Marine mesoscale vortex identification method and device

Abstract

The invention provides an ocean mesoscale vortex identification method and device, and the method comprises the steps: obtaining the input data of an ocean environment numerical forecasting product, and the input data at least comprise the sea surface height abnormality, the horizontal flow velocity field, the temperature and the salinity; preprocessing the input data to obtain processed marine environment data; recognizing vortexes in the processed marine environment data according to a plurality of preset vortex recognition algorithms to obtain a plurality of vortexes; wherein the preset multiple vortex recognition algorithms at least comprise an Okubo-Wei ss parameter method, a vector geometry method, a rotation invariance method and an SSH isoline method; performing clustering analysis according to the vortex centers of the plurality of vortexes to obtain a target vortex corresponding to the input data; performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex; and the feature parameters are displayed according to a preset display mode, so that the recognition accuracy and comprehensiveness are improved.

Description

Technical Field

[0001] The present invention belongs to the field of marine science and technology, and in particular relates to a method and a device for identifying marine mesoscale vortices. Background Art

[0002] Mesoscale eddies are one of the most important dynamic processes in the ocean, and have a significant impact on ocean circulation, material transport, energy exchange and other processes. Accurately identifying and characterizing the characteristic information of mesoscale eddies in the ocean has important scientific significance and application value for in-depth understanding of ocean dynamic processes, improving ocean numerical models, and guiding ocean observations. With the continuous improvement of ocean observation technology and numerical simulation capabilities, massive amounts of marine environmental data provide a rich data foundation for the study of mesoscale eddies. However, how to quickly and accurately identify and extract the characteristic information of mesoscale eddies from these massive data remains a challenging scientific problem.

[0003] Traditional manual identification methods are time-consuming and labor-intensive, and it is difficult to meet the needs of massive data processing. Most of the existing automatic identification methods are based on a single physical quantity or simple geometric features, which makes it difficult to fully and accurately characterize the complex characteristics of mesoscale eddies. Marine mesoscale eddies are important dynamic processes that are ubiquitous in the ocean and have a significant impact on processes such as ocean circulation, material transport, and energy exchange. Accurately identifying and characterizing the characteristic information of marine mesoscale eddies has important scientific significance and application value for in-depth understanding of ocean dynamic processes, improving ocean numerical models, and guiding ocean observations. With the continuous improvement of ocean observation technology and numerical simulation capabilities, massive marine environmental data provide a rich data basis for the study of mesoscale eddies. However, how to quickly and accurately identify and extract the characteristic information of mesoscale eddies from these massive data remains a challenging scientific problem. Traditional manual identification methods are time-consuming and labor-intensive, and it is difficult to meet the needs of massive data processing. Manual identification usually relies on expert experience, is highly subjective, and it is difficult to ensure the consistency and repeatability of the results. At the same time, manual identification methods are difficult to process large amounts of historical data and real-time data streams, and cannot meet the needs of marine scientific research and business applications. Therefore, developing automated mesoscale eddy identification methods has become one of the hot topics in marine science research in recent years.

[0004] At present, a variety of automated mesoscale eddy identification methods have been proposed and applied. These methods can be roughly divided into the following categories: First, the method based on physical parameters, among which the most widely used is the method based on the Okubo-Weiss parameter. The Okubo-Weiss parameter is defined as W = s_n^2 + s_s^2-ω^2, where s_n and s_s are the normal strain rate and shear strain rate, respectively, and ω is the relative vorticity. When W < 0, it means that rotation is dominant and there may be a vortex. Isern-Fontanet et al. (2003) first applied this method to satellite altimeter data to identify mesoscale eddies in the Mediterranean. Chelton et al. (2007) further improved this method by setting the threshold W_0 = -0.2σ_W (σ_W is the standard deviation of W) to identify eddies in the global ocean. However, the Okubo-Weiss method also has some limitations. First, it is sensitive to noise and may produce false vortex signals in altimeter data. Second, the choice of threshold is somewhat subjective, and different thresholds may lead to significant differences in identification results. In addition, this method makes it difficult to accurately determine the boundary of the vortex. In order to overcome these problems, some improved methods have been proposed. For example, Nenciol i et al. (2010) proposed a method based on vector geometry to identify vortices by analyzing the geometric characteristics of the velocity field. This method does not require the calculation of the derivative of the velocity field and is therefore less sensitive to noise. However, it requires setting multiple parameters, and the choice of parameters may affect the identification results.

[0005] The second is the method based on streamlines or trajectories. This type of method mainly uses the rotation characteristics of vortices to identify vortices by analyzing streamlines or particle trajectories. Sadarjoen and Post (2000) proposed a method based on streamline geometry to identify vortices by calculating the winding angle of streamlines. When the winding angle of streamlines exceeds 2π, a vortex is considered to exist. Chaigneau et al. (2008) applied this method to vortex identification in the South Pacific and compared it with the Okubo-Weiss method. Dong et al. (2011) proposed a method based on Lagrangian trajectories to identify vortices by analyzing the trajectory of a surface drifting buoy. This method first defines a "return point", which is the point on the drifting buoy trajectory closest to a certain point. By calculating the rotation angle between the return points, it can be determined whether there is a vortex. The advantage of this method is that it can be directly applied to observational data without interpolation or gridding. However, methods based on streamlines or trajectories also have some limitations. First, they may be affected by the background flow field, especially in strong current areas. Second, these methods usually require long time series data and are not very suitable for the analysis of instantaneous fields. Furthermore, they may have difficulty accurately determining the boundaries and strength of the vortex.

[0006] The third category is the method based on geometric features. This type of method mainly uses the geometric features of the vortex in the physical field for identification. The most commonly used is the closed contour method based on the sea surface height anomaly (SSHA). Chelton et al. (2011) proposed a global vortex identification method based on SSHA contours. They defined the vortex as a series of closed SSHA contours containing a local extreme point. The advantages of this method are intuitive, simple, and able to simultaneously determine the location and boundary of the vortex. However, this method also has some problems. First, it can only identify vortices with closed SSHA contours, and may miss some weak vortices or deformed vortices. Second, in strong current areas or coastal areas, the background flow field may interfere with the distribution of SSHA contours, resulting in identification errors. In addition, this method is difficult to apply to situations where there is no SSHA data, such as the internal layer of the numerical model. In order to overcome these problems, some improved methods have been proposed. For example, Yi et al. (2014) proposed a comprehensive identification method based on multiple physical quantities. They not only use SSHA, but also combine the distribution characteristics of physical quantities such as temperature and salinity to identify vortices. This method can improve the accuracy of identification, especially in complex ocean environments.

[0007] The fourth category is the method based on mathematical features. This type of method mainly uses the mathematical features of the vortex for identification. Among them, the most representative is the method based on singular point detection. McWilliams (1990) first proposed to use the singular points of the velocity field to identify the vortex center. A singular point refers to a point in the velocity field where the velocity is zero, which may correspond to the vortex center, saddle point or other special flow field structure. Dogliol i et al. (2007) further developed this method and proposed a vortex identification method based on discrete vector field topology. They identified key points (singular points) by analyzing the local linearization of the velocity field and judged whether a vortex exists based on the characteristics of the key points. The advantage of this method is that it can accurately locate the vortex center without setting a threshold. However, the method based on singular points also has some limitations. First, it may be affected by noise, especially in the observed data. Second, this method mainly focuses on the vortex center and it is difficult to give the boundary and other characteristic information of the vortex. In addition, in a complex flow field, there may be multiple singular points, which increases the difficulty of identification. With the development of artificial intelligence technology, some vortex identification methods based on machine learning have also been proposed. For example, Lguensat et al. (2018) proposed a vortex identification method based on convolutional neural networks. They used SSHA images as input and trained the network to identify the location and boundaries of vortices. This method can automatically learn the characteristics of vortices without manually setting thresholds or parameters. However, machine learning-based methods also face some challenges. First, they require a large amount of labeled data for training, and high-quality vortex labeled data is not easy to obtain. Second, the generalization ability of these methods needs to be verified, especially their performance in different sea areas or different data sources. In addition, machine learning methods are often difficult to explain their decision-making process, which may limit their application in scientific research.

[0008] Although a variety of vortex identification methods have been proposed, each method has its advantages, disadvantages and scope of application. In practical applications, it is often necessary to select an appropriate method based on the specific research purpose and data characteristics. At the same time, comparison and verification between different methods is also an important research direction. In addition, most of the existing methods focus on the detection and positioning of vortices, and lack systematic characterization and quantitative description of other important characteristics of vortices, such as impact depth, range radius, moving speed, intensity, etc. These characteristic information is of great significance for understanding the dynamic process of vortices, evaluating the environmental effects of vortices, and predicting the evolution trend of vortices. Therefore, how to provide an identification method for mesoscale vortices in the ocean has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0009] The purpose of the present invention is to provide a method and a device for identifying ocean mesoscale vortices.

