A method and device for processing data of offshore wind power equipment based on edge-cloud collaboration

Abstract

The present invention proposes a method and device for processing data of offshore wind power equipment based on edge-cloud collaboration. The method identifies various types of sensors installed on offshore wind power equipment and collects corresponding types of sensing data; performs edge computing processing on the collected sensing data of different types according to preset rules to obtain sensor monitoring data; and transmits the sensor monitoring data to the cloud for analysis and storage. Through the present invention, the monitoring data of offshore wind power equipment can be transmitted to land in real time to ensure the timeliness of the data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, computer equipment and storage medium for offshore wind power equipment data processing based on edge-cloud collaboration. Background Art

[0002] In order to cope with the severe environmental problems caused by climate warming, renewable energy technology has developed rapidly in recent years. Due to the relatively abundant wind energy conditions in the offshore sea, as well as the multiplier effect brought about by technological improvement and cost reduction, there has been a construction boom in offshore wind power in recent years, and the total installed capacity and number of offshore wind power projects have been rising year by year. However, since wind power generation relies on uncertain wind energy, the output power is unstable and cannot reach the rated power. There is a large power shortage space in the electrical equipment related to offshore wind power that has not been utilized, and the utilization of offshore energy is not sufficient.

[0003] On the other hand, ocean energy, as a renewable clean energy, does not occupy land and has an extremely abundant total amount. Tidal power generation technology was born under this background. Compared with solar energy and wind energy, tidal energy has a higher energy density and is predictable according to the movement of the earth, moon and sun, and its energy is relatively stable. Tidal energy uses the relatively mature principle of mechanical energy-electrical energy conversion of turbines. In recent years, a series of experiments and applications have been carried out at home and abroad, and the prospects are broad. However, because tidal energy is far away from land, the infrastructure cost of laying offshore power transmission, control and other equipment is very high, and it is easily damaged by severe sea conditions. The economic efficiency of building an offshore tidal power station alone is low.

[0004] With the advent of the era of large-scale offshore wind turbines, higher requirements are placed on key technologies and important supporting components of offshore wind power generation. For example, the tower, a key component of large wind turbines, is subjected to the harsh environment of typhoons, strong sandstorms, low temperatures and high salt fog under extreme weather conditions along the coast. In order to meet the requirements under the above-mentioned operating conditions, it is necessary to monitor the offshore wind power equipment in real time and collect and analyze the monitoring data.

[0005] However, since offshore wind power equipment is installed at sea, monitoring data needs to be transmitted back to land. However, the offshore environment is complex and communication is inconvenient, and sometimes the data cannot be transmitted to land in time. Summary of the invention

[0006] The present invention provides a method, device, computer equipment and storage medium for processing offshore wind power equipment data based on edge-cloud collaboration, aiming to transmit the monitoring data of offshore wind power equipment to land in real time to ensure the timeliness of the data.

[0007] To this end, the first object of the present invention is to propose an offshore wind power equipment data processing method based on edge-cloud collaboration, comprising:

[0008] Identify various types of sensors installed on offshore wind power equipment and collect corresponding types of sensing data; wherein the data acquisition is connected to the sensors installed on the offshore wind power equipment; wherein the sensors are divided into wired transmission sensors and wireless transmission sensors; the types of wired transmission sensors include at least vibration sensors, stress sensors, bolt tightening force sensors, and laser radars; the types of wireless transmission sensors include at least displacement, inclination sensors, and atmospheric and marine environment sensors;

[0009] According to preset rules, edge computing is performed on different types of collected sensing data to obtain sensor monitoring data;

[0010] The sensor monitoring data is transmitted to the cloud for analysis and storage.

[0011] Among them, in the step of performing edge computing processing on the collected different types of sensing data according to preset rules to obtain sensor monitoring data, the steps include:

[0012] Setting a preset data receiving frequency, and comparing the sampling frequencies of different types of sensing data with the preset data receiving frequency;

[0013] receiving different types of sensing data from the data collection according to a preset data receiving frequency;

[0014] By comparing the data receiving frequency with the sampling frequencies of different types of sensors, data processing is performed, and the data is sent and stored;

[0015] The sensors are synchronized according to the sampling frequencies of the sensing data of different types of sensors.

