Marine environment spatial data retrieval method

Abstract

The invention discloses a marine environment spatial data retrieval method, which comprises the following steps of: acquiring to-be-processed initial data of a marine environment, preprocessing the to-be-processed initial data, and storing the preprocessed to-be-processed initial data in a uniform data format required in a network mapping process to obtain initial data; performing network mapping processing according to the initial data to obtain a corresponding grid index; according to the method, the to-be-retrieved data is obtained, retrieval is performed according to the to-be-retrieved data and the grid index, and the corresponding retrieval result is obtained, so that comprehensive coverage and fine division of the marine environment spatial data are realized, and a solid foundation is provided for rapid retrieval of the marine environment spatial data; the system can realize conversion with information systems under various existing technical systems, and is convenient for cross-industry and cross-department information sharing and efficient service.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and in particular to a method for retrieving marine environment spatial data. Background Art

[0002] Marine environmental spatial data include but are not limited to various environmental parameters such as ocean temperature, salinity, current velocity, waves, sea ice, biomass, as well as static and dynamic information such as seabed topography, seabed geology, and marine pollution. It has the characteristics of large data volume, diverse types, and complex temporal and spatial changes. It is of great significance to the fields of marine scientific research, marine resource development, and marine environmental protection.

[0003] Traditional marine environmental spatial data retrieval methods mainly rely on the traditional longitude and latitude system or simple spatial division method. Such processing methods are very complex and inefficient. In addition, marine environmental spatial data from different sources, formats and accuracies lack unified data standards and interfaces between different systems, resulting in serious data island phenomena, making it difficult to achieve efficient integration and sharing, and there are delays or lags. In addition, it cannot meet the application scenarios where users have high real-time requirements. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method and system for retrieving marine environment spatial data.

[0005] According to a first aspect of the present invention, a method for retrieving marine environment spatial data comprises:

[0006] Acquire the to-be-processed initial data of the marine environment, and pre-process and store the to-be-processed initial data in a unified data format required in the network mapping process to obtain initial data;

[0007] Performing network mapping processing according to the initial data to obtain a corresponding grid index;

[0008] Acquire the data to be retrieved, perform retrieval according to the data to be retrieved and the grid index, and obtain corresponding retrieval results, wherein the data to be retrieved includes spatial information, time information, attribute information, and association relationship information;

[0009] The searching according to the data to be searched and the grid index includes:

[0010] When the data to be retrieved is spatial information, searching is performed according to the spatial information of the data to be retrieved and the grid index;

[0011] When the data to be retrieved is time information, performing retrieval according to the time information of the data to be retrieved and the grid index;

[0012] When the data to be retrieved is attribute information, performing retrieval according to the attribute information of the data to be retrieved and the grid index;

[0013] When the data to be retrieved is association relationship information, retrieval is performed according to the association relationship information of the data to be retrieved and the grid index.

[0014] The marine environment spatial data retrieval method according to the embodiment of the present invention has at least the following beneficial effects:

[0015] The method obtains the to-be-processed initial data of the marine environment, and pre-processes and stores the to-be-processed initial data into a unified data format required in a network mapping process to obtain the initial data; performs network mapping processing according to the initial data to obtain a corresponding grid index; obtains the to-be-searched data, searches according to the to-be-searched data and the grid index to obtain a corresponding search result, thereby achieving comprehensive coverage and fine division of marine environment spatial data, providing a solid foundation for rapid retrieval of marine environment spatial data, and being able to realize conversion with information systems under various existing technical systems, facilitating cross-industry and cross-departmental information sharing and efficient services.

[0016] According to some embodiments of the present invention, the unified data format is an initial calculation format for conversion into grid units.

[0017] According to some embodiments of the present invention, before performing network mapping processing according to the initial data to obtain a corresponding grid index, the method further includes:

[0018] Associating the grid index with the initial data, and constructing a relational model of the association relationship of the initial data using a preset relational modeling model;

[0019] Constructing a time retrieval space of the initial data according to the time information of the initial data and the grid index;

[0020] The retrieval space of the grid index is constructed by a preset index structure.

