Mesoscale vortex vertical structure processing method and device and electronic equipment

Abstract

The invention provides a method and a device for processing a mesoscale vortex vertical structure and electronic equipment, which can be applied to the technical field of underwater observation. The method comprises the following steps: acquiring respective profile data of a plurality of profiles of a single underwater glider in a mesoscale vortex; according to the respective profile data of the plurality of profiles, the respective vertical densities of the plurality of profiles are obtained, and the vertical densities represent the densities of the profiles at different depth positions in the mesoscale vortex; determining N section segmentation intervals from the plurality of sections according to the respective vertical densities of the plurality of sections, N being a positive integer; according to the vertical density difference of adjacent sections in each section of the N section segmented sections, a multivariate ordinal modal sequence of each section segmented section is constructed, and the multivariate ordinal modal sequences represent the structural features of the multiple sections of the mesoscale vortex at different depths; and obtaining a visualization result of the mesoscale vortex vertical structure according to the respective multivariate ordinal modal sequences of the N section segmentation 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 mesoscale ocean phenomena in the ocean, which play an important role in regulating global ocean heat, material transport, and climate change. Limited by observation methods and observation costs, it is difficult for people to continuously observe and accurately describe long-term and large-scale ocean mesoscale phenomena. In addition, the observation of the vertical structure of mesoscale eddies in related technologies is relatively complex and not intuitive enough. 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] Obtaining profile data of multiple sections of a single underwater glider in a mesoscale vortex;

[0006] According to the profile data of each of the plurality of profiles, a vertical density of each of the plurality of profiles is obtained, wherein the vertical density represents the density of the profile at different depths in the mesoscale vortex;

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

[0008] According to the vertical density difference of the adjacent sections in each of the N section segmentation intervals, construct a multivariate ordinal modal sequence for each of the N section segmentation intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of the multiple sections of the mesoscale vortex at different depths;

[0009] According to 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, used for acquiring profile data of each of multiple profiles of a single underwater glider in a mesoscale vortex;

[0012] A first obtaining module is used to obtain vertical densities of each of the plurality of cross sections according to 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;

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

[0014] A construction module, for constructing a multivariate ordinal modal sequence for each of the N section segment intervals according to the vertical density difference of the 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;

[0015] 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 sequences 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 above method when executed.

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

[0022] According to the embodiments of the present disclosure, profile data of each of multiple profiles of a single underwater glider in a mesoscale vortex is obtained; the vertical density of each of the multiple profiles is obtained according to the profile data of each of the multiple profiles; N profile segmentation intervals are determined from the multiple profiles according to the vertical density of each of the multiple profiles; a multivariate ordinal modal sequence of each of the N profile segmentation intervals is constructed according to the vertical density difference of adjacent profiles in each of the N profile segmentation intervals; 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 profile segmentation intervals, the vertical density of the profile of the mesoscale vortex is calculated through the profile data and the profile segmentation interval for characterizing its vertical structure is determined, it is proposed to construct an ordinal modal sequence from the vertical density of the profile of the mesoscale vortex, and it is proposed to characterize the visualization result 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 the observation of 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 in the present invention can be applied;

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

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

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

[0028] Figure 5 The following schematically shows a schematic diagram of constructing a univariate 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 shows an indicator visualization result according to an embodiment of the present disclosure;

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

[0032] Fig. 9 A block diagram of an electronic device suitable for implementing the method described above 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 exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, 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 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 existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0035] All terms (including technical and scientific terms) used herein 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 using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression 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 the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0038] In the related technologies, the widespread use of satellite altimeters and drifting floats has achieved many research results in the observation of mesoscale eddies, but there are also many problems, such as the observation object is limited to the surface structure of the mesoscale eddies, the vertical structure of the mesoscale eddies cannot be measured, and the acquisition of high-resolution field observation data is also very expensive. Although the profile buoy makes up for the shortcomings of satellite altimeters and drifting floats to a certain extent, its poor maneuverability makes it quickly leave the range of the mesoscale eddies, making it difficult to observe them continuously for a long time, and there are problems such as uneven and discontinuous temporal and spatial distribution of the observation data and low spatial resolution, which cannot meet the requirements for the characterization of the vertical structure of the mesoscale eddies.

