Method, device and electronic equipment for processing vertical structure of mesoscale vortex

Abstract

The present disclosure provides a method, device, and electronic device for processing the vertical structure of a mesoscale vortex, which can be applied to the field of underwater observation technology. The method includes: obtaining cross-sectional data of multiple sections of a single underwater glider in a mesoscale vortex; obtaining the vertical density of each of the multiple sections based on the cross-sectional data, wherein the vertical density represents the density of the section at different depths in the mesoscale vortex; determining N cross-sectional segment intervals from the multiple sections based on the vertical density of each of the multiple sections, where N is a positive integer; constructing a multivariate ordinal modal sequence for each of the N cross-sectional segment intervals based on the vertical density difference between adjacent sections in each of the N cross-sectional segment intervals, wherein the multivariate ordinal modal sequence represents the structural characteristics of multiple cross sections of the mesoscale vortex at different depths; and obtaining a visualization result of the vertical structure of the mesoscale vortex based on the multivariate ordinal modal sequence for each of the N cross-sectional segment intervals.

Description

Technical Field

[0001] The present disclosure relates to the field of underwater observation technology, and more specifically, to a method, device, and electronic equipment for processing the vertical structure of a mesoscale vortex. Background Art

[0002] Mesoscale eddies are widespread and complex ocean phenomena that play a crucial role in regulating global ocean heat, material transport, and climate change. Limited by observational methods and costs, continuous observation and accurate characterization of these oceanic mesoscale phenomena over long periods of time and over large areas are difficult. Furthermore, existing techniques for observing the vertical structure of mesoscale eddies are complex and less intuitive. Summary of the Invention

[0003] In view of this, the present disclosure provides a method, device and electronic equipment for processing the vertical structure of a mesoscale vortex.

[0004] One aspect of the present disclosure provides a method for processing a vertical structure of a mesoscale vortex, comprising:

[0005] Obtain profile data of multiple sections of a single underwater glider in a mesoscale vortex;

[0006] Obtaining vertical densities of the plurality of cross-sections according to the cross-sectional data of the plurality of cross-sections, wherein the vertical densities represent densities of the cross-sections at different depths in the mesoscale vortex;

[0007] Determining N section segment intervals from the plurality of sections according to respective vertical densities of the plurality of sections, where N is a positive integer;

[0008] constructing a multivariate ordinal modal sequence for each of the N section segment intervals based on the vertical density difference between adjacent sections in each of the N section segment intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of the multiple sections of the mesoscale vortex at different depths;

[0009] Based on the multivariate ordinal mode sequences of each of the N above-mentioned section segment intervals, the visualization results of the vertical structure of the above-mentioned mesoscale vortex are obtained.

[0010] Another aspect of the present disclosure provides a device for processing a vertical structure of a mesoscale vortex, comprising:

[0011] An acquisition module is used to obtain profile data of multiple profiles of a single underwater glider in a mesoscale vortex;

[0012] A first obtaining module is configured to obtain vertical densities of the plurality of cross sections according to the cross-sectional data of the plurality of cross sections, wherein the vertical densities represent densities of the cross sections at different depths in the mesoscale vortex;

[0013] a determination module, configured to determine N section segment intervals from the plurality of sections according to respective vertical densities of the plurality of sections, where N is a positive integer;

[0014] A construction module is used to construct a multivariate ordinal modal sequence for each of the N section segment intervals based on the vertical density difference between the adjacent sections in each of the N section segment intervals, wherein the multivariate ordinal modal sequence represents the structural characteristics of multiple sections of the mesoscale vortex at different depths;

[0015] The second obtaining module is used to obtain the visualization result of the vertical structure of the above-mentioned mesoscale vortex according to the multivariate ordinal mode sequence of each of the N above-mentioned section segment intervals.

[0016] Another aspect of the present disclosure provides an electronic device, comprising:

[0017] one or more processors;

[0018] a memory for storing one or more programs,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0020] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described above when executed.

[0021] Another aspect of the present disclosure provides a computer program product comprising computer executable instructions, which are used to implement the method described above when the instructions are executed.

[0022] According to an embodiment of the present disclosure, profile data of multiple sections of a single underwater glider in a mesoscale vortex is obtained; vertical densities of the multiple sections are obtained based on the profile data of the multiple sections; N profile segment intervals are determined from the multiple sections based on the vertical densities of the multiple sections; multivariate ordinal modal sequences of the N profile segment intervals are constructed based on the vertical density differences of adjacent sections in each of the N profile segment intervals; visualization results of the vertical structure of the mesoscale vortex are obtained based on the multivariate ordinal modal sequences of the N profile segment intervals, the vertical density of the section of the mesoscale vortex is calculated through the profile data and the profile segment interval for characterizing its vertical structure is determined, it is proposed to construct an ordinal modal sequence from the vertical density of the section of the mesoscale vortex, and it is proposed to characterize the visualization results of the vertical structure of the mesoscale vortex through the multivariate ordinal modal sequence, compared with the related art, the acquisition of data is simplified, the complexity of observing the mesoscale vortex is reduced, and the vertical structure of the mesoscale vortex can be visualized quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0024] Figure 1 Schematically illustrates an exemplary system architecture to which the method, apparatus, and electronic device for processing the vertical structure of a mesoscale vortex disclosed herein can be applied;

[0025] Figure 2 Schematically shows a flow chart of a method for processing a vertical structure of a mesoscale vortex according to an embodiment of the present disclosure;

[0026] Figure 3 Schematically shows a schematic diagram of cross-sectional data collection of a mesoscale vortex according to an embodiment of the present disclosure;

[0027] Figure 4 A schematic diagram schematically illustrates a vertical density visualization result and a cross-section segmentation interval according to an embodiment of the present disclosure;

[0028] Figure 5 Schematically shows a schematic diagram of constructing a unary ordinal modal sequence according to an embodiment of the present disclosure;

[0029] Figure 6 A schematic diagram schematically shows a visualization result of an ordinal modal recurrence diagram according to an embodiment of the present disclosure;

[0030] Figure 7 A schematic diagram schematically illustrates an indicator visualization result according to an embodiment of the present disclosure;

[0031] Figure 8A block diagram schematically illustrates a device for processing vertical structures of mesoscale vortices according to an embodiment of the present disclosure; and

[0032] Figure 9 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0033] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0034] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0035] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0036] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0037] In the embodiments of this disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of all data involved (including, but not limited to, user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information and maintain the security of user personal information and network security.