[0016] Wherein, when it is determined that the sampling frequency of the sensing data of the current processing type is the same as the preset data receiving frequency, the data is sent and stored at the preset data receiving frequency;

[0017] When it is determined that the sampling frequency of the sensing data of the current processing type is greater than the preset data receiving frequency, the characteristic value is calculated and selected, and the characteristic value is sent and stored at the set receiving frequency;

[0018] When it is determined that the sampling frequency of the sensing data of the current processing type is less than the preset data receiving frequency, data supplementation is performed to achieve data transmission and storage at the set receiving frequency.

[0019] Among them, the wireless transmission sensor outputs the sensing data through the 5G network.

[0020] Among them, the preset receiving frequency is 1Hz. If the actual sampling frequency of the sensor is greater than 1Hz, the characteristic value of the sampled data is calculated; wherein the calculated characteristic value is any one of the mean value, root mean square value, standard deviation, skewness, and kurtosis; if the actual sampling frequency of the sensor is less than 1Hz, data interpolation is performed according to 1Hz, or the mean value or straight line slope is calculated.

[0021] The received sensing data is stored and the data is transmitted according to the instructions of the host computer.

[0022] The second object of the present invention is to provide an offshore wind power equipment data processing device based on edge-cloud collaboration, comprising:

[0023] Data collection, used to identify the types of sensors installed on offshore wind power equipment and collect the corresponding types of sensing data generated;

[0024] The data processing module is used to perform edge computing processing on different types of collected sensing data according to preset rules to obtain sensor monitoring data;

[0025] The data transmission module is used to transmit the sensor monitoring data to the cloud for analysis and storage.

[0026] The third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method of the aforementioned technical solution is implemented.

[0027] The fourth object of the present invention is to provide a non-temporary computer-readable storage medium having a computer program stored thereon, which implements the method of the aforementioned technical solution when the computer program is executed by a processor.

[0028] Different from the prior art, the offshore wind power equipment data processing method based on edge-cloud collaboration provided by the present invention identifies various types of sensors installed on offshore wind power equipment and collects the corresponding types of sensing data generated; performs edge computing processing on the collected sensing data of different types according to preset rules to obtain sensor monitoring data; and transmits the sensor monitoring data to the cloud for analysis and storage. Through the present invention, the monitoring data of offshore wind power equipment can be transmitted to land in real time to ensure the timeliness of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention and / or additional aspects and advantages will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 It is a flow chart of a method for processing offshore wind power equipment data based on edge-cloud collaboration provided by the present invention.

[0031] Figure 2 It is a structural schematic diagram of an offshore wind power cloud-edge collaborative monitoring system in an offshore wind power equipment data processing method based on edge-cloud collaboration provided by the present invention.

[0032] Figure 3 It is a logical diagram of data processing in a method for processing offshore wind power equipment data based on edge-cloud collaboration provided by the present invention.

[0033] Figure 4 It is a structural schematic diagram of an offshore wind power equipment data processing device based on edge-cloud collaboration provided by the present invention.

[0034] Figure 5 It is a structural schematic diagram of a non-temporary computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0035] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0036] Figure 1 An embodiment of the present invention provides a method for processing offshore wind power equipment data based on edge-cloud collaboration. The method comprises the following steps:

[0037] Step 101, identifying various types of sensors installed on offshore wind power equipment, and collecting corresponding types of sensing data generated.

[0038] The data acquisition is connected to the sensors installed on the offshore wind power equipment; wherein the sensors are divided into wired transmission sensors and wireless transmission sensors; the types of the wired transmission sensors include at least vibration sensors, stress sensors, bolt tightening force sensors, and laser radars; the types of the wireless transmission sensors include at least displacement, inclination sensors, and atmospheric and marine environment sensors.

