A method and system for identifying and predicting ocean vortices

Abstract

The present invention discloses a method and system for identifying and predicting ocean vortices. The method includes: first, obtaining ocean vortex high-frequency ground wave radar signals; constructing a training set and a test set based on the ocean vortex high-frequency ground wave radar signals; training the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and constructing an initial model; performing feature extraction on the test set and testing the initial model based on the extracted features to obtain a target model that meets the test requirements; and performing identification and prediction on newly obtained ocean vortex high-frequency ground wave radar signals according to the target model to obtain an ocean vortex identification result. The embodiments of the present invention can improve the accuracy and timeliness of the prediction results and can be widely applied to the field of computer technology.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to a method and system for identifying and predicting ocean vortices. Background Art

[0002] An ocean vortex is a rotating water flow structure formed in the ocean. It carries various nutrients and nutrient elements including organisms, and has important impacts on the global ocean circulation, marine ecosystem, weather and climate, etc. Therefore, accurate identification and prediction of ocean vortices are crucial for aspects such as ocean resource development and maritime traffic safety. However, traditional observation means are limited by time and space resolution, and it is difficult to comprehensively and accurately monitor the formation and evolution of vortices. At the same time, numerical simulation methods have certain limitations in simulating ocean vortices: insufficiently refined parameterization schemes, limited computing resources, etc. These problems affect the accuracy and timeliness of prediction.

[0003] A mainstream ocean vortex identification technology is the direct identification technology based on satellite altimeter data. This technology has been used by many teams around the world for research and processing in the past two decades, and provides sea surface height time series products at the global or regional scale. Its main method is to use the echo signal of the satellite radar altimeter to calculate the distance from the satellite to a certain point under the satellite, and then combine the orbital height of the satellite to obtain the height of this point relative to the surface reference plane, and finally obtain the ocean surface feature image in the area. Then, by extracting the feature lines of the abnormal height of the ocean surface on the image, the purpose of vortex identification is achieved. At present, the satellite height data anomaly identification technology has developed relatively maturely, can realize automatic processing, and is suitable for real-time monitoring and large-scale data analysis.

[0004] Traditional hydrological data are mainly time-series data. Therefore, the standard recurrent neural network represented by LSTM (Long Short-Term Memory Network) is still the core model structure of deep learning hydrological forecasting at the present stage. This model is a "data-hungry" model, and the research depends on a large amount of fine data. Moreover, the resolution of the satellite altimeter data image limits the minimum vortex scale that can be identified, so the identification accuracy of small-scale or irregularly shaped ocean vortices is limited. Secondly, the phenomenon of ocean vortices is constantly changing dynamically with human and non-human environmental changes, and the existing vortex identification and prediction technologies lack the ability to respond to this dynamic change in a timely manner. In particular, hydrological modeling in river basins greatly affected by human activities is still a difficult problem. Summary of the Invention

[0005] The main purpose of the embodiments of the present invention is to propose a method and system for identifying and predicting ocean vortices, which can improve the accuracy and timeliness of prediction results.

[0006] To achieve the above object, one aspect of the embodiments of the present invention proposes a method for identifying and predicting ocean vortices, including the following steps:

[0007] Obtain ocean vortex high-frequency ground wave radar signals;

[0008] Construct a training set and a test set according to the ocean vortex high-frequency ground wave radar signals;

[0009] Train the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and construct an initial model;

[0010] Test the initial model by extracting features from the test set and based on the extracted features to obtain a target model that meets the test requirements;

[0011] According to the target model, identify and predict newly obtained ocean vortex high-frequency ground wave radar signals to obtain ocean vortex identification results.

[0012] In some embodiments, the method further includes:

[0013] After completing the identification and prediction of ocean vortices, use a knowledge graph to verify the vortex identification data.

[0014] In some embodiments, the expression of the black-winged kite optimization algorithm is: X i =BK lh +rand(BK ub -BK lb ), where X i represents the position of the i-th individual in the iteration process of the optimization algorithm; BK lb represents the lower bound of the j-th dimension of the black-winged kite; rand() represents a value randomly selected between [0, 1]; BK ub represents the upper bound of the j-th dimension of the black-winged kite;

[0015] In the initialization process, the black-winged kite optimization algorithm selects the individual with the best fitness value as the leader X L in the initial population; the expression of the leader X L is:

[0016] f best =min(f(X i ))

[0017] X L =X(find(f best ==f(X i )))

[0018] where f bestrepresents the minimum fitness value in the population; X L represents the leader in the initial population, which is the individual with the best fitness value; f(X i ) represents the fitness value of the i-th individual; X() represents the position of the individual that satisfies the function conditions;

[0019] The expression for the attack behavior of the black-winged kite optimization algorithm is:

[0020]

[0021] where represents the position of the i-th black-winged kite in the j-th dimension and the (t + 1)-th iteration step; r represents a random number; p is a calculated constant; T is the total number of iterations; t is the number of iterations completed so far; n is a control parameter related to the number of iterations, and its value is determined by the following expression:

[0022]

[0023] The expression for the migration behavior of the black-winged kite optimization algorithm is:

[0024]

[0025]

[0026] where represents the leading scorer of the j-th dimension of the black-winged kite at the t-th iteration so far; F i represents the current position of any black-winged kite in the j-th dimension obtained at the t-th iteration; F ri represents the fitness value of the random position of any black-winged kite in the j-th dimension at the t-th iteration; m is a control parameter, and its value is determined by the following expression:

[0027]

[0028] In some embodiments, after completing the identification and prediction of ocean vortices, a knowledge graph is used to verify the identification data of the vortices, including the following steps:

[0029] Collect and organize the basic attributes of the vortex knowledge graph; the entities of the vortex knowledge graph are the monitoring points on the sea surface, the entity relationship is the spatial distribution relationship of the monitoring points, and the entity attributes are object attributes and data attributes. The object attributes include the monitored physical quantity of the monitoring point, the early warning level of the single monitoring point, and the affiliated part, and the data attributes include three-dimensional coordinates, current monitoring data, and historical monitoring data;

[0030] Select a vortex anomaly point as a reference point. According to the multi-level relationship query ability of the knowledge graph, retrieve all monitoring points that have a spatial adjacency relationship with the reference point, and determine whether these adjacent monitoring points are abnormal; among them, the characteristics of the vortex anomaly point include sea surface height anomaly, sea surface temperature anomaly, and abnormal flow velocity change;

