A method, system, device and medium for constructing a marine surface layer flow field

Abstract

The application provides a kind of ocean surface layer flow field construction method, system, equipment and medium, related to ocean big data mining and ocean surface layer flow field construction field, comprising the following steps: obtaining the dynamic data of ship AIS in target sea area and carrying out abnormal data elimination;According to the division of spatial grid of ship density distribution carries out data aggregation;Based on the projection of ship speed vector synthesis relationship, ground speed, ground direction and ship heading to rectangular coordinate system, construct linear equation group;Combined with the assumption strategy of space-time uniformity, solve all linear equation groups simultaneously, obtain the surface layer flow component in grid;The flow field component of adjacent grid is fused by using optimal interpolation method, to generate global continuous surface layer flow field data set;The method realizes high-resolution, global continuous sea surface flow field dynamic reconstruction by adaptive space-time grid division, vector projection modeling and optimal interpolation fusion, effectively makes up the problem of insufficient coverage of traditional observation means in nearshore area.

Description

Technical Field

[0001] This invention relates to the fields of marine big data mining and marine surface flow field construction, and more specifically to a method, system, equipment and medium for marine surface flow field construction. Background Technology

[0002] Accurate monitoring and construction of ocean surface current fields are of vital practical significance for navigation safety route planning, pollutant dispersion prediction, search and rescue operation deployment, marine ecosystem research, and climate model validation. Especially in nearshore waters, busy shipping lanes, and estuaries, high spatiotemporal resolution surface current information is crucial foundational data for ensuring maritime safety and understanding regional ocean dynamic processes.

[0003] Currently, the mainstream methods for observing ocean surface currents mainly rely on satellite altimeters and drifting buoys. However, satellite altimeters suffer from significantly reduced data accuracy or even data loss in nearshore areas due to signal interference and echo problems, and their spatial resolution and time revisit period make it difficult to capture the complex and ever-changing details of nearshore flow fields. While drifting buoys can provide direct measurements, their deployment and maintenance costs are high, and their spatial coverage is sparse and random, making it difficult to form a dense and continuous observation network in specific areas (especially nearshore areas and shipping channels), resulting in a severe lack of flow field data in these key areas.

[0004] Meanwhile, the widespread application of Automatic Identification Systems (AIS) on merchant ships and large vessels continuously generates massive amounts of dynamic information such as ship position, speed above ground, heading above ground, and bow direction. Although existing research recognizes the potential of AIS data to reflect the marine environment and has made preliminary explorations, existing methods mostly focus on ship behavior itself (such as trajectory tracking and collision warning). The key deficiency lies in the lack of a systematic and engineering-applicable mathematical framework to efficiently and accurately invert the discrete, massive amounts of raw dynamic AIS data into a spatiotemporally gridded surface flow vector field. Existing attempts are often limited to local point or route analysis, failing to fully utilize the data's wide spatiotemporal coverage advantage, and failing to effectively solve the problem of separating the ship's own power, wind and wave influences from the target flow field.

[0005] Even when local flow field information is inferred using partial AIS data, the results are discrete and non-uniformly distributed, with numerous data gaps not covered by ship trajectories. Current technologies lack effective, physically consistent methods for data fusion and spatial expansion. Furthermore, simple spatial interpolation often ignores the physical continuity and dynamic constraints of ocean current fields, leading to unreasonable vortices, divergences, or discontinuities in the flow fields generated in blank areas. This reduces the physical consistency and usability of the global flow field dataset, failing to meet the needs of refined 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 continuous and physically consistent high spatiotemporal resolution ocean surface flow field construction across the entire region, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a method for constructing ocean surface flow fields, which aims to solve the problem of data sparsity in traditional ocean surface flow observation in near-shore high-ship-density areas. By mining the ocean dynamic characteristics hidden in the ship AIS information, it breaks through the spatiotemporal resolution limitation and generates a physically consistent, globally continuous high-precision surface flow field dataset, providing data support for marine environmental monitoring, shipping safety and other purposes.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

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

[0010] S1. Obtain dynamic AIS data of ships in the target sea area, including ship latitude and longitude coordinates and ground speed S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed.

[0011] S2. Data aggregation is performed by dividing the spatiotemporal grid according to the ship density distribution;

[0012] S3. Within each grid, the ground speed S is based on the ship velocity vector composition relationship. g Project the ground heading δ and the bow heading θ onto a rectangular coordinate system to construct a system of linear equations;

[0013] S4. Combining the strategy of spatiotemporal homogeneity assumption, solve all the linear equations in the same grid simultaneously to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur ;

[0014] S5. For grids 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.

