A method and device for inverting the flow field of ocean near-surface submesoscale processes

Abstract

The present application provides a method and device for inverting the flow field of ocean near-surface submesoscale processes. The method provided by the present application includes: screening representative sea areas of ocean near-surface submesoscale; obtaining structural image information of phytoplankton fronts and filaments of ocean submesoscale processes; obtaining displacement velocity field information at the same position; matching the structural image information and the displacement velocity field information to construct a first training sample set; correcting the first training sample set based on a second data set and a third data set to obtain a second training sample set; training an initial model using the second training sample set to obtain a flow field inversion model; obtaining multiple submesoscale sea surface water color images of a sea area to be detected; and processing the submesoscale sea surface water color images using the flow field inversion model to obtain a displacement velocity field. The method and device for inverting the flow field of ocean near-surface submesoscale processes provided by the present application are used to solve the problem of low inversion accuracy of the current ocean flow field inversion algorithm for submesoscale processes.

Description

Technical Field

[0001] This application relates to the technical field of ocean dynamic process identification, and particularly to a method and device for inverting the flow field of near-surface submesoscale processes in the ocean. Background Art

[0002] Submesoscale processes play a crucial role in ocean energy dissipation and are the key link connecting larger-scale equilibrium dynamics and smaller-scale non-equilibrium turbulence. Among them, the development of observation technologies is very important for studying submesoscale motions. The new generation of altimetry satellite data developed in the past, such as surface water and ocean topography, has improved the spatial resolution of ocean dynamic field data to about 2 kilometers, bringing new opportunities for the study of ocean submesoscale processes. However, its revisit cycle of about 20 days cannot capture the rapid changes in the submesoscale motions on the hourly to daily time scales. Therefore, traditional satellite-based altimetry data is difficult to capture the fine-scale structures of submesoscale processes and cannot meet the needs of more in-depth research on submesoscale motions. Summary of the Invention

[0003] In view of this, this application provides a method and device for inverting the flow field of near-surface submesoscale processes in the ocean to solve the problem of low inversion accuracy of the current ocean flow field inversion algorithm for submesoscale processes.

[0004] Specifically, this application is implemented through the following technical solutions:

[0005] In the first aspect of this application, a method for inverting the flow field of near-surface submesoscale processes in the ocean is provided. The method includes:

[0006] Screen the representative sea areas of near-surface submesoscale processes from the ocean area;

[0007] Use a geostationary ocean color imaging satellite to obtain the structural image information of the phytoplankton front and filaments of the submesoscale process in the representative sea area;

[0008] Use the first database to obtain the displacement velocity field information of the ocean submesoscale process at the same location;

[0009] Match the structural image information and the displacement velocity field information to construct a first training sample set;

[0010] Correct the first training sample set based on the second data set and the third data set to obtain a second training sample set; where the second data set includes sea surface height data, and the third data set includes the geostrophic flow field displacement velocity field data of the ocean large-scale process;

[0011] Use the second training sample set to train an initial model to obtain a flow field inversion model;

[0012] Use the geostationary ocean color imaging satellite to observe the sea area to be detected, and obtain multiple sub-meso-scale sea surface color images of the sea area to be detected;

[0013] Use the flow field inversion model to process the sub-meso-scale sea surface color image, and obtain the displacement velocity field of the surface sub-meso-scale process in the sea area to be detected.

[0014] The second aspect of the present application provides an apparatus for inverting the flow field of the ocean near-surface sub-meso-scale process. The apparatus includes an acquisition module, a construction module, and a processing module; wherein,

[0015] The acquisition module is used to screen the representative sea areas of the ocean near-surface sub-meso-scale from the ocean area;

[0016] The acquisition module is further used to use the geostationary ocean color imaging satellite to obtain the structural image information of the phytoplankton front and filaments of the sub-meso-scale process in the representative sea area;

[0017] The acquisition module is further used to use the first database to obtain the displacement velocity field information of the ocean sub-meso-scale process at the same location;

[0018] The construction module is used to match the structural image information and the displacement velocity field information, and construct a first training sample set;

[0019] The construction module is further used to correct the first training sample set based on the second data set and the third data set to obtain a second training sample set; wherein, the second data set includes sea surface height data, and the third data set includes geostrophic flow field displacement velocity field data of the ocean large-scale process;

[0020] The construction module is further used to use the second training sample set to train the initial model to obtain a flow field inversion model;

[0021] The processing module is used to use the geostationary ocean color imaging satellite to observe the sea area to be detected, and obtain multiple sub-meso-scale sea surface color images of the sea area to be detected;

[0022] The processing module is further used to use the flow field inversion model to process the sub-meso-scale sea surface color image, and obtain the displacement velocity field of the surface sub-meso-scale process in the sea area to be detected.

[0023] The method and device for inverting the flow field of the ocean near-surface submesoscale process provided by this application, in order to improve the model's ability to invert the flow field of the submesoscale process, first screen representative sea areas, select representative sea areas where the identification target and the interference background environment coexist, to improve the pertinence and representativeness of the samples; secondly, based on the samples composed of ocean data obtained from the representative sea areas, the structural image information of phytoplankton fronts and filaments and the displacement velocity field information at the same position are obtained. Secondly, the first training sample set composed of representative sea area data is corrected by using the second data set containing sea surface height data and the third data set containing geostrophic flow field displacement velocity field data of the ocean large-scale process. On the one hand, the fineness of the information increases, enabling the model to learn more details of the ocean submesoscale process; on the other hand, the large-scale information of the second and third data set samples corrects the submesoscale information of the first training sample set in the same dimension, enabling the model to learn more accurate feature information, thereby improving the fineness and accuracy of the model prediction, realizing the multi-scale information fusion from the submesoscale to the ocean large-scale, and helping to deeply understand the behavioral characteristics of the ocean submesoscale process in different scale environments. Further, the initial model is trained based on the second training sample set constructed from multi-source data, greatly optimizing the training data of the model. The corrected training sample set combines the advantages of multiple data, reduces the errors and uncertainties that may exist in a single data source, and improves the accuracy of the relationship between the submesoscale sea surface color image and the displacement velocity field learned by the flow field inversion model. Using the trained flow field inversion model to process the submesoscale sea surface color image of the sea area to be detected observed by the geostationary ocean color imaging satellite, the displacement velocity field of the surface submesoscale process in this sea area can be obtained. This process realizes the rapid and accurate conversion from satellite observation images to flow field information, providing strong data support for oceanographic research, ocean environment monitoring, ocean resource management, etc. Description of the Drawings

[0024] Figure 1 It is a flowchart of the first embodiment of the method for inverting the flow field of the ocean near-surface submesoscale process provided by this application;

[0025] Figure 2 It is a comparison diagram of the effects of the flow field inversion model and the traditional model at the same time shown in the exemplary embodiment of this application;

[0026] Figure 3 It is a schematic structural diagram of the first embodiment of the device for inverting the flow field of the ocean near-surface submesoscale process provided by this application. Detailed Embodiments

[0027] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application.