[0038] The widespread use of satellite altimeters and drifting floats has yielded numerous research results in the observation of mesoscale eddies. However, these technologies also present numerous challenges. For example, observations are limited to the surface structure of mesoscale eddies, preventing the measurement of their vertical structure. Furthermore, obtaining high-resolution in-situ observation data is prohibitively expensive. While profiling buoys have somewhat mitigated the shortcomings of satellite altimeters and drifting floats, their poor maneuverability quickly causes them to escape the mesoscale eddies, making long-term continuous observation difficult. Furthermore, the observed data suffers from uneven and discontinuous temporal and spatial distribution, as well as low spatial resolution, making them inadequate for characterizing the vertical structure of mesoscale eddies.

[0039] Research on the evolutionary characteristics of mesoscale eddies can be broadly divided into two categories: one involves statistically analyzing the overall evolutionary trends of eddies in a given ocean region from various physical oceanographic field data, such as current velocity, sea surface temperature, and sea surface height; the other involves long-term observation and statistical analysis of the eddy life cycle. While current research has revealed some rough characteristics of the evolution of eddies within a region, the methodologies are often based on complex, multi-parameter ocean dynamics models and require extensive empirical analysis, resulting in a complex and slow process.

[0040] Underwater gliders are a new type of unmanned autonomous underwater vehicle (UUV). Driven by buoyancy, they offer long endurance, durability, robustness, and autonomy, making them a crucial component of in-situ ocean observation technology. However, there is still a lack of effective methods for observing mesoscale eddies using underwater gliders, and practical applications are limited. In practice, these gliders are often deployed in large fleets, resulting in significant resource consumption.

[0041] Complex networks are a primary method for studying the dynamics of complex systems across different scales. With the development of complex network technology, its application to complex marine systems has become both a research hotspot and a challenge. Currently, few researchers have successfully applied complex network theory to effectively describe the vertical structural characteristics of mesoscale eddies. This is generally attributed to two reasons: first, due to disciplinary limitations, researchers have struggled to find effective complex network models to characterize mesoscale eddies; second, researchers have primarily focused on the surface characteristics of mesoscale eddies, often overlooking their more valuable vertical properties.

[0042] Therefore, the observation means and methods of mesoscale vortices in related technologies cannot quickly, intuitively and interpretably characterize the vertical structure of mesoscale vortices based on the in-situ observation data of a single underwater glider, which hinders further research on the three-dimensional structure of mesoscale vortices.

[0043] In view of this, an embodiment of the present disclosure provides a method for processing the vertical structure of a mesoscale vortex, including: obtaining profile data of each of multiple sections of a single underwater glider in a mesoscale vortex; obtaining the vertical density of each of the multiple sections based on the profile data of each of the multiple sections, wherein the vertical density characterizes the density of the section at different depths in the mesoscale vortex; determining N profile segmentation intervals from the multiple sections based on the vertical density of each of the multiple sections, where N is a positive integer; constructing a multivariate ordinal modal sequence for each of the N profile segmentation intervals based on the vertical density difference between adjacent sections in each of the N profile segmentation intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of multiple sections of the mesoscale vortex at different depths; obtaining a visualization result of the vertical structure of the mesoscale vortex based on the multivariate ordinal modal sequence for each of the N profile segmentation intervals.

[0044] Figure 1 An exemplary system architecture of the method, apparatus, and electronic device for processing the vertical structure of a mesoscale vortex according to the present disclosure is schematically shown.

[0045] It should be noted that Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0046] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0047] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software (for example only).

[0048] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0049] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0050] It should be noted that the method for processing the vertical structure of a mesoscale vortex provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the processing device for the vertical structure of a mesoscale vortex provided in the embodiment of the present disclosure can generally be set in the server 105. The method for processing the vertical structure of a mesoscale vortex provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the processing device for the vertical structure of a mesoscale vortex provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the method for processing the vertical structure of a mesoscale vortex provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or by other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the processing device for the vertical structure of the mesoscale vortex provided in the embodiment of the present disclosure can also be set in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0051] For example, the cross-sectional data of each of the multiple cross-sections in the mesoscale vortex may be originally stored in any one of the first terminal device 101, the second terminal device 102, or the third terminal device 103 (for example, the first terminal device 101, but not limited thereto), or stored on an external storage device and imported into the first terminal device 101. The first terminal device 101 may then locally execute the method for processing the vertical structure of the mesoscale vortex provided in the embodiment of the present disclosure, or send the image to be processed to another terminal device, server, or server cluster, and the other terminal device, server, or server cluster that receives the image to be processed may execute the method for processing the vertical structure of the mesoscale vortex provided in the embodiment of the present disclosure.

[0052] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0053] Figure 2 A flow chart of a method for processing the vertical structure of a mesoscale vortex according to an embodiment of the present disclosure is schematically shown.

[0054] like Figure 2 As shown, the method includes operations S210 to S250.

[0055] In operation S210 , profile data of each of a plurality of profiles of a single underwater glider in a mesoscale vortex is acquired.

[0056] In operation S220 , vertical densities of the plurality of cross sections are obtained according to the cross section data of the plurality of cross sections, wherein the vertical density represents the density of the cross section at different depths in the mesoscale vortex.

[0057] In operation S230 , N section segment intervals are determined from the multiple sections according to respective vertical densities of the multiple sections, where N is a positive integer.