[0031] Continuously retrieve and determine whether there are abnormal monitoring points outside the abnormal measurement point group that have a spatial adjacency relationship with any monitoring point inside the abnormal measurement point group. If so, incorporate the new abnormal monitoring points into the abnormal measurement point group for processing until there are no abnormal monitoring points outside the abnormal measurement point group that have a spatial adjacency relationship with it;

[0032] Classify all abnormal monitoring points in the sea surface vortex monitoring knowledge graph into different abnormal measurement point groups;

[0033] Select any two abnormal measurement point groups in the knowledge graph. With the help of the relationship query ability of the graph database, sequentially determine whether there is a data sequence similarity relationship between the corresponding monitoring points in the two abnormal measurement point groups. If the number of monitoring points with a data sequence similarity relationship is greater than 50% of the total number of monitoring points in the two abnormal measurement point groups, it is determined that these two abnormal measurement point groups have a data sequence similarity relationship;

[0034] Perform pairwise analysis on all abnormal measurement point groups in the knowledge graph to query abnormal measurement point groups with a data sequence similarity relationship;

[0035] Compare the detected vortex monitoring points with the results obtained by pattern recognition to verify the vortex recognition results. After the recognition is completed, use a regression prediction model composed of an optimized sparrow algorithm, a convolutional neural network, and a bidirectional gated recurrent unit to further predict the vortex.

[0036] In some embodiments, the step of selecting a vortex anomaly point as a reference point, retrieving all monitoring points that have a spatial adjacency relationship with the reference point according to the multi-level relationship query ability of the knowledge graph, and determining whether these adjacent monitoring points are abnormal includes the following steps:

[0037] By constructing an abnormal mesoscale vortex sample library, and then using sea surface height and sea surface temperature anomaly data, perform feature extraction and learning through a densely connected network to obtain fusion feature data that integrates sea surface height and sea surface temperature information;

[0038] Build recognition and judge the abnormality of the vortex at the monitoring point through an improved U-Net image segmentation model; among them, the construction process of the U-Net model is as follows: extract features from the monitoring data, including the vortex center point, vortex radius, and ocean parameters; associate the extracted features with the entities in the knowledge graph, and the output is the segmentation mask of the vortex generation area; U-Net adopts an encoder-decoder structure, where the encoder is used to extract features and the decoder is used to map the features to the segmentation result; an attention mechanism is added to U-Net to focus on key areas;

[0039] Train the U-Net model with the labeled segmentation data, supervise and learn the recognition of the vortex generation monitoring point, and use the cross-entropy loss function and Adam optimizer to optimize the model;

[0040] Among them, the cross-entropy loss function The expression is:

[0041]

[0042] Among them, N represents the number of samples; y i represents the true label; represents the probability predicted by the model.

[0043] In some embodiments, compare the detected vortex monitoring points with the results obtained by pattern recognition to verify the vortex recognition results; after the recognition is completed, use a regression prediction model to further predict the vortex, including the following steps:

[0044] Use 1D CNN to extract the features of the original data, discover the hidden relationships between multiple features, filter out noise and instability components, and then input the information processed by CNN into a bidirectional gated recurrent unit Bi-GRU for the next prediction;

[0045] During the prediction process of the bidirectional gated recurrent unit Bi-GRU, the output information at a certain moment t is the sum of the outputs of the forward hidden layer and the backward hidden layer;

[0046] Use the optimized sparrow algorithm to optimize the CNN–BiGRU model;

[0047] Introduce the normEve algorithm to optimize the obtained prediction model and complete the further prediction of the vortex.

[0048] In some embodiments, the use of the optimized sparrow algorithm to optimize the CNN–BiGRU model includes the following steps:

[0049] Initialize the population parameters, and the population parameters include the maximum number of iterations and the population size;

[0050] Initialize the parameters of CNN–BiGRU and set the parameter optimization interval;

[0051] Calculate the fitness of each sparrow currently, find the optimal and worst fitness values, and their corresponding positions;

[0052] After each iteration, recalculate the fitness of each sparrow, introduce a dynamic learning factor, and update the position of the discoverer; introduce the Levy strategy to update the position of the followers; update the position of the vigilant;

[0053] According to the current state of the sparrows, update the optimal and worst fitness values and their positions experienced by the entire sparrow population;

[0054] Judge whether the maximum iteration is satisfied. If so, assign the optimization result to CNN–BiGRU. Otherwise, continue the iteration until the maximum iteration is satisfied;

[0055] Input the optimized parameters to build a CNN–BiGRU prediction model.

[0056] Another aspect of the embodiments of the present invention also provides an identification and prediction system for ocean vortices, including:

[0057] The first module is used to obtain ocean vortex high-frequency ground wave radar signals;

[0058] The second module is used to construct a training set and a test set according to the ocean vortex high-frequency ground wave radar signals;

[0059] The third module is used to train the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and construct an initial model;

[0060] The fourth module is used to extract features from the test set and test the initial model based on the extracted features to obtain a target model that meets the test requirements;

[0061] The fifth module is used to identify and predict newly obtained ocean vortex high-frequency ground wave radar signals according to the target model to obtain an ocean vortex identification result.

[0062] To achieve the above object, another aspect of the embodiments of the present invention proposes an electronic device, where the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the foregoing method is implemented.

[0063] To achieve the above object, another aspect of the embodiments of the present invention proposes a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the foregoing method is implemented.

[0064] An embodiment of the present invention also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the foregoing method.

[0065] The embodiments of the present invention at least include the following beneficial effects: The present invention provides a method and a system for identifying and predicting ocean vortices. The solution first obtains ocean vortex high-frequency ground wave radar signals; constructs a training set and a test set according to the ocean vortex high-frequency ground wave radar signals; trains the training set through a black-winged kite optimization algorithm and a kernel extreme learning machine to obtain optimal kernel function parameters and a regularization factor, and constructs an initial model; performs feature extraction on the test set and tests the initial model based on the extracted features to obtain a target model that meets the test requirements; and performs identification and prediction on newly obtained ocean vortex high-frequency ground wave radar signals according to the target model to obtain an ocean vortex identification result. The embodiments of the present invention can improve the accuracy and timeliness of prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention;

[0067] Figure 2 is a flowchart of the overall steps provided by an embodiment of the present invention;

[0068] Figure 3 is a flowchart of the working process of a TSMFDE-BKA-KELM model for vortex identification and prediction provided by an embodiment of the present invention;

[0069] Figure 4 is a structural diagram of a bidirectional gated recurrent unit provided by an embodiment of the present invention;

[0070] Figure 5 is an optimization process diagram of a CNN–BiGRU model provided by an embodiment of the present invention;

[0071] Figure 6 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the appended claims.