[0010] According to a first aspect of the present invention, a method for identifying an ocean mesoscale eddy is provided, comprising:

[0011] Obtaining input data of marine environment numerical forecast products, wherein the input data at least includes sea surface height anomaly, horizontal current velocity field, temperature and salinity;

[0012] Preprocessing the input data to obtain processed marine environment data;

[0013] According to a plurality of vortex identification algorithms pre-set, vortices in the processed marine environment data are identified to obtain a plurality of vortices; wherein the plurality of vortex identification algorithms pre-set at least include an Okubo-Weiss parameter method, a vector geometry method, a rotation invariance method and an SSH contour method;

[0014] Performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data;

[0015] Extracting features of the target vortex to obtain feature parameters corresponding to the target vortex;

[0016] The characteristic parameters are displayed in a preset display mode.

[0017] Optionally, the step of obtaining input data of a marine environment numerical forecast product, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity, comprises:

[0018] A preset processing library is used to read the input data of the marine environment numerical forecast product, extract sea surface height anomaly data and horizontal flow velocity field, and obtain longitude and latitude grid information and time series data, and generate multidimensional data results from the sea surface height anomaly data, horizontal flow velocity field, longitude and latitude grid information and time series data.

[0019] Optionally, the input data is preprocessed to obtain processed marine environment data, wherein the preprocessing at least includes format conversion, quality control, interpolation and smoothing processing, including:

[0020] The input data is preprocessed to obtain processed marine environment data, wherein the preprocessing at least includes format conversion, quality control, interpolation and smoothing processing methods.

[0021] Optionally, the identifying of vortices in the processed ocean environment data according to a plurality of pre-set vortex identification algorithms to obtain a plurality of vortices includes:

[0022] Using the Okubo-Weiss parameter method to identify the processed ocean environment data and the retrograde vortex, obtaining a first identification result;

[0023] Using a vector geometry method to identify the processed ocean environment data and the retrograde vortex to obtain a second identification result;

[0024] The processed ocean environment data and the retrograde vortex identification are adopted according to the rotation invariance method to obtain a third identification result;

[0025] The processed ocean environment data and retrograde vortex identification described in the SSH contour method are used to obtain a fourth identification result;

[0026] The plurality of vortices are generated according to the first recognition result, the second recognition result, the third recognition result, and the fourth recognition result.

[0027] Optionally, performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data comprises:

[0028] The vortex centers whose spatial distances are less than a preset threshold are merged, the number of times each cluster center is recognized by different algorithms is calculated, and the vortex whose number of recognitions is greater than 1 is determined as the target vortex.

[0029] Optionally, the extracting features of the target vortex to obtain feature parameters corresponding to the target vortex includes:

[0030] The target vortex is subjected to feature extraction of life cycle characteristics, dynamic characteristics, intensity and structure, position and range to obtain characteristic parameters corresponding to the target vortex, wherein the position and range at least include the vortex center position, influence depth and range radius, the intensity and structure at least include vorticity intensity, kinetic energy intensity and vertical structure characteristics, the dynamic characteristics at least include Rossby number, Burger number and potential vorticity anomaly, and the life cycle characteristics at least include life span, moving speed and intensity change.

[0031] Optionally, displaying the characteristic parameter in a preset display mode includes:

[0032] Using a preset statistical analysis algorithm to perform statistical analysis on the characteristic parameters to obtain statistical analysis results;

[0033] Outputting the statistical analysis results as a data file, or visually displaying them in a graphic format;

[0034] Wherein, the preset statistical analysis algorithm at least includes a spatiotemporal distribution of the number of vortices or a statistical method of characteristic parameters;

[0035] The data files at least include a vortex catalog file, a vortex trajectory file and a gridded data file;

[0036] The graph at least includes a plan distribution graph, a vertical cross-sectional graph or a three-dimensional stereogram.

[0037] According to a second aspect of the present invention, there is provided a device for identifying an ocean mesoscale vortex, comprising:

[0038] An acquisition module is used to acquire input data of marine environment numerical forecast products, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity;

[0039] A preprocessing module, used for preprocessing the input data to obtain processed marine environment data;

[0040] An identification module, used for identifying vortices in the processed marine environment data according to a plurality of pre-set vortex identification algorithms to obtain a plurality of vortices; wherein the pre-set plurality of vortex identification algorithms at least include an Okubo-Weiss parameter method, a vector geometry method, a rotation invariance method and an SSH contour method;

[0041] A clustering module, used for performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data;

[0042] An extraction module, used for performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex;

[0043] The display module is used to display the characteristic parameters in a preset display mode.

[0044] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0045] In a fourth aspect, the present application illustrates a non-temporary computer-readable storage medium, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a method as described in any of the above aspects.

[0046] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above aspects.

[0047] The beneficial effects brought by the present invention are as follows:

[0048] It can be seen from the above scheme that the embodiment of the present invention provides a method and device for identifying ocean mesoscale vortices, which has the following beneficial effects: a system that comprehensively utilizes multi-source marine environmental data to automatically identify and characterize various characteristic information of mesoscale vortices has important theoretical significance and practical value. Such a system should be able to overcome the limitations of a single method and improve the accuracy and comprehensiveness of identification; at the same time, it should also be able to provide a variety of characteristic parameters of the vortex, and provide comprehensive information support for in-depth research on the dynamic process of the vortex. The present invention is aimed at the above needs and proposes a system for identifying characteristic information of ocean mesoscale vortices based on marine environment numerical forecast products. The system combines the advantages of multiple identification methods, can automatically process multiple physical quantity fields in marine environment numerical forecast products, and realizes rapid and accurate identification of multiple characteristic information such as mesoscale vortex position, influence depth, range radius, moving speed, intensity, etc. This will provide strong technical support for the study of ocean mesoscale vortices, and has important application prospects in promoting marine scientific research, improving marine environmental forecast capabilities, and promoting the development and utilization of marine resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a flow chart of a method for identifying an ocean mesoscale eddy provided according to an embodiment;

[0050] Figure 2 A schematic diagram of a flow chart of another method for identifying an ocean mesoscale eddy according to an embodiment;

[0051] Figure 3 This is a structural block diagram of a device for identifying ocean mesoscale eddies in the present application.

[0052] Figure 4 It is a block diagram of an electronic device of the present application.

[0053] Figure 5 It is a block diagram of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Reference Figure 1 , shows a flow chart of the steps of a method for identifying an ocean mesoscale vortex of the present application, which can be applied to electronic devices, wherein the method can specifically include the following steps:

[0056] S101, obtaining input data of marine environment numerical forecast products, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity;

[0057] S102, preprocessing the input data to obtain processed marine environment data;

[0058] S103, identifying vortices in the processed marine environment data according to a plurality of vortex identification algorithms pre-set to obtain a plurality of vortices; wherein the plurality of vortex identification algorithms pre-set at least include an Okubo-Weiss parameter method, a vector geometry method, a rotation invariance method, and an SSH contour method;

[0059] The Okubo-Weiss parameter method is a method for identifying and tracking mesoscale eddies in the ocean. This method determines the stability of fluid motion by calculating the Okubo-Weiss parameter (Ω) to identify eddies.

[0060] S104, performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data;

[0061] A method combining multiple algorithms is used to identify possible vortex structures from different angles, and the accuracy of detection is improved by cross-validation of multi-algorithm results. To improve the accuracy of identification, the system fuses the results of the above-mentioned multiple algorithms. The specific steps are as follows: First, cluster analysis is performed on the vortex centers identified by each algorithm, and the center points whose spatial distance is less than a preset threshold (such as 1 / 4 of the vortex characteristic scale) are merged. Then, the number of times each cluster center is identified by different algorithms is calculated. Only cluster centers that are simultaneously identified by at least two algorithms are finally confirmed as vortex centers. For the confirmed vortex center, the weighted average position of the identification results of each algorithm is taken as the final vortex center position, and the weight is determined according to the reliability and applicability of each algorithm.

[0062] S105, extracting features of the target vortex to obtain feature parameters corresponding to the target vortex;

[0063] For each vortex detected, its characteristic parameters such as center position, impact depth, range radius, moving speed, and intensity are calculated. The center position of the vortex has been determined in the vortex detection module and is expressed in longitude and latitude. The impact depth of the vortex is defined as the maximum depth at which the vortex structure can be detected in the vertical direction. The system starts from the surface layer and checks the recognition results of the vortex detection module layer by layer. When no vortex structure is detected in three consecutive layers, the depth of the last detected vortex is defined as the impact depth. If the vortex structure extends to the bottom layer, the depth of the bottom layer is defined as the impact depth.

