Ocean surface flow field construction method, system, equipment and medium

Abstract

The invention provides an ocean surface flow field construction method, system, device and medium, and relates to the field of ocean big data mining and ocean surface flow field construction, and the method comprises the following steps: obtaining ship AIS dynamic data of a target sea area, and removing abnormal data; dividing space-time grids according to ship density distribution to perform data aggregation; projecting the to-ground navigational speed, the to-ground course and the ship head navigational direction to a rectangular coordinate system based on a ship speed vector synthesis relation, and constructing a linear equation set; combining a space-time homogeneity hypothesis strategy, and simultaneously solving all linear equations to obtain the flow component of the inner surface layer of the grid; fusing the flow field components of the adjacent grids by using an optimal interpolation method to generate a global continuous surface flow field data set; according to the method, through adaptive space-time grid division, vector projection modeling and optimal interpolation fusion, high-resolution and global continuous sea surface flow field dynamic reconstruction is realized, and the problem that the coverage rate of a traditional observation means in a near-shore area is insufficient is effectively solved.

Description

Technical Field

[0001] The present invention relates to the fields of ocean big data mining and ocean surface flow field construction, and more particularly to an ocean surface flow field construction method, system, equipment and medium. Background Art

[0002] Accurate monitoring and analysis of ocean surface currents is crucial for safe shipping route planning, pollutant dispersion prediction, search and rescue deployment, marine ecosystem research, and climate model validation. Especially in nearshore waters, busy shipping lanes, and estuaries, high-temporal and spatial resolution surface current information is essential data for ensuring safe maritime activities and understanding regional ocean dynamics.

[0003] Currently, mainstream ocean surface current observation methods rely primarily on satellite altimeters and drifting buoys. However, satellite altimeters suffer from significant data accuracy degradation or even loss nearshore due to signal interference and echo issues. Furthermore, their spatial resolution and temporal revisit period make it difficult to capture the complex and ever-changing nearshore flow field details. While drifting buoys can provide direct measurements, their deployment and maintenance costs are high, and their spatial coverage is sparse and random. This makes it difficult to establish a dense, continuous observation network in specific areas (especially nearshore and shipping lanes), resulting in a severe shortage of flow field data in these critical regions.

[0004] At the same time, the widespread use of Automatic Identification Systems (AIS) on merchant ships and large vessels continuously generates massive amounts of dynamic information, including ship position, speed over ground, course over ground, and heading. While existing research recognizes the potential of AIS data to reflect the marine environment and has conducted preliminary explorations, existing methods primarily focus on ship behavior itself (e.g., trajectory tracking and collision warning). Key drawbacks include the lack of a systematic, engineering-ready mathematical framework to efficiently and accurately invert the discrete, massive raw AIS dynamic data into a spatiotemporal gridded surface flow vector field. Existing attempts are often limited to local point or route analysis, failing to fully utilize the data's wide spatial and temporal coverage and effectively addressing the challenge of separating the ship's own dynamics, wind and wave effects, from the target flow field.

[0005] Even if local flow field information is inferred using partial AIS data, the results are discrete and non-uniformly distributed, with large data gaps that are not covered by ship tracks. Existing technologies lack effective, physically consistent data fusion and spatial expansion methods. Furthermore, simple spatial interpolation often ignores the physical continuity and dynamic constraints of the ocean flow field, resulting in unreasonable vortices, divergence, or discontinuities in the flow field generated in the blank areas. This reduces the physical consistency and usability of the global flow field dataset and fails to meet the needs of sophisticated applications.

[0006] Therefore, how to design a method for constructing ocean surface flow fields that can overcome the insufficient coverage of traditional observation methods in nearshore and high-traffic-density sea areas and achieve full-area continuous and physically consistent high-temporal and spatial resolution ocean surface flow field construction is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0007] In view of this, the present invention provides a method for constructing ocean surface current fields, aiming to solve the data sparsity problem of traditional ocean surface current observations in nearshore areas with high ship density. By mining the ocean dynamic characteristics implicit in ship AIS information, the temporal and spatial resolution limitations are broken through, and a physically consistent, globally continuous, high-precision surface current field dataset is generated, providing data support for marine environmental monitoring, shipping safety, etc.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a method for constructing an ocean surface flow field, comprising the following steps:

[0010] S1. Obtain the AIS dynamic data of the ship in the target sea area, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data;

[0011] S2, divide the space-time grid according to the ship density distribution to perform data aggregation;

[0012] S3. In each grid, the ground speed S based on the ship speed vector synthesis relationship g , the ground direction δ and the bow heading θ are projected into the rectangular coordinate system to construct a linear equation system;

[0013] S4. Combined with the temporal and spatial homogeneity assumption strategy, all linear equations in the same grid are solved simultaneously to obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ;

[0014] S5. For grids without observation data, the optimal interpolation method is used to fuse the flow field components of adjacent grids to generate a global continuous surface flow field dataset.