[0015] Preferably, in step S1, the abnormal data removal includes:

[0016] The ship's speed vector above ground is decomposed into a meridional component S. gx and latitudinal component S gy Acceleration calculation based on time series and ;

[0017] Set dynamic threshold a th ,when If the ship is in a steering / speed change operation, then the corresponding AIS data point is removed.

[0018] Preferably, S2 includes:

[0019] The average daily number of ship trajectory points per unit sea area is used as the ship density ρ for spatiotemporal grid division.

[0020] When ρ≥10, a 1 / 8°×1 / 8° spatial grid and a 1-day time window are 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 step S3, the ship velocity vector composition relationship is expressed as follows:

[0024]

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

[0026] Preferably, in S3, the system of linear equations is represented as follows:

[0027]

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

[0029] Preferably, S4 includes:

[0030] For a grid containing n AIS data points, construct a matrix equation Ax = b with 2n rows × (n+2) columns; where, Let A represent an unknown vector, and let A be the coefficient matrix. and Composed of blocks according to ship serial numbers, the constant vector b is... and The structure is defined by i, which represents the index variable used to identify different ship data records.

[0031] The overdetermined equations are solved using the least squares method, and the meridional component u of the surface flow within the mesh is output.cur and latitudinal component v cur .

[0032] Preferably, S5 includes:

[0033] Establish the interpolation objective function;

[0034]

[0035] Where v represents the flow field vector, v obs Let R represent the observed flow field vector, Q represent the observation error covariance, and ∇v represent the flow field smoothing constraint weight.

[0036] By solving for the minimum value of the objective function through variational assimilation, a physically continuous flow field distribution can be obtained.

[0037] Secondly, the present invention provides a system for constructing ocean surface flow fields, comprising:

[0038] Data acquisition module: Used to acquire dynamic AIS data of ships in the target sea area, including ship latitude and longitude coordinates, ground speed S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed.

[0039] Grid partitioning module: used to divide spatiotemporal grids based on ship density distribution for data aggregation;

[0040] Equation building module: Used within each grid to calculate the ground speed S based on the ship velocity vector composition relationship. g Project the ground heading δ and the bow heading θ onto a rectangular coordinate system to construct a system of linear equations;

[0041] Component Solving Module: This module combines the spatiotemporal homogeneity assumption with the simultaneous solution of all linear equations within the same grid to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur ;

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

[0043] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for constructing ocean surface flow fields.

[0044] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for constructing ocean surface flow fields.

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

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

[0047] 1. This method adopts a ship density-adaptive spatiotemporal grid partitioning strategy, using fine spatiotemporal resolution in densely populated areas (such as nearshore areas and waterways) and reducing resolution in sparse areas to ensure data volume. It makes full use of the spatiotemporal coverage advantage 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 onto a rectangular coordinate system to construct an overdetermined linear equation system. Combined with the assumption of spatiotemporal homogeneity, the system is solved simultaneously to directly output the meridional and zonal components of the surface flow within the grid. This enables quantitative analysis from ship motion state to marine dynamic elements, breaking through the limitation that AIS data does not directly contain water flow information.

[0049] 3. The flow field calculation results of discrete grids are fused using the optimal interpolation method. An objective function is established through variational assimilation technology. Under the control of error covariance and smoothing constraints, a continuous flow field with a Gaussian distribution is generated. Data gaps in areas not covered by ship trajectories are eliminated, ensuring the physical consistency and full coverage of the output dataset in the spatiotemporal dimensions. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0051] Figure 1 A flowchart of a method for constructing a marine surface flow field is provided in an embodiment of the present invention;

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

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

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The flow field construction method provided in this 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 providing 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 on 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; no specific limitations are made here.

[0056] Example 1;

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

[0058] S1. Obtain dynamic AIS data of ships in the target sea area, including ship latitude and longitude coordinates and ground speed S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed.

[0059] S2. Data aggregation is performed by dividing the spatiotemporal grid according to the ship density distribution;

[0060] S3. Within each grid, the ground speed S is based on the ship velocity vector composition relationship. g Project the ground heading δ and the bow heading θ onto a rectangular coordinate system to construct a system of linear equations;

[0061] S4. Combining the strategy of spatiotemporal homogeneity assumption, solve all the linear equations in the same grid simultaneously to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur ;

[0062] S5. For grids 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.