[0058] In operation S240, a multivariate ordinal modal sequence is constructed for each of the N section segment intervals based on the vertical density difference between adjacent sections in each of the N section segment intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of multiple sections of the mesoscale vortex at different depths.

[0059] In operation S250 , a visualization result of the vertical structure of the mesoscale vortex is obtained according to the multivariate ordinal mode sequences of each of the N section segment intervals.

[0060] Figure 3 The figure schematically shows a schematic diagram of cross-sectional data collection of a mesoscale vortex according to an embodiment of the present disclosure.

[0061] like Figure 3 As shown, the position and range of the mesoscale eddy can be determined by satellite telemetry images, and a single underwater glider equipped with a temperature-salinity-depth sensor can be used to cross the mesoscale eddy and conduct in-situ observations, thereby obtaining the profile data of multiple sections of the single underwater glider in the mesoscale eddy.

[0062] According to an embodiment of the present disclosure, the maximum diving depth of the underwater glider may be 800 meters, and the sampling frequency of the sensor may be 0.25 Hz.

[0063] According to an embodiment of the present disclosure, the multiple cross sections may be continuous cross sections.

[0064] According to an embodiment of the present disclosure, the vertical density of each section can be obtained based on the section data of each section. The vertical density is the density of the section at different depths in the mesoscale vortex.

[0065] According to embodiments of the present disclosure, N profile segmentation intervals can be determined from multiple profiles based on the vertical density variation trends between the profiles. The vertical density variation trends between the profiles can be determined based on the vertical density of each profile at the same depth. The N profile segmentation intervals can be a collection of profiles with relatively large vertical density variation trends.

[0066] According to an embodiment of the present disclosure, the vertical density difference between adjacent sections within each of the N section segment intervals is the vertical density difference between two adjacent sections at the same depth. Based on the vertical density difference between adjacent sections within each of the N section segment intervals, a multivariate ordinal modal sequence can be constructed for each interval to extract the structural features implicit in the vertical density of the mesoscale vortex section, namely, the structural features of multiple sections of the mesoscale vortex at different depths.

[0067] According to an embodiment of the present disclosure, since the multivariate ordinal modal sequence characterizes the structural features of multiple sections of the mesoscale vortex at different depths, the visualization results of the mesoscale vortex in the vertical structure can be characterized according to the multivariate ordinal modal sequence.

[0068] According to an embodiment of the present disclosure, profile data of multiple sections of a single underwater glider in a mesoscale vortex is obtained; vertical densities of the multiple sections are obtained based on the profile data of the multiple sections; N profile segment intervals are determined from the multiple sections based on the vertical densities of the multiple sections; multivariate ordinal modal sequences of the N profile segment intervals are constructed based on the vertical density differences of adjacent sections in each of the N profile segment intervals; visualization results of the vertical structure of the mesoscale vortex are obtained based on the multivariate ordinal modal sequences of the N profile segment intervals, the vertical density of the section of the mesoscale vortex is calculated through the profile data and the profile segment interval for characterizing its vertical structure is determined, it is proposed to construct an ordinal modal sequence from the vertical density of the section of the mesoscale vortex, and it is proposed to characterize the visualization results of the vertical structure of the mesoscale vortex through the multivariate ordinal modal sequence, compared with the related art, the acquisition of data is simplified, the complexity of observing the mesoscale vortex is reduced, and the vertical structure of the mesoscale vortex can be visualized quickly and accurately.

[0069] According to an embodiment of the present disclosure, obtaining the vertical density of each of the multiple sections based on the respective cross-sectional data of the multiple sections may include: obtaining the vertical density of each of the multiple sections based on the respective temperature data, conductivity data and pressure data of the multiple sections.

[0070] According to an embodiment of the present disclosure, the profile data includes temperature data, conductivity data, and pressure data. The profile data of each profile can be preprocessed. The preprocessing can be to remove useless values ​​and abnormal values ​​from the profile data, and perform Kriging interpolation processing, as shown in the following formula (1):

[0071] (1)

[0072] in, At the measuring point The estimated value at is the weight coefficient.

[0073] According to an embodiment of the present disclosure, after preprocessing, time series data of preprocessed temperature data, conductivity data, and pressure data can be obtained.

[0074] According to an embodiment of the present disclosure, the vertical density of each section is calculated section by section using the seawater density formula for the preprocessed time series data of temperature data, conductivity data, and pressure data. The seawater density formula is as follows (2):

[0075] (2)

[0076] Where S is conductivity, T is temperature, and P is pressure. is the sea level density value, is the secant bulk modulus.

[0077] According to an embodiment of the present disclosure, N profile segmentation intervals are determined from multiple profiles based on the vertical densities of each of the multiple profiles, including: constructing a multivariate time series based on the vertical densities of each of the multiple profiles; dividing the multivariate time series into multiple segmentation time series based on multiple time segmentation points; modeling the multiple segmentation time series to obtain the mean vectors and covariance matrices of the multiple segmentation time series; obtaining the likelihood function of the multivariate time series with respect to the multiple time segmentation points based on the mean vectors and covariance matrices of the multiple segmentation time series; optimizing the likelihood function to determine N+1 target time segmentation points from the multiple time segmentation points; and determining N profile segmentation intervals from the multiple profiles based on the N+1 target time segmentation points.

[0078] According to an embodiment of the present disclosure, a multivariate time series is constructed from the vertical density of each cross section of the mesoscale vortex, such as , where s is the number of sections and L is the length of the section perpendicular to the density.

[0079] According to an embodiment of the present disclosure, a greedy Gaussian segmentation algorithm can be used to segment each multivariate time series. Segment. The Greedy Gaussian Segmentation (GGS) algorithm is a heuristic method for solving multivariate time series segmentation problems. GGS always produces a local optimal solution, such that changing any breakpoint ultimately does not improve the overall objective. GGS is based on a greedy strategy, segmenting the time series from left to right, each time determining the segmentation point by maximizing the ratio of the variance to the left and right of the segmentation point. This method is easily extended to multivariate time series segmentation problems with dimensions exceeding 1000 or of arbitrary length, and can be approximately solved in linear time.