[0028] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a", "the", and "said" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0029] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0030] Specific embodiments are given below to introduce the technical solutions of this application in detail.

[0031] Figure 1 This is a flowchart of the first embodiment of the method for inverting the flow field of ocean near-surface submesoscale processes provided by this application. Please refer to Figure 1 , the method provided in this embodiment may include:

[0032] S101. Screen representative sea areas of ocean near-surface submesoscale from ocean regions.

[0033] Specifically, the submesoscale process is a kind of movement in the ocean, and its horizontal scale is between 0.1 - 10 km. This scale range makes the submesoscale process occupy a unique spatial position in the ocean, different from both larger-scale ocean circulations and smaller-scale turbulent motions. Vertically, it changes at a speed of 100 m / day. This relatively fast vertical change speed plays a key role in the vertical exchange of matter and energy in the ocean. It can promote the transport of nutrients between different water layers, affect the growth and distribution of marine organisms such as phytoplankton, and thus have a chain reaction on the entire marine ecosystem.

[0034] Furthermore, the submesoscale process is a key link connecting the larger-scale balanced dynamics and the smaller-scale unbalanced turbulence. At larger scales, it is influenced by balanced dynamics such as ocean circulation. At the same time, through its own motion and interactions, it transfers energy and matter to smaller-scale turbulent motions, promoting the cascade transfer of ocean energy. Phytoplankton in the submesoscale process often form filamentous structures, and the diurnal variation of horizontal transport is significant. Such distribution and movement changes of phytoplankton not only affect their own survival and reproduction but also have an impact on the entire ocean food web.

[0035] Traditional submesoscale flow field inversion models cannot capture the rapid changes of submesoscale motions on the hourly to daily time scales, making it difficult to obtain complete information on the fine-scale structures of submesoscale processes. When dealing with complex filamentous structures (such as submesoscale vortices), it is difficult to accurately match, resulting in large velocity errors and angular errors, which affect the accurate identification and analysis of submesoscale processes.

[0036] Given these deficiencies of traditional submesoscale flow field inversion models, it is necessary to construct special samples. By screening representative submesoscale sea areas near the ocean surface, obtaining the structural image information of phytoplankton fronts and filaments in this sea area and the displacement velocity field information at the same location, and matching the two to construct the first training sample set, the physical relationship between the distribution of substances (phytoplankton) and the flow field in the ocean can be accurately captured. Using the second data set (sea surface height data) and the third data set (geostrophic flow field displacement velocity field data of large-scale ocean processes) to correct the first training sample set to obtain the second training sample set, realizing the multi-scale information fusion from submesoscale to large-scale ocean. The special samples constructed in this way can provide more comprehensive and accurate data for model training, helping the model learn more fine-grained details of submesoscale processes and more accurate feature information, thereby improving the model's ability to invert the flow field of submesoscale processes, making up for the defects of traditional models, and meeting the needs of more in-depth research on submesoscale motions.

[0037] Specifically, representative submesoscale sea areas near the ocean surface are screened from ocean regions, including:

[0038] (1) Obtain the motion conditions of submesoscale processes in each ocean region.

[0039] Specifically, the motion conditions of submesoscale processes in ocean regions include rich mesoscale vortices, submesoscale vortices, phytoplankton fronts, filaments and other characteristics. These motion conditions can provide more flow field change information in the study of submesoscale ocean process flow field inversion, enabling the model to fully learn the physical properties of submesoscale motions, thereby improving the analytical ability of submesoscale flow fields.

[0040] Furthermore, the movement of sub - mesoscale processes in each ocean region can be obtained through satellites, such as geostationary ocean color satellites and altimetry satellites.

[0041] (2)Obtain the characteristic differences between phytoplankton and filaments and the environment in each ocean region.

[0042] Specifically, after obtaining the movement of sub - mesoscale processes in each ocean region, the geostationary ocean color satellite (GOCI - II with a spatial resolution of 250m and 10 observations per day) can be used to obtain the ocean surface chlorophyll a (Chl - a) concentration data. The Chl - a concentration is an important indicator of phytoplankton biomass. By analyzing the distribution and changes of Chl - a concentration in different ocean regions, the abundance differences of phytoplankton can be understood. At the same time, ocean color images can show the aggregation patterns of phytoplankton, such as the areas where algal blooms form and the positions of phytoplankton fronts. These information helps to initially judge the distribution characteristics of phytoplankton in different regions and their relationship with the environment. According to factors such as ocean geography, climate, and ecology, each ocean region can be classified, such as the open ocean area, coastal area, upwelling area, tropical sea area, temperate sea area, polar sea area, etc. Compare the characteristic differences between phytoplankton and filaments in different types of regions and analyze their relationship with regional environmental characteristics (such as ocean circulation patterns, water temperature distribution, nutrient supply mechanisms, etc.).

[0043] Furthermore, based on the above - mentioned analysis results, a comprehensive assessment model is constructed to incorporate the characteristics of phytoplankton and filaments and environmental factors into a unified framework. The model can be a statistical model (such as multiple regression model, structural equation model, etc.) or a mechanism model based on physical - ecological processes, which is used to quantitatively predict the changes of phytoplankton and filament characteristics under different environmental conditions. Through model simulation, the characteristic differences between phytoplankton and filaments and the environment can be further verified.

[0044] Furthermore, obtaining the characteristic differences between phytoplankton and filaments and the environment in each ocean region can also include the following steps:

[0045] 2.1. Obtain multiple ocean color images of each ocean region, and each ocean color image includes phytoplankton, filaments, and the ocean region background environment at the same time.

[0046] Specifically, the water color image can display information such as the aggregation pattern of phytoplankton, the area where algal blooms form, and the position of the phytoplankton front. Each water color image contains phytoplankton, filaments, and the background environment of the ocean area at the same time. By obtaining multiple water color images of different ocean areas, the relationship between phytoplankton, filaments, and the background environment in different areas can be observed comprehensively and multi-dimensionally. The water color images of each ocean area can be obtained by a geostationary ocean color imager satellite.

[0047] 2.2. Identify the first boundaries of the phytoplankton and filaments from each of the water color images.