[0021] According to some embodiments of the present invention, the searching according to the spatial information of the data to be searched and the grid index includes:

[0022] Preprocessing the spatial information of the data to be retrieved to obtain a grid index range to be retrieved;

[0023] Calculate and obtain a first grid index set according to the grid index range to be searched;

[0024] According to the first grid index set, searching is performed using a regional search algorithm and a search space of the grid index to obtain initial data corresponding to the first grid index set;

[0025] The initial data of the first grid index set is visualized and outputted using a first visualization algorithm.

[0026] According to some embodiments of the present invention, the retrieving according to the time information of the data to be retrieved and the grid index includes:

[0027] Determine a corresponding grid index according to the time information of the data to be retrieved, wherein the time information of the data to be retrieved includes a time range, a time point, and a time period;

[0028] A search is performed according to the grid index of the time information of the data to be searched and the time search space to obtain initial data corresponding to the time information of the data to be searched.

[0029] According to some embodiments of the present invention, the initial data includes attribute information, and the searching according to the attribute information of the data to be searched and the grid index includes:

[0030] A search is performed based on the attribute information of the data to be searched and the attribute information of the initial data to obtain the initial data corresponding to the attribute information of the data to be searched.

[0031] According to some embodiments of the present invention, the searching according to the association relationship information of the data to be searched and the grid index includes:

[0032] Performing a search based on the association relationship information of the data to be retrieved and the relationship model to obtain initial data corresponding to the association relationship information of the data to be retrieved;

[0033] The initial data corresponding to the association relationship information of the data to be retrieved is visualized and outputted through a second visualization algorithm.

[0034] According to a second aspect of the present invention, a marine environment spatial data retrieval system comprises:

[0035] An acquisition module is used to acquire the initial data to be processed of the marine environment, and pre-process the initial data to be processed and store it in a unified data format required in the network mapping process to obtain the initial data;

[0036] A mapping module, used to perform network mapping processing according to the initial data to obtain a corresponding grid index;

[0037] The retrieval module is used to obtain the data to be retrieved, perform retrieval according to the data to be retrieved and the grid index, and obtain corresponding retrieval results. The data to be retrieved includes spatial information, time information, attribute information and association relationship information.

[0038] According to an embodiment of the third aspect of the present invention, an electronic device includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the above-mentioned marine environment spatial data retrieval method.

[0039] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned marine environment spatial data retrieval method.

[0040] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0042] Figure 1 It is a flow chart of a method for retrieving marine environment spatial data provided by one embodiment of the present invention;

[0043] Figure 2 It is a logical schematic diagram of a marine environment spatial data retrieval method provided by an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of spatial information retrieval provided by an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram of time information retrieval provided by an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of attribute information retrieval provided by an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of association relationship information retrieval provided by an embodiment of the present invention;

[0048] Figure 7 It is a structural schematic diagram of a marine environment spatial data retrieval system provided by an embodiment of the present invention;

[0049] Figure 8 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0051] In the description of the present invention, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0052] In the description of the present invention, it should be understood that descriptions involving orientation, such as orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0053] In the description of the present invention, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0054] Existing technologies often rely on traditional longitude and latitude systems or simple spatial division methods, which are particularly complex and inefficient in big data environments, especially when dealing with complex, multi-dimensional marine environmental spatial data.

[0055] In order to solve the existing defects, refer to Figure 1 An embodiment of the present application provides a method for retrieving marine environment spatial data. The method for retrieving marine environment spatial data includes the following steps S110 to S130.

[0056] Step S110: Acquire the to-be-processed initial data of the marine environment, and pre-process the to-be-processed initial data and store it in a unified data format required in the network mapping process to obtain initial data.

[0057] In this step, the initial data to be processed is preferably the marine environment data obtained from Beidou satellites and other sensors, including sea surface temperature, salinity, ocean currents, meteorological data, etc., in formats including csv, NetCDF, HDF5, etc. The initial data to be processed is stored as an initial calculation format for conversion into grid cells according to the needs of subsequent network mapping, wherein each initial data contains the spatial information, time information, attribute information and association relationship information of the corresponding data.

