Ocean front intelligent tracking method, device, medium and product

Abstract

The application discloses an ocean front intelligent tracking method and device, medium and product, relates to the field of ocean front tracking, and comprises the following steps: obtaining a target front surface of a previous time node and a plurality of to-be-judged front surfaces of a next time node; combining a double extreme value method and point-to-point, point-to-surface and surface-to-surface distance calculation sequences, determining one to-be-judged front surface most similar to the target front surface and marking the to-be-judged front surface as a most similar front surface; determining a front surface tracking result based on the width of the most similar front surface, the width of the target front surface and the distance between the target front surface and the most similar front surface; and the front surface tracking result is that the target front surface and the most similar front surface are the same ocean front surface, or the target front surface and the most similar front surface are different ocean front surfaces. The application can realize more accurate ocean front surface tracking.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ocean front tracking, and particularly relates to an ocean front intelligent tracking method, device, medium and product. BACKGROUND

[0002] In recent years, the huge energy carried by mesoscale phenomena in the ocean is recognized as the main energy form in the ocean, and there is a huge demand for tracking one of its main manifestations: ocean front. The existing ocean front intelligent tracking algorithms, such as Lagrange index and Long Short-term Memory Networks (LSTM), have the disadvantages of less information utilization, small research scale, artificial threshold parameter setting, and poor continuity. SUMMARY

[0003] The purpose of the present application is to provide an ocean front intelligent tracking method, device, medium and product, which can realize more accurate ocean front tracking.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides an ocean front intelligent tracking method, comprising:

[0006] obtaining a target front at a previous time node and a plurality of to-be-judged fronts at a next time node;

[0007] determining one to-be-judged front most similar to the target front and marking it as the most similar front in combination with the double extreme value method and the distance calculation sequence from point to point, from point to surface, and from surface to surface;

[0008] determining a front tracking result based on the width of the most similar front, the width of the target front, and the distance between the target front and the most similar front; the front tracking result is that the target front and the most similar front are the same ocean front, or the target front and the most similar front are different ocean fronts.

[0009] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement an ocean front intelligent tracking method.

[0010] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement an ocean front intelligent tracking method.

[0011] In a fourth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method for intelligent tracking of oceanic fronts.

[0012] According to the specific embodiments provided in the present application, the present application has the following technical effects: the present application provides a method for intelligent tracking of oceanic fronts, device, medium and product, defines three distances (point-to-point, point-to-surface, and surface-to-surface distances), i.e. distances in the metric space in functional analysis, determines the most similar front surface between two time nodes by double extremum, and then makes a decision based on the width of the most similar front surface, the width of the target front surface, and the distance between the target front surface and the most similar front surface, to determine whether it is the same front surface. Through the above steps, the present application realizes the fusion of double extremum and three distance decisions, solves the problem of less information used by the traditional oceanic front tracking algorithm, and can obtain better front tracking results. Moreover, since the two minimum values are accurately realized, optimization can be achieved, and more accurate front tracking results can be obtained. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0014] Figure 1 FIG. 1 is a diagram of an application environment of the method for intelligent tracking of oceanic fronts in an embodiment of the present application.

[0015] Figure 2 FIG. 2 is a flowchart of the method for intelligent tracking of oceanic fronts provided in an embodiment of the present application.

[0016] Figure 3 FIG. 3 is a diagram for calculating the first distance and the second distance provided in an embodiment of the present application.

[0017] Figure 4 FIG. 4 is a diagram for calculating the third distance and the front tracking result provided in an embodiment of the present application.

[0018] Figure 5 FIG. 5 is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of the present application.