[0039] Offshore wind power equipment includes blades and their control equipment, and various sensors are used to collect monitoring data for equipment monitoring. Vibration sensors, stress sensors, bolt tightening force sensors, lidar and other sensors are usually installed in the control equipment of wind power equipment, and the monitoring data is transmitted by wired mode; while displacement, inclination sensors and atmospheric and marine environment sensors are usually installed on the blades of wind power equipment and in the deep sea. Wired collection consumes a lot of energy, and wireless collection is adopted in the present invention. Due to the complex offshore environment and inconvenient communication, 5G macro stations are installed in the booster station and 5G / WiFi access points are installed inside the unit to achieve full network coverage of the offshore wind farm. In addition to the traditional sensors arranged inside the unit, wired / 5G / WiFi transmission can be used. In particular, environmental monitoring equipment that is difficult to transmit data by wire, such as meteorological sensors arranged on the top of the unit and hydrological sensors arranged on the sea surface, can use 5G to transmit data.

[0040] Step 102: Perform edge computing processing on the collected different types of sensing data according to preset rules to obtain sensor monitoring data.

[0041] Specifically, the following steps are included:

[0042] Setting a preset data receiving frequency, and comparing the sampling frequencies of different types of sensing data with the preset data receiving frequency;

[0043] receiving different types of sensing data from the data collection according to a preset data receiving frequency;

[0044] By comparing the data receiving frequency with the sampling frequencies of different types of sensors, data processing is performed, and the data is sent and stored;

[0045] The sensors are synchronized according to the sampling frequencies of the sensing data of different types of sensors.

[0046] When it is determined that the sampling frequency of the sensing data of the current processing type is the same as the preset data receiving frequency, the data is sent and stored at the preset data receiving frequency;

[0047] When it is determined that the sampling frequency of the sensing data of the current processing type is greater than the preset data receiving frequency, the characteristic value is calculated and selected, and the characteristic value is sent and stored at the set receiving frequency;

[0048] When it is determined that the sampling frequency of the sensing data of the current processing type is less than the preset data receiving frequency, data supplementation is performed to achieve data transmission and storage at the set receiving frequency.

[0049] Specifically, the preset receiving frequency is 1 Hz. If the actual sampling frequency of the sensor is greater than 1 Hz, the characteristic value of the sampled data is calculated; wherein the calculated characteristic value is any one of the mean value, root mean square value, standard deviation, skewness, and kurtosis; if the actual sampling frequency of the sensor is less than 1 Hz, data interpolation is performed according to 1 Hz, or the mean value or the slope of the straight line is calculated.

[0050] Step 103, transmitting the sensor monitoring data to the cloud for analysis and storage.

[0051] The method of the present invention relies on an offshore wind power cloud-edge collaborative monitoring system, the structure of which is as follows: Figure 2 As shown. The system includes various sensors connected to the wind turbine blades and the wind turbine control system, including vibration sensors, stress sensors, bolt tightening force sensors, laser radars, displacement, tilt sensors and atmospheric and marine environment sensors, which are connected to the corresponding positions of the wind turbine blades and the control system; the vibration sensors, stress sensors, bolt tightening force sensors and laser radars are respectively connected to an edge server, and the edge server is used to perform the operations of step 101 and step 102. The edge server receives the sensing data transmitted by the sensors connected to it, and processes the data to obtain sensor monitoring data.

[0052] Each type of wired transmission sensor is connected to an edge server, which stores the collected data and then determines the data collection frequency. The receiving frequency is set to 1 Hz. If the actual sampling frequency of the sensor is greater than 1 Hz, the characteristic value of the sampled data is calculated; wherein the calculated characteristic value is any one of the mean value, root mean square value, standard deviation, skewness, and kurtosis; if the actual sampling frequency of the sensor is less than 1 Hz, data interpolation is performed according to 1 Hz, or the mean value or the slope of the straight line is calculated.

[0053] The specific data processing process is as follows Figure 3 As shown in the figure. For sensor data with a frequency higher than 1 Hz, the edge server can extract data features and store the raw data. The raw data is processed in the edge server, and the processed data is used to implement fault warning using the built-in algorithm. The edge server forwards the data features to the cloud server, and the fault warning information is also forwarded to the cloud server.

[0054] For sensor data with a frequency equal to or close to 1 Hz, signal processing is performed in the edge server, and the processed data is applied with a built-in algorithm to achieve fault warning. The edge server forwards the original data features to the cloud server, and the fault warning information is also forwarded to the cloud server.