[0048] Specifically, through image recognition technology, such as object detection algorithms based on deep learning, a model capable of accurately identifying phytoplankton and filaments can be trained by learning a large number of known sample images of phytoplankton and filaments. This model can analyze features such as the color, texture, and shape of pixels in the water color image, separate the phytoplankton and filaments from the background environment, and then determine their boundaries to obtain the first boundaries. For another example, a convolutional neural network (CNN) can also be used. It extracts image features through multiple convolutional layers and pooling layers, and then determines whether a pixel belongs to phytoplankton and filaments through a classifier, and finally outlines their boundaries to obtain the first boundaries.

[0049] 2.3. Identify the second boundaries of the background environment from each of the water color images.

[0050] Specifically, after identifying the first boundaries of the phytoplankton and filaments in the water color image, the part of the water color image other than the phytoplankton and filaments is the background environment of the ocean area. Use edge detection algorithms in image processing, such as the Canny edge detection algorithm. This algorithm calculates the gradient intensity and direction of pixels in the image to find the places where the gray value changes significantly in the image, thereby determining the boundaries of the background environment to obtain the second boundaries. It is also possible to use a threshold segmentation method. According to the gray-scale characteristics of the image, a suitable threshold is set to distinguish the background environment from the phytoplankton and filaments, obtain the boundaries of the background environment, and then determine these boundaries as the second boundaries of the background environment.

[0051] 2.4. Calculate the overlapping area after the intersection of the first boundaries and the second boundaries in each of the water color images.

[0052] Specifically, after obtaining the first boundaries of phytoplankton and filaments and the second boundaries of the background environment in each water color image, the area calculation method in graphics is used to represent the boundaries as polygons, and the overlapping area is obtained by calculating the area of the intersection of the two polygons. For example, the vector cross product method can be used to calculate the area of a polygon. For two intersecting polygons (i.e., the polygons formed by the first boundary and the second boundary), find their intersection region and then calculate the area of the intersection region. This overlapping area reflects to a certain extent the overlapping degree between phytoplankton and filaments and the background environment, providing a data basis for subsequent calculation of feature differences.

[0053] 2.5. Calculate the weighted sum of the overlapping areas of all water color images to obtain the total overlapping area, and use the total overlapping area as the feature difference.

[0054] Specifically, since the quality and reliability of different water color images are different, in order to calculate the feature differences more accurately, it is necessary to weight the overlapping area of each water color image. The size of the weight is positively correlated with the recognition accuracy of the boundary. Accurately identifying the boundaries of phytoplankton, filaments and the background environment is crucial for calculating the overlapping area after their intersection. If the boundary recognition is inaccurate, the calculation of the overlapping area will deviate, resulting in unreliable calculation results of the feature differences. For example, if there is a misjudgment when identifying the first boundary of phytoplankton and filaments, and including parts that do not belong to phytoplankton within the boundary, it will make the calculated overlapping area larger, thus affecting the judgment of the feature differences. Therefore, the higher the boundary recognition accuracy, the greater the weight of the corresponding water color image in calculating the feature differences.

[0055] Furthermore, the weight can also be determined by identifying indicators such as the brightness, contrast, exposure, and clarity of the water color image to evaluate whether the water color image is clearly recognizable. These indicators reflect the quality of the water color image. The better the quality of the water color image, the higher the accuracy of boundary recognition. Compare these indicators with the standard indicators, calculate the ratio between them, and use this ratio as the weight corresponding to the water color image. The specific weight determination indicators are set according to actual needs and are not limited in this embodiment.

[0056] Furthermore, after obtaining the weight of each water color image, multiply the overlapping area of each water color image by its corresponding weight and then sum them up to obtain the total overlapping area. The total overlapping area comprehensively considers the information of multiple water color images and can more comprehensively and accurately reflect the feature differences between phytoplankton, filaments and the environment, providing a key basis for screening representative sea areas of the near-surface sub-mesoscale in the ocean.

[0057] (3)Screen each of the marine regions according to the feature differences to obtain the first screening result of the representative sea areas.

[0058] Specifically, the characteristic differences refer to the characteristic differences between phytoplankton and filaments in the sea area and the environment. After determining the characteristic differences of each sea area, the sea areas with characteristic differences greater than the characteristic difference threshold are retained. The representative sea areas in the first screening result include the sea areas with characteristic differences greater than the characteristic difference threshold in each sea area. It should be noted that the characteristic difference threshold is set according to actual needs and is not limited in this embodiment. It can be understood that a large characteristic difference means that the difference between phytoplankton and filaments and the environment is more significant, the characteristics of the sub-mesoscale processes in these sea areas are more obvious, and the information contained in the sea areas with large characteristic differences is more diverse. When training the model, the data of these sea areas can provide more valuable learning materials for the model, enabling the model to capture the relationship between more complex and subtle sub-mesoscale processes and environmental factors. Retaining the sea areas with large characteristic differences can avoid overfitting of the model to a specific local area. The marine environment is complex and diverse, and the sub-mesoscale processes in different regions are affected by a variety of factors. If only the sea areas with small characteristic differences are selected for training, the model may only learn the characteristics of the sub-mesoscale processes in some similar environments and have poor generalization ability when facing other different environments. However, the sea areas with large characteristic differences cover a wider range of marine environmental conditions. Selecting these sea areas for training can enable the model to learn the commonalities and differences of sub-mesoscale processes in various marine environments, enhance the adaptability to different marine environments, and improve the reliability of the model in performing flow field inversion in different sea areas.

[0059] (4) Determine the distribution characteristics of each representative sea area in the first screening result of the representative sea areas, select the first screening result of the representative sea areas according to the distribution characteristics to obtain the second screening result, and determine the representative sea areas according to the second screening result.

[0060] Specifically, in this embodiment, key attention is paid to the sea areas where active sub-mesoscale vortices, frontal activities, and the distribution of phytoplankton show obvious sub-mesoscale characteristics, which can provide obvious characteristic information and enable the model to better learn the relationship between frontal activities and the displacement velocity field of phytoplankton and sub-mesoscale processes.