[0063] This method employs a ship density-adaptive spatiotemporal grid partitioning strategy to project ship AIS dynamic data onto a Cartesian coordinate system to construct an overdetermined linear equation set. By combining the assumption of spatiotemporal homogeneity, it solves for the surface flow components within the grid and generates a global continuous flow field 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 gap in traditional satellite / buoy observations. At the same time, it achieves accurate analysis of ship motion data into marine dynamic elements through a mathematical transformation mechanism.

[0064] The following provides a further detailed explanation of each step in the above method;

[0065] In this embodiment, S1, the AIS dynamic data of ships in the target sea area is acquired, including the ship's latitude and longitude coordinates and its speed over land S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed.

[0066] The outlier removal process includes: decomposing the ship's ground speed vector into a meridional component S. gx and latitudinal component S gy Acceleration calculation based on time series and Set dynamic threshold a th ,when If the ship is in a steering / speed change operation, then the corresponding AIS data point is removed.

[0067] Specifically, different acceleration thresholds can be set according to the type of ship, with container ships and tankers using a... th =0.3m / s, fishing boats and tugboats use a th =0.5m / s, and when the time interval Δt between ship trajectory points is greater than 300 seconds, the data point is directly removed to avoid the problem of positioning error accumulation; it effectively avoids the interference of ship maneuvering on the flow velocity inversion, and at the same time, it directly removes long-term positioning interval data to suppress error accumulation from the source;

[0068] In addition, this step integrates ship static information, including filtering AIS data of non-transport ships based on ship type in the ship static information; filtering ship data with a draft ≤10 meters based on the draft in the ship static information; and verifying the uniqueness of ships and the continuity of their tracks through the Maritime Mobile Services Identifier (MMSI) for dynamic data.

[0069] It provides key screening criteria and physical attribute calibration for surface flow inversion. On the one hand, by filtering non-transport vessels and screening small and medium-sized vessels with a draft of ≤10 meters, it excludes vessel data with complex dynamic characteristics or significant interference from deep laminar flow, ensuring that the vessel motion response in the target dataset mainly reflects the effect of surface flow in the 0-10 meter range. On the other hand, it limits the effective water layer range affected by the flow based on the draft depth, ensuring the physical consistency between the flow field inversion signal and the target depth from the source.

[0070] By utilizing the unique MMSI identifier, accurate correlation of ship dynamic and static information is achieved, and a complete spatiotemporal behavior profile of the ship is constructed. This not only supports the verification of continuous trajectories of the same ship, but also allows for dynamic adjustment of data quality control thresholds based on ship type and draft parameters. This collaborative mechanism provides highly reliable data for subsequent spatiotemporal grid aggregation and flow field equation solving, avoiding flow field calculation deviations caused by ship dynamic heterogeneity.

[0071] In this embodiment S2, data aggregation is performed by dividing a spatiotemporal grid based on the ship density distribution; specifically, it includes:

[0072] The average daily number of ship trajectory points per unit sea area is used as the ship density ρ for spatiotemporal 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. High-density areas use fine spatiotemporal grids, while medium- and low-density areas gradually expand the spatiotemporal scale. This strategy breaks through the limitations of fixed grids, improves flow field resolution in data-rich areas, and ensures statistical significance through spatiotemporal aggregation in sparse areas, thereby achieving optimal spatial allocation of observation resources.

[0075] In this embodiment, S3, within each grid, the ground speed S is calculated based on the ship velocity vector composition relationship. g Project the ground heading δ and the ship's bow heading θ onto a rectangular coordinate system and construct a system of linear equations. The rectangular coordinate system is defined as follows: the positive x-axis is due east and the positive y-axis is due north. The angles of the ground heading δ and the ship's bow heading θ are measured as follows: with due north as 0°, rotate clockwise to the target direction as the positive angle, and the value range is 0°~360°.

[0076] The core of constructing a system of linear equations lies in transforming the geometric relationships of a ship's trajectory into a solvable mathematical form, where the vector composition relationship of the ship's velocity is expressed as:

[0077]

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

[0079] Furthermore, the system of linear equations can be expressed as:

[0080]

[0081] Among them, S g S represents the speed scalar of a ship relative to land. s u represents the speed scalar of a ship relative to the water surface. cur v cur These represent the meridional and latitudinal components of the surface flow, respectively.