[0064] The range radius of the vortex is calculated by a variety of methods. The system uses the contour method, velocity extreme value method and energy decay method, and takes the weighted average. In the contour method, the system searches for closed contour lines of the SSH (or vorticity) field from the center to the outside at the depth layer where the vortex center is located, calculates the area A contained in each closed contour line, and defines the range radius R as the radius of the equivalent circle: R = sqrt (A / π), and takes the R corresponding to the outermost closed contour line as the vortex range radius. In the velocity extreme value method, the system searches for the extreme points of the tangential velocity from the vortex center to the four main directions (east, west, south, and north), and calculates the average distance from these extreme points to the vortex center as the range radius. In the energy decay method, the system calculates the vortex kinetic energy field E = 0.5 (u^2 + v^2), calculates the change of E with the distance r from the vortex center to the outside, and finds the corresponding r when E decays to e^(-1) times the center value as the range radius.

[0065] The moving speed of the vortex is calculated by tracking the position change of the vortex center on continuous time steps. For each time step t, the system records the position of the vortex center (x_t, y_t), calculates the position difference between adjacent time steps Δx = x_t + 1-x_t, Δy = y_t + 1-y_t, and then calculates the moving speed u = Δx / Δt, v ​​= Δy / Δt, where Δt is the time step. In order to reduce the impact of instantaneous fluctuations, the system performs a 5-point sliding average on the calculated speed sequence.

[0066] The vortex strength can be characterized by a variety of physical quantities. The system calculates multiple indicators such as vorticity intensity, kinetic energy intensity, SSH anomaly intensity and tangential velocity intensity. For example, the vorticity intensity calculates the average relative vorticity ω_avg within the vortex range, and the intensity index I_ω=ω_avg / f, where f is the Coriolis parameter. The kinetic energy intensity calculates the average kinetic energy within the vortex range E_avg=0.5(u^2+v^2)_avg, and the intensity index I_E=E_avg / E_bg, where E_bg is the average kinetic energy of the background field.

[0067] The system also calculates a series of shape parameters that describe the geometric characteristics of the vortex, including eccentricity, irregularity, and tilt angle. The eccentricity is defined by selecting the two farthest points on the vortex boundary to define the major axis, and the longest line segment perpendicular to the major axis is defined as the minor axis. The calculation e = sqrt (1-(b / a)^2), where a and b are half the length of the major axis and the minor axis respectively. The irregularity is expressed by calculating the ratio of the actual area of ​​the vortex A_real and the equivalent circular area A_circle. The tilt angle calculates the angle between the major axis of the vortex and the meridian direction.

[0068] To describe the vertical structure of the vortex, the system calculates parameters such as the change of intensity with depth, the change of range radius with depth, and the tilt angle of the center position. For example, the system calculates the vorticity intensity I_ω(z) at each depth layer, and then fits the vertical profile of I_ω(z) to obtain the attenuation scale H_ω. Similarly, the system also calculates the vertical profile of the range radius R(z) to obtain the rate of change dR / dz. By calculating the change of the center position of the vortex with depth, the system fits the tilt angle of the center position α=arctan(dX / dZ), where X is the horizontal displacement and Z is the vertical depth.

[0069] The system also analyzes the thermo-salinity structure of the vortex, calculates the average temperature T_eddy within the vortex and the background temperature T_bg of the surrounding area, and obtains the temperature anomaly ΔT = T_eddy-T_bg. Similarly, the system also calculates the salinity anomaly and density anomaly. Based on this information, the system further calculates the heat content anomaly ΔQ = ρc_p∫ΔTdV and the salt content anomaly ΔS = ∫ΔSdV of the vortex, where c_p is the specific heat capacity of seawater and V is the vortex volume. The system also calculates the Brint-Visera frequency at the center of the vortex And compare it with the layer intensity of the background field.

[0070] In order to gain a deeper understanding of the dynamic characteristics of the vortex, the system calculated a series of dynamic parameters, including the ratio of relative vorticity to planetary vorticity ξ / f, Rossby deformation radius R_d=NH / f (where N is the Brint-Viserra frequency and H is the characteristic depth), Rossby number Ro=V_max / (fR) (where V_max is the maximum tangential velocity and R is the vortex radius), and Berg number Bu=(R_d / R)^2. The system also calculated the conversion rate between eddy kinetic energy and average kinetic energy, as well as the conversion rate between eddy potential energy and average potential energy. These parameters are crucial for understanding the energy budget and dynamic process of the vortex. In addition, the system also calculated the potential vorticity anomaly of the vortex. Where b is the buoyancy, which is of great significance for studying the stability and evolution of vortices.

[0071] The system calculates a series of life cycle characteristics by tracking vortices at consecutive time steps. First, the system records the time t_start when the vortex is first detected and the time t_end when it is last detected, and calculates the life span T = t_end-t_start. Then, the system records the position of the vortex center at each time step (x_t, y_t) and calculates the total movement distance L = Σ√((x_t+1-x_t)^2+(y_t+1-y_t)^2). Based on this information, the system calculates the average propagation speed V_prop = L / T. The system also records the change of vortex intensity over time I(t), calculates the growth rate of intensity dI / dt, and the change of vortex radius over time R(t), and calculates the growth rate of radius dR / dt. These parameters are helpful for studying the generation, development and extinction process of vortices.

[0072] The system also detects the merging and splitting events of vortices. When the distance between two vortices is less than the sum of their radii, the system will determine whether a merging event has occurred. If only one vortex remains in the next time step, and the area of ​​this vortex is close to the sum of the areas of the original two vortices, it is considered that a merging event has occurred. Similarly, when a vortex splits into two or more small vortices in the next time step, the system will record this splitting event. This information is of great value for studying the interaction and energy transfer process between vortices.

[0073] S106: Display the characteristic parameters according to a preset display mode.

[0074] The identified and extracted vortex feature information is output in various forms such as graphics, tables and data files, which is convenient for users to conduct visual analysis and subsequent research. In the plane distribution diagram, the system displays the detected vortex center position and boundary on the horizontal section, uses different colors to distinguish cyclonic vortices from anticyclonic vortices, and can superimpose background fields such as SSH, vorticity or flow fields. In the vertical section diagram, the system displays the vertical structure of the vortex along the specified section, and can display the distribution of physical quantities such as temperature, salinity, and density. The system also generates a three-dimensional stereogram and uses isosurfaces to display the three-dimensional structure of the vortex. Users can select different physical quantities (such as vorticity, Q value, etc.) as the basis of the isosurface.

[0075] In terms of statistical analysis, the system generates graphs showing the change of the number of vortices over time and heat maps of spatial distribution, as well as histograms and scatter plots of vortex characteristic parameters (such as radius, intensity, life span, etc.). The system also generates vortex trajectory graphs showing the movement trajectory of long-life vortices, which can be superimposed with background flow fields or terrain. These statistical graphs are helpful for studying the spatiotemporal patterns and long-term trends of vortex activity.

[0076] The system saves the vortex characteristic information as a data file in a standard format, which is convenient for other programs to read and analyze. The vortex catalog file contains the basic information of each detected vortex, such as ID, center position, time, radius, intensity, etc., in CSV or NetCDF format. The vortex trajectory file records the motion trajectory and characteristic evolution of each long-life vortex, also in CSV or NetCDF format. The system also generates a gridded data file, which interpolates the vortex characteristic information onto a regular grid, including statistics such as vortex existence probability, average intensity, average radius, etc., in NetCDF format.

[0077] To facilitate user interaction, the system provides a graphical user interface that allows users to select time periods and regions of interest, adjust parameters for vortex detection and feature extraction, generate and display various graphs and statistical results in real time, and perform detailed viewing and analysis of specific vortices. This interactive interface greatly improves the ease of use and flexibility of the system.

[0078] The implementation of this system takes full consideration of computational efficiency and scalability. The system adopts parallel computing strategies, such as time parallelism, space parallelism, and algorithm parallelism, to fully utilize the computing resources of multi-node clusters and multi-core processors. For example, the system can divide the research area into multiple sub-areas and process them in parallel on different computing nodes; data of different time steps can also be processed in parallel; and multiple vortex detection algorithms can be executed simultaneously. The system uses a hybrid parallel mode combining MPI (Message Passing Interface) and OpenMP to achieve the best parallel efficiency.

[0079] In terms of data management, the system adopts a data partitioning strategy to divide large data sets into manageable blocks to achieve distributed storage and processing of data. The system also implements a cache mechanism to store frequently accessed data in memory or cache to reduce I / O operations. For raw data and intermediate results, the system uses compression algorithms to reduce storage space and data transmission volume, further improving processing efficiency.

[0080] In order to improve the efficiency and accuracy of vortex detection and feature extraction, the system adopts a multi-scale analysis strategy. First, preliminary detection is performed on a coarse-resolution grid, and then precise positioning is performed on a fine-resolution grid. The system also implements adaptive parameter adjustment, automatically adjusting the parameters of the detection algorithm according to the local flow field characteristics. For example, in strong flow areas, the system automatically lowers the threshold of the Okubo-Weiss parameter to better capture the vortex structure.