[0073] It can be understood that the terms "first", "second", etc. used in the present invention may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0074] The terms "at least one", "a plurality of", "each", "any one", etc. used in the present invention, at least one includes one, two or more than two, a plurality includes two or more than two, each refers to each of the corresponding plurality, and any one refers to any one of the plurality.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0076] The method and system for identifying and predicting ocean vortices provided by the embodiments of the present invention relate to the field of computer technology. The method for identifying and predicting ocean vortices provided by the embodiments of the present invention can be applied to a terminal, a server, or software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application for implementing the method for identifying and predicting ocean vortices, etc., but is not limited to the above forms.

[0077] The present invention can be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0078] As Figure 1 shown, it is a schematic diagram of an implementation environment provided by the embodiments of the present invention. Referring to Figure 1 , this implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be network-connected wirelessly or wiredly to complete data transmission and exchange.

[0079] The server 101 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0080] In addition, the server 101 can also be a node server in a blockchain network. Among them, the blockchain is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms.

[0081] The terminal 102 can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. Among them, the terminal 102 can also be an in-vehicle terminal of various device types exemplified above, but is not limited thereto. The terminal 102 and the server 101 can be directly or indirectly connected through wired or wireless communication methods, and the embodiments of the present invention do not limit this here.

[0082] Exemplarily based on Figure 1 the shown implementation environment, the embodiments of the present invention provide a method for identifying and predicting ocean vortices. Taking the application of this method for identifying and predicting ocean vortices in the server 101 as an example for description, it can be understood that this method can also be applied to the terminal 102.

[0083] Referring to Figure 2 , Figure 2 is a flowchart of the method for identifying and predicting ocean vortices applied to the server provided by the embodiments of the present invention. The execution subject of this method can be any of the aforementioned computer devices (including servers or terminals). Referring to Figure 2 ,this method can include the following steps:

[0084] Obtain ocean vortex high-frequency ground wave radar signals;

[0085] Construct a training set and a test set according to the ocean vortex high-frequency ground wave radar signals;

[0086] Train the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and construct an initial model;

[0087] Extract features from the test set and test the initial model based on the extracted features to obtain a target model that meets the test requirements;

[0088] According to the target model, the newly acquired ocean vortex high-frequency ground wave radar signal is identified and predicted to obtain the ocean vortex identification result.

[0089] In some embodiments, the method further includes:

[0090] After completing the identification and prediction of the ocean vortex, a knowledge graph is used to verify the identification data of the vortex.

[0091] In some embodiments, the expression of the black-winged kite optimization algorithm is: X i = BK lh + rand(BK ub - BK lb ), where X i represents the position of the i-th individual in the iterative process of the optimization algorithm; BK lb represents the lower bound of the j-th dimension of the black-winged kite; rand() represents a value randomly selected between [0, 1]; BK ub represents the upper bound of the j-th dimension of the black-winged kite;

[0092] In the initialization process, the black-winged kite optimization algorithm selects the individual with the best fitness value as the leader X L in the initial population; the expression of the leader X L is:

[0093] f best = min(f(X i ))

[0094] X L = X(find(f best == f(X i )))

[0095] where f best represents the minimum value of the fitness in the population; X L represents the leader in the initial population, which is the individual with the best fitness value; f(X i ) represents the fitness value of the i-th individual; X() represents the position of the individual that satisfies the function condition;

[0096] The expression of the attack behavior of the black-winged kite optimization algorithm is:

[0097]

[0098] where represents the position of the i-th black-winged kite in the j-th dimension and the (t + 1)-th iteration step; r represents a random number; p is a calculated constant; T is the total number of iterations; t is the number of iterations completed so far; n is a control parameter related to the number of iterations, and its value is determined by the following expression:

[0099]

[0100] The expression for the migration behavior of the Black-winged Kite Optimization Algorithm is as follows:

[0101]

[0102] Where represents the leading scorer of the j-th dimension of the Black-winged Kite at the t-th iteration so far; F i represents the current position of the j-th dimension obtained by any Black-winged Kite at the t-th iteration; F ri represents the fitness value of the random position of any Black-winged Kite at the j-th dimension in the t-th iteration; m is a control parameter, and its value is determined by the following expression:

[0103]

[0104] In some embodiments, after completing the identification and prediction of ocean vortices, a knowledge graph is used to verify the identification data of the vortices, including the following steps:

[0105] Collect and organize the basic attributes of the vortex knowledge graph; the entities of the vortex knowledge graph are the monitoring points on the sea surface, the entity relationship is the spatial distribution relationship of the monitoring points, and the entity attributes are object attributes and data attributes. The object attributes include the monitored physical quantity of the monitoring point, the early warning level of a single monitoring point, and the affiliated part. The data attributes include three-dimensional coordinates, current monitoring data, and historical monitoring data;

[0106] Select a vortex anomaly point as the reference point, and according to the multi-level relationship query ability of the knowledge graph, retrieve all the monitoring points that have a spatial adjacent relationship with the reference point, and determine whether these adjacent monitoring points are abnormal; among them, the characteristics of the vortex anomaly point include abnormal sea surface height, abnormal sea surface temperature, and abnormal flow velocity change;

[0107] Continuously retrieve and determine whether there are abnormal monitoring points outside the abnormal measurement point group that have a spatial adjacent relationship with any monitoring point inside the abnormal measurement point group. If so, incorporate the new abnormal monitoring point into the abnormal measurement point group for processing until there are no abnormal monitoring points outside the abnormal measurement point group that have a spatial adjacent relationship with it;

[0108] Classify all the abnormal monitoring points in the sea surface vortex monitoring knowledge graph into different abnormal measurement point groups;

[0109] Arbitrarily select two groups of abnormal measurement points in the knowledge graph. With the help of the relationship query ability of the graph database, sequentially determine whether there is a data sequence similarity relationship between the corresponding measurement points in the two groups of abnormal measurement points. If the number of measurement points with a data sequence similarity relationship is greater than 50% of the total number of measurement points in the two groups of abnormal measurement points, then it is determined that the two groups of abnormal measurement points have a data sequence similarity relationship;

[0110] Perform pairwise analysis on all groups of abnormal measurement points in the knowledge graph, and query to obtain groups of abnormal measurement points with a data sequence similarity relationship;

[0111] Compare the detected vortex measurement points with the results obtained by pattern recognition to verify the vortex recognition results; after the recognition is completed, use a regression prediction model composed of an optimized sparrow algorithm, a convolutional neural network, and a bidirectional gated recurrent unit to further predict the vortex.

