix between predicted wind conditions and actual wind conditions based on the variances of all power generation and all Euclidean distances obtained.

[0090] The adjustment unit is used to adjust the energy storage capacity of the energy storage devices in the wind-storage system by using the relationship matrix between the predicted wind conditions and the actual wind conditions.

[0091] It also includes a data cleaning unit, which cleans the obtained historical wind resource data and historical wind power prediction data to remove outliers and obtain cleaned historical wind resource data and historical wind power prediction data.

[0092] The data partitioning unit includes a first data partitioning subunit, which includes:

[0093] The first sub-module of data partitioning is used to divide the cleaned historical wind resource data according to macro meteorological conditions, resulting in m types of historical wind resource sub-data.

[0094] The second sub-module of data partitioning is used to divide each type of historical wind resource sub-data according to atmospheric stability, resulting in n types of historical wind resource sub-data.

[0095] The third module of the data partitioning sub-data is used to abstract the time-series wind data in each type of historical wind resource sub-data into a wind spectrum, resulting in a variety of different wind conditions.

[0096] The data partitioning unit includes a second data partitioning subunit, which includes:

[0097] The first sub-module of data partitioning is used to divide the cleaned historical wind power prediction data according to macro meteorological conditions to obtain m types of historical wind power prediction sub-data.

[0098] The second sub-module of data partitioning is used to divide each type of historical wind power prediction sub-data according to atmospheric stability, resulting in n types of historical wind power prediction sub-data.

[0099] The third sub-module of data partitioning is used to abstract the time-series wind data in each type of historical wind power prediction sub-data into wind spectrum, thereby obtaining a variety of different predicted wind power data.

[0100] Example 6

[0101] This embodiment provides a processing device corresponding to the wind storage system pre-adjustment method considering wind resource uncertainty provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.

[0102] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the wind storage system pre-adjustment method considering wind resource uncertainty provided in Embodiment 1.

[0103] In some embodiments, the memory may be high-speed random access memory (RAM).

[0104] It may also include nonvolatile memory, such as at least one disk storage device.

[0105] In other embodiments, the processor can be a general-purpose processor of various types, such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0106] Example 7

[0107] The wind-storage system pre-adjustment method considering wind resource uncertainty in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded for executing the wind-storage system pre-adjustment method considering wind resource uncertainty described in Embodiment 1.

[0108] A computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device.

[0109] Computer-readable storage media may be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0110] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A pre-adjustment method for a wind-storage system considering the uncertainty of wind resources, characterized in that, Includes the following steps: Step 1: Obtain historical wind resource data and historical wind power prediction data; Step 2: Divide the obtained historical wind resource data and historical wind power prediction data into different categories to obtain various wind conditions and predicted wind power data. Step 3: Obtain the power generation data of the target wind turbine under normal power generation conditions for each wind condition. Step 4: Divide the power generation data corresponding to each wind condition into multiple wind speed buckets, and calculate the variance of power generation corresponding to each wind speed bucket. Step 5: Calculate the Euclidean distance between the obtained power generation data and the obtained predicted power generation data; Step 6: Establish a relationship matrix between predicted wind conditions and actual wind conditions based on the variances of all power generation and all Euclidean distances obtained. Step 7: Adjust the energy storage capacity of the energy storage devices in the wind-storage system using the obtained relationship matrix between predicted and actual wind conditions; after Step 1 and before Step 2, clean the obtained historical wind resource data and historical wind power prediction data to remove outliers, obtaining cleaned historical wind resource data and historical wind power prediction data; divide the cleaned historical wind resource data into various wind conditions, specifically as follows: The cleaned historical wind resource data is divided according to macro-meteorological conditions to obtain m types of historical wind resource sub-data. Each type of historical wind resource sub-data is divided according to atmospheric stability, resulting in n types of historical wind resource sub-data. The time-series wind data in each type of historical wind resource subdata is abstracted into a wind spectrum to obtain various different wind conditions.

2. The wind-storage system pre-adjustment method considering wind resource uncertainty according to claim 1, characterized in that, The cleaned historical wind power prediction data is divided into several different predicted wind power data sets. The specific method is as follows: The cleaned historical wind power prediction data is divided according to macro meteorological conditions to obtain m types of historical wind power prediction sub-data. The obtained historical wind power prediction sub-data for each category is divided according to atmospheric stability, resulting in n categories of historical wind power prediction sub-data. The time-series wind data in each type of historical wind power prediction subdata is abstracted into a wind spectrum to obtain various different predicted wind power data.

3. A wind-storage system pre-adjustment system considering the uncertainty of wind resources, characterized in that, include: The data acquisition unit is used to acquire historical wind resource data and historical wind power prediction data. The data partitioning unit is used to divide the obtained historical wind resource data and historical wind power prediction data into different types of wind conditions and predicted wind power data. The power generation data acquisition unit is used to acquire the power generation data of the target wind turbine under normal power generation conditions for each wind condition. The power generation variance calculation unit is used to divide the power generation data corresponding to each wind condition, obtain multiple wind speed buckets, and calculate the power generation variance corresponding to each wind speed bucket. The Euclidean distance calculation unit is used to calculate the Euclidean distance between the obtained power generation data and the obtained predicted power generation data. The relation matrix construction unit is used to establish a relation matrix between predicted wind conditions and actual wind conditions based on the variances of all power generation and all Euclidean distances obtained. The adjustment unit is used to adjust the energy storage capacity of the energy storage device in the wind storage system using the relationship matrix between the predicted wind conditions and the actual wind conditions; it also includes a data cleaning unit, which cleans the obtained historical wind resource data and historical wind power prediction data to remove outliers and obtain cleaned historical wind resource data and historical wind power prediction data. The data partitioning unit includes a first data partitioning subunit, which includes: The first sub-module of data partitioning is used to divide the cleaned historical wind resource data according to macro meteorological conditions, resulting in m types of historical wind resource sub-data. The second sub-module of data partitioning is used to divide each type of historical wind resource sub-data according to atmospheric stability, resulting in n types of historical wind resource sub-data. The third module of the data partitioning sub-data is used to abstract the time-series wind data in each type of historical wind resource sub-data into a wind spectrum, resulting in a variety of different wind conditions.

4. A wind-storage system pre-adjustment system considering wind resource uncertainty according to claim 3, characterized in that, The data partitioning unit includes a second data partitioning subunit, which includes: The first sub-module of data partitioning is used to divide the cleaned historical wind power prediction data according to macro meteorological conditions to obtain m types of historical wind power prediction sub-data. The second sub-module of data partitioning is used to divide each type of historical wind power prediction sub-data according to atmospheric stability, resulting in n types of historical wind power prediction sub-data. The third sub-module of data partitioning is used to abstract the time-series wind data in each type of historical wind power prediction sub-data into wind spectrum, thereby obtaining a variety of different predicted wind power data.

5. A processing apparatus, the processing apparatus comprising at least a processor and a memory, the memory storing a computer program, characterized in that, When the processor runs the computer program, it performs steps to implement the method of any one of claims 1 to 2.

6. A computer storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to perform the steps of the method according to any one of claims 1 to 2.

Images