[0081] The system's visualization module uses level of detail (LOD) technology to dynamically adjust the displayed detail level according to the view range. In the global view, the system only displays the main large-scale vortices, while in the local zoom view, more small-scale structures are displayed. The system also uses GPU for graphics rendering to improve the smoothness of 3D visualization. For large data sets, the system uses progressive loading, first displaying key information, and then gradually loading details to improve response speed and user experience.

[0082] The design of this system takes scalability into full consideration. Users can easily add new vortex detection algorithms or feature extraction methods. For example, if a user develops a new vortex identification algorithm, it can be easily integrated into the system by simply encapsulating it into a module that meets the system interface specifications. Similarly, if the user needs to calculate new vortex feature parameters, the corresponding calculation function can also be easily added to the feature extraction module.

[0083] The system also provides a rich set of secondary development interfaces, allowing advanced users to customize system functions according to specific needs. For example, users can develop specialized analysis modules, such as an analysis module for the interaction between vortices and ocean fronts, or an assessment module for the impact of vortices on marine ecosystems. These custom modules can be seamlessly integrated into the system, expanding the scope of application of the system.

[0084] In summary, the ocean mesoscale eddy characteristic information identification system based on the marine environment numerical forecast product proposed in this invention realizes the comprehensive, accurate and efficient identification and characterization of the ocean mesoscale eddy by comprehensively using a variety of advanced eddy detection and feature extraction methods. The modular design, parallel computing strategy, multi-scale analysis method and rich visualization functions of the system enable it to effectively process large-scale marine environmental data and provide strong technical support for marine scientific research and business applications. The high flexibility and scalability of the system provide a solid foundation for future continuous optimization and functional expansion, and is expected to play an important role in many fields such as ocean dynamics research, climate change research, and marine resource management.

[0085] The present invention proposes a marine mesoscale eddy characteristic information identification system based on marine environment numerical forecast products. The system comprehensively utilizes the advantages of multiple identification methods, adopts modular design, and can automatically process multiple physical quantity fields in marine environment numerical forecast products, such as sea surface height, temperature, salinity, flow rate, etc. The system mainly includes a data preprocessing module, a vortex detection module, a feature extraction module and a result output module. The vortex detection module adopts a method combining multiple algorithms, such as Okubo-Weiss parameter method, vector geometry method, etc., to identify possible vortex structures from different angles. The feature extraction module calculates characteristic parameters such as the center position, influence depth, range radius, moving speed, and intensity of each vortex detected. The extraction method of these characteristic parameters integrates multiple technologies, such as determining the vortex center by analyzing the rotation of the velocity vector, defining the vortex boundary by closed streamlines or contour lines, and estimating the vortex intensity by integrating kinetic energy or vorticity. The innovation of this system lies in the comprehensive utilization of multiple physical quantity fields and multiple identification algorithms, and improving the accuracy and comprehensiveness of identification through cross-validation. At the same time, the system can not only identify the location of the vortex, but also extract a variety of important characteristic parameters, providing comprehensive information support for in-depth research on the dynamic process of the vortex. This comprehensive vortex feature recognition method helps to overcome the limitations of a single method, improve the reliability and applicability of the recognition results, and provide strong technical support for the research and application of ocean mesoscale vortices.

[0086] The embodiment of the present application provides a system that can comprehensively utilize multi-source marine environmental data to automatically identify and characterize multiple characteristic information of mesoscale vortices, which has important theoretical significance and practical value. This system should be able to overcome the limitations of a single method and improve the accuracy and comprehensiveness of identification; at the same time, it should also be able to provide multiple characteristic parameters of the vortex, and provide comprehensive information support for in-depth research on the dynamic process of the vortex. The present invention is aimed at the above needs and proposes a marine mesoscale vortex characteristic information identification system based on marine environment numerical forecast products. The system combines the advantages of multiple identification methods, can automatically process multiple physical quantity fields in marine environment numerical forecast products, and realize rapid and accurate identification of multiple characteristic information such as mesoscale vortex position, impact depth, range radius, moving speed, intensity, etc. This will provide strong technical support for the study of marine mesoscale vortices, and has important application prospects in promoting marine scientific research, improving marine environmental forecast capabilities, and promoting the development and utilization of marine resources.

[0087] Another embodiment of the present application further supplements the method for identifying ocean mesoscale eddies provided in the above embodiment.

[0088] The present invention proposes a marine mesoscale eddy characteristic information identification system based on marine environment numerical forecast products. The system can automatically process various physical quantity fields in marine environment numerical forecast products, and comprehensively use various methods such as flow field characteristic analysis, geometric characteristic recognition, dynamic characteristic diagnosis, etc. to realize rapid and accurate identification of various characteristic information such as mesoscale eddy position, influence depth, range radius, moving speed, intensity, etc. The system adopts modular design, mainly including data preprocessing module, eddy detection module, feature extraction module and result output module. The modules communicate through standardized data interfaces to ensure the flexibility and scalability of the system. The core algorithm of the system is implemented in Python language, making full use of scientific computing libraries such as NumPy and SciPy to improve computing efficiency, using specialized multidimensional array data processing libraries such as xarray to process marine data, and using powerful visualization libraries such as Matplotlib and Cartopy to display results.

[0089] The specific technical solutions for each module are as follows:

[0090] Optionally, input data of marine environment numerical forecast products are obtained, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity, including:

[0091] The preset processing library is used to read the input data of the marine environment numerical forecast product, extract the sea surface height anomaly data and the horizontal velocity field, and obtain the longitude and latitude grid information and time series data, and generate multidimensional data results from the sea surface height anomaly data, horizontal velocity field, longitude and latitude grid information and time series data.

[0092] Optionally, the input data is preprocessed to obtain processed marine environment data, wherein the preprocessing at least includes format conversion, quality control, interpolation and smoothing processing methods, including:

[0093] The input data is preprocessed to obtain processed marine environment data, wherein the preprocessing at least includes format conversion, quality control, interpolation and smoothing processing methods.

[0094] The data preprocessing module is responsible for format conversion, quality control, interpolation and smoothing of the input marine environment numerical forecast products to ensure data consistency and reliability. The system specializes in processing marine numerical forecast products in NetCDF format and uses a special NetCDF processing library to read data.

[0095] The data preprocessing module first reads the marine environment numerical forecast product. Taking the NetCDF format as an example, the system uses the netCDF4 library to read the data. During the reading process, the system extracts key physical quantities such as sea surface height anomaly (SSH), horizontal velocity field (u, v), temperature (T), salinity (S), and obtains latitude and longitude grid information and time series data. This information is organized into a multidimensional array structure, including dimensions such as longitude, latitude, depth, time and physical quantity, which is convenient for subsequent processing and analysis. The system uses statistical methods to detect outliers in physical quantities. First, the mean and standard deviation of each physical quantity in each depth layer are calculated, and then the values ​​exceeding ±3 times the standard deviation of the mean are marked as potential outliers. For potential outliers, the system further considers their spatial continuity and temporal continuity to determine whether they are real outliers. For example, if an abnormally high SSH value is surrounded by normal values ​​in space and does not exist in the previous and next time steps, it is likely to be a real outlier. Data points confirmed as outliers will be replaced by interpolation of nearby valid values ​​to maintain the smoothness of the data field.

[0096] The system uses a variety of interpolation methods to handle missing values ​​of different physical quantities. For small-scale missing values, the linear interpolation method is used; for large-scale missing values, the optimal interpolation method is used. During the interpolation process, the system will consider the spatial correlation of the physical quantities to ensure the rationality of the interpolation results. For example, when processing the sea surface height field, the system will consider the influence of terrain to avoid inappropriate interpolation in land areas. Different physical quantities may have different spatial resolutions and vertical stratifications. The system interpolates all physical quantities onto a unified three-dimensional grid for subsequent processing. Bilinear interpolation is used in the horizontal direction and spline interpolation is used in the vertical direction to maintain the vertical structural characteristics of the physical quantities. This step ensures the spatial consistency of all physical quantities, laying the foundation for subsequent vortex detection and feature extraction.

[0097] In order to reduce the impact of high-frequency noise on vortex identification, the system smoothes the time series data. Here, the Lanczos filter is used to filter out high-frequency signals with a period of less than 2 days, while retaining the characteristic time scale of mesoscale vortices (usually several days to several months). During the filtering process, the system automatically adjusts the filter parameters to adapt to the dynamic characteristics of different sea areas. For example, in strong current areas, higher-frequency signals may need to be retained to capture rapidly changing vortex structures. In nearshore areas, the system masks the physical quantity field based on high-precision terrain data to ensure that the values ​​in the land area are invalid. At the same time, for complex terrain areas, the system uses a terrain-following coordinate system for vertical interpolation to improve the accuracy of near-bottom-layer data. This step is crucial for accurately identifying nearshore vortex structures, especially in dynamically active areas such as the shelf edge.