[0039] In the related technologies, the research on the evolution characteristics of mesoscale eddies can be roughly divided into two categories: one is to statistically analyze the overall evolution trend of eddies in a certain sea area from various physical ocean element fields, such as flow velocity, sea surface temperature, sea surface height and other field data; the other is to observe and statistically analyze the eddy life cycle as a research object for a long time. Although the current research has revealed the rough evolution characteristics of eddies in the region to a certain extent, from the perspective of research methods, most of them are based on complex and multi-parameter ocean dynamic models, and require a lot of human experience and judgment, and the process is complicated and slow.

[0040] Underwater gliders are a new type of unmanned autonomous underwater vehicle, which is driven by buoyancy and has the characteristics of long flight time, sustainability, robustness, autonomy, etc. It is one of the important underwater vehicles for building an in-situ ocean observation technology system. However, there is still a lack of good methods for using underwater gliders to observe mesoscale vortices, and there are few practical applications. In practice, they are often deployed in the form of large-scale networking formations, which consumes a lot of resources.

[0041] Complex networks are the main research method for the evolution of the situation between different scales of complex systems. With the development of complex network technology, its application in marine complex systems has become a research hotspot and difficulty. At present, few researchers can introduce complex network theory into the effective description of the vertical structural characteristics of mesoscale vortices. This is generally attributed to two reasons: on the one hand, due to the limitations of the subject area, it is difficult for researchers to find an effective complex network model to characterize the mesoscale vortex; on the other hand, the main focus of researchers is mostly on the surface characteristics of the mesoscale vortex, and they often ignore their more research-worthy vertical characteristics.

[0042] Therefore, the observation means and methods of mesoscale vortices in related technologies cannot quickly, intuitively and explainably 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 profiles of a single underwater glider in a mesoscale vortex; obtaining the vertical density of each of the multiple profiles based on the profile data of each of the multiple profiles, wherein the vertical density characterizes the density of the profile at different depths in the mesoscale vortex; determining N profile segmentation intervals from the multiple profiles based on the vertical density of each of the multiple profiles, 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 profiles in each of the N profile segmentation intervals, wherein the multivariate ordinal modal sequence characterizes the structural characteristics of multiple profiles 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 to which the present disclosure can be applied is schematically shown.

[0045] It should be noted that Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot 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 to provide a medium for 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] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the 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, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[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 (only as an example) that provides support for 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 the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0050] It should be noted that the processing method for the vertical structure of the 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 the mesoscale vortex provided in the embodiment of the present disclosure can generally be set in the server 105. The processing method for the vertical structure of the 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. Correspondingly, the processing device for the vertical structure of the 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 processing method for the vertical structure of the mesoscale vortex provided in the embodiment of the present disclosure can also be performed 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. Correspondingly, 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 profile data of each of the multiple profiles 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 may be imported into the first terminal device 101. Then, the first terminal device 101 may locally execute the processing method for the vertical structure of the mesoscale vortex provided in the embodiment of the present disclosure, or send the image to be processed to other terminal devices, servers, or server clusters, and the other terminal devices, servers, or server clusters that receive the image to be processed may execute the processing method for 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 only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[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 obtained.

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

[0057] In operation S230, N section segment intervals are determined from the multiple sections according to the 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 schematic diagram of cross-sectional data collection of a mesoscale vortex according to an embodiment of the present disclosure is shown schematically.

[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, a plurality of 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, and the vertical density is the density of the section at different depths in the mesoscale vortex.

[0065] According to an embodiment of the present disclosure, N section segment intervals can be determined from multiple sections according to the vertical density variation trend between each section. The vertical density variation trend between each section can be determined according to the size of the vertical density of each section at the same depth position. The N section segment intervals can be a set of sections with relatively large vertical density variation trends.

[0066] According to the embodiments of the present disclosure, the vertical density difference of adjacent sections in each of the N section segmentation intervals is the difference in vertical density of two adjacent sections at the same depth. According to the vertical density difference of adjacent sections in each of the N section segmentation intervals, a multivariate ordinal modal sequence of each interval can be constructed to extract the structural features implicit in the vertical density of the mesoscale vortex section, that is, the structural features of multiple sections of the mesoscale vortex at different depths.