[0082] In this step, by projecting the ship's velocity vector relationship onto a standard rectangular coordinate system, the complex ship kinematics problem is transformed into a quantifiable mathematical model. It establishes a rigorous geometric projection framework, which precisely decouples the coupling effect between the ship's own power and marine dynamic elements with a unified coordinate system and trigonometric function relationships. This provides a mathematically clear physical basis for the construction of linear equations, thereby transforming actual navigation data into solvable marine dynamic parameters.

[0083] In this embodiment S4, combining the spatiotemporal homogeneity assumption strategy, all linear equations within the same grid are solved simultaneously to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur Specifically, this includes:

[0084] For a grid containing n AIS data points, construct a matrix equation Ax = b with 2n rows × (n+2) columns; where x = Let A represent an unknown vector, and let A be the coefficient matrix. and Composed of blocks according to ship serial numbers, the constant vector b is... and The structure is defined by i, which represents the index variable used to identify different ship data records.

[0085] For a grid containing n AIS data points, construct a matrix equation Ax = b with 2n rows × (n+2) columns; where, Let A represent an unknown vector, and let A be the coefficient matrix. and Composed of blocks according to ship serial numbers, the constant vector b is... and The structure consists of an index variable, 'i', used to identify different ship data records; the overdetermined equations are solved using the least squares method, outputting the meridional component of the surface flow within the grid, 'u'. cur and latitudinal component v cur ;

[0086] Specifically, assuming each grid contains n AIS data points that meet the requirements, substituting them into the above system of linear equations results in 2n equations. Using the spatiotemporal homogeneity assumption strategy, we solve for n+2 unknowns and n S-values. s And two ocean current components;

[0087] The spatiotemporal homogeneity assumption strategy here refers to the assumption that all ships within the same spatiotemporal grid will encounter the same sea surface conditions during the process of constructing the ocean surface flow field using ship AIS data. That is, the surface flow components within the grid remain consistent within the set spatiotemporal range. It constructs a multi-ship collaborative inversion mechanism, which reduces the original three unknowns for each ship that needed to be solved independently into n+2 unknowns by sharing the ocean current component parameters within the same grid. This significantly reduces the complexity of solving the equations and effectively suppresses the interference of single-ship measurement errors by utilizing the statistical redundancy of multi-ship observation data, ultimately achieving a highly robust solution for the surface flow components.

[0088] Solve for n+2 unknowns and n S's. s The two ocean current components specifically include:

[0089]

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

[0091]

[0092] Furthermore, by solving the overdetermined system of equations using the least squares method, the matrix equation Ax=b formed by solving all the ship observation equations simultaneously is transformed into normal equations. By minimizing the sum of squares of the residuals of all equations, a unique solution vector that minimizes the global fitting error is obtained, which contains the constant meridional component u of the surface flow within the mesh. cur and latitudinal component v cur ;

[0093] This step is based on the assumption of spatiotemporal homogeneity to perform matrix modeling and solution of the overdetermined equations. 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 mathematical optimization mechanism.

[0094] In this embodiment, S5, for grids 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 including:

[0095] Establish the interpolation objective function;

[0096]

[0097] Where v represents the flow field vector, v obs Let R represent the observed flow field vector, Q represent the observation error covariance, and ∇v represent the flow field smoothing constraint weight.

[0098] By solving for the minimum value of the objective function through variational assimilation, a physically continuous flow field distribution can be obtained.

[0099] In this step, the optimal interpolation method of the variational assimilation framework is adopted, which breaks through the spatial limitations of discrete observation data. It not only suppresses the false flow field structure that may be introduced by traditional interpolation methods, but also makes the reconstruction results conform to the intrinsic motion law of ocean fluid through smoothness constraints, and finally generates a high-confidence continuous flow field dataset with global consistency.

[0100] The ocean surface flow field construction method proposed in this embodiment, through systematic step design and synergistic application of key technical features, forms a logically rigorous and feasible technical process. From the accurate screening and anomaly removal of AIS data, to adaptive grid generation based on ship density, to the 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 calculation through multi-stage 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 a system for constructing ocean surface flow fields, including:

[0103] Data acquisition module: Used to acquire dynamic AIS data of ships in the target sea area, including ship latitude and longitude coordinates, ground speed S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed.