[0098] During the reading process, the system extracts key physical quantities such as sea surface height anomaly (SSH) and horizontal velocity field (u, v), and obtains latitude and longitude grid information and time series data. This information is organized into a multidimensional array structure, including longitude, latitude, depth, time and physical quantity dimensions, which is convenient for subsequent processing and analysis.

[0099] The system uses statistical methods to detect outliers of physical quantities. First, the mean and standard deviation of each physical quantity at each depth layer are calculated, and then the values ​​exceeding ±3 times the standard deviation of the mean are marked as potential outliers. For potential outliers, the system further considers their spatial continuity and temporal continuity to determine whether they are real anomalies. Data points confirmed as outliers will be replaced by the average of the adjacent valid values ​​to maintain the smoothness of the data field. Different physical quantities may have different spatial resolutions and vertical stratifications. The system interpolates all physical quantities onto a unified three-dimensional grid for subsequent processing. Bilinear interpolation is used in the horizontal direction and spline interpolation is used in the vertical direction to maintain the vertical structural characteristics of the physical quantities. This step ensures the spatial consistency of all physical quantities and lays the foundation for subsequent vortex detection and feature extraction. In order to reduce the impact of high-frequency noise on vortex identification, the system smoothes the time series data and uses a Lanczos filter to filter out high-frequency signals with a period of less than 2 days, while retaining the characteristic time scale of mesoscale vortices (usually several days to several months). During the filtering process, the system automatically adjusts the filter parameters to adapt to the dynamic characteristics of different sea areas. For example, in areas of strong currents, it may be necessary to retain higher-frequency signals to capture rapidly changing vortex structures. In nearshore areas, the system masks the physical quantity field based on high-precision terrain data to ensure that the values ​​in the land area are invalid. At the same time, for areas with complex terrain, the system uses a terrain-following coordinate system for vertical interpolation to improve the accuracy of near-bottom-layer data. This step is crucial for accurately identifying nearshore vortex structures, especially in dynamically active areas such as the shelf edge.

[0100] The vortex detection module is the core part of this system. It uses a combination of multiple algorithms to identify possible vortex structures from different angles and improves the accuracy of detection through cross-validation of multiple algorithm results. It mainly includes the following methods: Okubo-Weiss parameter method, vector geometry method, rotation invariance method and sea surface height anomaly (SSH) contour method.

[0101] Optionally, according to a plurality of vortex identification algorithms pre-set, vortices in the processed ocean environment data are identified to obtain a plurality of vortices, including:

[0102] The Okubo-Weiss parameter method is used to identify the processed marine environmental data and the retrograde vortex, and the first identification result is obtained;

[0103] The processed marine environment data and retrograde vortices are identified using the vector geometry method to obtain the second identification result;

[0104] The processed marine environment data and retrograde vortices are identified using the rotational invariance method to obtain the third identification result;

[0105] The processed marine environment data and retrograde vortices are identified using the SSH contour method to obtain the fourth identification result;

[0106] Based on the first, second, third, and fourth identification results, multiple vortices are generated.

[0107] Specifically, in the Okubo - Weiss parameter method, the system calculates the Okubo - Weiss parameter W for each grid point, which is defined as W = s_n^2 + s_s^2 - ω^2, where s_n and s_s are the normal strain rate and shear strain rate respectively, and ω is the relative vorticity.

[0108] The system first calculates the standard deviation σ_W of W for the entire domain, and then sets the threshold W_0 = - 0.2σ_W. At each depth layer, the system searches for connected regions that satisfy W < W_0. For each connected region, its area is calculated, and only regions with an area larger than a preset threshold (such as 9 grid points) are considered potential vortices. Within each potential vortex region, the system searches for local minimum points of W as candidate points for the vortex center. The vector geometry method is based on the algorithm proposed by Nencioli et al. (2010) and identifies vortices by analyzing the geometric characteristics of the velocity field.

[0109] The system defines a search window (such as a 7×7 grid) around each grid point and then checks the following four conditions: the u - component changes sign on the east and west sides of the vortex center and its magnitude increases outward; the v - component changes sign on the north and south sides of the vortex center and its magnitude increases outward; the velocity magnitude reaches a local minimum at the vortex center; the velocity vector rotates around the vortex center and the direction change of adjacent vectors accumulates to 360°. Points that satisfy all conditions are marked as candidate points for the vortex center. The rotational invariance method uses the characteristics of the velocity gradient tensor to identify vortices. The system first calculates the velocity gradient tensor The eigenvalues λ_1 and λ_2 are obtained, and then the rotation invariant Q = -0.5(λ_1^2 + λ_2^2) is calculated. At each depth layer, the system searches for connected regions where Q > 0. For each connected region, its area is calculated, and only regions with an area greater than a preset threshold are considered potential vortices. Within each potential vortex region, the system searches for local maximum points of Q as candidate points for the vortex center. The SSH contour method uses the closed contours of the SSH field to identify vortices. The system searches for local extreme points (maximum or minimum) in the SSH field. Starting from each extreme point, the search range is gradually expanded to find closed SSH contours. When the first non-closed contour is found, the previous closed contour is defined as the vortex boundary. The system calculates the area of each closed region, and only regions with an area greater than a preset threshold are considered potential vortices.

[0110] First is the method based on the Okubo-Weiss parameter. The system calculates the Okubo-Weiss parameter W at each grid point, which is defined as W = s_n^2 + s_s^2 - ω^2, where s_n and s_s are the normal strain rate and shear strain rate respectively, and ω is the relative vorticity. The system first calculates the standard deviation σ_W of W for the entire domain, and then sets the threshold W_0 = -0.2σ_W. At each depth layer, the system searches for connected regions that satisfy W < W_0. For each connected region, its area is calculated, and only regions with an area greater than a preset threshold (such as 9 grid points) are considered potential vortices. Within each potential vortex region, the system searches for local minimum points of W as candidate points for the vortex center.

[0111] Second is the vector geometry method. This method is based on the algorithm proposed by Nencioli et al. (2010) and identifies vortices by analyzing the geometric features of the velocity field. The system defines a search window (such as a 7×7 grid) around each grid point and then checks the following four conditions: the u component changes sign on the east and west sides of the vortex center and its magnitude increases outward; the v component changes sign on the north and south sides of the vortex center and its magnitude increases outward; the velocity magnitude reaches a local minimum at the vortex center; the velocity vector rotates around the vortex center and the direction change of adjacent vectors accumulates to 360°. Points that satisfy all conditions are marked as candidate points for the vortex center.

[0112] The third method is the method based on rotational invariance. This method uses the characteristics of the velocity gradient tensor to identify vortices. The system first calculates the velocity gradient tensor The eigenvalues ​​λ_1 and λ_2 are calculated, and then the rotation invariant Q = -0.5(λ_1^2+λ_2^2) is calculated. At each depth layer, the system searches for connected areas with Q>0. For each connected area, its area is calculated, and only areas with an area greater than a preset threshold are considered potential vortices. In each potential vortex area, the system searches for the local maximum point of Q as a candidate point for the vortex center.

[0113] The fourth method is based on the sea surface height anomaly (SSH) contour. The system searches for local extreme points (maximum or minimum) in the SSH field. Starting from each extreme point, the search range is gradually expanded to find closed SSH contours. When the first contour that is no longer closed is found, the previous closed contour is defined as the vortex boundary. The system calculates the area of ​​each closed area, and only areas with an area greater than a preset threshold are considered potential vortices.

[0114] Optionally, cluster analysis is performed according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data, including:

[0115] The vortex centers whose spatial distance is less than a preset threshold are merged, and the number of times each cluster center is recognized by different algorithms is calculated. The vortex with a recognition number greater than 1 is determined as the target vortex.

[0116] To improve the accuracy of recognition, the system fuses the results of the above-mentioned multiple algorithms. The specific steps are as follows: First, cluster analysis is performed on the vortex centers identified by each algorithm, and the center points whose spatial distance is less than a preset threshold (such as 1 / 4 of the vortex characteristic scale) are merged. Then, the number of times each cluster center is recognized by different algorithms is calculated. Only cluster centers that are simultaneously recognized by at least two algorithms are finally confirmed as vortex centers. For the confirmed vortex center, the average position of the recognition results of each algorithm is taken as the final vortex center position. This multi-algorithm fusion method significantly improves the reliability of vortex identification, especially in complex marine environments.

[0117] Optionally, feature extraction is performed on the target vortex to obtain feature parameters corresponding to the target vortex, including:

[0118] The life cycle characteristics, dynamic characteristics, intensity and structure, position and range of the target vortex are extracted to obtain characteristic parameters corresponding to the target vortex, wherein the position and range at least include the vortex center position, influence depth and range radius, the intensity and structure at least include vorticity intensity, kinetic energy intensity and vertical structure characteristics, the dynamic characteristics at least include Rossby number, Berger number and potential vorticity anomaly, and the life cycle characteristics at least include lifespan, moving speed and intensity change.

