Sea wave height determination method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a sea wave height determination method and device, a storage medium and electronic equipment, and the method comprises the steps: segmenting target point cloud data into a plurality of time sequence frames, and determining a wave crest region and a wave trough region of sea waves according to a local extreme point in the target point cloud data, each time sequence frame is used for indicating the sea surface shape at each moment; according to the wave crest area, the wave trough area and the multiple time sequence frames, a target sea wave contour model is fitted, and the target sea wave contour model is used for indicating the sea wave height and the three-dimensional shape of the sea wave at each time point; and determining the sea wave height at any time point based on the target sea wave contour model. According to the method, the problem that high-precision sea wave height data cannot be provided under complex sea conditions such as high storm waves and changeable tides in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the technical field of sea surface dynamic monitoring. Specifically, it relates to a method and device for determining wave height, a storage medium, and an electronic device. Background Art

[0002] In the field of marine environmental monitoring, accurately grasping the wave height is crucial for ensuring the safe operation of offshore wind power facilities, promoting marine scientific research, and guaranteeing the safety of maritime navigation. However, traditional monitoring technologies face severe challenges in complex sea conditions and cannot provide high-precision wave height data, which has become a key issue restricting the development of the industry.

[0003] First, contact measurement devices, such as buoy wave gauges, due to their characteristic of directly contacting the waves, are vulnerable to physical damage under high wind and wave conditions, resulting in impaired continuity and stability of data collection. In addition, the buoy may deviate from its predetermined position due to strong winds and waves, causing changes in the measurement point and affecting the accuracy and consistency of the data.

[0004] Second, remote sensing technology, although capable of providing observational data for a vast sea area, is difficult to capture the details of instantaneous wave changes due to low observational frequency and limited data resolution. Especially in nearshore complex waters, its accuracy is greatly reduced. Severe weather and cloud cover will also seriously interfere with the collection and analysis of remote sensing data.

[0005] In summary, in the face of complex sea conditions such as high winds and waves, changing tides, etc., the limitations of traditional monitoring technologies are obvious and cannot meet the requirements of modern marine monitoring and research for high-precision and real-time data.

[0006] In view of the problem in the related technology that high-precision wave height data cannot be provided in the face of complex sea conditions such as high winds and waves, changing tides, etc., no effective solution has been proposed yet.

[0007] Therefore, it is necessary to improve the related technology to overcome the defects in the related technology. Summary of the Invention

[0008] Embodiments of this application provide a method and device for determining wave height, a storage medium, and an electronic device to at least solve the problem in the related technology that high-precision wave height data cannot be provided in the face of complex sea conditions such as high winds and waves, changing tides, etc.

[0009] According to an embodiment of the present application, a method for determining the sea wave height is provided, including: dividing the target point cloud data into multiple time series frames, and determining the peak region and trough region of the sea wave according to the local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment; fitting a target sea wave contour model according to the peak region, the trough region and the multiple time series frames, where the target sea wave contour model is used to indicate the sea wave height and the three-dimensional shape of the sea wave at each time point; determining the sea wave height at any time point based on the target sea wave contour model.

[0010] In an exemplary embodiment, before dividing the target point cloud data into multiple time series frames, the method further includes: sending a laser wave to the sea surface through a transmitter of a lidar to obtain the original point cloud data; screening the original point cloud data to remove the error points and interference data in the original point cloud data; mapping the screened original point cloud data to a target grid, and performing a resampling operation on the original point cloud data mapped to the target grid; performing a coordinate conversion operation on the resampled original point cloud data to obtain the target point cloud data, where the coordinate system corresponding to the target point cloud data is a coordinate system with the position corresponding to the lidar as the origin.

[0011] In an exemplary embodiment, dividing the target point cloud data into multiple time series frames includes: obtaining the time stamp of each first data point in the target point cloud data; dividing the target point cloud data into multiple data subsets based on a preset time interval and the time stamp, where each data subset includes: multiple first data points with time stamps within each preset time interval, and each data subset contains: multiple first data points within each preset time interval; constructing the multiple time series frames according to the multiple data subsets.

[0012] In an exemplary embodiment, before determining the crest region and trough region of the ocean wave based on the local extreme points in the target point cloud data, the method further includes: determining the neighborhood range corresponding to each first data point in the target point cloud data; determining the first theoretical height of each first data point according to the three-dimensional coordinates of each first data point, and determining the second theoretical height of other data points according to the three-dimensional coordinates of other data points within the neighborhood range corresponding to each first data point; when it is determined that the first theoretical height of a second data point among multiple first data points in the target point cloud data is greater than the second theoretical height of other data points within the neighborhood range corresponding to the second data point, determining the second data point as a local maximum point; when it is determined that the first theoretical height of a third data point among multiple first data points in the target point cloud data is less than the second theoretical height of other data points within the neighborhood range corresponding to the third data point, determining the third data point as a local minimum point, where the local extreme points include: the local maximum points and the local minimum points.

[0013] In an exemplary embodiment, determining the crest region and trough region of the ocean wave based on the local extreme points in the target point cloud data includes: performing clustering analysis on the local maximum points to determine a first cluster corresponding to the local maximum points, where the local extreme points include: the local maximum points; determining the crest region according to the first cluster; and performing clustering analysis on the local minimum points to determine a second cluster corresponding to the local minimum points, where the local extreme points include: the local minimum points; determining the trough region according to the second cluster.