[0061] Furthermore, analyze the distribution characteristics of each representative sea area in the first screening result of representative sea areas, such as geographical location distribution, marine region type distribution, and the influence distribution of ocean circulation systems, etc. When determining the geographical location distribution of each representative sea area, determine the latitude position of each representative sea area on the earth and clarify whether it is in the tropics, temperate zone or frigid zone. The sea areas in different latitude zones have significant differences in light, temperature and climate conditions, and these factors will greatly affect the marine ecosystem and marine dynamic processes; when determining the marine region type distribution of each representative sea area, check which type the representative sea area belongs to, such as the oceanic region, coastal region, upwelling region, etc. Because there are differences in the ocean circulation patterns, water temperature distributions, and nutrient supply mechanisms in different types of regions. For example, the coastal region is greatly affected by the land, rich in nutrients, with rapid growth of phytoplankton, and the submesoscale processes may be more complex; the upwelling region will bring deep-sea nutrients to the surface, promoting the massive reproduction of phytoplankton, and its submesoscale flow field also has unique characteristics; when determining the influence distribution of ocean circulation systems on each representative sea area, it is necessary to study which ocean circulation system the representative sea area is affected by. Large ocean circulations such as the Kuroshio and the Gulf Stream will have a profound impact on the water temperature, salinity and flow field of the surrounding sea areas, and thus affect the submesoscale processes. The modes of material transport and energy transfer in the submesoscale processes in the sea areas affected by different circulation systems are different, and the distribution and movement of phytoplankton will also vary.

[0062] Furthermore, to enable the model to learn the flow field characteristics of submesoscale processes in various marine environments, select areas as evenly as possible from the first screening result of representative sea areas. Cover sea areas in different oceans, such as the Pacific Ocean, the Atlantic Ocean, the Indian Ocean, etc., to ensure that the model can learn the characteristics of submesoscale processes in different ocean environments; select sea areas in different latitude zones, including the tropics, temperate zones and frigid zones, to enable the model to adapt to submesoscale processes under different temperature and light conditions; select areas affected by different ocean circulation systems, such as sea areas affected by warm currents and cold currents, to let the model understand the action mechanisms of different circulations on submesoscale processes, and focus on sea areas with active submesoscale vortices, frontal activities, and obvious submesoscale characteristics in the distribution of phytoplankton. These sea areas can provide obvious characteristic information, which helps the model to better learn the relationship between frontal activities and the displacement velocity field between phytoplankton and submesoscale processes. For example, in sea areas where submesoscale vortices are active, phytoplankton will show a specific distribution pattern under the action of the vortices. By studying these sea areas, we can understand more deeply the influence of submesoscale flow fields on the distribution of phytoplankton.

[0063] Determine the areas in the second screening results obtained through the above screening as representative sea areas. In subsequent research, using the data on submesoscale processes in these representative sea areas to train the model can better simulate the displacement velocity field of submesoscale processes, improve the accuracy and reliability of the flow field inversion model, and provide strong data support for oceanographic research, ocean environmental monitoring, and ocean resource management, etc.

[0064] S102. Use a geostationary ocean color imaging satellite to obtain the structural image information of phytoplankton fronts and filaments of the submesoscale processes in the representative sea areas.

[0065] Specifically, the structural image information of phytoplankton fronts and filaments includes the shape characteristics, texture characteristics, color characteristics, and boundary characteristics of phytoplankton and filaments, etc. The structural image information of phytoplankton fronts and filaments can reflect the changes in ocean color and the distribution characteristics of phytoplankton, thereby reflecting submesoscale processes. A geostationary ocean color imaging satellite is a type of satellite used for observing the ocean, with relatively high spatial resolution. For example, the spatial resolution can reach 250 m, and it can clearly observe phenomena such as submesoscale vortices on the order of several kilometers in the ocean, especially in areas such as phytoplankton fronts, filaments, and the edges of mesoscale vortices. At the same time, it has high temporal resolution. For example, it can observe 8 times a day, and can capture the changes in ocean processes on the hourly scale. It is located in a geostationary orbit and can continuously observe a specific area.

[0066] S103. Use the first database to obtain the displacement velocity field information of the ocean submesoscale processes at the same location.

[0067] Specifically, the first database is the MITgcm LLC4320 simulation product obtained from the data portal of the National Aeronautics and Space Administration of the United States. MITgcm based on the LLC (Latitude - Longitude Cap, latitude - longitude polar cap) grid configuration has a nominal horizontal resolution of 1 / 48° (about 2 km) and a temporal resolution of 1 hour. The LLC4320 simulation includes 90 vertical layers, with a vertical resolution of about 1 m near the sea surface and about 30 m at 500 m, optimizing the resolution of upper ocean processes. This first dataset provides key variables such as sea level anomaly, temperature, salinity, and velocity per hour in the native grid.

[0068] Furthermore, search for the displacement velocity field information of the ocean submesoscale processes at the same location in the first dataset. The displacement velocity field contains information such as the velocity, displacement, vortices, and spatio - temporal changes of ocean water body movement, which can ensure the continuity of the studied submesoscale processes in time and space and accurately track their evolution process at a specific location.

[0069] S104. Match the structural image information and the displacement velocity field information to construct a first training sample set.

[0070] Specifically, matching the structural image information and the displacement velocity field information includes:

[0071] (1) Extract the structural features of the phytoplankton front and the filaments in the structural image information.

[0072] Specifically, use image segmentation and feature extraction techniques to identify and extract the structural features of the phytoplankton front and filaments from the structural image, and convert them into a structured data format for subsequent analysis, such as vector representations of edge contours, texture features, etc.

[0073] (2) Perform spatial grid processing on the displacement velocity field information, and extract the displacement features and velocity features of each grid unit of the displacement velocity field information.

[0074] Specifically, for the displacement velocity field information obtained from the first database, perform spatial grid processing on it. Each grid unit corresponds to a certain area (such as corresponding to an image pixel or pixel block), and extract the displacement features and velocity features of each grid unit. The implementation process of feature extraction can refer to the description in related technologies and will not be elaborated here.

[0075] (3) Process the structural image information and the displacement velocity field information according to the spatial scale and time series, and match the displacement velocity field information with the structural features of the phytoplankton front and the filaments.

[0076] Specifically, use GIS technology to match based on geographical coordinates from the spatial scale, and obtain structural image information and displacement velocity field information that are in the same geographical reference system, have consistent resolutions, and have a corresponding relationship pixel by pixel or region by region. Perform time calibration on the structural image information and the displacement velocity field information from the time series to obtain structurally continuous and synchronous structural image information and displacement velocity field information. Then, by analyzing the correlation between the structural image information and the displacement velocity field information after spatial scale and time series processing, the matching of the structural image information and the displacement velocity field information is achieved.