[0015] Preferably, in S1, removing abnormal data includes:

[0016] Decompose the ship's ground speed vector into the longitudinal component S gx and the latitudinal component S gy , calculate acceleration based on time series and ;

[0017] Set dynamic threshold a th ,when Or if the ship status is turning / speed changing operation, the corresponding AIS data point will be eliminated.

[0018] Preferably, the S2 includes:

[0019] The average daily number of ship track points per unit sea area is calculated as the ship density ρ for space-time grid division:

[0020] When ρ ≥ 10, a 1 / 8° × 1 / 8° spatial grid and a 1-day time window were used;

[0021] When 3≤ρ<10, a 1 / 8°×1 / 8° spatial grid and a 3-day time window are used;

[0022] When ρ < 3, a 1 / 4° × 1 / 4° spatial grid and a 5-day time window are used.

[0023] Preferably, in S3, the ship velocity vector synthesis relationship is expressed as:

[0024]

[0025] in, represents the velocity vector of the ship relative to the land, represents the velocity vector of the ship relative to the water surface, Represents the seawater surface velocity vector.

[0026] Preferably, in S3, the linear equations are expressed as:

[0027]

[0028] Among them, S g The speed scalar of the ship relative to the land, S s Represents the speed scalar of the ship relative to the water surface, u cur 、v cur They represent the meridional and latitudinal components of the surface flow, respectively.

[0029] Preferably, the S4 includes:

[0030] For a grid containing n AIS data, construct a 2n-row × (n+2)-column matrix equation Ax=b; where Represents the unknown vector, and the coefficient matrix A is given by and The blocks are constructed according to the ship serial number, and the constant vector b is composed of and Composition, i represents the index variable, which is used to identify different ship data records;

[0031] Solve the overdetermined equations by the least squares method and output the meridional component u of the surface flow in the gridcur and the latitudinal component v cur .

[0032] Preferably, the S5 includes:

[0033] Establish interpolation objective function;

[0034]

[0035] Where v represents the flow field vector, v obs represents the observed flow field vector, R represents the observation error covariance, Q represents the flow field smoothing constraint weight, and ∇v represents the gradient of the flow field vector;

[0036] The minimum value of the objective function is solved by variational assimilation to obtain the physically continuous flow field distribution.

[0037] In a second aspect, the present invention provides a system for constructing an ocean surface flow field, comprising:

[0038] Data acquisition module: used to obtain the AIS dynamic data of ships in the target sea area, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data;

[0039] Grid division module: used to divide the space-time grid according to the ship density distribution for data aggregation;

[0040] Equation building block: used to calculate the ground speed S based on the ship velocity vector synthesis relationship in each grid. g , the ground direction δ and the bow heading θ are projected into the rectangular coordinate system to construct a linear equation system;

[0041] Component solving module: It is used to combine the temporal and spatial homogeneity assumption strategy to solve all the linear equations in the same grid and obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ;

[0042] Flow field reconstruction module: It is used to fuse the flow field components of adjacent grids using the optimal interpolation method for grids without observation data to generate a global continuous surface flow field dataset.

[0043] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for constructing an ocean surface flow field when executing the computer program.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for constructing an ocean surface flow field when executed by a processor.

[0045] The descriptions of the second to fourth aspects of the present invention can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second to fourth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0046] It can be seen from the above technical solutions that compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. This method uses a spatiotemporal grid division strategy that is adaptive to ship density. It adopts a fine spatiotemporal resolution in areas with dense ship populations (such as offshore areas and waterways), while reducing the resolution in sparse areas to ensure data volume. It fully utilizes the spatiotemporal coverage advantages of AIS data, significantly improving the spatial resolution and data update frequency of the surface flow field.

[0048] 2. Based on the ship motion vector decomposition model, AIS dynamic data is projected into a rectangular coordinate system to construct an overdetermined linear equation system. Combined with the assumption of spatiotemporal homogeneity, the system solves the equations simultaneously and directly outputs the meridional and latitudinal components of the surface current within the grid. This enables quantitative analysis from the ship's motion state to ocean dynamic factors, overcoming the limitation that AIS data does not directly contain water flow information.