[0014] In an exemplary embodiment, fitting a target ocean wave profile model according to the crest region, the trough region, and the multiple time series frames includes: determining the fitting coefficients in the polynomial function according to the three-dimensional coordinates of multiple crest points in the crest region and the three-dimensional coordinates of multiple trough points in the trough region, and fitting a preliminary ocean wave profile model according to the fitting coefficients; determining the first time when the transmitter of the lidar sends a first laser wave to each crest point and the second time when the receiver of the lidar receives a first reflected wave corresponding to each first laser wave; determining the first actual height of each crest point according to a first formula, where the first formula is: h1 is the first actual height, c is the speed of light, t2 is the second time, and t1 is the first time; determining the third time when the transmitter of the lidar sends a second laser wave to each trough point and the fourth time when the receiver of the lidar receives a second reflected wave corresponding to each second laser wave; determining the second actual height of each trough point according to a second formula, where the second formula is: h2 is the second actual height, t4 is the fourth time, and t3 is the third time; adjusting the fitting coefficient according to the first actual height and the second actual height, and adjusting the preliminary ocean wave profile model according to the adjusted fitting coefficient to obtain the target ocean wave profile model.

[0015] In an exemplary embodiment, after determining the ocean wave height at any time point based on the target ocean wave profile model, the method further includes: obtaining historical wave characteristic data, where the historical wave characteristic data includes at least one of the following: historical wave height data, historical wave period data, and historical wave frequency data; determining a wave characteristic threshold according to the historical wave characteristic data, where the wave characteristic threshold includes at least one of the following: wave height threshold, wave period threshold, and wave frequency threshold; determining the target ocean wave heights at multiple time points in a second time period according to the target ocean wave profile model, and determining the wave period and wave frequency of the waves in the second time period according to the multiple target ocean wave heights; comparing the wave characteristic data in the second time period with the wave characteristic threshold to obtain a target comparison result, where the wave characteristic data includes: the multiple target ocean wave heights, the wave period, and the wave frequency, and the target comparison result includes: a first comparison result of comparing each target ocean wave height with the wave height threshold, a second comparison result of comparing the wave period with the wave period threshold, and a third comparison result of comparing the wave frequency with the wave frequency threshold; and determining that there is an extreme wave event in the second time period and triggering an alarm when the target comparison result indicates that any wave characteristic data is greater than the wave characteristic threshold.

[0016] According to another embodiment of the present application, there is provided a device for determining ocean wave height, including: a segmentation module configured to segment target point cloud data into multiple time series frames, and determine the wave crest region and wave trough region of the ocean wave according to local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment; a fitting module configured to fit a target ocean wave profile model according to the wave crest region, the wave trough region, and the multiple time series frames, where the target ocean wave profile model is used to indicate the ocean wave height and the three-dimensional shape of the ocean wave at each time point; and a determination module configured to determine the ocean wave height at any time point based on the target ocean wave profile model.

[0017] According to still another embodiment of the present application, there is further provided a computer-readable storage medium storing a computer program, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0018] According to another embodiment of the present application, an electronic device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0019] According to another embodiment of the present application, a computer program product is further provided, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0020] Through the embodiments of the present application, the target point cloud data is segmented into multiple time series frames for indicating the sea surface shape at each moment, and the wave crest area and wave trough area of the sea wave are determined according to the local extreme points in the target point cloud data; according to the wave crest area, wave trough area and multiple time series frames, a target sea wave profile model for indicating the sea wave height and three-dimensional shape at each time point is fitted; the sea wave height in any time period is determined based on the target sea wave profile model. That is to say, through the local extreme points in the target point cloud data, the wave crest area and wave trough area of the sea wave are determined in the embodiments of the present application, and then the target sea wave profile model is fitted according to the wave crest area, wave trough area and multiple time series frames, and further the sea wave height at any time point can be determined through the target sea wave profile model. Through the embodiments of the present application, the problem that in the related art, high-precision sea wave height data cannot be provided in the face of complex sea conditions such as high wind waves and variable tides can be solved, and thus high-precision sea wave height data can be provided even in the face of complex sea conditions such as high wind waves and variable tides. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0023] Figure 1 is a hardware structure block diagram of a computer terminal device for a method of determining the sea wave height according to an embodiment of the present application;

[0024] Figure 2 is a flowchart of a method of determining the sea wave height according to an embodiment of the present application;

[0025] Figure 3 is a structure block diagram of a device for determining the sea wave height according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In the following, embodiments of the present application will be described in detail with reference to the accompanying drawings and in conjunction with the embodiments.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence.

[0028] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal device or a similar computing device. Taking the operation on a computer terminal device as an example, Figure 1 is a hardware structure block diagram of a computer terminal device for a method of determining the sea wave height according to an embodiment of the present application. As Figure 1 shown, the computer terminal device may include one or more ( Figure 1 only one is shown in Figure 1 Figure) processors 102 (the processors 102 may include, but are not limited to, processing devices such as microprocessor MCUs or programmable logic devices FPGAs) and a memory 104 for storing data. Among them, the above-mentioned computer terminal device may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown in Figure 1 Figure is only schematic and does not limit the structure of the above-mentioned computer terminal device. For example, the computer terminal device may further include more or fewer components than

[0029] Figure shows, or has a different configuration from

[0030] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by a communication provider of a computer terminal device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] In this embodiment, a method for determining the sea wave height is provided, which is applied to Figure 1 the computer terminal device in Figure 2 is a flowchart of the method for determining the sea wave height according to the embodiment of the present application, as shown in Figure 2 shown, and the process includes the following steps:

[0032] Step S202: Divide the target point cloud data into multiple time series frames, and determine the wave crest region and wave trough region of the sea wave according to the local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment;

[0033] Step S204: Fit a target sea wave contour model according to the wave crest region, the wave trough region, and the multiple time series frames, where the target sea wave contour model is used to indicate the sea wave height and the three-dimensional shape of the sea wave at each time point;

[0034] Step S206: Determine the sea wave height at any time point based on the target sea wave contour model.