[0055] For sensor data with a frequency less than 1 Hz, the edge server interpolates the data and stores the original data in the edge server for signal processing. The processed data is applied with a built-in algorithm to realize fault warning. The edge server forwards the interpolated data features to the cloud server, and the fault warning information is also forwarded to the cloud server.

[0056] The warning information determined in advance by the fault warning module in the edge server can be directly sent to the controller of the unit in the form of control instructions, and the instruction information received by the edge server can also be sent from the channel to the unit controller for execution.

[0057] By extracting data features, we can calculate the mean, root mean square value, standard deviation, skewness, kurtosis, etc. of the data. We can also calculate the interquartile range, that is, select the signal at the same time to calculate the interquartile range (IQR), as shown in formula (1):

[0058] iqr=SS(75)-SS(25) (1)

[0059] Where iqr is the interquartile range, SS(75) and SS(25) represent the signals of the third quartile (i.e. 75% N) and the first quartile (i.e. 25% N), respectively, and N is the number of data.

[0060] Fault warning can be calculated based on the upper and lower limits SS shown in formulas (2) and (3). max , SSmin calculation is out of limit, thus issuing a warning signal.

[0061] SS min =SS(25)-iqr×c (2)

[0062] SS max =SS(75)+iqr×c (3)

[0063] Among them, c is an adjustable parameter, which is generally set to 1.5.

[0064] After processing, the data packet is sent to the LAN switch through the ring network switch connected to the corresponding edge server, and then passes through the on-site monitoring server, router, and firewall in sequence, and is output to the upper device for cloud storage analysis and display on the centralized control screen. Furthermore, the edge computing server can simultaneously transmit data to the remote centralized control center and the on-site unit control PLC to participate in the control.

[0065] In order to implement the embodiment, the present invention also proposes an offshore wind power equipment data processing device based on edge-cloud collaboration, such as Figure 4 As shown, including:

[0066] The data acquisition module 310 is used to identify the types of sensors installed on the offshore wind power equipment and collect the corresponding types of sensing data generated;

[0067] The data processing module 320 is used to perform edge computing processing on the collected different types of sensing data according to preset rules to obtain sensor monitoring data;

[0068] The data transmission module 330 is used to transmit the sensor monitoring data to the cloud for analysis and storage.

[0069] In order to implement the embodiment, the present invention also proposes another computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, offshore wind power equipment data processing as in the embodiment of the present invention is implemented.

[0070] like Figure 5 As shown, the non-transitory computer-readable storage medium includes a memory 810 of instructions, an interface 830, and the instructions can be executed by a processor 820 according to the offshore wind power equipment data processing device to complete the method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0071] In order to implement the embodiment, the present invention further proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, offshore wind power equipment data processing as in the embodiment of the present invention is implemented.

[0072] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0073] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0074] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0075] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0076] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the described embodiment, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0077] A person skilled in the art may understand that all or part of the steps of implementing the method of the embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0078] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0079] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the embodiments are exemplary and cannot be construed as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the embodiments within the scope of the present invention.