[0049] 3. The optimal interpolation method is used to fuse the flow field calculation results of the discrete grid. The objective function is established through the variational assimilation technique. A Gaussian distributed continuous flow field is generated under the control of error covariance and smoothness constraints. Data gaps in areas not covered by the ship trajectory are eliminated to ensure the physical consistency and full coverage of the output data set in the temporal and spatial dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0051] Figure 1 A flow chart of a method for constructing an ocean surface flow field provided by an embodiment of the present invention;

[0052] Figure 2 A structural framework diagram of an ocean surface flow field construction system provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] The flow field construction method provided in the embodiments of the present application can be applied to a flow field construction server, which can be either hardware or software. When the flow field construction server is hardware, it can be implemented as a distributed server cluster that provides flow field construction services, or it can be implemented as a single server. When the flow field construction server is software, it can be installed in the servers listed above. It can be implemented as multiple software programs or software modules, or it can be implemented as a single software program or software module, without specific limitations here.

[0056] Example 1;

[0057] like Figure 1 As shown, this embodiment provides a method for constructing an ocean surface flow field, comprising the following steps:

[0058] S1. Obtain the AIS dynamic data of the ship in the target sea area, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data;

[0059] S2, divide the space-time grid according to the ship density distribution to perform data aggregation;

[0060] S3. In each grid, the ground speed S based on the ship speed vector synthesis relationship g , the ground direction δ and the bow heading θ are projected into the rectangular coordinate system to construct a linear equation system;

[0061] S4. Combined with the temporal and spatial homogeneity assumption strategy, all linear equations in the same grid are solved simultaneously to obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ;

[0062] S5. For grids without observation data, the optimal interpolation method is used to fuse the flow field components of adjacent grids to generate a global continuous surface flow field dataset.

[0063] This method uses a spatiotemporal grid division strategy that is adaptive to ship density to project ship AIS dynamic data into a rectangular coordinate system to construct an overdetermined linear equation system. The surface flow components within the grid are solved by combining the spatiotemporal uniformity assumption, and the global continuous flow field is generated by fusing discrete observation data based on the optimal interpolation method. This method significantly improves the spatiotemporal resolution of the surface flow field in nearshore high-traffic density areas, filling the gaps in traditional satellite / buoy observations. At the same time, through a mathematical transformation mechanism, it achieves the precise interpretation of ship motion data into ocean dynamic elements.

[0064] The following further describes each step in the above method in detail;

[0065] In this embodiment, S1, the dynamic data of the ship AIS in the target sea area is obtained, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data;

[0066] Among them, the abnormal data removal includes: decomposing the ship's speed vector to the ground into the longitudinal component S gx and the latitudinal component S gy , calculate acceleration based on time series and ; Set dynamic threshold a th ,when Or if the ship status is turning / speed changing operation, the corresponding AIS data point will be eliminated;

[0067] Specifically, differentiated acceleration thresholds can be set according to the type of ship, with container ships and oil tankers using a th =0.3m / s, fishing boats and tugboats use a th =0.5m / s, and when the time interval Δt of the ship trajectory point is greater than 300 seconds, the data point is directly eliminated to avoid the problem of positioning error accumulation; this effectively avoids the interference of ship maneuvering on velocity inversion, and at the same time directly eliminates the data with long positioning intervals, suppressing error accumulation at the source;

[0068] In addition, this step simultaneously integrates static ship information, including filtering AIS data of non-transport vessels based on the ship type in the static ship information; screening data of ships with a draft of ≤10 meters based on the draft in the static ship information; and verifying the unique identification and trajectory continuity of dynamic data using the Maritime Mobile Service Identity (MMSI).

[0069] It provides key screening basis and physical property calibration for surface current inversion. On the one hand, by filtering non-transport ships and screening small and medium-sized ships with a draft of ≤10 meters, it excludes ship data with complex dynamic characteristics or significant interference from deep currents, ensuring that the ship motion response in the target data set mainly reflects the effects of 0-10 meter surface currents; on the other hand, based on the draft depth, it limits the effective water layer range affected by the ship's flow, ensuring the physical consistency of the flow field inversion signal and the target depth from the source.