[0077] Specifically, in terms of spatial scale, using Geographic Information System (GIS) technology, the structural image and the displacement velocity field are accurately matched based on geographical coordinates. Ensure that each pixel or region in the image can accurately correspond to the corresponding position in the velocity field. This is the basis for establishing the connection between the two. For example, through means such as satellite positioning information and map projection transformation, the structural image information and the displacement velocity field information are aligned in the same geographical reference system for subsequent correlation analysis. If the resolutions of the structural image and the displacement velocity field are inconsistent, resolution coordination processing is required. Interpolation algorithms such as bilinear interpolation and nearest neighbor interpolation can be used to interpolate the velocity field data to the same or similar resolution as the image, so that the two are matched in spatial scale, facilitating the establishment of the correspondence between the structure and the velocity pixel by pixel or region by region.

[0078] Furthermore, in terms of time series, according to the satellite observation time and the time stamp of the velocity field data, time calibration operations are carried out to make the two reflect the ocean state at the same moment or a similar moment. For data with inconsistent time resolutions, methods such as time interpolation or averaging can be used to construct a continuous and synchronous data set in time. A series of continuous structural images are matched and analyzed with the corresponding displacement velocity field time series. Observe the evolution process of the phytoplankton front and filamentous structures over time, as well as the corresponding changes in the velocity field, and analyze the dynamic correlation between the two in the time dimension. For example, by calculating the correlation between the change rate of the structural characteristics and the change in the velocity field, the time lag effect and periodic change law of the interaction between the structure and the flow field in the ocean submesoscale process are revealed.

[0079] Furthermore, based on the extracted structural characteristics, displacement characteristics, and velocity characteristics of the phytoplankton front and the filamentous structures, statistical analysis or machine learning methods are used to construct a mathematical model between the structural image information and the displacement velocity field information. For example, in one embodiment, regression analysis is used to establish a quantitative relationship model between the length of the phytoplankton front and the magnitude of the local flow field velocity; a neural network model is used to learn the complex mapping relationship between the filamentous texture structure and the vorticity of the surrounding velocity field. By analyzing the linear or non-linear correlation between the structural image information and the displacement velocity field information, the displacement velocity field information is matched with the structural image information.

[0080] S105. Correct the first training sample set based on the second data set and the third data set to obtain a second training sample set; wherein, the second data set includes sea surface height data, and the third data set includes geostrophic flow field displacement velocity field data of the large-scale ocean process.

[0081] Specifically, although the first training sample set can provide detailed information on submesoscale processes, its description of large-scale ocean processes is relatively limited. The sea surface height data in the second data set and the geostrophic flow field displacement velocity field data of large-scale ocean processes in the third data set can provide background information on large-scale ocean circulation. Since submesoscale ocean processes occur and evolve under the background of large-scale ocean circulation, by using the second and third data sets to correct the first training sample set, the first training sample set can contain more comprehensive ocean dynamic information, forming a complete information chain from large scale to submesoscale, which helps the model to more accurately understand the behavioral characteristics of submesoscale processes in different scale environments and improve the accuracy and reliability of submesoscale process prediction.

[0082] Furthermore, the correction of the first training sample set based on the second and third data sets includes:

[0083] (1) Construct a transfer function based on the geostrophic balance relationship, which is used to calculate the geostrophic flow field displacement velocity field of the second data set.

[0084] Specifically, in the ocean, there is a geostrophic balance relationship between geostrophic flow and sea surface height anomaly, and the transfer function can be constructed through the following function:

[0085] ;

[0086] Where is the Coriolis parameter;

[0087] is the geostrophic flow velocity;

[0088] is the acceleration due to gravity;

[0089] is the sea surface height anomaly;

[0090] is the horizontal direction.

[0091] For the second data set, substituting its sea surface height data into this transfer function can obtain the geostrophic flow field displacement velocity field corresponding to each data in the second data set, which can be directly compared with the displacement velocity field in the first training sample set in the same geographical area and time range, and the consistency and differences between the two can be judged based on the geostrophic balance relationship.

[0092] (2) Compare the geostrophic flow field displacement velocity fields of the second and third data sets with the displacement velocity field of the first training sample set at the same location and within the same time range to obtain the displacement velocity field difference.

[0093] Specifically, the first training sample set has high spatial and temporal resolutions, while the third data set has a lower resolution, which reflects the ocean dynamic field at a larger scale. To make them corresponding, spatial filtering can be performed on the displacement velocity field in the first training sample set. For example, a low-pass filter can be used to extract its large-scale geostrophic flow component, so that it matches the data in the third data set in terms of scale, and then the difference in the displacement velocity field between the first training sample set and the third data set can be obtained.

[0094] (3) Correct the first training sample set according to the second data set and the difference in the displacement velocity field between the third data set and the first training sample set.

[0095] Specifically, combining the above description, analyze the difference in the displacement velocity field between the second data set and the first training sample set and the difference in the displacement velocity field between the third data set and the first training sample set. According to the difference in the displacement velocity field, correct the data in the first training sample set. In specific implementation, the data in the first training sample set and the corresponding difference in the displacement velocity field can be weighted and summed, and the data after weighted summation is used to replace the original data in the first training sample set.

[0096] S106. Use the second training sample set to train the initial model to obtain a flow field inversion model.

[0097] Specifically, the training process of the flow field inversion model includes:

[0098] (1) Input the second training sample set into the initial model;

[0099] (2) The initial model learns the relationship between the submesoscale sea surface water color image and the displacement velocity field from the second training sample set.

[0100] Specifically, it can be understood that the second training sample set is a set of high-quality data after correction, which contains submesoscale sea surface water color images and corresponding displacement velocity field information. These data come from multiple data sources, integrating ocean information of different scales and types, providing rich and accurate learning materials for the initial model. The sea surface water color image can reflect the distribution of phytoplankton, filaments, etc. on the ocean surface, while the displacement velocity field describes the motion state of ocean water. There is a complex physical connection between the two. For example, the aggregation and diffusion of phytoplankton are often affected by the ocean current field, and the change of the ocean current field may also interact with the growth and distribution of phytoplankton. By inputting the second training sample set into the initial model, the model can access the information of the interconnection of various factors in the real ocean environment, so as to learn the potential internal relationship pattern between the submesoscale sea surface water color image and the displacement velocity field.

[0101] (3) Determine the loss function. With the goal of minimizing the loss function, iterate the parameters of the flow field inversion model so that the difference between the predicted value and the true value of the displacement velocity field is less than the threshold.

[0102] Specifically, the loss function can be determined through the following steps:

[0103] (1) For each pixel position in the submesoscale sea surface water color image, obtain the predicted value and the true value of its displacement velocity vector respectively.