[0035] Through the above steps, the target point cloud data is divided into multiple time series frames for indicating the sea surface shape at each moment, and the wave crest region and wave trough region of the sea wave are determined according to the local extreme points in the target point cloud data; a target sea wave contour model for indicating the sea wave height and three-dimensional shape at each time point is fitted according to the wave crest region, the wave trough region, and the multiple time series frames; the sea wave height at any time period is determined based on the target sea wave contour model. That is to say, in the embodiment of the present application, the wave crest region and wave trough region of the sea wave are determined through the local extreme points in the target point cloud data, and then the target sea wave contour model is fitted according to the wave crest region, the wave trough region, and the multiple time series frames, and then the sea wave height at any time point can be determined through the target sea wave contour model. Through the embodiment of the present application, the problem that in the related art, high-precision sea wave height data cannot be provided in the face of complex sea conditions such as high winds and waves and changing tides can be solved, and thus high-precision sea wave height data can be provided even in the face of complex sea conditions such as high winds and waves and changing tides.

[0036] Optionally, before splitting the target point cloud data into multiple time series frames in the above step S202, the method further includes: sending a laser wave to the sea surface through a transmitter of a lidar to obtain raw point cloud data; filtering the raw point cloud data to remove error points and interference data in the raw point cloud data; mapping the filtered raw point cloud data to a target grid, and performing a resampling operation on the raw point cloud data mapped to the target grid; performing a coordinate transformation operation on the resampled raw point cloud data to obtain the target point cloud data, where the coordinate system corresponding to the target point cloud data is a coordinate system with the position corresponding to the lidar as the origin.

[0037] It can be understood that before performing data splitting on the target point cloud data, it is necessary to obtain the target point cloud data. Specifically: the transmitter of the lidar emits laser pulses (i.e., laser waves) towards the sea surface. After these laser pulses are reflected by the sea surface, they are captured by the receiver of the lidar, thereby obtaining the reflection information of each point on the sea surface. Based on the ranging principle of the lidar, the spatial coordinates and reflection intensity of each point (i.e., each data point in the point cloud data) can be recorded to form the raw point cloud data.

[0038] The raw point cloud data may contain error points and interference data caused by various factors, such as atmospheric interference, floating objects on the sea surface, or measurement errors of the lidar itself. Therefore, it is necessary to perform data filtering on the raw point cloud data to remove the noise in the raw point cloud data. The steps of data filtering can be to use specific algorithms and threshold conditions to identify and exclude these inaccurate or irrelevant data points.

[0039] The filtered raw point cloud data is mapped onto a preset target grid to convert the irregularly distributed point cloud data into a grid form. Further, by performing a resampling operation on the data in the grid, it is ensured that the data is evenly distributed in space.

[0040] To unify the data processing flow, it is necessary to perform a coordinate transformation on the resampled raw point cloud data. The data is transformed into a coordinate system with the lidar position as the origin. The purpose of this processing is to facilitate subsequent analysis and fitting operations, ensure that all data is in the same reference framework, and thus avoid calculation errors caused by coordinate system differences. The data set after coordinate transformation is the target point cloud data.

[0041] Optionally, splitting the target point cloud data into multiple time series frames in step S202 above includes: obtaining the timestamp of each first data point in the target point cloud data; dividing the target point cloud data into multiple data subsets based on a preset time interval and the timestamp, where each data subset includes: multiple first data points whose timestamps are within each preset time interval, and each data subset contains: multiple first data points within each preset time interval; constructing the multiple time series frames according to the multiple data subsets.

[0042] It can be understood that it is necessary to perform data splitting on the target point cloud data to generate multiple time series frames. Specifically: Each first data point collected from the lidar is attached with the timestamp when it is recorded. The timestamp is the exact moment when each data point is generated, providing a time attribute for each data point, enabling the data points to be correctly sorted in the entire data stream according to the generation order.

[0043] Data subset division based on a preset time interval: Due to the high-frequency data acquisition characteristics of the lidar, the data volume is huge and continuous. For the convenience of processing and analysis, it is necessary to split these continuous data streams at preset time intervals. The selection of the preset time interval depends on the monitoring requirements and data processing capabilities. Usually, it is to obtain enough data to describe the dynamic changes of the ocean waves without overly increasing the computational burden.

[0044] Each data subset contains all the first data points within each preset time interval, and it can be determined whether a first data point is a data point within this data subset according to its timestamp.

[0045] Once the data subsets within each preset time interval are divided, these data subsets can be converted into time series frames. A time series frame contains the three-dimensional coordinate information of all data points within that time interval, as well as their corresponding timestamps. These time series frames are arranged in timestamp order to form a complete sequence, reflecting the continuous change of the ocean wave height over time.

[0046] Among them, each time series frame represents the state of the ocean waves at a certain time point. By comparing the data of different time series frames, the evolution process of the ocean waves over time can be observed and analyzed.

[0047] Optionally, before determining the wave crest region and the wave trough region of the sea wave according to the local extreme points in the target point cloud data in the above step S202, the method further includes: determining the neighborhood range corresponding to each first data point in the target point cloud data; determining the first theoretical height of each first data point according to the three-dimensional coordinates of each first data point, and determining the second theoretical height of other data points according to the three-dimensional coordinates of other data points within the neighborhood range corresponding to each first data point; when it is determined that the first theoretical height of a second data point among multiple first data points in the target point cloud data is greater than the second theoretical height of other data points within the neighborhood range corresponding to the second data point, determining the second data point as a local maximum point; when it is determined that the first theoretical height of a third data point among multiple first data points in the target point cloud data is less than the second theoretical height of other data points within the neighborhood range corresponding to the third data point, determining the third data point as a local minimum point, where the local extreme points include: the local maximum points and the local minimum points.