[0070] The MMSI unique identifier is used to accurately associate a ship's dynamic and static information and build a complete archive of its spatiotemporal behavior. This not only supports the verification of the continuous trajectory of the same ship, but also dynamically adjusts the data quality control threshold based on the ship type and draft parameters. This collaborative mechanism provides highly reliable data for subsequent spatiotemporal grid aggregation and flow field equation solution, avoiding deviations in flow field calculations caused by ship dynamic heterogeneity.

[0071] In this embodiment, S2, data aggregation is performed by dividing the space-time grid according to the ship density distribution; specifically, the following steps are performed:

[0072] The average daily number of ship track points per unit sea area is calculated as the ship density ρ for space-time grid division:

[0073] When ρ ≥ 10, a 1 / 8° × 1 / 8° spatial grid and a 1-day time window are used; when 3 ≤ ρ < 10, a 1 / 8° × 1 / 8° spatial grid and a 3-day time window are used; when ρ < 3, a 1 / 4° × 1 / 4° spatial grid and a 5-day time window are used;

[0074] This step implements hierarchical grid division based on the spatial distribution characteristics of ship density. Fine spatiotemporal grids are used in high-density areas, while the spatiotemporal scales are gradually relaxed in medium and low-density areas. This strategy breaks through the limitations of fixed grids, improves flow field resolution in data-rich areas, ensures statistical significance through spatiotemporal aggregation in sparse areas, and achieves the optimal spatial configuration of observation resources.

[0075] In this embodiment S3, in each grid, the ground speed S is calculated based on the ship speed vector synthesis relationship. g , the ground course δ and the bow heading θ are projected into a rectangular coordinate system to construct a linear equation system. The rectangular coordinate system is defined as follows: the positive direction of the x-axis is due east, and the positive direction of the y-axis is due north. The angles of the ground course δ and the bow heading θ are measured as follows: with due north as 0°, the angles rotated clockwise to the target direction are positive angles, and the value range is 0° to 360°.

[0076] The core of constructing a linear equation system is to transform the geometric relationship of the ship's track into a solvable mathematical form, where the synthetic relationship of the ship's velocity vector is expressed as:

[0077]

[0078] in, represents the velocity vector of the ship relative to the land, represents the velocity vector of the ship relative to the water surface, Represents the seawater surface velocity vector.

[0079] Furthermore, the linear equations are expressed as:

[0080]

[0081] Among them, S g The speed scalar of the ship relative to the land, S s Represents the speed scalar of the ship relative to the water surface, u cur 、v cur represent the meridional and latitudinal components of the surface flow, respectively;

[0082] In this step, the complex ship kinematics problem is converted into a quantifiable mathematical model by projecting the ship velocity vector relationship into a standard rectangular coordinate system. A rigorous geometric projection framework is established to accurately decouple the coupling between the ship's own power and the ocean power elements with a unified coordinate system and trigonometric function relationship, providing a mathematical basis with clear physical meaning for the construction of a set of linear equations, thereby converting actual navigation data into solvable ocean power parameters.

[0083] In this embodiment S4, combined with the temporal and spatial homogeneity assumption strategy, all linear equations in the same grid are solved simultaneously to obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ; Specifically include:

[0084] For a grid containing n AIS data, construct a matrix equation Ax=b with 2n rows × (n+2) columns; where x= Represents the unknown vector, and the coefficient matrix A is given by and The blocks are constructed according to the ship serial number, and the constant vector b is composed of and Composition, i represents the index variable, which is used to identify different ship data records;

[0085] For a grid containing n AIS data, construct a 2n-row × (n+2)-column matrix equation Ax=b; where Represents the unknown vector, and the coefficient matrix A is given by and The blocks are constructed according to the ship serial number, and the constant vector b is composed of and The structure is composed of i, which represents the index variable, used to identify different ship data records; the overdetermined equations are solved by the least squares method, and the meridional component u of the surface flow in the grid is output. cur and the latitudinal component v cur ;

[0086] Specifically, assuming that there are n AIS data that meet the requirements in each grid, there are 2n equations when they are put into the above linear equations. Using the time-space homogeneity assumption strategy, we can solve n+2 unknowns and n S s and 2 current components;

[0087] The spatiotemporal uniformity assumption strategy here means that in the process of constructing the ocean surface current field using ship AIS data, it is assumed that all ships within the same spatiotemporal grid will encounter the same sea surface conditions, that is, the surface current components within the grid remain consistent within the set spatiotemporal range. It constructs a multi-ship collaborative inversion mechanism. By sharing the current component parameters within the same grid, the three unknown quantities for each ship that originally need to be solved independently are reduced in dimension and aggregated into n+2 unknown quantities. This not only significantly reduces the complexity of solving the equation, but also utilizes the statistical redundancy of multi-ship observation data to effectively suppress the interference of single-ship measurement errors, ultimately achieving a highly robust solution for the surface current components.