[0104] Specifically, when processing the submesoscale sea surface water color image, each pixel position may correspond to different flow field characteristics. By obtaining the predicted value and the true value of the displacement velocity vector for each pixel position respectively, the detailed distribution of the flow field in the image space can be accurately described. The displacement velocity vector contains two important physical quantities, namely displacement and velocity. Displacement describes the position change of the water body within a certain time interval, while velocity represents the speed and direction of the displacement. In the ocean flow field, these two quantities are interrelated and jointly determine the dynamic characteristics of the flow field.

[0105] (2) Calculate the square of the displacement and the square of the velocity in the predicted value and the true value of the displacement velocity vector.

[0106] (3) Calculate the difference between the square of the displacement and the square of the velocity, and take the square root of the difference to obtain the loss function.

[0107] Specifically, the loss function can be expressed by the following formula:

[0108] ;

[0109] where, is the displacement vector in the true value of the displacement velocity vector;

[0110] is the velocity vector in the true value of the displacement velocity vector;

[0111] is the displacement vector in the predicted value of the displacement velocity vector;

[0112] is the velocity vector in the predicted value of the displacement velocity vector.

[0113] Further, the structure of the flow field inversion model will be introduced below:

[0114] The flow field inversion model includes: a feature extraction module, a convolution module, an iterative update module, and an output flow upsampling module;

[0115] The feature extraction module is used to extract features from the sub-mesoscale sea surface water color image to obtain a dense feature map. The resolution of the dense feature map is low resolution, and the dense feature map includes a feature map of the temperature gradient and a feature map of the texture feature of the sea area to be detected;

[0116] The convolution module includes multiple convolution kernels with different sizes, and the multiple convolution kernels are used to calculate the correlation of the dense feature map;

[0117] The iterative update module is used to learn and predict the movement trends of the filaments and the phytoplankton front according to the update gate and reset gate mechanisms, combining the current input of the dense feature map and the correlation volume, to obtain the flow field prediction value;

[0118] The output flow upsampling module is used to process the flow field prediction value to restore the fine displacement changes of the filaments and the phytoplankton front on the ocean surface, and obtain the predicted displacement velocity field of the ocean sub-mesoscale process.

[0119] S107. Use the geostationary ocean color imaging satellite to observe the sea area to be detected, and obtain multiple sub-mesoscale sea surface water color images of the sea area to be detected.

[0120] S108. Use the flow field inversion model to process the sub-mesoscale sea surface water color image to obtain the displacement velocity field of the surface sub-mesoscale process of the sea area to be detected.

[0121] Specifically, using the flow field inversion model to process the sub-mesoscale sea surface water color image includes:

[0122] (1) Extract features from the sub-mesoscale sea surface water color image to obtain a dense feature map.

[0123] Specifically, in combination with the above description, use the feature extraction module in the flow field inversion model to extract features from the sub-mesoscale sea surface water color image, which is implemented by a convolutional neural network and consists of three levels of downsampling. This feature extraction module can extract complex feature information (such as temperature gradient and texture features, etc.) from two sets of consecutive sub-mesoscale sea surface water color images, and map the input image (256 pixels × 256 pixels) to a low-resolution dense feature map with an initial image resolution of 1 / 8. For the filament structure and front area in the ocean, this layer can capture their unique temperature gradient and texture features, and convert these features into a vector representation that can be processed by the model, providing a basis for accurately identifying and analyzing the movement and changes of filaments and fronts.

[0124] (2) Calculate the correlation between the dense feature maps.

[0125] Specifically, a four-layer correlation pyramid structure is employed. Through hierarchical pooling operations, while maintaining high-resolution image information, the visual similarity between two sets of consecutive input dense feature maps is effectively calculated. For filaments and fronts, their corresponding positions in consecutive images can be accurately located. Even when they undergo complex changes such as distortion and rotation, their similarity features can be accurately identified, thereby providing a key basis for accurately calculating the displacement velocity and ensuring that the model can effectively handle the dynamic changes of filaments and fronts. The implementation process of calculating the correlation between dense feature maps can refer to the description in related technologies and will not be elaborated here.

[0126] (3) Combining the dense feature maps and the correlation to learn and predict the movement trends of the filaments and the phytoplankton fronts, obtaining a flow field prediction value.

[0127] Specifically, taking the dense feature maps and their correlation as inputs, the flow field inversion model can learn the complex relationship between the movement trends of filaments and phytoplankton fronts and marine environmental factors. Through learning a large number of training samples, the flow field inversion model gradually establishes a mapping model between the feature maps and the movement trends. Based on the learned mapping model, when a new sub-mesoscale sea surface water color image is input, the flow inversion model can predict the movement trends of filaments and phytoplankton fronts according to the extracted dense feature maps and their correlation, and then obtain a flow field prediction value. This prediction value not only contains the velocity magnitude and direction information of the ocean water body in this area, but also reflects the influence of the movement of filaments and fronts on the flow field and the driving effect of the flow field on them.

[0128] (4) Processing the flow field prediction value to obtain a predicted displacement velocity field.

[0129] Specifically, the flow field prediction value is a vector field containing various information and needs to be further processed to obtain a predicted displacement velocity field for analysis. The velocity vectors in the flow field prediction value can be decomposed according to spatial coordinates to obtain the velocity components in each direction, and then these components can be combined into a displacement velocity field through a certain algorithm so that it can accurately represent the displacement changes of the ocean water body at the sub-mesoscale. At the same time, the velocity field may also be smoothed to remove the influence of noise and outliers and improve the accuracy and reliability of the displacement velocity field.

[0130] (5) Using the predicted displacement velocity field to calculate the sub-mesoscale kinetic energy spectrum, Chl-a concentration spectrum and their flux changes.

[0131] Specifically, the predicted displacement velocity field provides basic data for calculating the submesoscale kinetic energy spectrum, Chl-a concentration spectrum, and their flux variations. The submesoscale kinetic energy spectrum can be obtained by performing spectral analysis on the displacement velocity field, which reflects the distribution of ocean kinetic energy at different wavenumbers (or spatial scales) and helps to study the transfer and dissipation mechanisms of ocean energy within the submesoscale range. The Chl-a concentration spectrum is calculated based on the Chl-a concentration information in the sea surface water color image. Combining with the displacement velocity field, the distribution characteristics of Chl-a concentration at the submesoscale and its interaction relationship with the flow field can be analyzed. Further, by calculating the flux variation, the transfer rate and direction of substances (such as phytoplankton-related substances represented by Chl-a) and energy during the submesoscale process can be understood, further revealing the laws of material cycling and energy flow in the ocean ecosystem, and providing important quantitative indicators for deeply understanding the physical, chemical, and biological interactions in the ocean submesoscale process.