[0119] The feature extraction module calculates the characteristic parameters such as the center position, influence depth, range radius, moving speed, and intensity for each vortex detected. The center position of the vortex has been determined in the vortex detection module and is expressed in longitude and latitude. The influence depth of the vortex is defined as the maximum depth at which the vortex structure can be detected in the vertical direction. The system starts from the surface layer and checks the recognition results of the vortex detection module layer by layer. When no vortex structure is detected in three consecutive layers, the depth of the last detected vortex is defined as the influence depth. If the vortex structure extends to the bottom layer, the depth of the bottom layer is defined as the influence depth. The range radius of the vortex is calculated by a variety of methods. The system uses the contour method, the velocity extreme method, and the energy attenuation method, and takes the average value. In the contour method, the system searches for the closed contour lines of the SSH (or vorticity) field from the center to the outside at the depth layer where the vortex center is located, calculates the area A contained in each closed contour line, and defines the range radius R as the radius of the equivalent circle: R = sqrt (A / π), and takes the R corresponding to the outermost closed contour line as the vortex range radius. In the velocity extreme value method, the system searches for the extreme points of the tangential velocity from the vortex center to the four main directions (east, west, south, and north), and calculates the average distance from these extreme points to the vortex center as the range radius. In the energy decay method, the system calculates the vortex kinetic energy field E = 0.5 (u^2 + v^2), and calculates the change of E with distance r from the vortex center outward, and finds the corresponding r when E decays to e^(-1) times the center value as the range radius.

[0120] The moving speed of the vortex is calculated by tracking the position change of the vortex center on continuous time steps. For each time step t, the system records the position of the vortex center (x_t, y_t), calculates the position difference between adjacent time steps Δx = x_t + 1-x_t, Δy = y_t + 1-y_t, and then calculates the moving speed u = Δx / Δt, v ​​= Δy / Δt, where Δt is the time step. In order to reduce the impact of instantaneous fluctuations, the system performs a 5-point sliding average on the calculated velocity sequence. The vortex intensity can be characterized by a variety of physical quantities. The system calculates multiple indicators such as vorticity intensity, kinetic energy intensity, SSH anomaly intensity and tangential velocity intensity. For example, the vorticity intensity calculates the average relative vorticity ω_avg within the vortex range, and the intensity index I_ω = ω_avg / f, where f is the Coriolis parameter. Kinetic energy intensity calculation: the average kinetic energy within the vortex range is E_avg=0.5(u^2+v^2)_avg, and the intensity index is I_E=E_avg / E_bg, where E_bg is the average kinetic energy of the background field.

[0121] The system also calculates a series of shape parameters that describe the geometric characteristics of the vortex, including eccentricity, irregularity, and inclination angle. The eccentricity is defined by selecting the two farthest points on the vortex boundary as the major axis, and the longest line segment perpendicular to the major axis is defined as the minor axis. The calculation e = sqrt (1-(b / a)^2), where a and b are half the length of the major axis and the minor axis, respectively. Irregularity is expressed by calculating the ratio of the actual area of ​​the vortex A_real and the equivalent circular area A_circle. The inclination angle calculates the angle between the major axis of the vortex and the meridian direction. To describe the vertical structure of the vortex, the system calculates parameters such as the change of intensity with depth, the change of range radius with depth, and the inclination angle of the center position. The system also analyzes the thermosalt structure of the vortex, calculates the average temperature T_eddy within the vortex range and the background temperature T_bg of the surrounding area, and obtains the temperature anomaly ΔT = T_eddy-T_bg. Similarly, the system also calculates salinity anomalies and density anomalies. Based on this information, the system further calculates the vortex's heat content anomaly ΔQ = ρc_p∫ΔTdV and salt content anomaly ΔS = ∫ΔSdV, where c_p is the specific heat capacity of seawater and V is the vortex volume.

[0122] In order to gain a deeper understanding of the dynamic characteristics of the vortex, the system calculated a series of dynamic parameters, including the ratio of relative vorticity to planetary vorticity ξ / f, Rossby deformation radius R_d=NH / f (where N is the Brint-Viserra frequency and H is the characteristic depth), Rossby number Ro=V_max / (fR) (where V_max is the maximum tangential velocity and R is the vortex radius), and Burg number Bu=(R_d / R)^2. The system also calculated the conversion rate between vortex kinetic energy and average kinetic energy, as well as the conversion rate between vortex potential energy and average potential energy. These parameters are crucial for understanding the energy budget and dynamic process of the vortex. In addition, the system also calculated the potential vorticity anomaly of the vortex. Where b is the buoyancy, which is of great significance for studying the stability and evolution of vortices.

[0123] The system calculates a series of life cycle characteristics by tracking vortices on consecutive time steps. First, the system records the time t_start when the vortex is first detected and the time t_end when it is last detected, and calculates the life span T = t_end-t_start. Then, the system records the position of the vortex center at each time step (x_t, y_t) and calculates the total movement distance L = Σ√((x_t+1-x_t)^2+(y_t+1-y_t)^2). Based on this information, the system calculates the average propagation speed V_prop = L / T. The system also records the change of vortex intensity over time I(t), calculates the growth rate of intensity dI / dt, and the change of vortex radius over time R(t), and calculates the growth rate of radius dR / dt. These parameters help to study the generation, development and extinction process of vortices. In particular, the system also detects the merging and splitting events of vortices. When the distance between two vortices is less than the sum of their radii, the system determines whether a merging event has occurred. If only one vortex remains in the next time step, and the area of ​​this vortex is close to the sum of the areas of the original two vortices, a merger event is considered to have occurred. Similarly, when a vortex splits into two or more small vortices in the next time step, the system will record this splitting event. This information is of great value for studying the interaction and energy transfer process between vortices.

[0124] Optionally, the characteristic parameters are displayed in a preset display mode, including:

[0125] Using a preset statistical analysis algorithm to perform statistical analysis on the characteristic parameters to obtain statistical analysis results;

[0126] Output statistical analysis results as data files or visualize them in graphics;

[0127] Wherein, the preset statistical analysis algorithm at least includes the spatiotemporal distribution of the number of vortices or the statistical method of characteristic parameters;

[0128] The data files at least include a vortex catalog file, a vortex trajectory file and a gridded data file;

[0129] The graphics include at least a plan distribution diagram, a vertical section diagram or a three-dimensional stereogram.

[0130] Specifically, the result output module outputs the identified and extracted vortex feature information in various forms such as graphics, tables and data files, which is convenient for users to conduct visual analysis and subsequent research. In the plane distribution map, the system displays the detected vortex center position and boundary on the horizontal section, uses different colors to distinguish cyclonic vortices from anticyclonic vortices, and can superimpose background fields such as SSH, vorticity or flow fields. In the vertical section map, the system displays the vertical structure of the vortex along the specified section, and can display the distribution of physical quantities such as temperature, salinity, and density. The system also generates a three-dimensional stereogram, using isosurfaces to display the three-dimensional structure of the vortex. Users can select different physical quantities (such as vorticity, Q value, etc.) as the basis of the isosurface. In terms of statistical analysis, the system generates a graph of the change of the number of vortices over time and a thermal map of the spatial distribution, as well as a histogram and scatter plot of vortex characteristic parameters (such as radius, intensity, life, etc.). The system also generates a vortex trajectory map to display the movement trajectory of long-life vortices, which can be superimposed with background flow fields or terrain. These statistical charts help study the temporal and spatial patterns and long-term trends of vortex activity.

[0131] The system saves the vortex feature information as a data file in a standard format, which is convenient for other programs to read and analyze. The vortex catalog file contains basic information of each detected vortex, such as ID, center position, time, radius, intensity, etc., in NetCDF format. The vortex trajectory file records the motion trajectory and feature evolution of each long-life vortex, also in NetCDF format. The system also generates a gridded data file, interpolating the vortex feature information onto a regular grid, including statistics such as vortex existence probability, average intensity, average radius, etc., in NetCDF format. To facilitate user interaction, the system provides a graphical user interface that allows users to select time periods and areas of interest, adjust parameters for vortex detection and feature extraction, generate and display various graphics and statistical results in real time, and conduct detailed viewing and analysis of specific vortices. This interactive interface greatly improves the ease of use and flexibility of the system.

[0132] The implementation of this system takes full consideration of computational efficiency and scalability. The system adopts parallel computing strategies, such as time parallelism, space parallelism, and algorithm parallelism, to fully utilize the computing resources of multi-node clusters and multi-core processors. For example, the system can divide the study area into multiple sub-areas and process them in parallel on different computing nodes; data of different time steps can also be processed in parallel; and multiple vortex detection algorithms can be executed simultaneously. The system uses a hybrid parallel mode combining MPI (Message Passing Interface) and OpenMP to achieve optimal parallel efficiency. In terms of data management, the system adopts a data block strategy to divide large data sets into manageable blocks to achieve distributed storage and processing of data. The system also implements a cache mechanism to store frequently accessed data in memory or cache to reduce I / O operations. For raw data and intermediate results, the system uses compression algorithms to reduce storage space and data transmission volume, further improving processing efficiency.