[0048] It can be understood that the embodiments of the present application also need to determine the wave crest region and the wave trough region of the sea wave. The wave crest region and the wave trough region are determined based on local extreme points. Therefore, before determining the wave crest region and the wave trough region, it is necessary to determine local extreme points. Specifically:

[0049] Determine a neighborhood range for each first data point. The neighborhood range is usually set based on the distribution density of data points and the typical scale of sea waves. After determining the neighborhood range, the first theoretical height and the second theoretical height can be calculated: for each first data point, the first theoretical height can be calculated according to its three-dimensional coordinates, that is, the vertical distance of each first data point relative to the sea surface. At the same time, for other data points within the neighborhood range of each data point, calculate their second theoretical height, that is, the vertical distance of each other data point relative to the sea surface, which is also calculated based on three-dimensional coordinates.

[0050] Determine local maximum points: When it is recognized that the first theoretical height of a second data point is greater than the second theoretical heights of all other data points within its neighborhood range, this point is marked as a local maximum point. These points usually correspond to the wave crest positions of sea waves because near the wave crest, the sea surface height is higher than the surrounding area, forming local highest points.

[0051] Determine local minimum points: Similarly, when the first theoretical height of a third data point is less than the second theoretical heights of all other data points within its neighborhood range, this point is marked as a local minimum point. These points usually correspond to the wave trough positions of sea waves because near the wave trough, the sea surface height is lower than the surrounding area, forming local lowest points.

[0052] Optionally, determining the wave crest region and wave trough region of the ocean wave according to the local extreme points in the target point cloud data in step S202 includes: performing clustering analysis on the local maximum points to determine the first clustering cluster corresponding to the local maximum points, where the local extreme points include: the local maximum points; determining the wave crest region according to the first clustering cluster; and, performing clustering analysis on the local minimum points to determine the second clustering cluster corresponding to the local minimum points, where the local extreme points include: the local minimum points; determining the wave trough region according to the second clustering cluster.

[0053] It can be understood that after determining the local extreme points, the wave crest region and the wave trough region can be determined. Specifically:

[0054] All the identified local maximum points (i.e., the data points with the highest height in the neighborhood) are used as the objects of clustering analysis. Through clustering analysis, these local maximum points can be automatically grouped according to the similarity of their spatial positions to form multiple first clustering clusters. Each clustering cluster contains local maximum points that are adjacent or aggregated in space, and these points together form the wave crest of the ocean wave in terms of height.

[0055] Clustering analysis usually adopts algorithms such as K-means, DBSCAN or density-based clustering algorithms, which can effectively identify the aggregation regions of data points. Even in complex sea conditions, they can accurately capture the outline of the wave crest. This process helps to distinguish different wave crests, even if they are close to or overlap each other in the time series.

[0056] Once the first clustering cluster is determined, the data point region included in each clustering cluster is defined as the wave crest region. These regions form the outline of the wave crest in three-dimensional space, providing clear wave crest position information for subsequent calculation of the ocean wave height.

[0057] Clustering analysis of local minimum points: Similar to the processing of local maximum points, clustering analysis is performed on all local minimum points (i.e., the data points with the lowest height in the neighborhood) to form a second clustering cluster. Each cluster contains local minimum points that are adjacent or aggregated in space, and together they form the wave trough of the ocean wave. After the second clustering cluster is determined, the data point region represented by each cluster is defined as the wave trough region.

[0058] Optionally, fitting the target ocean wave contour model according to the wave crest region, the wave trough region, and the plurality of time series frames in step S204 above includes: determining fitting coefficients in a polynomial function according to three-dimensional coordinates of a plurality of wave crest points in the wave crest region and three-dimensional coordinates of a plurality of wave trough points in the wave trough region, and fitting a preliminary ocean wave contour model according to the fitting coefficients; determining a first time when the transmitter of the lidar sends a first laser wave to each wave crest point and a second time when the receiver of the lidar receives a first reflected wave corresponding to each first laser wave; determining a first actual height of each wave crest point according to a first formula, where the first formula is: h1 is the first actual height, c is the speed of light, t2 is the second time, and t1 is the first time; determining a third time when the transmitter of the lidar sends a second laser wave to each wave trough point and a fourth time when the receiver of the lidar receives a second reflected wave corresponding to each second laser wave; determining a second actual height of each wave trough point according to a second formula, where the second formula is: h2 is the second actual height, t4 is the fourth time, and t3 is the third time; adjusting the fitting coefficients according to the first actual height and the second actual height, and adjusting the preliminary ocean wave contour model according to the adjusted fitting coefficients to obtain the target ocean wave contour model.

[0059] It can be understood that after determining the wave crest region and the wave trough region, the target ocean wave contour model can be constructed. Specifically: Fitting of the preliminary ocean wave contour model: Based on the three-dimensional coordinates of a plurality of wave crest points in the wave crest region and a plurality of wave trough points in the wave trough region, the fitting coefficients in the polynomial function are preliminarily determined. The fitting coefficients reflect the shape and dynamic changes of ocean waves and are key parameters for constructing the ocean wave contour model. By using the least squares method or other optimization algorithms, the parameters of the polynomial function fitting these data points can be solved, thereby obtaining a preliminary ocean wave contour model.

[0060] Calculation of the actual height of the wave crest point: For each wave crest point, record the first time when the lidar transmitter sends a first laser wave to it and the second time when the receiver receives the first reflected wave reflected by the first laser wave. According to the lidar ranging principle, by calculating half of the product of the speed of light (c) and the time difference (t2 - t1), the true vertical height (h1) of the wave crest point relative to the lidar can be obtained.

[0061] Calculation of the actual height of the wave trough point: Similarly, for each wave trough point, record the third time when the lidar transmitter sends a second laser wave to it and the fourth time when the receiver receives the second reflected wave reflected by the second laser wave. By the same principle, calculating half of the product of the speed of light (c) and the time difference (t4 - t3) can obtain the true vertical height (h2) of the wave trough point.