[0058] In this embodiment, the relevant data of physical, chemical and biological parameters are collected through various ocean observation platforms (such as buoys, ships, satellites, drones, etc.), wherein the relevant data include temperature, salinity, flow velocity, flow direction, nutrient concentration, dissolved oxygen level, chlorophyll a concentration, etc., and then the relevant data are cleared of duplicate or incomplete records, abnormal values ​​are detected, and it is decided whether to repair, remove or mark them and use interpolation or other statistical methods to estimate and process missing values, so as to ensure the accuracy and reliability of the data. Then all data are converted into the same unit and coordinate system to ensure that the timestamps are consistent, such as using UTC as the standard time zone, and then the relevant data are normalized for subsequent data processing. In practical applications, according to the specific project requirements, the data processing flow can be adjusted accordingly, and the efficient organization and indexing of marine environmental spatial data are realized. The grid-based data management method not only simplifies the complexity of data retrieval, but also greatly improves the retrieval speed, so that users can obtain the required marine environmental information more quickly.

[0059] Step S120: Perform network mapping processing according to the initial data to obtain a corresponding grid index.

[0060] In this step, the initial data is mapped to the network to obtain the corresponding grid index for subsequent data retrieval. Based on the binary integer characteristics of Beidou grid coding, spatial calculations are transformed from traditional longitude and latitude floating-point operations to bit operations based on binary coding, further improving computing efficiency and providing strong technical support for marine scientific research. Moreover, the marine environment spatial data rapid retrieval engine based on Beidou grid division can break the data island phenomenon and realize cross-system and cross-platform data integration and sharing. Through a unified grid coding and indexing system, marine environment spatial data from different sources, different formats, and different precisions can be effectively integrated together to provide users with more comprehensive and accurate data support. At the same time, it supports input and output of multiple data formats, which is convenient for users to export and analyze data according to their needs. Improve the accuracy and timeliness of marine environmental monitoring.

[0061] In this embodiment, the network index is first calculated by dividing the earth's surface into multiple grid cells based on the Beidou grid, so that for each initial data, its geographic coordinates (latitude φ and longitude λ) are converted into the index of the Beidou grid. The specific formula is as follows:

[0062]

[0063] Among them, Δλ and is the grid spacing of longitude and latitude, N λ is the number of grids per latitudinal layer, λ min and is the starting longitude and latitude of the grid, ensuring that the longitude and latitude start from the minimum value of the grid.

[0064] Then the grid index of each initial data is associated with the data content and stored in an efficient index structure. Preferably, the grid index is stored through an R-tree or a KD-tree, which specifically includes using the R-tree index structure to store the grid information of the spatial data to support fast spatial queries, and using timestamps as indexes in combination with R-tree indexes to achieve efficient time dimension retrieval.

[0065] Step S130: Acquire the data to be retrieved, perform retrieval based on the data to be retrieved and the grid index, and obtain corresponding retrieval results. The data to be retrieved includes spatial information, time information, attribute information, and association relationship information.

[0066] In this step, the data type of the data to be retrieved is determined according to the data to be retrieved, and corresponding retrieval operations are performed through the data to be retrieved and the grid index according to different data types to obtain corresponding retrieval results, wherein the data to be retrieved includes spatial information, time information, attribute information and association relationship information.

[0067] In some embodiments, before performing network mapping processing according to the initial data in step S120 to obtain the corresponding grid index, the method further includes the following steps:

[0068] Associating the grid index with the initial data, and building a relational model of the association relationship of the initial data using a preset relational modeling model;

[0069] Constructing the time retrieval space of the initial data according to the time information of the initial data and the grid index;

[0070] The retrieval space of the grid index is constructed through the preset index structure.