[0132] Further, Figure 2 The following is a comparison chart of the effects of the flow field inversion model and the traditional model at the same time shown in the exemplary embodiments of the present application. Please refer to Figure 2 , Figure 2 In row (a) of, the velocity field data of the flow field inversion model and the traditional models (the maximum correlation coefficient algorithm and the physical constraint convergence algorithm) are shown. The ground truth data is the accurate velocity field information as a reference; the velocity field of the flow field inversion model is obtained by using the flow field inversion model proposed in the present application; the velocity field obtained by the maximum correlation coefficient algorithm represents the traditional maximum cross-correlation algorithm; the velocity field obtained by the physical constraint convergence algorithm is based on the optimized algorithm of decorrelation scale analysis. Figure 2 In row (b) of, the average velocity error (EPE) data of the flow field inversion model and the traditional models (the maximum correlation coefficient algorithm and the physical constraint convergence algorithm) are shown. Figure 2 In row (c) of, the average angle error (AEE) data of the flow field inversion model and the traditional models (the maximum correlation coefficient algorithm and the physical constraint convergence algorithm) are shown. In terms of the endpoint error, the average velocity error of the flow field inversion model is 0.14 m / s, the average velocity error of the maximum correlation coefficient algorithm is 0.40 m / s, and the average velocity error of the physical constraint convergence algorithm is 0.39 m / s; in terms of the average angle error, the average angle error of the flow field inversion model is 5.58°, the average angle error of the maximum correlation coefficient algorithm is 12.41°, and the average angle error of the physical constraint convergence algorithm is 11.91°. It can be clearly seen from these data that the flow field inversion model provided by the present application performs better in error control and can simulate the velocity field more accurately.

[0133] The method provided in this embodiment screens representative sea areas, matches and analyzes the structural image information of phytoplankton fronts and filaments with the displacement velocity field information at the same location, can accurately capture the physical relationship between the material distribution (phytoplankton) and the flow field in the ocean, uses the ocean data of the representative sea areas to form the first training sample set, and corrects it using the second data set and the third data set to obtain the second training sample set, achieving multi-scale information fusion from sub-mesoscale to large ocean scale, which helps to deeply understand the behavioral characteristics of ocean sub-mesoscale processes in different scale environments. Further, training the initial model with the second training sample set constructed based on multi-source data greatly optimizes the training data of the model. The corrected training sample set combines the advantages of multiple data sources, reduces the errors and uncertainties that may exist in a single data source, and improves the accuracy of the relationship between the sub-mesoscale sea surface color image and the displacement velocity field learned by the flow field inversion model. Using the trained flow field inversion model to process the sub-mesoscale sea surface color image of the sea area to be detected observed by the geostationary ocean color imaging satellite can obtain the displacement velocity field of the sub-mesoscale process on the surface of the sea area. This process realizes the rapid and accurate conversion from satellite observation images to flow field information, providing strong data support for oceanography research, ocean environmental monitoring, ocean resource management, etc. In addition, by calculating and analyzing the sub-mesoscale kinetic energy spectrum, Chl-a concentration spectrum and their flux changes, the laws of transmission and transformation of ocean energy and matter at the sub-mesoscale can be revealed, providing an important basis for the development of the basic theory of ocean science.

[0134] Corresponding to the foregoing embodiment of a method for inverting the flow field of ocean near-surface sub-mesoscale processes, this application also provides an embodiment of an apparatus for inverting the flow field of ocean near-surface sub-mesoscale processes.

[0135] Figure 3 It is a schematic structural diagram of the first embodiment of the apparatus for inverting the flow field of ocean near-surface sub-mesoscale processes provided by this application. Please refer to Figure 3 , the apparatus provided in this embodiment includes an acquisition module 310, a construction module 320 and a processing module 330; wherein,

[0136] The acquisition module 310 is used to screen representative sea areas of ocean near-surface sub-mesoscale from the ocean area;

[0137] The acquisition module 310 is further used to obtain the structural image information of phytoplankton fronts and filaments of the ocean sub-mesoscale process by using a geostationary ocean color imaging satellite;

[0138] The acquisition module 310 is further used to obtain the displacement velocity field information of the ocean sub-mesoscale process at the same location by using the first database;

[0139] The construction module 320 is configured to match the structural image information and the displacement velocity field information to construct a first training sample set;

[0140] The construction module 320 is further configured to correct the first training sample set based on a second data set and a third data set to obtain a second training sample set; wherein, the second data set includes sea surface height data, and the third data set includes geostrophic flow field displacement velocity field data of large-scale ocean processes;

[0141] The construction module 320 is further configured to train an initial model using the second training sample set to obtain a flow field inversion model;

[0142] The processing module 330 is configured to observe a sea area to be detected using the geostationary ocean color imaging satellite to obtain multiple sub-mesoscale sea surface color images of the sea area to be detected;

[0143] The processing module 330 is further configured to process the sub-mesoscale sea surface color images using the flow field inversion model to obtain a displacement velocity field of the surface sub-mesoscale process of the sea area to be detected.

[0144] The device of this embodiment can be used to execute Figure 1 the steps of the method embodiment shown. The specific implementation principle and process are similar and will not be elaborated here.

[0145] For the implementation process of the functions and roles of each unit in the above device, please refer to the implementation process of the corresponding steps in the above method for details, which will not be elaborated here.

[0146] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0147] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for inverting the flow field of marine near-surface submesoscale processes, characterized in that The method includes: Screening representative sea areas of the ocean near-surface submesoscale from ocean regions; Obtaining structural image information of phytoplankton fronts and filaments of the submesoscale process in the representative sea area by using a geostationary ocean color imaging satellite; Obtaining displacement velocity field information of the ocean submesoscale process at the same location by using a first database; Matching the structural image information and the displacement velocity field information to construct a first training sample set; Correcting the first training sample set based on a second data set and a third data set to obtain a second training sample set; wherein, the second data set includes sea surface height data, and the third data set includes geostrophic flow field displacement velocity field data of the ocean large-scale process; Wherein, the correcting the first training sample set based on the second data set and the third data set includes: Constructing a conversion function based on the geostrophic balance relationship, and the conversion function is used to calculate the geostrophic flow field displacement velocity field of the second data set; Comparing the geostrophic flow field displacement velocity fields of the second data set and the third data set with the displacement velocity field of the first training sample set at the same location and within the same time range to obtain a displacement velocity field difference; Correcting the first training sample set according to the displacement velocity field difference between the second data set and the third data set and the first training sample set; Training an initial model by using the second training sample set to obtain a flow field inversion model; Wherein, the training process of the flow field inversion model includes: Inputting the second training sample set into the initial model; The initial model learns the relationship between the submesoscale sea surface color image and the displacement velocity field from the second training sample set; Determining a loss function, aiming to minimize the loss function, iterating the parameters of the flow field inversion model, so that the difference between the predicted value of the displacement velocity field and the true value of the displacement velocity field is less than a threshold; The determining the loss function includes: For each pixel position in the submesoscale sea surface color image, respectively obtaining its predicted displacement velocity vector and true displacement velocity vector; Calculating the square of the displacement and the square of the velocity in the predicted displacement velocity vector and the true displacement velocity vector; Calculating the difference between the square of the displacement and the square of the velocity, and taking the square root of the difference to obtain the loss function; Observing a sea area to be detected by using the geostationary ocean color imaging satellite to obtain multiple submesoscale sea surface color images of the sea area to be detected; Processing the submesoscale sea surface color image by using the flow field inversion model to obtain the displacement velocity field of the surface submesoscale process of the sea area to be detected.