[0112] In some embodiments, select one vortex anomaly point as a reference point. According to the multi-level relationship query ability of the knowledge graph, retrieve all measurement points that have a spatial adjacent relationship with the reference point, and determine whether these adjacent measurement points are abnormal, including the following steps:

[0113] By constructing an abnormal mesoscale vortex sample library, and then using sea surface height and sea surface temperature anomaly data, perform feature extraction and learning through a densely connected network to obtain fusion feature data that integrates sea surface height and sea surface temperature information;

[0114] Construct an identification through an improved U-Net image segmentation model to judge the abnormal situation of the vortex at the measurement point; among them, the construction process of the U-Net model is: extract features from the monitoring data, including the vortex center point, vortex radius, and ocean parameters; associate the extracted features with the entities in the knowledge graph, and the output is the segmentation mask of the vortex generation area; U-Net adopts an encoder-decoder structure, where the encoder is used to extract features, and the decoder is used to map the features to the segmentation result; an attention mechanism is added to U-Net to focus on key areas;

[0115] Train the U-Net model with the labeled segmentation data, supervise the learning of the identification of the vortex generation measurement points, and use the cross-entropy loss function and the Adam optimizer to optimize the model;

[0116] Among them, the cross-entropy loss function The expression is:

[0117]

[0118] Among them, N represents the number of samples; y i represents the true label; represents the probability predicted by the model.

[0119] In some embodiments, the detected vortex monitoring points are compared with the results obtained by pattern recognition to verify the vortex recognition results; after the recognition is completed, a regression prediction model is used to further predict the vortex, including the following steps:

[0120] Use 1D CNN to extract the features of the original data, explore the implicit relationships between multiple features, and filter out noise and instability components, and then input the information processed by CNN into a bidirectional gated recurrent unit Bi-GRU for the next prediction;

[0121] During the prediction process of the bidirectional gated recurrent unit Bi-GRU, the output information at a certain moment t is the sum of the outputs of the forward hidden layer and the backward hidden layer;

[0122] Use the optimized sparrow algorithm to optimize the CNN–BiGRU model;

[0123] Introduce the normEve algorithm to optimize the obtained prediction model and complete the further prediction of the vortex.

[0124] In some embodiments, the use of the optimized sparrow algorithm to optimize the CNN–BiGRU model includes the following steps:

[0125] Initialize the population parameters, where the population parameters include the maximum number of iterations and the population size;

[0126] Initialize the CNN–BiGRU parameters and set the parameter optimization interval;

[0127] Calculate the fitness of each current sparrow, find the optimal and worst fitness values, and their corresponding positions;

[0128] After each iteration, recalculate the fitness of each sparrow, introduce a dynamic learning factor, and update the position of the discoverer; introduce the Levy strategy to update the position of the follower; update the position of the vigilant;

[0129] According to the current state of the sparrow, update the optimal and worst fitness values and their positions experienced by the entire sparrow population;

[0130] Judge whether the maximum iteration is satisfied. If so, assign the optimization result to the CNN–BiGRU, otherwise continue to iterate until the maximum iteration is satisfied;

[0131] Input the optimized parameters to build a CNN–BiGRU prediction model.

[0132] Next, taking a specific application scenario as an example, the specific implementation process of the present invention will be described in detail:

[0133] In the embodiments of the present invention, high-frequency ground wave radar is used to collect images. Compared with the images obtained by traditional satellite altimeters, this image collection method has higher resolution and accuracy. In addition, the high-frequency ground wave radar is relatively less affected by factors such as sea surface temperature and light visibility, which is beneficial to obtaining more accurate data. The present invention uses data assimilation technology to combine observation data with numerical simulation results, and on the basis of accurately identifying and predicting ocean vortex data, further corrections are made to ensure the timeliness of the results.

[0134] As Figure 3 shown, in the embodiments of the present invention, the time-shift theory is used to replace the traditional coarse-graining process in the multi-scale fluctuation dispersion entropy (MFDE). The time-shift multi-scale fluctuation dispersion entropy is proposed. Through simulation experiments, it is proved that the proposed method has good robustness to the time series length, noise resistance and feature extraction ability, and solves the problem of insufficient coarse-graining of traditional multi-scale entropy. Then, the black-winged kite optimization algorithm (BKA) with strong portability, strong optimization ability and fast convergence speed is selected to jointly optimize the regularization parameter and kernel function parameter of the kernel extreme learning machine (KELM), and a BKA-KELM classifier is established, which solves the problem that the hyperparameters (kernel function parameter, regularization parameter) of KELM are difficult to adjust. Finally, the features extracted by TSMFDE are input into the classifier to complete the pattern recognition work.

[0135] Explanation of the BKA algorithm and related formulas:

[0136] X i = BK lb + rand(BK ub - BK lb )

[0137] where i is an integer between 1 and pop, and X i represents the position of the i-th individual in the iterative process of the optimization algorithm; BKlb and BKub are the lower and upper bounds of the j-th dimension of the black-winged kite respectively, and rand is a value randomly selected between [0, 1].

[0138] In the initialization process, BKA selects the individual with the best fitness value as the leader XL in the initial population, which is considered to be the optimal position of the black-winged kite. The following is the mathematical representation of the initial leader XL, taking the minimum value as an example.

[0139] f best = min(f(X i ))

[0140] X L = X(find(f best == f(X i )))

[0141] Among them, f best represents the minimum fitness value in the population; X L represents the leader in the initial population, that is, the individual with the best fitness value; f(X i ) represents the fitness value of the i-th individual; X() represents the position of the individual that satisfies the conditions of this function;

[0142] Attack behavior:

[0143]

[0144] and respectively represent the position of the i-th black-winged kite in the j-th dimension and the (t + 1)-th iteration step; r is a random number between 0 and 1, and p is a calculated constant; T is the total number of iterations, and t is the number of iterations completed so far.