[0080] According to an embodiment of the present disclosure, for each of the multiple multivariate time series, the following operations are performed:

[0081] S1, set T = s as the number of time segmentation points, divide the multivariate time series into k + 1 segmented time series, then each segmented time series 𝑖 can be modeled as having a mean vector and covariance matrix The multivariate Gaussian distribution can be expressed as the following formula (3):

[0082] (3)

[0083] in, represents the mean vector of the i-th segment time series, Represents the covariance matrix of the i-th segment time series.

[0084] S2, use the likelihood function to evaluate the observation value x corresponding to each time segmentation point t t How to adapt to its Gaussian distribution to quantify the fitting of the entire multivariate time series, the likelihood function can be expressed as follows:

[0085] (4)

[0086] Among them, b is the time segmentation point of the multivariate time series, is the set of mean vectors of all segmented time series, is the set of covariance matrices of all segmented time series, x t is the observation value at time segmentation point t, Represents the covariance matrix of the segmented time series where the time segmentation point t is located, It represents the mean vector of the segmented time series at the time segmentation point t, and n represents the dimension of the variable.

[0087] S3, the covariance regularization term is introduced to reduce the estimation error caused by high dimension. The covariance regularization term can be expressed as the following formula (5):

[0088] (5)

[0089] in, is the regularization parameter.

[0090] S4, using the greedy Gaussian segmentation algorithm to adjust the time segmentation point b to maximize the greedy optimization objective function to effectively search for multivariate time series The structural changes in , and the location of the target time segmentation point are determined. The greedy optimization objective function can be expressed as the following formula (6):

[0091] (6)

[0092] Where C is a constant term, S(i) is the empirical covariance matrix of the i-th segment time series, I is the identity matrix, and b i and b i-1 are the values ​​of the i-th and i-1-th time division points b.

[0093] Figure 4 A schematic diagram schematically illustrates a vertical density visualization result and a cross-section segmentation interval according to an embodiment of the present disclosure.

[0094] like Figure 4 As shown, in this embodiment, N=3, that is, the target time segmentation points are 4, namely b0, b1, b2 and b3, and the corresponding multivariate time series of the vertical density of the segmented mesoscale vortex can be expressed as , and , where the three segment ranges are b 0~1 =0,…,b1-b0; b 1~2 =b1-b0,…,b2-b1; b 2~3 =b2-b1,…,b3-b2, L is the length of the vertical density section.

[0095] According to an embodiment of the present disclosure, a multivariate ordinal modal sequence of each of the N profile segmentation intervals is constructed based on the vertical density differences of adjacent profiles in each of the N profile segmentation intervals, including: obtaining a vertical density difference sequence of each of the N profile segmentation intervals based on the vertical density differences of adjacent profiles in each of the N profile segmentation intervals; constructing a unary ordinal modal sequence of each of the N profile segmentation intervals based on the vertical density difference sequence of each of the N profile segmentation intervals; constructing a multivariate ordinal modal sequence of each of the N profile segmentation intervals based on the unary ordinal modal sequence of each of the N profile segmentation intervals.

[0096] According to an embodiment of the present disclosure, for each of the N profile segment intervals, a vertical density difference sequence can be obtained according to the vertical density difference between adjacent profiles within the profile segment interval, as shown in the following formula (7):

[0097] (7)

[0098] Among them, m=1, 2,…, s-1, T=L.

[0099] According to the embodiment of the present disclosure Figure 4 In the case of N=3, the vertical density difference sequences of the three profile segment intervals can be expressed as: 、 and .

[0100] According to the embodiments of the present disclosure, the vertical density difference sequence characterizes the characteristics of the profile within the profile segmentation interval in the depth direction and the horizontal direction. Therefore, the unary ordinal modal sequence can be the characteristics of adjacent profiles only in the vertical direction. Multiple unary ordinal modal sequences can be established. According to the unary ordinal modal sequences of each interval of the N profile segmentation intervals, the multivariate ordinal modal sequences of each of the N profile segmentation intervals can be constructed, thereby obtaining a multivariate ordinal modal sequence that can characterize the structural characteristics of multiple profiles of the mesoscale vortex at different depths.

[0101] According to the embodiments of the present disclosure, the density relationship between the profiles is fully utilized to obtain a multivariate ordinal modal sequence, so that the vertical structure in the mesoscale vortex can be effectively extracted.

[0102] According to an embodiment of the present disclosure, based on the vertical density difference sequences of each interval of the N profile segmentation intervals, a unary ordinal modal sequence of each interval of the N profile segmentation intervals is constructed, including: based on the vertical density difference sequences of each interval of the N profile segmentation intervals and a preset sliding window, a window sequence set of each profile of the N profile segmentation intervals along the depth direction is obtained, wherein the window sequence set includes multiple window sequences with the same length as the preset sliding window; based on the vertical density difference of each of the multiple window sequences in the window sequence set, the ordinal number of each window sequence of the N profile segmentation intervals is determined, wherein the ordinal number of the window sequence represents the order of the vertical density difference in the window sequence; based on the ordinal number of each window sequence of the N profile segmentation intervals, a unary ordinal modal sequence of each interval of the N profile segmentation intervals is constructed.

[0103] According to an embodiment of the present disclosure, the preset sliding window may be moved in a vertical density difference sequence with a set size and step length to obtain a window sequence set of each profile of N profile segment intervals along the depth direction.