[0067] According to the embodiments 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 the embodiments of the present disclosure, profile data of each of multiple profiles of a single underwater glider in a mesoscale vortex is obtained; the vertical density of each of the multiple profiles is obtained according to the profile data of each of the multiple profiles; N profile segmentation intervals are determined from the multiple profiles according to the vertical density of each of the multiple profiles; a multivariate ordinal modal sequence of each of the N profile segmentation intervals is constructed according to the vertical density difference of adjacent profiles in each of the N profile segmentation intervals; 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 profile segmentation intervals, the vertical density of the profile of the mesoscale vortex is calculated through the profile data and the profile segmentation interval for characterizing its vertical structure is determined, it is proposed to construct an ordinal modal sequence from the vertical density of the profile of the mesoscale vortex, and it is proposed to characterize the visualization result 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 the observation of 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 according to the respective section data of the multiple sections may include: obtaining the vertical density of each of the multiple sections according to 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, and 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 the embodiments 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] Among them, 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 section segmentation intervals are determined from multiple sections according to the vertical densities of each of the multiple sections, including: constructing a multivariate time series according to the vertical densities of each of the multiple sections; dividing the multivariate time series into multiple segmentation time series according to 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 according to 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 section segmentation intervals from the multiple sections according to 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 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. Greedy Gaussian Segmentation (GGS) is a heuristic method that can be used to solve the multivariate time series segmentation problem. GGS always produces a local optimal solution, so that any breakpoint in the end cannot continue to improve the overall goal. GGS is based on a greedy strategy, that is, segmenting the time series one by one from left to right, and each time determining the segmentation point by maximizing the variance ratio of the left and right sides of the segmentation point. This method can be easily extended to multivariate time series segmentation problems with dimensions exceeding 1000 or with arbitrary length, and can approximately solve the problem 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 the 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 observed value x corresponding to each time segmentation point t t How to adapt to its Gaussian distribution to quantify the fit 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 observed value at time segmentation point t, Represents the covariance matrix of the segmented time series at time segmentation point t, 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 the and determine the location of the target time segmentation point, the greedy optimization objective function can be expressed as the following formula (6):

[0091] (6)

[0092] Among them, 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 of a vertical density visualization result and a cross-section segmentation interval according to an embodiment of the present disclosure is schematically shown.

[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 profile 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 of the profile.

[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 segmentation intervals, a vertical density difference sequence may be obtained according to the vertical density difference of adjacent profiles within the profile segmentation interval, as shown in the following formula (7):

[0097] (7)

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

[0099] According to the embodiments of the present disclosure Figure 4 In the case of N=3, the vertical density difference sequences of the three section 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 univariate ordinal modal sequence can be the characteristics of adjacent profiles only in the vertical direction. Multiple univariate ordinal modal sequences can be established. According to the univariate 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 the 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 of the N profile segmentation intervals, a unary ordinal modal sequence of each of the N profile segmentation intervals is constructed, including: based on the vertical density difference sequences of each of the N profile segmentation intervals and a preset sliding window, a window sequence set of each 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 differences of each of the multiple window sequences in the window sequence set, the ordinal of each of the N profile segmentation intervals is determined, wherein the ordinal of the window sequence represents the order of the vertical density differences in the window sequence; based on the ordinal of each of the N profile segmentation intervals, a unary ordinal modal sequence of each 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 along the depth direction of N profile segmentation intervals.

[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 preset sliding window size. For example, if the preset sliding window size 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" ", there are 5!=120 possible ordinal modes, " " 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 univariate ordinal modal sequences of the N profile segment intervals can be constructed.

[0105] Figure 5 A schematic diagram of constructing a univariate ordinal modal sequence according to an embodiment of the present disclosure is schematically shown.

[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 univariate ordinal modal sequences of the respective intervals shown in (b).

[0107] According to the embodiment of the present disclosure, the one-dimensional ordinal modal sequence constructed from each window sequence is arranged in the order of conversion to obtain the one-dimensional ordinal modal sequence of any sub-signal in each section segmentation interval. Finally, the multivariate ordinal modal sequence is obtained from the profile data in each section segmentation interval. Figure 4 In the case of N=3, the three section segment 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 a mesoscale vortex is obtained according to the multivariate ordinal modal sequences of each of the N section segmentation intervals, which may include: obtaining a visualization result of an ordinal modal recursion diagram of the vertical structure of a mesoscale vortex according to the multivariate ordinal modal sequences of each of the N section segmentation intervals; obtaining an indicator visualization result of the vertical structure of a mesoscale vortex according to the visualization result of the ordinal modal recursion diagram of the vertical structure of a 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 visualization result of the ordinal modal recursive graph of the vertical structure of the mesoscale vortex.