[0145] Migration behavior:

[0146]

[0147] Among them, represents the leading scorer of the j-th dimension of the black-winged kite in the t-th iteration so far. and respectively represent the position of the i-th black-winged kite in the j-th dimension and the (t + 1)-th iteration step; F i represents the current position of any black-winged kite in the j-th dimension obtained in the t-th iteration; F ri represents the fitness value of the random position of any black-winged kite in the j-th dimension in the t-th iteration;

[0148] After the recognition is completed, the embodiment of the present invention plans to use a knowledge graph to verify the recognition data of the vortex. A knowledge graph is a new technology that uses a graph model to describe knowledge and model the association relationships between all things, and has capabilities such as multi-level relationship query and knowledge analysis and reasoning. A knowledge graph is a graph database composed of a large number of triples {(head entity, relationship, tail entity) or (entity, attribute, attribute value)}, and its key elements include entities, entity relationships, and entity attributes.

[0149] For the step of using a knowledge graph to verify the recognition data of the vortex, it specifically includes the following steps:

[0150] 1. Collect and organize the basic attributes of the vortex knowledge graph: Its entities are the monitoring points on the sea surface, and the entity relationships are 1) the spatial distribution relationship of the monitoring points, that is, whether the observation points are spatially connected, and 2) the data sequence relationship of the monitoring points. In this study, the dynamic time warping (DTW) algorithm, which is good at dealing with complex data sequence relationships, is used to determine the data sequence similarity relationship of the sea surface monitoring points. Its entity attributes are object attributes (the monitored physical quantity of the monitoring point, the early warning level of a single monitoring point, and the affiliated part) and data attributes (three-dimensional coordinates, current monitoring data, and historical monitoring data). In this study, the Neo4j database is used to construct the sea surface vortex monitoring knowledge graph. The fixed instructions for constructing the graph database are batch processed in the way of Python programming, and a large number of triples in the text are batch imported into the Neo4j database to complete the construction of the sea surface vortex monitoring knowledge graph.

[0151] 2. Use the established sea surface vortex knowledge graph to identify vortex anomaly points:

[0152] 1) Query the attributes of all monitoring points in the sea surface vortex monitoring knowledge graph;

[0153] 2) Select any one of the vortex anomaly points as the reference point. With the help of the multi-level relationship query ability of the knowledge graph, all the monitoring points with spatial adjacent relationships to the reference point are retrieved, and it is judged whether these adjacent monitoring points are abnormal. The characteristics of vortex anomaly points mainly include sea surface height anomaly, sea surface temperature anomaly, and abnormal velocity change. In this study, an abnormal mesoscale vortex sample library is mainly constructed, and then using SSHA (sea surface height) and SSTA (sea surface temperature anomaly) data, feature extraction and learning are carried out through a densely connected network to obtain fusion feature data that combines SSH (sea surface height) and SST (sea surface temperature) information. Then, an improved U-Net image segmentation model is used to construct the recognition to judge the abnormal situation of the vortex at the monitoring point. The construction of the U-Net model extracts features from the monitoring data, such as vortex features (center point, radius), ocean parameters, etc., associates the extracted features with the entities in the knowledge graph to enrich the model input (including remote sensing images and extracted features), and the output is the segmentation mask of the vortex generation area.

[0154] The U-Net adopts an encoder-decoder structure, where the encoder is used to extract features and the decoder is used to map the features to the segmentation results. Through this structure, multi-scale information can be captured. Through skip connections, the feature maps of the encoder layer can be directly transmitted to the corresponding decoder layer, which can retain high-resolution information and help improve the segmentation accuracy. Since this model uses multiple data such as SSH and SST for feature learning at the same time, through the forward propagation of the network, the model can automatically extract features related to vortex anomalies. At the same time, in the process of improving the U-Net, an attention mechanism is added to help the model focus on more important feature regions, thereby improving the recognition accuracy of vortex anomalies. At the final output, the result output by the model is a segmentation map, which reflects the vortex generation area and the normal area.

[0155] After that, the U-Net model will be trained using the labeled segmentation data to supervise the learning of the identification of vortex generation monitoring points and the model will be optimized using the cross-entropy loss function and the Adam optimizer.

[0156] The cross-entropy loss function is used to calculate the difference between the true label distribution and the model-predicted label distribution. For a binary classification problem, the definition of the cross-entropy loss function is as follows:

[0157]

[0158] where: N is the number of samples. y i is the true label (0 or 1). is the probability predicted by the model (between 0 and 1).

[0159] For a multi-classification problem, the definition of the cross-entropy loss function is:

[0160]

[0161] where, C is the number of classes; y i is the one-hot encoding of the true label; is the predicted probability of the model for each class.

[0162] 3) According to the method in step 2), continue to retrieve and judge whether there are abnormal monitoring points outside the abnormal monitoring point group that have a spatial adjacency relationship with any monitoring point inside the abnormal monitoring point group. If there are, incorporate the new abnormal monitoring points into the abnormal monitoring point group, and repeat this process until there are no abnormal monitoring points outside the abnormal monitoring point group that have a spatial adjacency relationship with it;

[0163] 4) Repeat steps 2) and 3) until all abnormal monitoring points in the sea surface vortex monitoring knowledge graph are classified into different abnormal monitoring point groups;

[0164] 5) Arbitrarily select two groups of abnormal measurement points in the knowledge graph, and by virtue of the relationship query ability of the graph database, successively determine whether there is a data sequence similarity relationship between the corresponding monitoring points in the two groups of abnormal measurement points. If the number of monitoring points with a data sequence similarity relationship is greater than 50% of the total number of monitoring points in the two groups of abnormal measurement points, it is considered that these two groups of abnormal measurement points have a data sequence similarity relationship;

[0165] 6) Repeat step 5), conduct pairwise analysis on all groups of abnormal measurement points in the knowledge graph, and query all groups of abnormal measurement points with a data sequence similarity relationship.

[0166] 7) Compare the detected vortex monitoring points with the results obtained by pattern recognition to verify the vortex recognition results.

[0167] After the recognition is completed, the present invention intends to adopt the ISSA–CNN–BiGRU regression prediction model to further predict the vortex.

[0168] The specific process is as follows:

[0169] 1. Convolutional neural network.