[0104] According to an embodiment of the present disclosure, an ordinal mode is constructed for each window sequence, and a corresponding label is set according to the size of the preset sliding window. For example, if the size of the preset sliding window is 5, then " "," "," "," "and" "Five labels, five labels can characterize the size order of vertical density difference in the window sequence, all possible ordinal modes are 5!=120, for " "Ordinal modality can be obtained" ", all possible ordinal modes are 5!=120, " " corresponds to the 19th type, then " The corresponding ordinal mode is After obtaining the ordinal numbers of the window sequences of the N profile segment intervals, the unary ordinal modal sequences of the N profile segment intervals can be constructed.

[0105] Figure 5 The figure schematically shows a construction diagram of a unary ordinal modal sequence according to an embodiment of the present disclosure.

[0106] like Figure 5 As shown in (a), for any vertical density difference sequence in any section segment interval, it is divided into many window sequences of length 5 by a sliding window of size 5 and step length 1. For each window sequence, the ordinal mode is constructed to obtain the following: Figure 5 The unary ordinal modal sequences of the respective intervals shown in (b).

[0107] According to the embodiment of the present disclosure, the unary ordinal modal sequence constructed from each window sequence is arranged in the order of conversion to obtain the unary ordinal modal sequence of any sub-signal within each section segmentation interval. Finally, the multivariate ordinal modal sequence is obtained from the profile data within each section segmentation interval, and combined with Figure 4 In the case of N=3, the three section intervals can be expressed as: 、 and .in, =0,…,b1- b0; = b1- b0,…,b2-b1; =b2-b1,…,b3-b2; J=T–4=L-4.

[0108] According to the embodiments of the present disclosure, through the ordinal modal sequence construction method of the present disclosure, the structural features implicit in the vertical density of the mesoscale vortex profile can be extracted, so that the mesoscale vertical structure can be more accurately visualized.

[0109] According to an embodiment of the present disclosure, a visualization result of the vertical structure of the mesoscale vortex is obtained according to the multivariate ordinal modal sequence of each of the N section segment intervals, which may include: obtaining a visualization result of the ordinal modal recursion diagram of the vertical structure of the mesoscale vortex according to the multivariate ordinal modal sequence of each of the N section segment intervals; obtaining an indicator visualization result of the vertical structure of the mesoscale vortex according to the visualization result of the ordinal modal recursion diagram of the vertical structure of the mesoscale vortex.

[0110] According to the embodiments of the present disclosure, based on the multivariate ordinal modal sequences of each section segment interval, it is proposed to construct a multivariate weighted ordinal modal recursive network to characterize the vertical structure of the mesoscale vortex, and obtain the ordinal modal recursive diagram visualization result of the vertical structure of the mesoscale vortex.

[0111] According to an embodiment of the present disclosure, based on the visualization results of the ordinal modal recurrence diagram of the mesoscale vortex vertical structure, a complex network index for the mesoscale vortex vertical structure can be constructed to obtain the visualization results of the index of the mesoscale vortex vertical structure.

[0112] According to an embodiment of the present disclosure, a visualization result of the ordinal modal recursion diagram of the vertical structure of the mesoscale vortex is obtained according to the multivariate ordinal modal sequence of each of the N section segment intervals, which may include: selecting M unary ordinal modal sequences from the multivariate ordinal modal sequence of each of the N section segment intervals for phase space reconstruction to obtain a phase space trajectory vector sequence of the M unary ordinal modal sequences; and obtaining a visualization result of the ordinal modal recursion diagram of the vertical structure of the mesoscale vortex according to the phase space trajectory vector sequence of the M unary ordinal modal sequences of each multivariate ordinal modal sequence.

[0113] According to an embodiment of the present disclosure, for a unary ordinal modal sequence of any sub-signal in each multi-element ordinal modal sequence , reconstruct its phase space and obtain the phase space trajectory vector sequence , which can be expressed as formula (8):

[0114] (8)

[0115] Where m is the embedding dimension, which is determined by the false nearest neighbor method; τ is the delay time, which is determined by the autocorrelation function method.

[0116] For any unary ordinal modal sequence Phase space trajectory vector sequence , the ordinal modal recurrence diagram is defined by the distance between any two phase space trajectory vectors in the phase space as follows (9):

[0117] (9)

[0118] Where OMR represents the ordinal modal recursive matrix, represents the unit step function, represents the norm, Indicates the set threshold. If the OMR value is 1, it is displayed in black in the ordinal modal recurrence diagram. If the OMR value is 0, it is displayed in white in the ordinal modal recurrence diagram.

[0119] Figure 6 A schematic diagram schematically shows the visualization result of the ordinal modal recurrence diagram according to an embodiment of the present disclosure.

[0120] When N=3, for three multivariate ordinal modal sequences 、 and

[0121] For each of the , we randomly select 6 unary ordinal modal sequences to represent the ordinal modal recursive graph, and the visualization results are as follows Figure 6 As shown, the vertical structure of the mesoscale vortex can be preliminarily and intuitively portrayed.

[0122] According to an embodiment of the present disclosure, the intrinsic nonlinear dynamic vertical structural characteristics of the mesoscale vortex are visualized by visualizing the intrinsic texture structure of the ordinal modal recurrence diagram visualization result.

[0123] According to an embodiment of the present disclosure, based on the visualization results of the ordinal modal recursion graph of the mesoscale vortex vertical structure, the index visualization results of the mesoscale vortex vertical structure are obtained, including: based on the visualization results of the ordinal modal recursion graph of the mesoscale vortex vertical structure, obtaining the cross-recursion rate of the ordinal modal recursion graph; based on the cross-recursion rate of the ordinal modal recursion graph, obtaining the average weighted clustering coefficient and graph energy of each multivariate ordinal modal sequence; based on the average weighted clustering coefficient and graph energy of each multivariate ordinal modal sequence, obtaining the index visualization results of the mesoscale vortex vertical structure.

[0124] According to an embodiment of the present disclosure, a multivariate weighted ordinal modal recursive network can be constructed to characterize the vertical structure of the mesoscale vortex, and the vertical structure of the mesoscale vortex can be characterized based on indicator data.