[0133] In order to improve the efficiency and accuracy of vortex detection and feature extraction, the system adopts a multi-scale analysis strategy. First, preliminary detection is performed on a coarse-resolution grid, and then precise positioning is performed on a fine-resolution grid. The system also implements adaptive parameter adjustment to automatically adjust the parameters of the detection algorithm according to the local flow field characteristics. For example, in strong flow areas, the system automatically lowers the threshold of the Okubo-Weiss parameter to better capture the vortex structure. The system's visualization module uses level of detail (LOD) technology to dynamically adjust the displayed detail level according to the view range. In the global view, the system only displays the main large-scale vortices, while in the local zoomed-in view, more small-scale structures are displayed. The system also uses GPU for graphics rendering to improve the smoothness of 3D visualization. For large data sets, the system adopts a progressive loading method, which displays key information first and then gradually loads details to improve response speed and user experience.

[0134] The design of this system takes scalability into full consideration. Users can easily add new vortex detection algorithms or feature extraction methods. For example, if a user develops a new vortex identification algorithm, it can be easily integrated into the system by simply encapsulating it into a module that meets the system interface specifications. Similarly, if the user needs to calculate new vortex characteristic parameters, the corresponding calculation function can also be easily added to the feature extraction module. The system also provides a wealth of secondary development interfaces, allowing advanced users to customize system functions according to specific needs. For example, users can develop specialized analysis modules, such as an analysis module for the interaction between vortices and ocean fronts, or an assessment module for the impact of vortices on marine ecosystems. These custom modules can be seamlessly integrated into the system, expanding the application scope of the system.

[0135] In summary, the ocean mesoscale eddy characteristic information identification system based on the marine environment numerical forecast product proposed in the present invention realizes the comprehensive, accurate and efficient identification and characterization of the ocean mesoscale eddy by comprehensively using a variety of advanced eddy detection and feature extraction methods. The system's modular design, parallel computing strategy, multi-scale analysis method and rich visualization functions enable it to effectively process large-scale marine environmental data and provide strong technical support for marine scientific research and business applications. The high flexibility and scalability of the system provide a solid foundation for future continuous optimization and functional expansion, and is expected to play an important role in many fields such as ocean dynamics research, climate change research, and marine resource management.

[0136] First, the system significantly improves the accuracy and comprehensiveness of ocean mesoscale eddy identification. By comprehensively using a variety of advanced eddy detection algorithms such as the Okubo-Weiss parameter method, vector geometry method, rotational invariance method, and SSH contour method, and combining cross-validation of multi-algorithm results, the system effectively overcomes the limitations of a single algorithm. This multi-algorithm fusion method greatly improves the reliability of eddy identification, especially in complex marine environments such as strong current areas or nearshore waters. For example, in the strong western boundary current area, traditional methods often find it difficult to accurately distinguish eddy structures from background strong current fields. However, by comprehensively analyzing the results of multiple physical fields and multiple identification algorithms, the system can better capture the characteristic signals of eddies and significantly reduce the false detection rate and missed detection rate. At the same time, the system can simultaneously process multiple physical fields such as sea surface height, temperature, salinity, and flow velocity, making full use of the rich information in the marine environment numerical forecast products, further improving the comprehensiveness and accuracy of eddy identification. This comprehensive and accurate eddy identification capability has laid a solid foundation for in-depth research on the dynamic process and environmental effects of mesoscale eddies.

[0137] Secondly, this system realizes the comprehensive characterization of vortex characteristics, greatly expanding the breadth and depth of mesoscale vortex research. Unlike the traditional method that only focuses on the position of the vortex, this system can extract a variety of important characteristic parameters of the vortex, including the center position, influence depth, range radius, movement speed, intensity, shape parameters, vertical structure, thermal and salt characteristics, dynamic characteristics, etc. This comprehensive feature extraction provides rich information support for in-depth research on the physical properties and environmental effects of the vortex. For example, by analyzing the vertical structure of the vortex, we can better understand the influence mechanism of the vortex on the mixing and vertical transport of the upper ocean; by tracking the movement speed and path of the vortex, we can quantitatively evaluate the contribution of the vortex to the transport of matter and energy; by analyzing the thermal and salt characteristics of the vortex, we can evaluate the effect of the vortex on the characteristics of local water masses and the ecological environment. In particular, a series of dynamic parameters calculated by the system, such as the ratio of relative vorticity to planetary vorticity, Rossby deformation radius, Rossby number, Berg number, etc., provide key information for in-depth understanding of the dynamic mechanism of the vortex. These detailed characteristic information not only expands the dimension of mesoscale eddy research, but also provides oceanographers with unprecedented research opportunities, which helps to deepen the understanding of mesoscale processes in the ocean and promote the development of ocean dynamics theory.

[0138] Furthermore, this system significantly improves the efficiency of massive data processing and provides technical support for large-scale, long-term eddy research. The system realizes the automation of the entire process from data preprocessing to result output, greatly improving the processing efficiency of massive marine environmental data. By adopting multiple parallel computing strategies such as time parallelism, space parallelism and algorithm parallelism, the system makes full use of modern computer hardware resources and can quickly process large-scale four-dimensional ocean data sets. For example, for daily average data with a resolution of 1 / 12° covering the world, this system can complete a year of data processing and eddy identification in a few hours, while traditional manual identification methods may take months or even longer. This efficient data processing capability makes it possible to conduct statistical analysis of eddies over long time series and large spatial ranges, providing strong support for studying the temporal and spatial variation laws and long-term trends of eddy activities. Especially in the context of climate change, this large-scale data processing capability provides the necessary technical means to study the impact of mesoscale eddies on global ocean circulation and climate systems.

[0139] In addition, this system provides a wealth of visualization and analysis tools, which greatly facilitates researchers to conduct quantitative analysis and comparative studies on vortex characteristics. The system integrates a variety of visualization methods, including two-dimensional plane diagrams, vertical profile diagrams, three-dimensional stereograms, etc., which can intuitively display the structure and evolution process of vortices. The system also provides a variety of statistical analysis tools, such as the spatiotemporal distribution diagram of the number of vortices, the statistical distribution diagram of vortex characteristic parameters, and the vortex trajectory diagram. These visualization and analysis tools not only improve research efficiency, but also provide the possibility of discovering new scientific problems. For example, through the vortex trajectory diagram, the generation area, propagation path and extinction area of ​​vortices can be intuitively analyzed, revealing the spatial pattern of vortex activity and its relationship with seabed topography and large-scale circulation; through the statistical distribution diagram of vortex characteristic parameters, the differences in vortex characteristics in different sea areas or different seasons can be studied, and then the environmental factors affecting vortex characteristics can be explored. This multi-dimensional visualization and statistical analysis capability provides strong technical support for the comprehensive study of mesoscale vortices.

[0140] This system also realizes the tracking of the entire life cycle of vortices, providing valuable information for studying the dynamic process of vortices. The system can automatically track the entire process of vortex generation, development, and extinction, and record the evolution of vortex characteristics over time. This full life cycle tracking function provides unprecedented opportunities for studying the dynamic process of vortices. For example, by analyzing the temporal changes in vortex intensity, the growth and decay mechanisms of vortices can be studied, and the impact of environmental factors on vortex evolution can be evaluated; by identifying vortex merger and splitting events, the interaction process between vortices can be explored, revealing the energy transfer mechanism between different scales. This information is of great value for understanding the life cycle characteristics of vortices and predicting the evolution trend of vortices, and also provides a scientific basis for improving vortex parameterization schemes in ocean numerical models.

[0141] In general, the marine mesoscale eddy characteristic information identification system proposed in this invention has significant advantages in improving the accuracy of eddy identification, comprehensively characterizing eddy characteristics, improving data processing efficiency, enriching analysis tools, and realizing full life cycle tracking. The system not only provides a powerful tool for the scientific research of marine mesoscale eddies, but also provides strong support for practical applications such as marine environmental monitoring and forecasting, and marine resource management. By promoting the development of marine numerical models, promoting interdisciplinary research, and optimizing marine observation strategies, this system is expected to play an important role in deepening the understanding of marine dynamic processes, improving the ability to forecast the marine environment, and responding to the challenges of global change, and make positive contributions to the development of marine science and the progress of marine affairs.

[0142] like Figure 2 As shown in the figure, the overall architecture of the ocean mesoscale eddy characteristic information identification system based on the ocean environment numerical forecast product is shown. The system mainly consists of the following core modules:

[0143] Marine environment numerical prediction products: The input data source of the system includes key physical quantities such as sea surface height anomaly, horizontal flow velocity field, temperature, salinity, etc.