[0111] According to the embodiments of the present disclosure, based on the visualization results of the ordinal modal recursive graph of the vertical structure of the mesoscale vortex, it is possible to construct the index of the vertical structure of the mesoscale vortex for a complex network and obtain the visualization results of the index of the vertical structure of the mesoscale vortex.

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

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

[0114] (8)

[0115] Among them, 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 The 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, and 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 a visualization result of an ordinal modal recursion diagram according to an embodiment of the present disclosure.

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

[0121] For each of them, we randomly select 6 univariate ordinal modal sequences to represent the ordinal modal recursive graph. The visualization result is as follows Figure 6 As shown, we can preliminarily and intuitively characterize the vertical structure of the mesoscale vortex.

[0122] According to an embodiment of the present disclosure, the intrinsic texture structure of the ordinal modal recurrence diagram visualization result is used to visualize the intrinsic nonlinear dynamic vertical structure characteristics of the mesoscale vortex.

[0123] According to an embodiment of the present disclosure, based on the visualization results of the ordinal modal recursion graph of the vertical structure of the mesoscale vortex, an index visualization result of the vertical structure of the mesoscale vortex is obtained, including: based on the visualization results of the ordinal modal recursion graph of the vertical structure of the mesoscale vortex, 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 result of the vertical structure of the mesoscale vortex.

[0124] According to the embodiments 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-recursively perform cross-recursion on them to obtain a (L-4)×(L-4) recursive matrix, as shown in the following formula (10):

[0126] (10)

[0127] Among them, 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 recursive points in each ordinal modal recursive graph, the cross-recursive rate OMCRR of each ordinal modal recursive 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, treat it as a node and set the crossover 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. , 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, i.e., the elements in the weight matrix W; represents the weighted clustering coefficient of any node v; is the eigenvalue 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. Therefore, the vertical structure of the mesoscale vortex can be characterized by using the multivariate weighted ordinal modal recursive network.

[0138] According to the embodiments of the present disclosure, the vertical structure of the mesoscale vortex is quickly, intuitively and interpretably characterized by the in-situ observation data (i.e., profile data) of the mesoscale vortex of a single underwater glider. By constructing a multivariate ordinal modal sequence, the in-situ observation data of the mesoscale vortex collected by a single underwater glider can be quickly and effectively mined for spatiotemporal features, and the ordinal modal recursive graph is used to intuitively characterize the inherent nonlinear dynamic vertical structural characteristics of the mesoscale vortex. By constructing a multivariate weighted ordinal modal recursive network and using network indicators, the vertical structure of the mesoscale vortex is deeply interpretably characterized.

[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 the profile data of each of the multiple profiles of a single underwater glider in the mesoscale vortex.

[0143] The first obtaining module 820 is used to obtain the vertical density of each of the multiple sections according to 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 section segment intervals based on the vertical density difference of 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.

[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 sequences of each of the N section segment intervals.

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

[0152] A first construction unit is used 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 a plurality of window sequences having the same length as the preset sliding window;

[0153] A second construction unit is used to determine the ordinal number of each window sequence of the N profile segment intervals according to the vertical density difference 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 difference in the window sequence;

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

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

[0159] The first obtaining unit is used to select M univariate ordinal modal sequences from the multivariate ordinal modal sequences of the N section segment intervals for phase space reconstruction, and obtain the phase space trajectory vector sequence of the M univariate 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 univariate 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 a mesoscale vortex vertical structure includes:

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

[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] Thirdly, a subunit is obtained, which is used to obtain the indicator visualization results of the vertical structure of the mesoscale vortex according to 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 a plurality of sections according to respective vertical densities of the plurality of sections includes:

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

[0167] A second determination 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 multiple segmented time series to obtain the mean vector and covariance matrix of each 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 multiple time segmentation points according to the mean vectors and covariance matrices of the multiple segmented time series;

[0170] A fifth determination submodule is used to optimize the likelihood function and determine N+1 target time segmentation points from the multiple 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; the first obtaining module 820 for obtaining the vertical density of each of the multiple profiles according to 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 sections according to the temperature data, conductivity data and pressure data of each of the multiple sections.