[0125] According to an embodiment of the present disclosure, for a unary ordinal modal sequence of two sub-signals in any multi-ary ordinal modal sequence and , we can get the phase space trajectory vector sequence and , cross-recursion can obtain a (L-4)×(L-4) recursive matrix, as shown in the following formula (10):

[0126] (10)

[0127] Where i, j = 1,…, L-4, and i is not equal to j.

[0128] According to an embodiment of the present disclosure, in order to quantitatively characterize the density of recursion points in each ordinal modal recursion graph, the cross-recursion rate OMCRR of each ordinal modal recursion graph is as follows (11):

[0129] (11)

[0130] For any multivariate ordinal modal sequence, we can obtain a The cross-recurrence rate matrix of .

[0131] For any sub-signal in a multivariate ordinal modal sequence, take it as a node and set the cross recursion rate As the edge weight between nodes i and j. Among them, 10% of the sum of the standard deviation values ​​of each sub-signal is used as the threshold The embedding dimension m and the delay time τ are determined using the false nearest neighbor method and the correlation function method, respectively.

[0132] Through the above steps, any multivariate ordinal modal sequence can be mapped to a multivariate weighted ordinal modal recursive network, and the network index weighted clustering coefficient and graph energy can be used to quantitatively characterize the characteristics of the network, further characterizing the vertical structural characteristics of the mesoscale vortex. In this embodiment, for the multivariate ordinal modal sequence , its weighted clustering coefficient The graph energy GE(W) can be expressed as:

[0133] (12)

[0134] (13)

[0135] in, The weight between nodes v and j, that is, the elements in the weight matrix W; represents the weighted clustering coefficient of any node v; are the eigenvalues ​​of the weight matrix W, and n represents the number of eigenvalues.

[0136] Figure 7 A schematic diagram schematically shows an indicator visualization result according to an embodiment of the present disclosure.

[0137] like Figure 7 As shown, for three multivariate ordinal modal sequences 、 and , and calculate their weighted clustering coefficients and graph energy respectively, thereby using a multivariate weighted ordinal modal recursive network to characterize the vertical structure of the mesoscale vortex.

[0138] According to the embodiments of the present disclosure, the vertical structure of mesoscale vortices is rapidly, intuitively, and interpretably characterized using in-situ observation data (i.e., profile data) of mesoscale vortices collected by a single underwater glider. By constructing a multivariate ordinal modal sequence, the in-situ observation data of mesoscale vortices collected by a single underwater glider can be rapidly and effectively mined for spatiotemporal features. Ordinal modal recursion diagrams are used to intuitively characterize the inherent nonlinear dynamic vertical structure of the mesoscale vortex. A multivariate weighted ordinal modal recursion network is constructed and network metrics are used to provide a deep, interpretable characterization of the vertical structure of the mesoscale vortex.

[0139] According to the embodiments of the present disclosure, the vertical structure of the mesoscale vortex can be deeply and interpretably characterized through indicator visualization results.

[0140] Figure 8 A block diagram of a device for processing the vertical structure of a mesoscale vortex according to an embodiment of the present disclosure is schematically shown.

[0141] like Figure 8 As shown, the processing device 800 for the vertical structure of the mesoscale vortex includes an acquisition module 810, a first obtaining module 820, a determination module 830, a construction module 840 and a second obtaining module 850.

[0142] The acquisition module 810 is used to acquire profile data of multiple profiles of a single underwater glider in a mesoscale vortex.

[0143] The first obtaining module 820 is used to obtain the vertical density of each of the multiple sections based on the section data of each of the multiple sections, wherein the vertical density represents the density of the section at different depths in the mesoscale vortex.

[0144] The determination module 830 is used to determine N section segment intervals from the multiple sections according to the vertical densities of the multiple sections, where N is a positive integer.

[0145] Construction module 840 is used to construct a multivariate ordinal modal sequence for each of the N profile segment intervals based on the vertical density difference between adjacent profiles in each of the N profile segment intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of multiple profiles of the mesoscale vortex at different depths.

[0146] The second obtaining module 850 is used to obtain the visualization result of the vertical structure of the mesoscale vortex according to the multivariate ordinal mode sequence of each of the N section segment intervals.

[0147] According to an embodiment of the present disclosure, the construction module 840 for constructing a multivariate ordinal modal sequence for each of the N section segment intervals based on the vertical density difference between adjacent sections in each of the N section segment intervals includes:

[0148] The first construction submodule is used to obtain a vertical density difference sequence of each of the N section segment intervals according to the vertical density differences of adjacent sections in each of the N section segment intervals;

[0149] The second construction submodule is used to construct a unary ordinal modal sequence of each of the N section segment intervals according to the vertical density difference sequence of each of the N section segment intervals;

[0150] The third construction submodule is used to construct a multivariate ordinal modal sequence for each of the N profile segment intervals based on the unary ordinal modal sequence of each of the N profile segment intervals.

[0151] According to an embodiment of the present disclosure, a second construction submodule for constructing a unary ordinal modal sequence of each of the N profile segment intervals based on a vertical density difference sequence of each of the N profile segment intervals includes:

[0152] A first construction unit is configured to obtain a window sequence set of each section of the N section segment intervals along the depth direction according to each vertical density difference sequence of the N section segment intervals and a preset sliding window, wherein the window sequence set includes multiple window sequences having the same length as the preset sliding window;

[0153] The second construction unit is configured to determine the ordinal number of each window sequence of the N profile segment intervals according to the vertical density differences of each of the plurality of window sequences in the window sequence set, wherein the ordinal number of the window sequence represents the order of the vertical density differences in the window sequence;

[0154] The third construction unit is used to construct a unary ordinal modal sequence of each of the N profile segment intervals according to the ordinal number of the window sequence of each of the N profile segment intervals.