[0170] Use 1DCNN to extract the features of the original data, discover the implicit relationships between multiple features and filter out the noise and instability components, and then input the information processed by CNN into the Bi-GRU network for the next prediction.

[0171] 2. Bidirectional gated recurrent unit (BiGRU). The bidirectional gated recurrent unit in the embodiment of the present invention is as Figure 4 shown.

[0172] 3. The output information at a certain moment t is the sum of the outputs of the forward hidden layer and the backward hidden layer, and the calculation formula is as follows:

[0173]

[0174] 4. ISSA optimizes the CNN–BiGRU model. Refer to Figure 5 , and this step specifically includes the following 1)-7):

[0175] 1) Initialize the population parameters. Such as the maximum number of iterations, population size, etc.

[0176] 2) Initialize the CNN–BiGRU parameters and set the parameter optimization interval.

[0177] 3) Calculate the fitness of each sparrow currently, find the optimal and worst fitness values, and the corresponding positions.

[0178] 4) Recalculate the fitness of each sparrow after each iteration, introduce a dynamic learning factor, and update the position of the discoverer; introduce the Levy strategy to update the position of the followers; update the position of the vigilant ones.

[0179] 5) Update the optimal, worst fitness values and their positions experienced by the entire sparrow population according to the current state of the sparrows.

[0180] 6) Determine whether the maximum number of iterations is satisfied. If so, assign the optimization result to CNN–BiGRU; otherwise, return to 3) and continue the iteration.

[0181] 7) Build a CNN–BiGRU prediction model by inputting the optimized parameters.

[0182] After the prediction is completed, the subsequent vortex data will continue to be followed up to verify the accuracy of the prediction results.

[0183] 5. Introduce the normEve algorithm to optimize the obtained prediction model.

[0184] This algorithm combines the advantages of the Eve optimization algorithm and the AdaNorm optimization algorithm, and proposes a new optimization algorithm, normEve. The convergence speed of the new algorithm is better than that of the traditional Adam optimization algorithm, and it also improves the problem that the Eve optimization algorithm has a slow convergence speed when the subsample size is small. It has the characteristics of fast convergence speed and better convergence performance, and has a better effect on model optimization.

[0185] In summary, the present invention has the following characteristics:

[0186] (1). Generally speaking, under the premise that there may be a large amount of noise in the ocean vortex high-frequency ground wave radar signal, the TSMFDE feature extraction method is more stable at any scale and can effectively distinguish different noises, verifying that the TSMFDE has good temporal length robustness and recognition ability, and the advantage of good anti-noise performance.

[0187] (2). Aiming at the problem that it is difficult to adjust the hyperparameters of KELM, the BKA algorithm with strong portability and not easy to fall into the optimal solution is introduced to optimize the two parameters of the kernel function parameter and the regularization factor of KELM. Through experimental comparison, it can be seen that the BKA algorithm effectively improves the classification accuracy of the model. This is an optimization of the recognition and prediction method KELM.

[0188] (3). The TSMFDE–BKA–KELM model of this method is a model for ocean vortex recognition and prediction with good recognition and prediction accuracy, that is, the TSMFDE–BKA–KELM method has the advantage of high accuracy.

[0189] (4) The BKA algorithm is adopted for optimization, solving the problem that the regularization parameters and kernel function parameters of the kernel extreme learning machine (KELM) are difficult to adjust.

[0190] (5) The method adopts the knowledge graph method to verify the recognition results. The knowledge graph is a new technology that uses a graph model to describe knowledge and model the association relationships between all things, and has capabilities such as multi-level relationship query and knowledge analysis and reasoning.

[0191] (6) The improved U-Net image model is used to identify vortex points. During the improvement of the U-Net, an attention mechanism is added to help the model focus on more important feature regions, thereby improving the recognition accuracy of vortex anomalies.

[0192] (7) In the judgment of vortex anomalies at sea surface monitoring points based on the knowledge graph, the DenseNet (Dense Convolutional Network) is used as a tool for feature extraction and learning. By connecting each layer to its subsequent layers, features can be reused, reducing information loss. This feature reuse helps the network learn richer feature representations. Especially when dealing with ocean data, complex vortex structures can be better captured. In addition, since each layer is connected to all the previous layers, the DenseNet improves the propagation of gradients, making the training process more stable. For complex ocean phenomena, this stability helps the network effectively learn the features of vortices under big data.

[0193] (8) The DenseNet can extract multi-scale features at different levels. This is particularly important for ocean monitoring because the scales of vortices can vary, and the learning of multi-scale features enhances the adaptability of the model to different types of vortices. In ocean monitoring, it is usually necessary to fuse multiple data (such as sea surface height anomaly and sea surface temperature anomaly). And this connection method can naturally integrate these different data features together, helping the model learn more effective vortex feature judgments.

[0194] (9) A prediction model is established based on the neural network for data preprocessing, network structure design, and change prediction. And the ISSA is used to optimize the CNN–BiGRU model, making the predicted values more in line with the real-time situation.

[0195] (10) The cross-entropy loss function is used to optimize the U-Net model. The identification of ocean vortices involves the classification of each pixel. The cross-entropy loss function can effectively measure the difference between the true label pixel distribution and the model prediction result, and is very suitable for pixel-level classification problems. In addition, the cross-entropy loss function imposes a large penalty on misclassifications, especially for low-probability predictions. This makes it easier for the model to adjust parameters during training to reduce mispredictions of rare classes, thereby improving the overall performance of the model. Most importantly, the cross-entropy loss function is relatively simple and intuitive, easy to implement, and widely supported in many deep learning frameworks, facilitating further model training and tuning.

[0196] Compared with the prior art, the present invention has the following advantages:

[0197] (1) High-frequency ground wave radar is used to collect images, effectively improving the resolution and accuracy of the images. Moreover, the high-frequency ground wave radar is relatively less affected by factors such as sea surface temperature and light visibility, which is beneficial to obtaining more accurate data.

[0198] (2) Data assimilation technology is adopted to combine the observed data with the numerical simulation results to further correct the ocean vortex data.

[0199] (3) A prediction model is established based on a neural network for data preprocessing, network structure design, and change prediction.

[0200] (4) Finally, the results are output in the form of visual images, which is intuitive and clear.

[0201] (5) The results are verified multiple times to ensure the accuracy of the results.