[0088] Solve n+2 unknowns and n S s and two current components specifically include:

[0089]

[0090] This system of equations is represented by the matrix equation Ax=b:

[0091]

[0092] Furthermore, the overdetermined equations are solved by the least squares method, and the matrix equation Ax=b formed by the combination of all ship observation equations is transformed into the normal equation By minimizing the sum of squares of all equation residuals, the unique solution vector that minimizes the global fitting error is obtained, that is, the surface flow component u that is constant in the grid cur and the latitudinal component v cur ;

[0093] This step performs matrix modeling and solution of the overdetermined equations based on the assumption of spatiotemporal homogeneity. By combining the vector equations of all ships in the same grid, a sparse matrix system with strong constraints is constructed. The least squares method is used to effectively fuse multi-source observation data while suppressing random errors. This not only significantly reduces the dependence of the uniqueness of the solution on the accuracy of single-ship data, but also realizes the coordinated inversion of surface flow components and ship water speed through a mathematical optimization mechanism.

[0094] In this embodiment S5, for a grid without observation data, the flow field components of adjacent grids are fused using the optimal interpolation method to generate a global continuous surface flow field dataset; specifically, the following steps are performed:

[0095] Establish interpolation objective function;

[0096]

[0097] Where v represents the flow field vector, v obs represents the observed flow field vector, R represents the observation error covariance, Q represents the flow field smoothing constraint weight, and ∇v represents the gradient of the flow field vector;

[0098] The minimum value of the objective function is solved by variational assimilation to obtain the physically continuous flow field distribution.

[0099] In this step, the optimal interpolation method of the variational assimilation framework is adopted to break through the spatial limitations of discrete observation data. It not only suppresses the false flow field structures that may be introduced by traditional interpolation methods, but also makes the reconstruction results conform to the intrinsic movement laws of ocean fluids through smoothness constraints, and ultimately generates a high-confidence continuous flow field dataset that is consistent across the entire domain.

[0100] The ocean surface flow field construction method proposed in this embodiment, through a systematic step design and coordinated application of key technical features, forms a set of logically rigorous and operationally feasible technical processes, from precise screening and anomaly elimination of AIS data, to adaptive grid division based on ship density, and then to mathematical transformation of velocity vectors, simultaneous solution of equations, and interpolation generation of the global flow field. It fully explores the value of ship AIS data in surface flow field inversion and improves the accuracy, reliability, and continuity of flow field calculations through multi-step optimization, providing a practical and effective solution for the efficient construction of ocean surface flow fields.

[0101] Example 2;

[0102] like Figure 2 As shown, this embodiment provides an ocean surface flow field construction system, including:

[0103] Data acquisition module: used to obtain the AIS dynamic data of ships in the target sea area, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data;

[0104] Grid division module: used to divide the space-time grid according to the ship density distribution for data aggregation;

[0105] Equation building block: used to calculate the ground speed S based on the ship velocity vector synthesis relationship in each grid. g , the ground direction δ and the bow heading θ are projected into the rectangular coordinate system to construct a linear equation system;

[0106] Component solving module: It is used to combine the temporal and spatial homogeneity assumption strategy to solve all the linear equations in the same grid and obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ;

[0107] Flow field reconstruction module: It is used to fuse the flow field components of adjacent grids using the optimal interpolation method for grids without observation data to generate a global continuous surface flow field dataset.

[0108] The system acquires and filters AIS data of ships in the target sea area through the data acquisition module. The grid division module divides the adaptive space-time grid according to the ship density. The equation construction module converts the ship velocity vector relationship into a solvable linear equation system. The component solution module combines the space-time uniformity assumption to jointly solve the surface flow components of each grid. The flow field reconstruction module generates a global continuous flow field through optimal interpolation and data fusion. The modules work together to realize the precise construction of the ocean surface flow field based on AIS data.

[0109] Example 3;

[0110] like Figure 3 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned ocean surface flow field construction method is implemented.

[0111] Example 4;

[0112] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned ocean surface flow field construction method is implemented.