[0144] Data preprocessing module: responsible for format conversion, quality control, interpolation and smoothing of input data to ensure data consistency and reliability.

[0145] Vortex detection module: the core part of the system, which uses a combination of multiple algorithms to identify possible vortex structures. It mainly includes the following detection algorithms: 3.1 Okubo-Weiss parameter method: Identify vortices based on flow field deformation and rotation characteristics. 3.2 Vector geometry method: Identify vortices by analyzing the geometric characteristics of the velocity field. 3.3 Rotational invariance method: Identify vortices using the characteristics of the velocity gradient tensor. 3.4 SSH contour method: Identify vortices using closed contours of the sea surface height anomaly field. 3.5 Multi-algorithm fusion: Fusion the results of the above multiple algorithms to improve the accuracy of detection.

[0146] Feature extraction module: For each vortex detected, various feature parameters are calculated and extracted, including: 4.1 Position and range: vortex center position, impact depth, range radius, etc. 4.2 Strength and structure: vorticity intensity, kinetic energy intensity, vertical structure characteristics, etc. 4.3 Dynamic characteristics: Rossby number, Berg number, potential vorticity anomaly, etc. 4.4 Life cycle characteristics: lifespan, moving speed, intensity change, etc.

[0147] Result output module: Output the identified and extracted vortex feature information in various forms, including: 5.1 Graphic visualization: plane distribution diagram, vertical section diagram, three-dimensional stereogram, etc. 5.2 Data file: vortex catalog file, vortex trajectory file, grid data file, etc. 5.3 Statistical analysis: spatiotemporal distribution of vortex quantity, characteristic parameter statistics, etc.

[0148] The system architecture diagram clearly shows the entire data processing flow from input to final output, as well as the connection between modules. Through this modular design, the system realizes the comprehensive, accurate and efficient identification and characterization of ocean mesoscale eddies, providing strong technical support for marine scientific research and business applications.

[0149] It should be noted that each implementable method in this embodiment may be implemented separately, or may be implemented in combination in any combination without conflict, and this application is not limited thereto.

[0150] Another embodiment of the present application provides an ocean mesoscale vortex identification device, which is used to execute the ocean mesoscale vortex identification method provided in the above embodiment.

[0151] like Figure 3, which is a schematic diagram of the structure of the device for identifying ocean mesoscale vortices provided in an embodiment of the present application. The device for identifying ocean mesoscale vortices includes an acquisition module 301, a preprocessing module 302, an identification module 303, a clustering module 304, an extraction module 305 and a display module 306, wherein:

[0152] The acquisition module 301 is used to acquire input data of the marine environment numerical forecast product, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity;

[0153] The preprocessing module 302 is used to preprocess the input data to obtain processed marine environment data;

[0154] The identification module 303 is used to identify vortices in the processed marine environment data according to a plurality of vortex identification algorithms that are pre-set to obtain a plurality of vortices; wherein the plurality of vortex identification algorithms that are pre-set include at least an Okubo-Weiss parameter method, a vector geometry method, a rotation invariance method, and an SSH contour method;

[0155] The clustering module 304 is used to perform cluster analysis according to the vortex centers of the multiple vortices to obtain a target vortex corresponding to the input data;

[0156] The extraction module 305 is used to extract the characteristics of the target vortex and obtain characteristic parameters corresponding to the target vortex;

[0157] The display module 306 is used to display the characteristic parameters in a preset display mode.

[0158] Regarding the device in this embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0159] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0160] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0161] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] Figure 4 800 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0163] Reference Figure 4 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0164] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0165] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0166] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0167] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0168] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0169] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0170] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0171] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0172] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0173] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. 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, an optical data storage device, etc.

[0174] Figure 519 is a block diagram of a computer-readable storage medium 1900 shown in the present application. For example, the computer-readable storage medium 1900 may be provided as a server.

[0175] Reference Figure 5 , the computer-readable storage medium 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0176] The computer readable storage medium 1900 may also include a power supply component 1926 configured to perform power management of the computer readable storage medium 1900, a wired or wireless network interface 1950 configured to connect the computer readable storage medium 1900 to a network, and an input / output (I / O) interface 1958. The computer readable storage medium 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0177] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0178] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course 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, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0179] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0180] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0182] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0185] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0186] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0187] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for identifying ocean mesoscale eddies, characterized in that: include: Obtaining input data of marine environment numerical forecast products, wherein the input data at least includes sea surface height anomaly, horizontal current velocity field, temperature and salinity; Preprocessing the input data to obtain processed marine environment data; According to a plurality of vortex identification algorithms pre-set, vortices in the processed marine environment data are identified to obtain a plurality of vortices; wherein the plurality of vortex identification algorithms pre-set at least include an Okubo-Weiss parameter method, a vector geometry method, a rotation invariance method and an SSH contour method; Performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data; Extracting features of the target vortex to obtain feature parameters corresponding to the target vortex; The characteristic parameters are displayed in a preset display mode.

2. The method for identifying ocean mesoscale eddies according to claim 1, characterized in that: The step of obtaining the input data of the marine environment numerical forecast product, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity, comprises: A preset processing library is used to read the input data of the marine environment numerical forecast product, extract sea surface height anomaly data and horizontal flow velocity field, and obtain longitude and latitude grid information and time series data, and generate multidimensional data results from the sea surface height anomaly data, horizontal flow velocity field, longitude and latitude grid information and time series data.

3. The method for identifying ocean mesoscale eddies according to claim 1, characterized in that: The preprocessing of the input data to obtain processed marine environment data, wherein the preprocessing at least includes format conversion, quality control, interpolation and smoothing processing, including: The input data is preprocessed to obtain processed marine environment data, wherein the preprocessing at least includes format conversion, quality control, interpolation and smoothing processing methods.

4. The method for identifying ocean mesoscale eddies according to claim 1, characterized in that: The method of identifying vortices in the processed ocean environment data according to the pre-set multiple vortex identification algorithms to obtain multiple vortices includes: Using the Okubo-Weiss parameter method to identify the processed ocean environment data and the retrograde vortex, obtaining a first identification result; Using a vector geometry method to identify the processed ocean environment data and the retrograde vortex to obtain a second identification result; The processed ocean environment data and the retrograde vortex identification are adopted according to the rotation invariance method to obtain a third identification result; The processed ocean environment data and retrograde vortex identification described in the SSH contour method are used to obtain a fourth identification result; The plurality of vortices are generated according to the first recognition result, the second recognition result, the third recognition result, and the fourth recognition result.

5. The method for identifying ocean mesoscale eddies according to claim 1, characterized in that: The step of performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data comprises: The vortex centers whose spatial distances are less than a preset threshold are merged, the number of times each cluster center is recognized by different algorithms is calculated, and the vortex whose number of recognitions is greater than 1 is determined as the target vortex.

6. The method for identifying ocean mesoscale eddies according to claim 1, characterized in that: The step of extracting features of the target vortex to obtain feature parameters corresponding to the target vortex includes: The target vortex is subjected to feature extraction of life cycle characteristics, dynamic characteristics, intensity and structure, position and range to obtain characteristic parameters corresponding to the target vortex, wherein the position and range at least include the vortex center position, influence depth and range radius, the intensity and structure at least include vorticity intensity, kinetic energy intensity and vertical structure characteristics, the dynamic characteristics at least include Rossby number, Burger number and potential vorticity anomaly, and the life cycle characteristics at least include life span, moving speed and intensity change.

7. The method for identifying ocean mesoscale eddies according to claim 1, characterized in that: The displaying of the characteristic parameters in a preset display mode includes: Using a preset statistical analysis algorithm to perform statistical analysis on the characteristic parameters to obtain statistical analysis results; Outputting the statistical analysis results as a data file, or visually displaying them in a graphic format; Wherein, the preset statistical analysis algorithm at least includes a spatiotemporal distribution of the number of vortices or a statistical method of characteristic parameters; The data files at least include a vortex catalog file, a vortex trajectory file and a gridded data file; The graph at least includes a plan distribution graph, a vertical cross-sectional graph or a three-dimensional stereogram.

8. A device for identifying ocean mesoscale eddies, characterized in that: include: An acquisition module is used to acquire input data of marine environment numerical forecast products, wherein the input data at least includes sea surface height anomaly, horizontal flow velocity field, temperature and salinity; A preprocessing module, used for preprocessing the input data to obtain processed marine environment data; An identification module, used for identifying vortices in the processed marine environment data according to a plurality of pre-set vortex identification algorithms to obtain a plurality of vortices; wherein the pre-set plurality of vortex identification algorithms at least include an Okubo-Weiss parameter method, a vector geometry method, a rotation invariance method and an SSH contour method; A clustering module, used for performing cluster analysis according to the vortex centers of the plurality of vortices to obtain a target vortex corresponding to the input data; An extraction module, used for performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex; The display module is used to display the characteristic parameters in a preset display mode.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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