[0020] In order to make the purposes, characteristics and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] The ocean front intelligent tracking method provided in the embodiments of the present application can be applied to an application environment as shown in Figure 1 . The terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be separately arranged, or integrated on the server 104, or placed on a cloud or other server. The terminal 102 can send the front surface information of multiple time nodes to the server 104. After receiving the front surface information, the server 104 first determines a target front surface of a previous time node and multiple to-be-judged front surfaces of a next time node; then determines one to-be-judged front surface most similar to the target front surface and marks it as the most similar front surface, in combination with the double extreme value method and the distance calculation sequence from point to point, from point to surface, and from surface to surface; and finally determines a front surface tracking result based on the width of the most similar front surface, the width of the target front surface, and the distance between the target front surface and the most similar front surface. The server 104 can feed back the front surface tracking result to the terminal 102. In addition, in some embodiments, the ocean front intelligent tracking method can also be implemented by the server 104 or the terminal 102 alone.

[0022] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0023] In an exemplary embodiment, as shown in Figure 2 , an ocean front intelligent tracking method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or both the terminal and the server. In the embodiments of the present application, the method is taken as an example applied to the server 104 in Figure 1 , and includes the following steps 201 to 203.

[0024] Step 201: Obtain the target front at the previous time node and multiple fronts to be evaluated at the next time node. The time node can be a day, that is, it is necessary to obtain the front information of two adjacent days, or it can obtain the front information of multiple days within a certain time length. Then, extract the front information of any two adjacent days as the data basis for subsequent processing, realize the tracking and comparison of all fronts of two adjacent days, and thus realize the front tracking of multiple days in the time series.

[0025] Additionally, when acquiring frontal information, it can be recorded and stored in an Excel spreadsheet. In this spreadsheet, the first column is the serial number (e.g., 1, 2, 3, 4, 5…), the second column is the front's ID, the third column is the front's width, the fourth column is the front's intensity, the fifth column is the front's length, and the sixth column and subsequent columns are the latitude and longitude of each point on the front. In subsequent calculations, this information can be directly retrieved from the Excel spreadsheet.

[0026] Step 202: Combining the double maximum / minimum method and the distance calculation order of point-to-point, point-to-surface, and surface-to-surface, determine the front to be evaluated that is most similar to the target front and mark it as the most similar front. In one application example, step 202 includes the following steps (21)-(25).

[0027] (21) Select any front to be evaluated as the current evaluation front.

[0028] (22) For any point on the target front, calculate the distance between the point and any point on the current evaluation front, and mark it as the first distance; that is, the distance calculation from a point on one front to a point on another front is realized, specifically, as follows: Figure 3 As shown, the formula for calculating the first distance is:

[0029] .

[0030] in, Indicates the target front The j Each front point and the current assessment of the front. The The distance between each point of attack refers to the first distance; This indicates the resolution after converting latitude and longitude into distance. Indicates the target front The j The longitude of the point of attack Indicates the target front The j The latitude of the point of attack; Indicates the current assessment of the front. The The longitude of the point of attack Indicates the current assessment of the front. The The latitude of the front point, M, indicates the current front being evaluated. The number of points of attack ; N Indicates the target front The number of points of attack .

[0031] With target front Taking the first front point as an example, the corresponding first distance The calculation formula is: .in, 1 indicates the target front The longitude of the first front. 1 indicates the target front The latitude of the first front.

[0032] (23) Take the minimum value among all the first distances as the distance between the front point and the current evaluation front, and mark it as the second distance; that is, the process of determining the first minimum value is realized, and the distance between the point and the surface is also realized. Specifically, the formula for calculating the second distance is:

[0033] .

[0034] in, Indicates the target front The j Each front point and the current assessment of the front. The distance between them refers to the second distance; Refers to the first distance.

[0035] With target front Taking the first front point as an example, the corresponding second distance The calculation formula is: .

[0036] in, Indicates the target front The first point of attack and the current assessment of the front The distance between them refers to the second distance; Refers to the target front The first distance corresponding to the first point of attack.

[0037] (24) The second distances corresponding to all front points on the target front are averaged to obtain the distance between the target front and the current evaluation front, and this distance is marked as the third distance; that is, the process of calculating the distance between the front at the previous time node and the front at the next time node is realized. Specifically, as follows: Figure 4 As shown, the formula for calculating the third distance is:

[0038] .