[0155] According to an embodiment of the present disclosure, the second obtaining module 850 for obtaining a visualization result of the vertical structure of the mesoscale vortex according to the multivariate ordinal mode sequence of each of the N section segment intervals includes:

[0156] The first submodule is used to obtain the visualization result of the ordinal mode recurrence diagram of the vertical structure of the mesoscale vortex according to the multivariate ordinal mode sequence of each of the N section segment intervals;

[0157] The second submodule is used to obtain the indicator visualization results of the mesoscale vortex vertical structure based on the ordinal mode recurrence diagram visualization results of the mesoscale vortex vertical structure.

[0158] According to an embodiment of the present disclosure, a second obtaining submodule for obtaining a visualization result of an ordinal mode recurrence diagram of a mesoscale vortex vertical structure based on a multivariate ordinal mode sequence of each of N section segment intervals includes:

[0159] The first obtaining unit is used to select M unary ordinal modal sequences from the multivariate ordinal modal sequences of the N section segment intervals to perform phase space reconstruction, and obtain a phase space trajectory vector sequence of the M unary ordinal modal sequences;

[0160] The second obtaining unit is used to obtain the visualization result of the ordinal mode recursion diagram of the vertical structure of the mesoscale vortex according to the phase space trajectory vector sequence of M unary ordinal mode sequences of each multivariate ordinal mode sequence.

[0161] According to an embodiment of the present disclosure, a first obtaining unit for obtaining an index visualization result of a mesoscale vortex vertical structure according to an ordinal modal recurrence diagram visualization result of the mesoscale vortex vertical structure includes:

[0162] First, a subunit is obtained, which is used to obtain the cross-recurrence rate of the ordinal mode recurrence diagram based on the visualization results of the ordinal mode recurrence diagram of the vertical structure of the mesoscale vortex;

[0163] Second, a subunit is obtained, which is used to obtain the average weighted clustering coefficient and graph energy of each multivariate ordinal modal sequence according to the cross-recurrence rate of the ordinal modal recurrence graph;

[0164] The third subunit is obtained, which is used to obtain the indicator visualization results of the mesoscale vortex vertical structure based on the average weighted clustering coefficient and graph energy of each multivariate ordinal mode sequence.

[0165] According to an embodiment of the present disclosure, the determining module 830 for determining N section segment intervals from the plurality of sections according to the vertical densities of the plurality of sections includes:

[0166] The first determination submodule is used to construct a multivariate time series based on the vertical densities of the multiple profiles;

[0167] The second determining submodule is used to divide the multivariate time series into a plurality of segmented time series according to a plurality of time segmentation points;

[0168] The third determination submodule is used to model the multiple segmented time series and obtain the mean vector and covariance matrix of the multiple segmented time series;

[0169] The fourth determination submodule is used to obtain the likelihood function of the multivariate time series with respect to the multiple time segmentation points according to the mean vectors and covariance matrices of the multiple segmented time series;

[0170] A fifth determination submodule is configured to optimize the likelihood function and determine N+1 target time segmentation points from the plurality of time segmentation points;

[0171] The sixth determination submodule is used to determine N section segmentation intervals from multiple sections according to N+1 target time segmentation points.

[0172] According to an embodiment of the present disclosure, the profile data includes temperature data, conductivity data, and pressure data; and the first obtaining module 820 for obtaining the vertical density of each of the multiple profiles based on the profile data of each of the multiple profiles includes:

[0173] The third obtaining submodule is used to obtain the vertical density of each of the multiple profiles based on the temperature data, conductivity data and pressure data of each of the multiple profiles.

[0174] According to the embodiments of the present invention, any number of modules, sub-modules, units, and sub-units, or at least part of the functions of any number of them, can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, sub-modules, units, and sub-units can be at least partially implemented as a computer program module, which can perform the corresponding functions when the computer program module is executed.

[0175] For example, any number of the acquisition module 810, the first acquisition module 820, the determination module 830, the construction module 840, and the second acquisition module 850 can be combined into a single module / unit / sub-unit, or any one of these modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functionality of one or more of these modules / units / sub-units can be combined with at least part of the functionality of other modules / units / sub-units and implemented in a single module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the acquisition module 810, the first acquisition module 820, the determination module 830, the construction module 840, and the second acquisition module 850 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of these. Alternatively, at least one of the acquisition module 810 , the first obtaining module 820 , the determination module 830 , the construction module 840 and the second obtaining module 850 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0176] It should be noted that the processing device part for the vertical structure of the mesoscale vortex in the embodiment of the present disclosure corresponds to the processing method part for the vertical structure of the mesoscale vortex in the embodiment of the present disclosure. The description of the processing device part for the vertical structure of the mesoscale vortex refers to the processing method part for the vertical structure of the mesoscale vortex, which will not be repeated here.

[0177] Figure 9 A block diagram of an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure is schematically shown. Figure 9 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0178] like Figure 9As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0179] Various programs and data required for the operation of the electronic device 900 are stored in the RAM 903. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. The processor 901 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and / or the RAM 903. It should be noted that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0180] According to an embodiment of the present disclosure, electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to bus 904. Electronic device 900 may also include one or more of the following components connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 908 including a hard disk; and a communication section 909 including a network interface card such as a LAN card or modem. Communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 910 as needed, so that computer programs read from the removable media can be installed into storage section 908 as needed.

[0181] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0182] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0183] According to embodiments of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0184] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 902 and / or the RAM 903 described above and / or one or more memories other than the ROM 902 and the RAM 903 .

[0185] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method for processing the vertical structure of the mesoscale vortex provided by the embodiment of the present disclosure.

[0186] When the computer program is executed by the processor 901, the above functions defined in the system / device of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0187] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0188] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0189] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, and all of these combinations and / or couplings fall within the scope of the present disclosure.