[0062] Adjustment of fitting coefficients and optimization of the sea wave profile model: After obtaining the actual heights of the wave crest points and wave trough points, these height information are fed back into the preliminary sea wave profile model to further adjust the fitting coefficients of the polynomial function. This process compares the differences between the actual heights and the predicted heights of the preliminary model, and uses iterative optimization methods such as gradient descent or genetic algorithms to adjust the model parameters to minimize the model prediction error and improve the fitting accuracy of the model. After multiple iterative adjustments, an objective sea wave profile model that accurately reflects the dynamic characteristics of the sea waves can be finally obtained.

[0063] Optionally, after determining the sea wave height at any time point based on the objective sea wave profile model in step S206 above, the method further includes: obtaining historical wave characteristic data, where the historical wave characteristic data at least includes one of the following: historical wave height data, historical wave period data, and historical wave frequency data; determining a wave characteristic threshold according to the historical wave characteristic data, where the wave characteristic threshold at least includes one of the following: wave height threshold, wave period threshold, and wave frequency threshold; determining the target sea wave heights at multiple time points in a second time period according to the objective sea wave profile model, and determining the wave period and wave frequency of the waves in the second time period according to the multiple target sea wave heights; comparing the wave characteristic data in the second time period with the wave characteristic threshold to obtain a target comparison result, where the wave characteristic data includes: the multiple target sea wave heights, the wave period, and the wave frequency, and the target comparison result includes: a first comparison result of comparing each target sea wave height with the wave height threshold, a second comparison result of comparing the wave period with the wave period threshold, and a third comparison result of comparing the wave frequency with the wave frequency threshold; in the case where the target comparison result indicates that any wave characteristic data is greater than the wave characteristic threshold, determining that there is an extreme wave event in the second time period and triggering an alarm.

[0064] It can be understood that after the sea wave height at any time point can be determined, it is possible to determine whether there is an extreme wave event in the second time period. Specifically: Obtaining historical wave characteristic data: Collect historical wave characteristic data, which at least includes historical wave height data, historical wave period data, and historical wave frequency data. The acquisition of historical wave characteristic data can be achieved through long-term monitoring records, marine environment research materials, or data from other monitoring stations in the same region.

[0065] Determine the wave characteristic thresholds: Based on historical wave characteristic data, analyze and determine the wave characteristic thresholds, including the wave height threshold, wave period threshold, and wave frequency threshold. These wave characteristic thresholds represent the common range of wave characteristics under normal ocean conditions. Usually, the thresholds are set as the outliers in the historical data, that is, the wave characteristic values exceeding a certain percentile (such as 95% or 99%) to identify possible extreme wave events.

[0066] Analyze the wave characteristics of the target sea wave profile model: Using the target sea wave profile model, calculate the target sea wave heights at multiple time points within the second time period (i.e., a certain time window within the monitoring period). Based on these target sea wave heights, further analyze and calculate the wave period (the time interval between adjacent wave crests or troughs) and wave frequency (the number of wave crests or troughs per unit time).

[0067] Compare the wave characteristic data with the wave characteristic thresholds: Compare the wave characteristic data (including the target sea wave height, wave period, and wave frequency) within the second time period with the previously set wave characteristic thresholds to obtain the target comparison result. The target comparison result includes the first comparison result (the comparison of each target sea wave height with the wave height threshold), the second comparison result (the comparison of the wave period with the wave period threshold), and the third comparison result (the comparison of the wave frequency with the wave frequency threshold).

[0068] Identify extreme wave events and trigger alarms: If the target comparison result indicates that any wave characteristic data (target sea wave height, wave period, or wave frequency) is greater than the corresponding wave characteristic threshold, it will be determined that there is an extreme wave event within the second time period. Extreme wave events may pose a threat to offshore facilities, so the alarm mechanism will be triggered to send an alarm to the relevant operators or control systems in a timely manner, so as to take necessary safety measures or adjust the operating state of the offshore wind power equipment.

[0069] To better understand the process of the above method for determining the sea wave height, the following will further illustrate the implementation method flow of the above determination of the sea wave height in combination with optional embodiments, but it is not used to limit the technical solutions of the embodiments of the present application.

[0070] An optional embodiment of the present application provides a method for monitoring the sea wave height based on the fitting of lidar and multi-dimensional data. The lidar technology can achieve high-resolution marine environment monitoring by emitting laser light and detecting the time difference and intensity change of the reflected signal. Combining lidar data with a multi-dimensional fitting algorithm can accurately extract the sea wave height information, especially suitable for dynamic monitoring under complex sea conditions. Specifically:

[0071] Step 1: Installation and configuration of lidar equipment:

[0072] The lidar should be installed on the tower of an offshore wind turbine, a fixed platform or a ship. The location should ensure an open view to avoid obstruction and affect the monitoring range. The equipment should have functions such as anti-salt fog, waterproof and anti-vibration to adapt to the harsh marine environment. The equipment parameter settings include laser wavelength, scanning frequency and detection angle, etc., to ensure high-precision capture of sea surface fluctuations. The laser wavelength should be selected in a moderate range, which can not only meet the measurement accuracy but also avoid being interfered by the atmosphere.

[0073] Step 2: Data acquisition and preprocessing:

[0074] The lidar scans the sea surface at a high frequency to obtain point cloud data in real time, and records the spatial coordinates and reflection intensity of each point.

[0075] The collected raw point cloud data may contain noise and needs to be preprocessed:

[0076] Remove error points and data interfered by floating objects;

[0077] Use the grid method to resample the raw point cloud data to ensure uniform distribution of the data;

[0078] Perform coordinate transformation on the raw point cloud data to unify the data into a coordinate system with the sensor (i.e., the lidar) as the origin to obtain the target point cloud data.