2. The method according to claim 1, wherein The matching the structural image information and the displacement velocity field information includes: Extracting the structural features of the phytoplankton fronts and the filaments in the structural image information; Performing spatial grid processing on the displacement velocity field information, and extracting the displacement features and velocity features of each grid unit of the displacement velocity field information; Process the structural image information and the displacement velocity field information according to the spatial scale and time series, and match the displacement velocity field information with the structural characteristics of the phytoplankton front and the structural characteristics of the filaments.

3. The method according to claim 1, wherein The screening of representative submesoscale sea areas in the ocean from ocean regions includes: Obtain the movement conditions of submesoscale processes in each ocean region; Obtain the characteristic differences between phytoplankton and filaments and the environment in each ocean region; Screen each ocean region according to the characteristic differences to obtain the first screening result of representative sea areas; Determine the distribution characteristics of each representative sea area in the first screening result of representative sea areas, select the first screening result of representative sea areas according to the distribution characteristics to obtain the second screening result, and determine the representative sea areas according to the second screening result.

4. The method according to claim 3, characterized in that, The obtaining of the characteristic differences between phytoplankton and filaments and the environment in each ocean region includes: Obtain multiple water color images of each ocean region, and each water color image includes phytoplankton, filaments, and the background environment of the ocean region at the same time; Identify the first boundaries of the phytoplankton and filaments from each water color image; Identify the second boundaries of the background environment from each water color image; Calculate the overlapping area after the intersection of the first boundary and the second boundary in each water color image; Calculate the weighted sum of the overlapping areas of all water color images to obtain the total overlapping area, and use the total overlapping area as the characteristic difference.

5. The method according to claim 1, characterized in that The flow field inversion model includes: a feature extraction module, a convolution module, an iterative update module, and an output flow upsampling module; The feature extraction module is used to extract features from the submesoscale sea surface water color image to obtain a dense feature map. The resolution of the dense feature map is low resolution, and the dense feature map contains a feature map of the temperature gradient and a feature map of the texture features of the sea area to be detected; The convolution module includes multiple convolution kernels with different sizes, and the multiple convolution kernels are used to calculate the correlation of the dense feature map; The iterative update module is used to learn and predict the movement trends of the filaments and the phytoplankton front according to the update gate and reset gate mechanisms, in combination with the current input dense feature map and the correlation volume, to obtain a flow field prediction value; The output flow upsampling module is used to process the flow field prediction value to restore the subtle displacement changes of the filaments and the phytoplankton front on the ocean surface, and obtain the predicted displacement velocity field of the ocean submesoscale process.

6. The method according to claim 1, wherein The processing of the submesoscale sea surface water color image by using the flow field inversion model includes: Extract features from the submesoscale sea surface water color image to obtain a dense feature map; Calculate the correlation between the dense feature maps; Learn and predict the movement trends of the filaments and the phytoplankton front in combination with the dense feature map and the correlation to obtain a flow field prediction value; Process the flow field prediction value to obtain a predicted displacement velocity field; Use the predicted displacement velocity field to calculate the submesoscale kinetic energy spectrum, Chl-a concentration spectrum, and their flux changes.

7. An apparatus for inverting the flow field of a near-surface sub-mesoscale process in the ocean, characterized in that, The device includes an acquisition module, a construction module, and a processing module; wherein, the acquisition module is configured to screen a representative sea area of the ocean near-surface submesoscale from the ocean area; the acquisition module is further configured to use a geostationary ocean color imaging satellite to obtain structural image information of phytoplankton fronts and filaments of the submesoscale process in the representative sea area; the acquisition module is further configured to use a first database to obtain displacement velocity field information of the ocean submesoscale process at the same location; the construction module is configured to match the structural image information and the displacement velocity field information to construct a first training sample set; the construction module is further configured to correct the first training sample set based on a second data set and a third data set to obtain a second training sample set; wherein, the second data set includes sea surface height data, and the third data set includes geostrophic current field displacement velocity field data of the ocean large-scale process; wherein, the correction of the first training sample set based on the second data set and the third data set includes: constructing a conversion function based on the geostrophic balance relationship, and the conversion function is used to calculate the geostrophic current field displacement velocity field of the second data set; comparing the geostrophic current field displacement velocity fields of the second data set and the third data set with the displacement velocity field of the first training sample set at the same location and within the same time range to obtain a displacement velocity field difference; correcting the first training sample set according to the displacement velocity field difference between the second data set and the third data set and the first training sample set; the construction module is further configured to use the second training sample set to train an initial model to obtain a flow field inversion model; wherein, the training process of the flow field inversion model includes: inputting the second training sample set into the initial model; the initial model learns the relationship between the submesoscale sea surface color image and the displacement velocity field from the second training sample set; determining a loss function, aiming to minimize the loss function, iterating the parameters of the flow field inversion model, so that the difference between the predicted value of the displacement velocity field and the true value of the displacement velocity field is less than a threshold; the determination of the loss function includes: for each pixel position in the submesoscale sea surface color image, respectively obtaining its predicted displacement velocity vector and true displacement velocity vector; calculating the square of the displacement and the square of the velocity in the predicted displacement velocity vector and the true displacement velocity vector; calculating the difference between the square of the displacement and the square of the velocity, and taking the square root of the difference to obtain the loss function; the processing module is configured to use the geostationary ocean color imaging satellite to observe a sea area to be detected, and obtain multiple submesoscale sea surface color images of the sea area to be detected; the processing module is further configured to use the flow field inversion model to process the submesoscale sea surface color image to obtain the displacement velocity field of the surface submesoscale process of the sea area to be detected.

Images