[0190] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for processing the vertical structure of a mesoscale vortex, comprising: Obtain profile data of multiple sections of a single underwater glider in a mesoscale vortex; Obtaining vertical densities of the plurality of cross-sections according to the cross-sectional data of the plurality of cross-sections, wherein the vertical densities represent densities of the cross-sections at different depths in the mesoscale vortex; Determining N section segment intervals from the plurality of sections according to respective vertical densities of the plurality of sections, where N is a positive integer; Constructing a multivariate ordinal modal sequence for each of the N section segment intervals based on the vertical density difference between adjacent sections in each of the N section segment intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of multiple sections of the mesoscale vortex at different depths; According to the multivariate ordinal modal sequences of each of the N section segment intervals, a visualization result of the vertical structure of the mesoscale vortex is obtained.

2. The method according to claim 1, wherein The step of constructing a multivariate ordinal modal sequence for each of the N section segment intervals according to the vertical density difference between adjacent sections in each of the N section segment intervals comprises: Obtaining a vertical density difference sequence of each of the N section segment intervals according to the vertical density differences of adjacent sections in each of the N section segment intervals; Constructing a unary ordinal modal sequence of each of the N section segment intervals according to the vertical density difference sequence of each of the N section segment intervals; According to the unary ordinal modal sequence of each of the N section segment intervals, a multivariate ordinal modal sequence of each of the N section segment intervals is constructed.

3. The method according to claim 2, wherein: The step of constructing a unary ordinal modal sequence of each of the N section segment intervals according to the vertical density difference sequence of each of the N section segment intervals comprises: Obtaining, based on the vertical density difference sequences of the N profile segment intervals and a preset sliding window, a window sequence set of each profile of the N profile segment intervals along the depth direction, wherein the window sequence set includes multiple window sequences having the same length as the preset sliding window; Determining, according to vertical density differences of each of the plurality of window sequences in the window sequence set, ordinal numbers of the window sequences of the N profile segment intervals, wherein the ordinal numbers of the window sequences represent the order of the vertical density differences in the window sequences; According to the ordinal numbers of the window sequences of the N profile segment intervals, unary ordinal modal sequences of the N profile segment intervals are constructed.

4. The method according to claim 2 or 3, wherein: Obtaining a visualization result of the mesoscale vortex vertical structure according to the multivariate ordinal mode sequence of each of the N section segment intervals includes: Obtaining a visualization result of an ordinal mode recurrence diagram of the vertical structure of the mesoscale vortex according to the multivariate ordinal mode sequences of each of the N section segment intervals; According to the visualization results of the ordinal modal recurrence diagram of the mesoscale vortex vertical structure, the indicator visualization results of the mesoscale vortex vertical structure are obtained.

5. The method according to claim 4, wherein Obtaining a visualization result of an ordinal mode recurrence diagram of the mesoscale vortex vertical structure according to the multivariate ordinal mode sequence of each of the N section segment intervals includes: Selecting M of the univariate ordinal modal sequences from the respective multivariate ordinal modal sequences of the N section segment intervals to perform phase space reconstruction, and obtaining phase space trajectory vector sequences of the M univariate ordinal modal sequences; According to the phase space trajectory vector sequence of the M unary ordinal modal sequences of each of the multivariate ordinal modal sequences, a visualization result of the ordinal modal recursion diagram of the vertical structure of the mesoscale vortex is obtained.

6. The method according to claim 5, wherein: The method of obtaining an index visualization result of the mesoscale vortex vertical structure according to the visualization result of the ordinal modal recurrence diagram of the mesoscale vortex vertical structure includes: According to the visualization result of the ordinal mode recurrence diagram of the vertical structure of the mesoscale vortex, a cross recurrence rate of the ordinal mode recurrence diagram is obtained; Obtaining an average weighted clustering coefficient and graph energy of each of the multivariate ordinal modal sequences according to the cross-recurrence rate of the ordinal modal recurrence graph; According to the average weighted clustering coefficient and graph energy of each of the multivariate ordinal modal sequences, the indicator visualization results of the mesoscale vortex vertical structure are obtained.

7. The method according to any one of claims 1 to 4, wherein Determining N section segment intervals from the plurality of sections according to respective vertical densities of the plurality of sections includes: constructing a multivariate time series based on the vertical densities of the plurality of profiles; Dividing the multivariate time series into a plurality of segmented time series according to a plurality of time segmentation points; Modeling the plurality of segmented time series to obtain respective mean vectors and covariance matrices of the plurality of segmented time series; Obtaining a likelihood function of the multivariate time series with respect to the plurality of time segmentation points according to the respective mean vectors and covariance matrices of the plurality of segmented time series; Optimizing the likelihood function to determine N+1 target time segmentation points from the plurality of time segmentation points; According to the N+1 target time segmentation points, N section segmentation intervals are determined from the plurality of sections.

8. The method according to any one of claims 1 to 4, wherein The profile data includes temperature data, conductivity data and pressure data; The step of obtaining the vertical density of each of the plurality of cross sections according to the cross section data of each of the plurality of cross sections comprises: The vertical density of each of the plurality of cross sections is obtained according to the temperature data, the conductivity data, and the pressure data of each of the plurality of cross sections.

9. A device for processing vertical structures of mesoscale vortices, wherein: include: An acquisition module is used to obtain profile data of multiple profiles of a single underwater glider in a mesoscale vortex; A first obtaining module is configured to obtain vertical densities of each of the plurality of cross sections based on the cross section data of each of the plurality of cross sections, wherein the vertical density represents the density of the cross section at different depths in the mesoscale vortex; a determination module, configured to determine N section segment intervals from the plurality of sections according to respective vertical densities of the plurality of sections, where N is a positive integer; A construction module is used to construct a multivariate ordinal mode sequence for each of the N section segment intervals based on the vertical density difference between adjacent sections in each of the N section segment intervals, wherein the multivariate ordinal mode sequence represents the structural characteristics of multiple sections of the mesoscale vortex at different depths; The second obtaining module is used to obtain the visualization result of the vertical structure of the mesoscale vortex according to the multivariate ordinal mode sequence of each of the N section segment intervals.

10. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 8.

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