[0079] Step 3: Wave profile extraction and fitting modeling:

[0080] The preprocessed point cloud data (i.e., the target point cloud data) is segmented into time series frames, and each frame represents the sea surface shape at a certain moment. By finding the local extreme points in the point cloud, the wave crest and trough regions of the sea waves can be initially identified.

[0081] In order to more accurately restore the wave profile, a multi-dimensional data fitting method is adopted to fit the scattered points in the point cloud into a continuous surface. Commonly used methods include polynomial fitting or spline interpolation, and the fitting error is calculated to optimize the fitting surface.

[0082] Step 4: Wave height calculation:

[0083] After the wave profile fitting is completed, the vertical distance between the wave crest and trough positions is extracted as the wave height. This process needs to be carried out through the following formula:

[0084] Let the emission point of the lidar be the origin, and the three-dimensional coordinates of the wave surface point cloud be the input data to establish a height calculation model:

[0085] Determine the geometric relationship between the emission angle of the laser wave and the wave surface reflection point, and calculate the height of each point through the lidar ranging formula: The height of each point is equal to the vertical distance from the laser emission point to the reflection point. It can be calculated through the above first formula and second formula.

[0086] According to the wave model, the wave surface height is fitted as a function of time and space. A common fitting model is a polynomial function. Specifically:

[0087] The height surface can be expressed as a function of multiple parameters, where the wave height depends on the wavelength, wave speed, and time period. The formula is as follows: x and y are horizontal position coordinates; t is time; a and b are fitting coefficients; T is the wave period; c is the offset.

[0088] The fitting parameters are solved by the least squares method, and the collected point cloud data is matched with the theoretical model to obtain the wave height at each time point.

[0089] Step 5: Wave characteristic analysis and abnormal wave identification:

[0090] Extract the wave height data in the time series, calculate the wave characteristic indicators, such as significant wave height, wave period, and frequency. Combine historical data and statistical models to identify abnormal wave characteristics. The system can detect sudden extreme wave events through threshold settings or machine learning classification models.

[0091] Step 6: System integration and real-time monitoring:

[0092] The lidar monitoring system is integrated with the remote monitoring platform, and the real-time wave data is transmitted to the control center. The characteristics such as wave height, period, and energy are displayed through a visualization interface, providing decision-making support for the operation and scheduling of offshore wind turbines.

[0093] In summary, the method for monitoring sea wave height based on lidar and multi-dimensional data fitting, combined with high-resolution laser detection and advanced data processing algorithms, realizes the dynamic and accurate monitoring of sea waves under complex sea conditions.

[0094] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), including several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0095] In this embodiment, a device for determining the sea wave height is further provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated here. As used hereinafter, the term "module" may be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0096] Figure 3 is a structural block diagram of a device for determining the sea wave height according to an embodiment of the present application. As Figure 3 shown, the device includes:

[0097] A segmentation module 32, configured to segment the target point cloud data into multiple time series frames, and determine the wave crest region and wave trough region of the sea wave according to the local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment;

[0098] A fitting module 34, configured to fit a target sea wave contour model according to the wave crest region, the wave trough region, and the multiple time series frames, where the target sea wave contour model is used to indicate the sea wave height and the three-dimensional shape of the sea wave at each time point;

[0099] A determination module 36, configured to determine the sea wave height at any time point based on the target sea wave contour model.

[0100] With the above device, the target point cloud data is segmented into multiple time series frames for indicating the sea surface shape at each moment, and the wave crest region and wave trough region of the sea wave are determined according to the local extreme points in the target point cloud data; a target sea wave contour model for indicating the sea wave height and three-dimensional shape at each time point is fitted according to the wave crest region, the wave trough region, and the multiple time series frames; the sea wave height at any time period is determined based on the target sea wave contour model. That is to say, in the embodiment of the present application, the wave crest region and wave trough region of the sea wave are determined through the local extreme points in the target point cloud data, and then the target sea wave contour model is fitted according to the wave crest region, the wave trough region, and the multiple time series frames, and further the sea wave height at any time point can be determined through the target sea wave contour model. Through the embodiment of the present application, the problem that in the related art, high-precision sea wave height data cannot be provided in the face of complex sea conditions such as high winds and waves and variable tides can be solved, and thus high-precision sea wave height data can be provided even in the face of complex sea conditions such as high winds and waves and variable tides.

[0101] In an exemplary embodiment, the segmentation module 32 is further configured to send a laser wave to the sea surface through a transmitter of a lidar to obtain original point cloud data; screen the original point cloud data to remove error points and interference data in the original point cloud data; map the screened original point cloud data to a target grid, and perform a resampling operation on the original point cloud data mapped to the target grid; perform a coordinate conversion operation on the resampled original point cloud data to obtain the target point cloud data, wherein the coordinate system corresponding to the target point cloud data is a coordinate system with the position corresponding to the lidar as the origin.

[0102] In an exemplary embodiment, the segmentation module 32 is further configured to obtain the timestamp of each first data point in the target point cloud data; divide the target point cloud data into multiple data subsets based on a preset time interval and the timestamp, wherein each data subset includes: multiple first data points whose timestamps are within each preset time interval, and each data subset contains: multiple first data points within each preset time interval; construct the multiple time series frames according to the multiple data subsets.

[0103] In an exemplary embodiment, the segmentation module 32 is further configured to determine the neighborhood range corresponding to each first data point in the target point cloud data; determine the first theoretical height of each first data point according to the three-dimensional coordinates of each first data point, and determine the second theoretical height of other data points according to the three-dimensional coordinates of other data points within the neighborhood range corresponding to each first data point; when it is determined that the first theoretical height of a second data point among the multiple first data points in the target point cloud data is greater than the second theoretical height of other data points within the neighborhood range corresponding to the second data point, determine the second data point as a local maximum point; when it is determined that the first theoretical height of a third data point among the multiple first data points in the target point cloud data is less than the second theoretical height of other data points within the neighborhood range corresponding to the third data point, determine the third data point as a local minimum point, wherein the local extreme points include: the local maximum points and the local minimum points.