Claims

1. A method for processing offshore wind power equipment data based on edge-cloud collaboration, characterized in that: include: Identify the various types of sensors installed on offshore wind power equipment and collect the corresponding types of sensing data generated; The data acquisition is connected to the sensors installed on the offshore wind power equipment; wherein the sensors are divided into wired transmission sensors and wireless transmission sensors; the types of wired transmission sensors include at least vibration sensors, stress sensors, bolt tightening force sensors, and laser radars; the types of wireless transmission sensors include at least displacement and inclination sensors and atmospheric and marine environment sensors; According to preset rules, edge computing is performed on different types of collected sensing data to obtain sensor monitoring data; Transmitting the sensor monitoring data to the cloud for analysis and storage; Among them, in the step of performing edge computing processing on the collected different types of sensing data according to preset rules to obtain sensor monitoring data, the steps include: Setting a preset data receiving frequency, and comparing the sampling frequencies of different types of sensing data with the preset data receiving frequency; receiving different types of sensing data from the data collection according to the preset data receiving frequency; When it is determined that the sampling frequency of the sensing data of the current processing type is the same as the preset data receiving frequency, signal processing is performed in the edge server at the preset data receiving frequency, and the processed data is applied with a built-in algorithm to implement fault warning. The edge server forwards the original data features and fault warning information to the cloud server; When it is determined that the sampling frequency of the sensing data of the current processing type is greater than the preset data receiving frequency, the edge server extracts data features and stores the original data, performs signal processing on the original data in the edge server, applies the built-in algorithm to the processed data to realize fault warning, and the edge server forwards the data features and the fault warning information to the cloud server; When it is determined that the sampling frequency of the sensing data of the current processing type is less than the preset data receiving frequency, the edge server interpolates the data and stores the original data in the edge server for signal processing. The processed data is applied with the built-in algorithm to realize fault warning. The edge server forwards the interpolated data features and fault warning information to the cloud server to realize data transmission and storage at the set receiving frequency. Synchronize the sensors according to the sampling frequency of the sensing data of different types of sensors; The preset data receiving frequency is 1 Hz. If the actual sampling frequency of the sensor is greater than 1 Hz, the characteristic value of the sampled data is calculated; wherein the calculated characteristic value is any one of the mean value, root mean square value, standard deviation, skewness, and kurtosis; if the actual sampling frequency of the sensor is less than 1 Hz, data interpolation is performed at 1 Hz.

2. The offshore wind power equipment data processing method based on edge-cloud collaboration according to claim 1 is characterized in that: The wireless transmission sensor outputs the sensing data through the 5G network.

3. The offshore wind power equipment data processing method based on edge-cloud collaboration according to claim 2 is characterized in that: The received sensing data is stored and transmitted according to the instructions of the host computer.

4. An offshore wind power equipment data processing device based on edge-cloud collaboration, characterized in that: include: A data acquisition module is used to identify the types of sensors installed on offshore wind power equipment and collect the corresponding types of sensing data generated; The data acquisition is connected to the sensors installed on the offshore wind power equipment; wherein the sensors are divided into wired transmission sensors and wireless transmission sensors; the types of wired transmission sensors include at least vibration sensors, stress sensors, bolt tightening force sensors, and laser radars; the types of wireless transmission sensors include at least displacement and inclination sensors and atmospheric and marine environment sensors; The data processing module is used to perform edge computing processing on different types of collected sensing data according to preset rules to obtain sensor monitoring data; A data transmission module, used to transmit the sensor monitoring data to the cloud for analysis and storage; Wherein, the data processing module is specifically used for: Setting a preset data receiving frequency, and comparing the sampling frequencies of different types of sensing data with the preset data receiving frequency; receiving different types of sensing data from the data collection according to the preset data receiving frequency; When it is determined that the sampling frequency of the sensing data of the current processing type is the same as the preset data receiving frequency, signal processing is performed in the edge server at the preset data receiving frequency, and the processed data is applied with a built-in algorithm to implement fault warning. The edge server forwards the original data features and fault warning information to the cloud server; When it is determined that the sampling frequency of the sensing data of the current processing type is greater than the preset data receiving frequency, the edge server extracts data features and stores the original data, performs signal processing on the original data in the edge server, applies the built-in algorithm to the processed data to realize fault warning, and the edge server forwards the data features and the fault warning information to the cloud server; When it is determined that the sampling frequency of the sensing data of the current processing type is less than the preset data receiving frequency, the edge server interpolates the data and stores the original data in the edge server for signal processing. The processed data is applied with the built-in algorithm to realize fault warning. The edge server forwards the interpolated data features and fault warning information to the cloud server to realize data transmission and storage at the set receiving frequency. Synchronize the sensors according to the sampling frequency of the sensing data of different types of sensors; The preset data receiving frequency is 1 Hz. If the actual sampling frequency of the sensor is greater than 1 Hz, the characteristic value of the sampled data is calculated; wherein the calculated characteristic value is any one of the mean value, root mean square value, standard deviation, skewness, and kurtosis; if the actual sampling frequency of the sensor is less than 1 Hz, data interpolation is performed at 1 Hz.

5. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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