[0039] in, Indicates the target front Compared with the current assessment front The distance between them refers to the third distance; Indicates the target front Upper j Each front point and the current assessment of the front. The distance between them.

[0040] (25) Based on the third distance corresponding to any of the fronts to be evaluated, select the front with the smallest third distance as the front most similar to the target front, and mark it as the most similar front. This achieves the process of determining the second minimum value. Specifically, the formula for calculating the most similar front is:

[0041] .

[0042] in, Indicates the most similar front; Indicates the target front With the front to be evaluated The distance between them P This indicates the number of fronts to be evaluated. ; argmin () indicates the value of the independent variable when the function is being minimized.

[0043] In addition, the distances obtained in the above calculations all refer to geographical distances (km).

[0044] Step 203: Determine the front tracking result based on the width of the most similar front, the width of the target front, and the distance between the target front and the most similar front; the front tracking result is that the target front and the most similar front are the same ocean front, or that the target front and the most similar front are different ocean fronts.

[0045] When the target front and the most similar front are different oceanic fronts, it indicates that the target front did not appear at the later time point and has disappeared. In practical applications, the target front can be marked at the previous time point as the last time point of that front.

[0046] In a specific application, the width of the most similar front (i.e., the width of the target front) is averaged. Then, the average value is compared with the distance between the target front and the most similar front. If the distance between the target front and the most similar front is less than or equal to the average value, it indicates that the target front and the most similar front are the same oceanic front; if the distance between the target front and the most similar front is greater than the average value, it indicates that the target front and the most similar front are different oceanic fronts. Specifically, the function formula for the front tracking result is:

[0047] : This indicates that the target front and the most similar front are the same oceanic front; The target front and the most similar front are represented by different oceanic fronts; among them, Indicates the target front Most similar front The distance between them Indicates the target front width, Indicates the most similar front The width.

[0048] In another exemplary embodiment of this application, the target front at the previous time point has five front points, and the front to be evaluated at the next time point has four front points. The corresponding intelligent ocean front tracking process is as follows: Figure 3 As shown, for the target front The first front point on the surface is calculated, and its relationship with the current evaluation front is determined. The first distance between each point in the middle: ,Depend on Figure 3 It can be seen from this that the minimum value appears At that time, therefore the target front The first point of attack on the front to the current judging front The second distance for .like Figure 4 As shown, for the target front The third distance between two fronts can be obtained by averaging the second distances of the five front points:

[0049] .

[0050] If the current assessment is the front The frontal system is already at a later time point (such as the next day) and is a mid-range target front. Given the recent frontal system, further decisions can be made accordingly. Figure 4 It can be clearly seen from the middle Therefore, they are different ocean fronts.

[0051] In an application example, tracking results of multiple fronts in the latitude and longitude range of 105-118°E and 4-21°N in two adjacent days can be collected, and numbers are marked on the fronts to represent their IDs, and the same ID represents the same ocean front. When tracking for multiple consecutive days, the tracking display can be performed through the ID of the front. If the ID of a front disappears in a later time node, it means that the front has disappeared.

[0052] In summary, the present application relates to the fields of functional analysis, optimization method and intelligent identification of mesoscale phenomena in the ocean, and provides an optimization tracking algorithm, which does not need to set a threshold, has strong robustness, and combines physical oceanography knowledge, and meets actual needs. In addition, three distances, i.e., point-to-point, point-to-face and face-to-face, are defined, which are progressive, and the distances can be mathematically strictly verified to meet the three properties of distance in a metric space, i.e., positive definiteness, symmetry and triangle inequality, so that the distances can be brought into the framework of functional analysis and used for other tracking problems, and have a wide application range.

[0053] In an example embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram of the computer device can be as shown in Figure 5 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the intelligent front tracking method for the ocean.