[0071] In this step, the preset relational modeling model is preferably a relational model suitable for the marine environment based on the entity-relationship (ER) model. First, the grid index is associated with the initial data so that each initial data corresponds to a grid index, which is convenient for subsequent data retrieval based on the index. Then, the preset relational modeling model is used to construct a relational model of the association relationship of the initial data. By constructing the relational model, users are allowed to perform in-depth analysis and retrieval based on data relationships. Common association relationships include the relationship between data types and products: such as a certain type of marine data is used to generate specific marine environmental products; the relationship between products and time periods: such as the generation or use of a certain type of product within a specific time period; the relationship between spatial location and time period: such as environmental changes in a certain geographical location within a specific time period. Then, the time retrieval space of the initial data is constructed based on the time information of the initial data and the grid index, and the retrieval space of the grid index is constructed through the preset index structure.

[0072] In this embodiment, data and its associated relationships are modeled, wherein the relational modeling method includes an entity-relationship (ER) model: used to describe data entities and the relationships between them; a graph model: using nodes to represent entities and edges to represent the relationships between entities; and a matrix model: using a matrix to represent the degree of association between entities.

[0073] Define key entities in the data (such as data types, products, time periods, etc.) and the relationships between them, and express the relationships between key entities through entity-relationship diagrams (ER diagrams) or other forms.

[0074] Assume there are two entity sets E1 and E2, and the relationship set between them is R, which can be expressed as:

[0075] R={(e1,e2)|e1∈E1,e2∈E2};

[0076] Among them, R can represent any binary relationship (such as inclusion, dependency, generation, etc.).

[0077] In this embodiment, first, each initial data is recorded with a timestamp, which is usually the UTC time accurate to seconds or minutes. i The timestamp is t i To speed up time retrieval, it is preferred to construct a time index structure (such as a B-tree, R-tree or time partition) based on timestamps, sort the data according to timestamps, and then create an index structure based on timestamps, such as creating index nodes by hour, day, month, etc.

[0078] In some embodiments, performing retrieval according to the data to be retrieved and the grid index in step S130 includes the following steps:

[0079] When the data to be retrieved is spatial information, retrieval is performed based on the spatial information and grid index of the data to be retrieved;

[0080] When the data to be retrieved is time information, the retrieval is performed according to the time information and the grid index of the data to be retrieved;

[0081] When the data to be retrieved is attribute information, the retrieval is performed according to the attribute information of the data to be retrieved and the grid index;

[0082] When the data to be retrieved is association relationship information, the retrieval is performed according to the association relationship information of the data to be retrieved and the grid index.

[0083] In this step, corresponding retrieval operations are performed according to the type of data to be retrieved, including when the data to be retrieved is spatial information, retrieval is performed based on the spatial information and grid index of the data to be retrieved; when the data to be retrieved is time information, retrieval is performed based on the time information and grid index of the data to be retrieved; when the data to be retrieved is attribute information, retrieval is performed based on the attribute information and grid index of the data to be retrieved; when the data to be retrieved is association information, retrieval is performed based on the association information and grid index of the data to be retrieved, thereby effectively performing retrieval operations according to different types of information to better serve the research and application needs of the marine environment.

[0084] In this embodiment, refer to Figure 2 This embodiment provides a data retrieval engine service module, which supports users to search through spatial information, temporal information, attribute information and association relationship information, and executes corresponding retrieval operations through the spatial retrieval engine, temporal retrieval engine, attribute retrieval engine and association relationship retrieval engine respectively, and then obtains corresponding retrieval results.

[0085] Among them, the spatial search engine realizes the space-based WYSIWYG resource rapid query and retrieval function, mainly realizing the retrieval of intelligent space, submitting spatial search conditions in a visual and interactive way, and the background spatial search engine completes the search and returns the search results to the module for display. By providing a search function page, in a graphical way, you can directly select the area to be searched on the page, and you can zoom in, move, change the range of the selected area, and zoom in and out the scale of the selected area.

[0086] The time search engine realizes the time series search of various marine environmental information products based on time series association. Users submit extraction conditions of different time series (extraction by time periods such as morning, afternoon, and evening, by fixed periods such as day, season, ten-day period, month, season, and year, as well as random time), and the background completes the search and extraction of marine environmental information products based on the time search engine system, and visualizes the extracted results.