[0104] In an exemplary embodiment, the segmentation module 32 is further configured to perform clustering analysis on the local maximum points to determine the first cluster corresponding to the local maximum points, wherein the local extreme points include: the local maximum points; determine the peak region according to the first cluster; and perform clustering analysis on the local minimum points to determine the second cluster corresponding to the local minimum points, wherein the local extreme points include: the local minimum points; determine the trough region according to the second cluster.

[0105] In an exemplary embodiment, the fitting module 34 is further configured to determine fitting coefficients in a polynomial function based on three-dimensional coordinates of a plurality of wave crest points within the wave crest region and three-dimensional coordinates of a plurality of wave trough points within the wave trough region, and fit a preliminary sea wave profile model based on the fitting coefficients; determine a first time when a transmitter of the lidar sends a first laser wave to each wave crest point and a second time when a receiver of the lidar receives a first reflected wave corresponding to each first laser wave; determine a first actual height of each wave crest point according to a first formula, where the first formula is: h1 is the first actual height, c is the speed of light, t2 is the second time, and t1 is the first time; determine a third time when the transmitter of the lidar sends a second laser wave to each wave trough point and a fourth time when the receiver of the lidar receives a second reflected wave corresponding to each second laser wave; determine a second actual height of each wave trough point according to a second formula, where the second formula is: h2 is the second actual height, t4 is the fourth time, and t3 is the third time; adjust the fitting coefficients according to the first actual height and the second actual height, and adjust the preliminary sea wave profile model according to the adjusted fitting coefficients to obtain the target sea wave profile model.

[0106] In an exemplary embodiment, the determination module 36 is further configured to obtain historical wave characteristic data, where the historical wave characteristic data includes at least one of the following: historical wave height data, historical wave period data, and historical wave frequency data; determine a wave characteristic threshold according to the historical wave characteristic data, where the wave characteristic threshold includes at least one of the following: wave height threshold, wave period threshold, and wave frequency threshold; determine target sea wave heights at a plurality of time points in a second time period according to the target sea wave profile model, and determine a wave period and a wave frequency of the waves in the second time period according to the plurality of target sea wave heights; compare the wave characteristic data in the second time period with the wave characteristic threshold to obtain a target comparison result, where the wave characteristic data includes: the plurality of target sea wave heights, the wave period, and the wave frequency, and the target comparison result includes: a first comparison result of comparing each target sea wave height with the wave height threshold, a second comparison result of comparing the wave period with the wave period threshold, and a third comparison result of comparing the wave frequency with the wave frequency threshold; in a case where the target comparison result indicates that any wave characteristic data is greater than the wave characteristic threshold, determine that there is an extreme wave event in the second time period and trigger an alarm.

[0107] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above-mentioned modules are all located in the same processor; or, the above-mentioned various modules are respectively located in different processors in any combination form.

[0108] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. Wherein, the computer program is set to execute the steps in any one of the above method embodiments when running.

[0109] Optionally, in this embodiment, the above storage medium can be set to store program code for executing the following steps:

[0110] S1, dividing the target point cloud data into multiple time series frames, and determining the wave crest region and wave trough region of the sea wave according to the local extreme points in the target point cloud data, wherein each time series frame is used to indicate the sea surface shape at each moment;

[0111] S2, fitting a target sea wave contour model according to the wave crest region, the wave trough region and the multiple time series frames, wherein the target sea wave contour model is used to indicate the sea wave height and the three-dimensional shape of the sea wave at each time point;

[0112] S3, determining the sea wave height at any time point based on the target sea wave contour model.

[0113] In an exemplary embodiment, the above computer-readable storage medium may include but is not limited to: various media such as USB flash drives, read-only memories (ROM for short), random access memories (RAM for short), mobile hard disks, magnetic disks or optical discs that can store computer programs.

[0114] An embodiment of the present application also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is set to run the computer program to execute the steps in any one of the above method embodiments.

[0115] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. Wherein, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0116] Optionally, in this embodiment, the above processor can be set to execute the following steps through a computer program:

[0117] S1. Split the target point cloud data into multiple time series frames, and determine the wave crest region and wave trough region of the ocean wave according to the local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment;

[0118] S2. Fit a target ocean wave contour model according to the wave crest region, the wave trough region and the multiple time series frames, where the target ocean wave contour model is used to indicate the ocean wave height and the three-dimensional shape of the ocean wave at each time point;

[0119] S3. Determine the ocean wave height at any time point based on the target ocean wave contour model.

[0120] An embodiment of the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0121] Another embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0122] An embodiment of the present application also provides a computer program. The computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in any one of the above method embodiments.

[0123] Optionally, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0124] S1. Split the target point cloud data into multiple time series frames, and determine the wave crest region and wave trough region of the ocean wave according to the local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment;

[0125] S2. Fit a target ocean wave contour model according to the wave crest region, the wave trough region and the multiple time series frames, where the target ocean wave contour model is used to indicate the ocean wave height and the three-dimensional shape of the ocean wave at each time point;

[0126] S3. Determine the ocean wave height at any time point based on the target ocean wave contour model.

[0127] Specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0128] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a sequence different from that here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0129] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining the wave height, characterized in that, Including: Dividing the target point cloud data into multiple time series frames, and determining the wave crest region and wave trough region of the sea wave according to the local extreme points in the target point cloud data, wherein each time series frame is used to indicate the sea surface shape at each moment; Fitting a target sea wave contour model according to the wave crest region, the wave trough region and the multiple time series frames, wherein the target sea wave contour model is used to indicate the sea wave height and the three-dimensional shape of the sea wave at each time point; Determining the sea wave height at any time point based on the target sea wave contour model.