[0054] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an example embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. The processor executes the computer program to implement the steps in each method embodiment described above.

[0055] In an exemplary embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, implements the steps of any of the above method embodiments.

[0056] In an exemplary embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0057] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0058] It can be understood by those skilled in the art that all or part of the processes in the above embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0059] The database involved in each of the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each of the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0060] Any combination of the technical features of the above embodiments can be made, and in order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0061] The principles and implementation modes of the present application are described by applying specific examples herein, and the above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for intelligent tracking of oceanic fronts, characterized in that, The intelligent ocean front tracking method comprises: obtaining a target front surface at a previous time node and a plurality of to-be-judged front surfaces at a next time node; determining one to-be-judged front surface most similar to the target front surface and marking it as a most similar front surface in combination with a double extreme value method and a point-to-point, point-to-surface and surface-to-surface distance calculation sequence; determining a front surface tracking result based on a width of the most similar front surface, a width of the target front surface and a distance between the target front surface and the most similar front surface; the front surface tracking result is that the target front surface and the most similar front surface are the same ocean front surface or the target front surface and the most similar front surface are different ocean front surfaces; a functional formula of the front surface tracking result is: : the target front is characterized as the same ocean front as the most similar front; : the target front is characterized as a different ocean front from the most similar front; wherein, represents the distance between the target facet A1 and the most similar facet represents the width of the target facet A1 facet, represents the width of the most similar facet .​ 2. The ocean front intelligent tracking method according to claim 1, characterized in that, determining one to-be-judged front surface most similar to the target front surface and marking it as a most similar front surface in combination with a double extreme value method and a point-to-point, point-to-surface and surface-to-surface distance calculation sequence, comprising: selecting any to-be-judged front surface as a current judgment front surface; calculating a distance between any front point on the target front surface and any front point on the current judgment front surface and marking it as a first distance; taking a minimum value in all the first distances as a distance between the front point and the current judgment front surface and marking it as a second distance; averaging the second distances corresponding to all the front points on the target front surface to obtain a distance between the target front surface and the current judgment front surface and marking it as a third distance; selecting a to-be-judged front surface with the minimum third distance as one to-be-judged front surface most similar to the target front surface and marking it as a most similar front surface according to the third distance corresponding to any to-be-judged front surface.

3. The ocean front intelligent tracking method according to claim 2, wherein, a calculation formula of the first distance is: ； in, Indicates the target front The j Each front point and the current assessment of the front. The The distance between each point of attack refers to the first distance; This indicates the resolution after converting latitude and longitude into distance. Indicates the target front The j The longitude of the point of attack Indicates the target front The j The latitude of the point of attack; Indicates the current assessment of the front. The The longitude of the point of attack Indicates the current assessment of the front. The The latitude of the front point, M, represents the current front being evaluated. The number of points of attack ; N Indicates the target front The number of points of attack .

4. The ocean front intelligent tracking method according to claim 2, wherein, a calculation formula of the second distance is: ； in, Indicates the target front The j Each front point and the current assessment of the front. The distance between them refers to the second distance; M refers to the first distance, and M represents the current frontal area being assessed. The number of points of attack.

5. The ocean front intelligent tracking method of claim 2, wherein, a calculation formula of the third distance is: ； in, Indicates the target front Compared with the current assessment front The distance between them refers to the third distance; Indicates the target front Upper j Each front point and the current assessment of the front. The distance between them; N Indicates the target front The number of points of attack.

6. The ocean front intelligent tracking method of claim 2, wherein, a calculation formula of the most similar front surface is: ； wherein represents the most similar front; represents the target front between the distance, between the distance, P represents the number of fronts to be judged, ; argmin () represents the argument value at which the minimum value of the function value is sought.

7. A computer device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the intelligent ocean front tracking method in any one of claims 1-6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the intelligent ocean front tracking method in any one of claims 1-6.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the intelligent ocean front tracking method in any one of claims 1-6.

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