[0087] The attribute retrieval engine supports query retrieval by different types of marine environment data; supports query retrieval by marine elements; provides query retrieval by the name of the actual observation platform, longitude and latitude, etc.; and supports combined query retrieval of these query methods.

[0088] Relationship retrieval helps users discover and understand the relationships between different entities in large-scale data sets, and conduct relevant retrieval and analysis. Users can retrieve and extract marine environmental information products based on the relationship between data and products, and the relationship between products and time periods, and display the relationship between objects in a visual way to explore the value contained in the data.

[0089] In some embodiments, the step of searching according to the spatial information and grid index of the data to be searched includes the following steps:

[0090] Preprocess the spatial information of the data to be retrieved to obtain the grid index range to be retrieved;

[0091] Calculate and obtain a first grid index set according to the grid index range to be searched;

[0092] According to the first grid index set, searching is performed using a regional search algorithm and a search space of the grid index to obtain initial data corresponding to the first grid index set;

[0093] The initial data of the first grid index set is visualized and outputted using a first visualization algorithm.

[0094] In this step, the first visualization algorithm is preferably Leaflet, D3.js or Three.js technology. First, the spatial information of the data to be retrieved needs to be preprocessed to obtain the grid index range to be retrieved, and then the first grid index set is calculated based on the grid index range to be retrieved, wherein the first grid index set refers to all grid indexes within the grid index range to be retrieved, and then according to the first grid index set, the regional search algorithm and the retrieval space of the grid index are used to perform retrieval to obtain the initial data corresponding to the first grid index set, and finally the initial data of the first grid index set is visualized and output through the first visualization algorithm.

[0095] In this embodiment, refer to Figure 3 When the user searches for spatial information, the spatial search function is started. The spatial search function searches based on multi-source marine environmental data. First, a spatial search page is created for the user. The user can determine the spatial range selection on the spatial search page, that is, input the data to be searched, and then search and process according to the spatial information of the data to be searched, obtain the corresponding search results and output them.

[0096] In an implementation of this embodiment, a spatial search page is first created: the spatial search function is implemented using technologies such as HTML, CSS, and JavaScript.

[0097] Draw the graphics or maps that need to be retrieved on the page, use the appropriate graphics library to visualize and interact with the graphics, and then use the box selection function to monitor the user's mouse events to achieve the selection of the spatial range. You can also draw the box selection area according to the event coordinates, zoom in, zoom out and translate the graphic area, and support updating the display area of ​​the graphic by monitoring the user's operation events. It supports changing the range of the selected area, changing the shape and size of the selected area according to the user's operation, and then performing retrieval processing based on the spatial information of the data to be retrieved, passing the area range selected by the user to the back-end server for subsequent retrieval logic processing, and displaying the results to the user.

[0098] In this embodiment, the retrieval of spatial information specifically includes: first, obtaining the retrieval conditions input by the user through the visual interface, including but not limited to the user being able to select a rectangular, polygonal, circular or any shaped area; the user can define the retrieval area by clicking, dragging, etc. with the mouse.

[0099] Based on the user-specified geographic area (e.g., a rectangle), convert the user-specified geographic area into a grid index range by entering the coordinates of the lower left corner of the area and the upper right corner coordinates Calculate the bottom left and top right network indices:

[0100]

[0101] Among them, λ0 and φ0 are the starting longitude and latitude of the grid, respectively, and Δλ and Δφ are the steps of longitude and latitude. Then, all grid cells within the calculated index range are extracted for subsequent data retrieval.

[0102] According to the calculated grid index, the initial data in the corresponding grid cell is extracted from the index structure (such as R-tree) generated in the preprocessing stage, and all grid cells overlapping the user-specified area are determined by using a region search algorithm:

[0103] R query ={D|Overlap(D,R input )};

[0104] Where D is the grid unit, R inputis the search area specified by the user, and the Overlap function is used to determine whether the data unit overlaps with the search area. All the initial data in the grid unit are extracted, and filtered according to precise spatial conditions (such as whether the point is within the user-defined polygon) to obtain the final initial data set.