[0202] (6) The normEve algorithm is used to optimize the prediction model to obtain more accurate data in a short time.

[0203] Another aspect of the embodiment of the present invention also provides an identification and prediction system for ocean vortices, including:

[0204] The first module is used to obtain high-frequency ground wave radar signals of ocean vortices;

[0205] The second module is used to construct a training set and a test set according to the high-frequency ground wave radar signals of ocean vortices;

[0206] The third module is used to train the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and construct an initial model;

[0207] The fourth module is used to perform feature extraction on the test set and test the initial model based on the extracted features to obtain a target model that meets the test requirements;

[0208] The fifth module is configured to identify and predict the newly acquired ocean vortex high-frequency ground wave radar signals according to the target model, so as to obtain the ocean vortex identification result.

[0209] It can be understood that the content in the above method embodiments is applicable to the present system embodiment. The functions specifically implemented by the present system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0210] An embodiment of the present invention further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned method for identifying and predicting ocean vortices is implemented. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0211] It can be understood that the content in the above method embodiments is applicable to the present device embodiment. The functions specifically implemented by the present device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0212] Please refer to Figure 6 , Figure 6 , which shows the hardware structure of an electronic device according to another embodiment. The electronic device includes:

[0213] A processor 601, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention;

[0214] A memory 602, which can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 602 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 602, and the processor 601 is called to execute the method for identifying and predicting ocean vortices in the embodiments of the present invention;

[0215] An input / output interface 603, which is used to implement information input and output;

[0216] A communication interface 604 for implementing communication and interaction between this device and other devices, which can achieve communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WI FI, Bluetooth, etc.);

[0217] A bus 605 for transmitting information between various components of the device (such as a processor 601, a memory 602, an input / output interface 603, and a communication interface 604);

[0218] Among them, the processor 601, the memory 602, the input / output interface 603, and the communication interface 604 are communicatively connected to each other inside the device through the bus 605.

[0219] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method for identifying and predicting ocean vortices.

[0220] It can be understood that the content in the above method embodiments is applicable to this storage medium embodiment. The functions specifically implemented by this storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0221] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0222] It should be noted that in various specific embodiments of the present invention, when it comes to performing relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when an embodiment of the present invention needs to obtain sensitive personal information of the user, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or jumping to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for enabling the normal operation of the embodiment of the present invention will be obtained.

[0223] The embodiments described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0224] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0225] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0226] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0227] The terms "first," "second," "third," "fourth," and the like (if any) in the description of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or apparatus.

[0228] It should be understood that in the present invention, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist simultaneously. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or its similar expressions refer to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0229] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0230] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0231] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0232] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0233] The preferred embodiments of the embodiments of the present invention have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of the present invention. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall fall within the scope of the rights of the embodiments of the present invention.

Claims

1. An identification and prediction method for ocean vortices, characterized in that It includes the following steps: Obtain the ocean vortex high-frequency ground wave radar signal; Construct a training set and a test set according to the ocean vortex high-frequency ground wave radar signal; Train the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and construct an initial model; Extract features from the test set and test the initial model based on the extracted features to obtain a target model that meets the test requirements; According to the target model, identify and predict the newly obtained ocean vortex high-frequency ground wave radar signal to obtain the ocean vortex identification result; The expression of the Black-winged Kite optimization algorithm is: X i = BK lb + rand(BK ub - BK lb ), where X i represents the position of the i-th individual in the iterative process of the optimization algorithm; BK lb represents the lower bound of the j-th dimension of the Black-winged Kite; rand() represents a value randomly selected between [0, 1]; BK ub represents the upper bound of the j-th dimension of the Black-winged Kite; During the initialization process, the Black-winged Kite optimization algorithm selects the individual with the best fitness value as the leader X in the initial population L ; the leader X L has the following expression: f best = min(f(X i )) X L = X(find(f best == f(X i ))) Among them, f best represents the minimum fitness value in the population; X L represents the leader in the initial population, which is the individual with the best fitness value; f(X i ) represents the fitness value of the i-th individual; X() represents the position of the individual that satisfies the conditions of this function; The expression of the attack behavior of the black-winged kite optimization algorithm is: Among them, represents the position of the i-th black-winged kite at the j-th dimension and the (t + 1)-th iteration step; r represents a random number; p is a calculated constant; T is the total number of iterations; t is the number of iterations completed so far; n is a control parameter related to the number of iterations, and its value is determined by the following expression: The expression of the migration behavior of the black-winged kite optimization algorithm is: Among them, represents the leading scorer of the j-th dimension of the black-winged kite at the t-th iteration so far; F i represents the current position of the j-th dimension obtained by any black-winged kite in the t-th iteration; F ri represents the fitness value of the random position of any black-winged kite in the j-th dimension in the t-th iteration; m is a control parameter, and its value is determined by the following expression:

2. The identification and prediction method of an ocean vortex according to claim 1, wherein The method further includes: After completing the identification and prediction of ocean vortices, use a knowledge graph to verify the identification data of the vortices.

3. The method for identifying and predicting an ocean vortex according to claim 2, wherein After completing the identification and prediction of ocean vortices, using a knowledge graph to verify the identification data of the vortices includes the following steps: Collect and organize the basic attributes of the vortex knowledge graph; the entities of the vortex knowledge graph are the monitoring points on the sea surface, the entity relationship is the spatial distribution relationship of the monitoring points, and the entity attributes are object attributes and data attributes. The object attributes include the monitoring physical quantity of the monitoring point, the single-point warning level and the affiliated part, and the data attributes include three-dimensional coordinates, current monitoring data and historical monitoring data; Select a vortex anomaly point as the reference point, and according to the multi-level relationship query ability of the knowledge graph, retrieve all the monitoring points that have a spatial adjacent relationship with the reference point, and judge whether these adjacent monitoring points are abnormal; among them, the characteristics of the vortex anomaly point include sea surface height anomaly, sea surface temperature anomaly and flow velocity change anomaly; Continuously retrieve and judge whether there are abnormal monitoring points outside the abnormal measurement point group that have a spatial adjacent relationship with any monitoring point inside the abnormal measurement point group. If so, incorporate the new abnormal monitoring point into the abnormal measurement point group for processing until there are no abnormal monitoring points outside the abnormal measurement point group that have a spatial adjacent relationship with it; Classify all abnormal monitoring points in the sea surface vortex monitoring knowledge graph into different abnormal measurement point groups; Select any two abnormal measurement point groups in the knowledge graph, and with the help of the relationship query ability of the graph database, judge in turn whether there is a data sequence similarity relationship between the corresponding monitoring points in the two abnormal measurement point groups. If the number of monitoring points with a data sequence similarity relationship is greater than 50% of the total number of monitoring points in the two abnormal measurement point groups, it is determined that the two abnormal measurement point groups have a data sequence similarity relationship; Conduct pairwise analysis on all abnormal measurement point groups in the knowledge graph to query abnormal measurement point groups with a data sequence similarity relationship; Compare the detected vortex monitoring points with the results obtained by pattern recognition to verify the vortex identification result; after the identification is completed, use a regression prediction model composed of an optimized sparrow algorithm, a convolutional neural network, and a bidirectional gated recurrent unit to further predict the vortex.