[0174] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way 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 them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0175] For example, any multiple 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 in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one 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 by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in a suitable combination of any of them. 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 specifically refers to the processing method part for the vertical structure of the mesoscale vortex, which will not be repeated here.

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

[0178] like Fig. 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 part 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 dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an 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] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0180] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 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 contains 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 without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0183] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an 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 may be used by or in combination 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, and the computer program contains a 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 executed. 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 based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 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, etc., or any suitable combination of the above.

[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 computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of 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 through the Internet).

[0189] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, 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 may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented 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, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0190] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of 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: Obtaining profile data of multiple sections of a single underwater glider in a mesoscale vortex; According to the respective cross-sectional data of the plurality of cross-sections, a vertical density of the respective cross-sections is obtained, wherein the vertical density represents the density of the cross-section at different depth positions in the mesoscale vortex; Determine N section segment intervals from the multiple sections according to the vertical densities of the multiple sections, where N is a positive integer; Constructing a multivariate ordinal modal sequence for each of the N section segment intervals according to the vertical density difference between the adjacent sections in each of the N section segment intervals, wherein the multivariate ordinal modal sequence characterizes the structural features of the multiple sections of the mesoscale vortex at different depths; According to the multivariate ordinal modal sequences of each of the N section segment intervals, the 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 of each of the N section segment intervals according to the vertical density difference of the adjacent sections in each of the N section segment intervals comprises: According to the vertical density differences of adjacent sections in each of the N section segmentation intervals, a vertical density difference sequence of each of the N section segmentation intervals is obtained; Constructing a univariate 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 sequences of each of the N profile segment intervals, a multi-ary ordinal modal sequence of each of the N profile 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: According to the vertical density difference sequences of the N section segment intervals and the preset sliding window, a window sequence set of the sections of the N section segment intervals along the depth direction is obtained, wherein the window sequence set includes a plurality of window sequences having the same length as the preset sliding window; Determining the ordinal number of each of the N section 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; According to the ordinal numbers of the window sequences of the N profile segmentation intervals, unary ordinal modal sequences of the N profile segmentation intervals are constructed.

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

5. The method according to claim 4, wherein: The method of obtaining a visualization result of an ordinal mode recursion diagram of the vertical structure of the mesoscale vortex according to the multivariate ordinal mode sequences of each of the N section segment intervals includes: Selecting M univariate ordinal modal sequences from the respective multivariate ordinal modal sequences of the N section segment intervals for 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 univariate ordinal modal sequences of each of the multivariate ordinal modal sequences, the 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 visualization result of the index of the mesoscale vortex vertical structure is obtained according to the visualization result of the ordinal modal recurrence diagram of the mesoscale vortex vertical structure, including: 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; According to the cross-recursion rate of the ordinal modal recursive graph, an average weighted clustering coefficient and graph energy of each of the multivariate ordinal modal sequences are obtained; 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 the respective vertical densities of the plurality of sections comprises: constructing a multivariate time series according to the vertical densities of each of the plurality of profiles; According to a plurality of time segmentation points, the multivariate time series is divided into a plurality of segmented time series; Modeling the plurality of segmented time series to obtain respective mean vectors and covariance matrices of the plurality of segmented time series; Obtaining the likelihood function of the multivariate time series with respect to the multiple time segmentation points according to the respective mean vectors and covariance matrices of the multiple segmented time series; Optimizing the likelihood function to determine N+1 target time division points from the plurality of time division points; According to the N+1 target time division 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 multiple cross sections is obtained according to the temperature data, the conductivity data and the pressure data of each of the multiple cross sections.

9. A device for processing the vertical structure of a mesoscale vortex, wherein: include: An acquisition module, used for acquiring profile data of each of multiple profiles of a single underwater glider in a mesoscale vortex; A first obtaining module is used to obtain vertical densities of each of the multiple sections according to the respective section data of the multiple sections, wherein the vertical density represents the density of the 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, used for constructing a multivariate ordinal modal sequence for each of the N section segment intervals according to the vertical density difference of the 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; 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 sequences 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 implement the method according to any one of claims 1 to 8.

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