2. The method according to claim 1, characterized in that, Before dividing the target point cloud data into multiple time series frames, the method further includes: Sending a laser wave to the sea surface through the emitter of the lidar to obtain the original point cloud data; Filtering the original point cloud data to remove the error points and interference data in the original point cloud data; Mapping the filtered original point cloud data to a target grid, and performing a resampling operation on the original point cloud data mapped to the target grid; Performing a coordinate transformation operation on the resampled original point cloud data to obtain the target point cloud data, wherein the coordinate system corresponding to the target point cloud data is a coordinate system with the position corresponding to the lidar as the origin.

3. The method according to claim 1, characterized in that, Dividing the target point cloud data into multiple time series frames, including: Obtaining the timestamp of each first data point in the target point cloud data; Dividing the target point cloud data into multiple data subsets based on a preset time interval and the timestamp, wherein each data subset includes: multiple first data points with timestamps within each preset time interval, and each data subset contains: multiple first data points within each preset time interval; Constructing the multiple time series frames according to the multiple data subsets.

4. The method according to claim 1, characterized in that, Before determining the wave crest region and wave trough region of the sea wave according to the local extreme points in the target point cloud data, the method further includes: Determining the neighborhood range corresponding to each first data point in the target point cloud data; Determining the first theoretical height of each first data point according to the three-dimensional coordinates of each first data point, and determining the second theoretical height of other data points according to the three-dimensional coordinates of other data points within the neighborhood range corresponding to each first data point; When it is determined that the first theoretical height of a second data point among the multiple first data points in the target point cloud data is greater than the second theoretical height of other data points within the neighborhood range corresponding to the second data point, determining the second data point as a local maximum point; When it is determined that the first theoretical height of a third data point among the multiple first data points in the target point cloud data is less than the second theoretical height of other data points within the neighborhood range corresponding to the third data point, determining the third data point as a local minimum point, wherein the local extreme points include: the local maximum points and the local minimum points.

5. The method according to claim 1, wherein Determining the wave crest region and wave trough region of the sea wave according to the local extreme points in the target point cloud data, including: Performing a clustering analysis on the local maximum points to determine the first cluster corresponding to the local maximum points, wherein the local extreme points include: the local maximum points; Determine the peak region according to the first clustering cluster; and, Perform clustering analysis on the local minimum points to determine the second clustering cluster corresponding to the local minimum points, where the local extreme points include: the local minimum points; Determine the trough region according to the second clustering cluster.

6. The method according to claim 1, characterized in that, Fitting a target ocean wave profile model based on the peak region, the trough region, and the plurality of time series frames, including: Determine the fitting coefficients in the polynomial function according to the three-dimensional coordinates of the plurality of peak points in the peak region and the three-dimensional coordinates of the plurality of trough points in the trough region, and fit a preliminary ocean wave profile model according to the fitting coefficients; Determine the first time when the transmitter of the lidar sends the first laser wave to each peak point and the second time when the receiver of the lidar receives the first reflected wave corresponding to each first laser wave; Determine the first actual height of each of the peak points according to the first formula, where the first formula is: h1 is the first actual height, c is the speed of light, t2 is the second time, and t1 is the first time; Determine the third time when the transmitter of the lidar sends the second laser wave to each trough point and the fourth time when the receiver of the lidar receives the second reflected wave corresponding to each second laser wave; Determine the second actual height of each of the trough points according to the second formula, where the second formula is: h2 is the second actual height, t4 is the fourth time, and t3 is the third time; Adjust the fitting coefficients according to the first actual height and the second actual height, and adjust the preliminary ocean wave profile model according to the adjusted fitting coefficients to obtain the target ocean wave profile model.

7. The method according to claim 1, wherein After determining the ocean wave height at any time point based on the target ocean wave profile model, the method further includes: Obtain historical wave characteristic data, where the historical wave characteristic data includes at least one of the following: historical wave height data, historical wave period data, and historical wave frequency data; Determine a wave characteristic threshold according to the historical wave characteristic data, where the wave characteristic threshold includes at least one of the following: a wave height threshold, a wave period threshold, and a wave frequency threshold; Determine the target ocean wave heights at multiple time points in a second time period according to the target ocean wave profile model, and determine the wave period and wave frequency of the waves in the second time period according to the multiple target ocean wave heights; Compare the wave characteristic data in the second time period with the wave characteristic threshold to obtain a target comparison result, where the wave characteristic data includes: the multiple target ocean wave heights, the wave period, and the wave frequency, and the target comparison result includes: a first comparison result of comparing each target ocean wave height with the wave height threshold, a second comparison result of comparing the wave period with the wave period threshold, and a third comparison result of comparing the wave frequency with the wave frequency threshold; When the target comparison result indicates that any wave characteristic data is greater than the wave characteristic threshold, determine that there is an extreme wave event in the second time period and trigger an alarm.

8. A device for determining the wave height, characterized in that, Include: A segmentation module, configured to segment the target point cloud data into a plurality of time series frames, and determine the peak region and the trough region of the ocean wave according to the local extreme points in the target point cloud data, where each time series frame is used to indicate the sea surface shape at each moment; A fitting module, configured to fit a target ocean wave profile model according to the wave crest region, the wave trough region, and the plurality of time series frames, wherein the target ocean wave profile model is used to indicate the ocean wave height at each time point and the three-dimensional shape of the ocean wave; A determination module, configured to determine the ocean wave height at any time point based on the target ocean wave profile model.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when running, executes the method according to any one of claims 1 to 7.

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

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