[0113] In the above embodiments provided in this application, it should be understood that the disclosed methods, systems, devices, and media can be implemented in other ways. The above-described methods, systems, devices, and media embodiments are merely illustrative. For example, the division of modules or units is merely a logical functional division, and other division methods may be used in actual implementation. Each functional unit may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0114] The units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. Computer-readable media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory, random access memory, electrical carrier signals, telecommunications signals, and software distribution media.

[0116] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for constructing an ocean surface flow field, characterized in that: The following steps are involved: S1. Obtain the AIS dynamic data of the ship in the target sea area, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data; S2, divide the space-time grid according to the ship density distribution to perform data aggregation; S3. In each grid, the ground speed S based on the ship speed vector synthesis relationship g , the ground direction δ and the bow heading θ are projected into the rectangular coordinate system to construct a linear equation system; S4. Combined with the temporal and spatial homogeneity assumption strategy, all linear equations in the same grid are solved simultaneously to obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ; S5. For grids without observation data, the optimal interpolation method is used to fuse the flow field components of adjacent grids to generate a global continuous surface flow field dataset.

2. The method for constructing an ocean surface flow field according to claim 1, characterized in that: In S1, removing abnormal data includes: Decompose the ship's ground speed vector into the longitudinal component S gx and the latitudinal component S gy , calculate acceleration based on time series and ; Set dynamic threshold a th ,when Or if the ship status is turning / speed changing operation, the corresponding AIS data point will be eliminated.

3. The method for constructing an ocean surface flow field according to claim 1, characterized in that: The S2 includes: The average daily number of ship track points per unit sea area is calculated as the ship density ρ for space-time grid division: When ρ ≥ 10, a 1 / 8° × 1 / 8° spatial grid and a 1-day time window were used; When 3≤ρ<10, a 1 / 8°×1 / 8° spatial grid and a 3-day time window are used; When ρ < 3, a 1 / 4° × 1 / 4° spatial grid and a 5-day time window are used.

4. The method for constructing an ocean surface flow field according to claim 1, characterized in that: In S3, the ship velocity vector synthesis relationship is expressed as: ； in, represents the velocity vector of the ship relative to the land, represents the velocity vector of the ship relative to the water surface, Represents the seawater surface velocity vector.

5. The method for constructing an ocean surface flow field according to claim 1, characterized in that: In S3, the linear equations are expressed as: ； Among them, S g The speed scalar of the ship relative to the land, S s Represents the speed scalar of the ship relative to the water surface, u cur 、v cur They represent the meridional and latitudinal components of the surface flow, respectively.

6. The method for constructing an ocean surface flow field according to claim 5, characterized in that: The S4 includes: For a grid containing n AIS data, construct a 2n-row × (n+2)-column matrix equation Ax=b; where Represents the unknown vector, and the coefficient matrix A is given by and The blocks are constructed according to the ship serial number, and the constant vector b is composed of and Composition, i represents the index variable, which is used to identify different ship data records; Solve the overdetermined equations by the least squares method and output the meridional component u of the surface flow in the grid cur and the latitudinal component v cur .

7. The method for constructing an ocean surface flow field according to claim 1, characterized in that: The S5 includes: Establish interpolation objective function; ； Where v represents the flow field vector, v obs represents the observed flow field vector, R represents the observation error covariance, Q represents the flow field smoothing constraint weight, and ∇v represents the gradient of the flow field vector; The minimum value of the objective function is solved by variational assimilation to obtain the physically continuous flow field distribution.

8. A system for constructing ocean surface flow fields, characterized in that: include: Data acquisition module: used to obtain the AIS dynamic data of ships in the target sea area, including the ship's latitude and longitude coordinates, speed over the ground S g , ground heading δ and bow heading θ, and remove abnormal data; Grid division module: used to divide the space-time grid according to the ship density distribution for data aggregation; Equation building block: used to calculate the ground speed S based on the ship velocity vector synthesis relationship in each grid. g , the ground direction δ and the bow heading θ are projected into the rectangular coordinate system to construct a linear equation system; Component solving module: It is used to combine the temporal and spatial homogeneity assumption strategy to solve all the linear equations in the same grid and obtain the meridional component u of the surface flow in the grid. cur and the latitudinal component v cur ; Flow field reconstruction module: It is used to fuse the flow field components of adjacent grids using the optimal interpolation method for grids without observation data to generate a global continuous surface flow field dataset.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the ocean surface flow field construction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for constructing an ocean surface flow field according to any one of claims 1 to 7 is implemented.

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