[0104] Grid partitioning module: used to divide spatiotemporal grids based on ship density distribution for data aggregation;

[0105] Equation building module: Used within each grid to calculate the ground speed S based on the ship velocity vector composition relationship. g Project the ground heading δ and the bow heading θ onto a rectangular coordinate system to construct a system of linear equations;

[0106] Component Solving Module: This module combines the spatiotemporal homogeneity assumption with the simultaneous solution of all linear equations within the same grid to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur ;

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

[0108] The system acquires and filters AIS data of ships in the target sea area through a data acquisition module, divides the grid according to the ship density into suitable spatiotemporal grids through a grid generation module, transforms the ship velocity vector relationship into a solvable linear system of equations through an equation system construction module, solves the component solution module by combining the assumption of spatiotemporal homogeneity to obtain the surface flow components of each grid through simultaneous solution, and generates a continuous flow field across the entire domain by fusing the data through optimal interpolation. The modules work together to achieve accurate 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, it implements the above-described method for constructing ocean surface flow fields.

[0111] Example 4;

[0112] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for constructing ocean surface flow fields.

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

[0114] The units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory, random access memory, electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0116] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for constructing ocean surface flow fields, characterized in that, Includes the following steps: S1. Obtain dynamic AIS data of ships in the target sea area, including ship latitude and longitude coordinates and ground speed S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed. S2. Data aggregation is performed by dividing the spatiotemporal grid according to the ship density distribution; S3. Within each grid, the ground speed S is based on the ship velocity vector composition relationship. g Project the ground heading δ and the bow heading θ onto a rectangular coordinate system to construct a system of linear equations; S4. Combining the strategy of spatiotemporal homogeneity assumption, solve all the linear equations in the same grid simultaneously to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur ; S5. For grids 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.

2. The method for constructing a marine surface flow field according to claim 1, characterized in that, In step S1, the abnormal data removal includes: The ship's speed vector above ground is decomposed into a meridional component S. gx and latitudinal component S gy Acceleration calculation based on time series and ; Set dynamic threshold a th ,when If the ship is in a steering / speed change operation, then the corresponding AIS data point is removed.

3. The method for constructing a marine surface flow field according to claim 1, characterized in that, S2 includes: The average daily number of ship trajectory points per unit sea area is used as the ship density ρ for spatiotemporal grid division. 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.

4. The method for constructing a marine surface flow field according to claim 1, characterized in that, In S3, the ship velocity vector composition relationship is expressed as follows: ； in, This represents the velocity vector of the ship relative to the land. This represents the velocity vector of the ship relative to the water surface. This represents the surface velocity vector of seawater.

5. The method for constructing a marine surface flow field according to claim 1, characterized in that, In S3, the system of linear equations is expressed as: ； Among them, S g S represents the speed scalar of a ship relative to land. s u represents the speed scalar of a ship relative to the water surface. cur v cur These represent the meridional and latitudinal components of the surface flow, respectively.

6. The method for constructing a marine surface flow field according to claim 5, characterized in that, S4 includes: For a grid containing n AIS data points, construct a matrix equation Ax = b with 2n rows × (n+2) columns; where, Let A represent an unknown vector, and let A be the coefficient matrix. and Composed of blocks according to ship serial numbers, the constant vector b is... and The structure is defined by i, which represents the index variable used to identify different ship data records. The overdetermined equations are solved using the least squares method, and the meridional component u of the surface flow within the mesh is output. cur and latitudinal component v cur .

7. The method for constructing a marine surface flow field according to claim 1, characterized in that, S5 includes: Establish the interpolation objective function; ； Where v represents the flow field vector, v obs Let R represent the observed flow field vector, Q represent the observation error covariance, and ∇v represent the flow field smoothing constraint weight. By solving for the minimum value of the objective function through variational assimilation, a physically continuous flow field distribution can be obtained.

8. A system for constructing ocean surface flow fields, characterized in that, include: Data acquisition module: Used to acquire dynamic AIS data of ships in the target sea area, including ship latitude and longitude coordinates, ground speed S. g The heading δ relative to the ground and the heading θ of the ship's bow are calculated, and abnormal data is removed. Grid partitioning module: used to divide spatiotemporal grids based on ship density distribution for data aggregation; Equation building module: Used within each grid to calculate the ground speed S based on the ship velocity vector composition relationship. g Project the ground heading δ and the bow heading θ onto a rectangular coordinate system to construct a system of linear equations; Component Solving Module: This module combines the spatiotemporal homogeneity assumption with the simultaneous solution of all linear equations within the same grid to obtain the meridional component u of the surface flow within the grid. cur and latitudinal component v cur ; Flow field reconstruction module: Used to fuse the flow field components of adjacent grids using the optimal interpolation method for grids without observation data, generating 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, characterized in that, When the processor executes the computer program, it implements the ocean surface flow field construction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the ocean surface flow field construction method as described in any one of claims 1 to 7.

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