4. The identification and prediction method of an ocean vortex according to claim 3, characterized in that, Select a vortex anomaly point as the reference point, and according to the multi-level relationship query ability of the knowledge graph, retrieve all monitoring points that have a spatial adjacent relationship with the reference point, and judge whether these adjacent monitoring points are abnormal, including the following steps: By constructing a sample library of anomalous mesoscale vortices, and then using sea surface height and sea surface temperature anomaly data, feature extraction and learning are carried out through a densely connected network to obtain fusion feature data that integrates sea surface height and sea surface temperature information; Construct an identification through an improved U-Net image segmentation model to judge the anomaly of the vortex at the monitoring point; among them, the construction process of the U-Net model is: extract features from the monitoring data, including the vortex center point, vortex radius, and ocean parameters; associate the extracted features with the entities in the knowledge graph, and the output is the segmentation mask of the vortex generation area; U-Net adopts an encoder-decoder structure, where the encoder is used to extract features, and the decoder is used to map the features to the segmentation result; an attention mechanism is added to U-Net to focus on key areas; Use the labeled segmentation data to train the U-Net model, supervise and learn the identification of the vortex generation monitoring points, and use the cross-entropy loss function and Adam optimizer to optimize the model; Among them, the cross-entropy loss function has the following expression: Among them, N represents the number of samples; y i represents the true label; represents the probability predicted by the model.

5. The method for identifying and predicting an ocean vortex according to claim 3, wherein Compare the detected vortex monitoring points with the results obtained by pattern recognition to verify the vortex recognition results; after the recognition is completed, use a regression prediction model to further predict the vortex, including the following steps: Use 1D CNN to extract the features of the original data, discover the hidden relationships between multiple features, filter out noise and instability components, and then input the information processed by CNN into a bidirectional gated recurrent unit Bi-GRU for the next prediction; During the prediction process of the bidirectional gated recurrent unit Bi-GRU, the output information at a certain moment t is the sum of the outputs of the forward hidden layer and the backward hidden layer; Use the optimized sparrow algorithm to optimize the CNN–BiGRU model; Introduce the normEve algorithm to optimize the obtained prediction model to complete the further prediction of the vortex.

6. The identification and prediction method of an ocean vortex according to claim 5, wherein The optimization of the CNN–BiGRU model using the optimized sparrow algorithm includes the following steps: Initialize the population parameters, and the population parameters include the maximum number of iterations and the population size; Initialize the CNN–BiGRU parameters and set the parameter optimization interval; Calculate the fitness of each sparrow currently, find the optimal and worst fitness values, and their corresponding positions; After each iteration, recalculate the fitness of each sparrow, introduce a dynamic learning factor, and update the position of the discoverer; introduce the Levy strategy to update the position of the follower; update the position of the vigilant; According to the current state of the sparrows, update the optimal, worst fitness values and their positions experienced by the entire sparrow population; Judge whether the maximum iteration is satisfied. If it is satisfied, assign the optimization result to CNN–BiGRU, otherwise continue to iterate until the maximum iteration is satisfied; Input the optimized parameters to build a CNN–BiGRU prediction model.

7. An identification and prediction system for ocean vortices, characterized in that, Including: The first module is used to obtain the ocean vortex high-frequency ground wave radar signal; The second module is used to construct a training set and a test set according to the ocean vortex high-frequency ground wave radar signal; The third module is used to train the training set through the black-winged kite optimization algorithm and the kernel extreme learning machine to obtain the optimal kernel function parameters and regularization factors, and construct an initial model; The fourth module is used to extract features from the test set and test the initial model based on the extracted features to obtain a target model that meets the test requirements; The fifth module is used to identify and predict the newly acquired ocean vortex high-frequency ground wave radar signal according to the target model to obtain an ocean vortex identification result; The expression of the Black-winged Kite optimization algorithm is: X i = BK lb + rand(BK ub - BK lb ), where X i represents the position of the i-th individual in the iterative process of the optimization algorithm; BK lb represents the lower bound of the j-th dimension of the Black-winged Kite; rand() represents a value randomly selected between [0, 1]; BK ub represents the upper bound of the j-th dimension of the Black-winged Kite; During the initialization process, the Black-winged Kite optimization algorithm selects the individual with the best fitness value as the leader X in the initial population L ; the leader X L has the following expression: f best = min(f(X i )) X L = X(find(f best == f(X i ))) Among them, f best represents the minimum value of fitness in the population; X L represents the leader in the initial population, which is the individual with the best fitness value; f(X i ) represents the fitness value of the i-th individual; X() represents the position of the individual that satisfies the conditions of this function; The expression of the attack behavior of the black-winged kite optimization algorithm is: Among them, represents the position of the i-th black-winged kite at the j-th dimension and the (t + 1)-th iteration step; r represents a random number; p is a calculated constant; T is the total number of iterations; t is the number of iterations completed so far; n is a control parameter related to the number of iterations, and its value is determined by the following expression: The expression of the migration behavior of the black-winged kite optimization algorithm is: Among them, represents the leading scorer of the j-th dimension of the black-winged kite in the t-th iteration so far; F i represents the current position of the j-th dimension obtained by any black-winged kite in the t-th iteration; F ri represents the fitness value of the random position of any black-winged kite in the j-th dimension in the t-th iteration; m is a control parameter, and its value is determined by the following expression:

8. An electronic device, characterized in that, It includes a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a program, and the program is executed by the processor to implement the method according to any one of claims 1-6.

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