[0105] Finally, in order to achieve intuitive spatial data display and user interaction, this embodiment preferably adopts the following graphics libraries and technologies: Leaflet: for visualization and interaction of two-dimensional maps; D3.js: for rich charts and data-driven visualization; Three.js: for display and interaction of three-dimensional geographic information data.

[0106] Leaflet is an open source JavaScript library that is mainly used to create interactive maps. Two-dimensional maps can be used to display maps of ocean areas, including geographic features such as coastlines and islands; markers and pop-up windows can be used to add markers (such as monitoring stations) at specific locations and provide information pop-up windows; heat maps can be used to display data density or intensity at different locations in a region, such as ocean temperature distribution; vector layers can be used to load and display vector data, such as ship routes, ocean current directions, etc.

[0107] D3.js (Data-Driven Documents) is a collection of technologies based on Web standards that is used to generate dynamic, interactive graphics. It uses time series charts to show ocean parameters that change over time, such as the changing trends of seawater temperature and salinity; uses scatter plots and bubble charts to compare the relationship between multiple variables, such as the relationship between temperature and salinity; combines GeoJSON data with geographic path maps to draw complex geographic contours and paths, such as ocean current patterns; and uses interactive controls to add sliders, buttons and other controls to allow users to explore data views under different conditions.

[0108] Three.js is a cross-browser library written in JavaScript for creating and displaying animated 3D computer graphics. It builds a 3D model of the seabed terrain through 3D terrain, allowing users to observe the seabed topography from different angles; it converts ocean surface data (such as wind speed and wave height) into 3D form through stereoscopic data display, and more intuitively displays the spatial distribution; it provides an immersive ocean exploration experience through virtual reality / augmented reality, combined with VR / AR technology; it simulates natural phenomena such as tidal movement and storm tracks through dynamic simulation and animation effects.

[0109] This embodiment combines different visualization technologies as needed to achieve the best visualization effect, which can not only provide multi-dimensional information presentation but also enhance user experience. Specifically, it includes:

[0110] Use Leaflet to visualize and interact with 2D maps. Leaflet is a lightweight JavaScript library for building interactive map applications. The specific steps are: create an HTML container to hold the map; initialize a Leaflet map and set the center point and zoom level; load the map basemap (such as OpenStreetMap or a custom basemap); and add data points or areas to the map.

[0111] The Leaflet plug-in Leaflet.draw allows users to select areas on the map by drawing rectangles, circles, or polygons. The specific steps are: introduce the Leaflet.draw plug-in; add a toolbar so that users can choose to draw polygons, rectangles, or circles; listen to user drawing events, obtain the coordinates of the area selected by the user, and trigger data retrieval after the selection.

[0112] The Leaflet.heat plug-in of Leaflet can be used to display the density and distribution of data points through a heat map. The specific steps are: introduce the Leaflet.heat plug-in; pass the longitude and latitude and weight (such as pollutant concentration) of the data point to the heat map renderer.

[0113] Data-driven visualization is achieved by using D3.js. D3.js is a powerful JavaScript library for creating dynamic and interactive graphs and charts based on data. It can be used with SVG, Canvas, and HTML to provide users with customized visualization effects.

[0114] By using D3.js to create an interactive scatter plot, you can show the distribution of data points. The specific steps are to create an SVG container; set the size and color of the circle according to the location and attributes (such as concentration) of the geographic data point; and add mouse interaction events (such as hover and click) to each point.

[0115] You can also use D3.js to create a line chart that shows data changes over time for analyzing time series data. The specific steps are to create a time axis as the x-axis; create a vertical axis to show the monitoring value; draw the data points in chronological order and connect these points with lines.

[0116] You can also interact with user input through D3.js, such as using sliders or selection boxes to dynamically filter displayed data. The specific steps are to create an input control (such as a slider); listen for change events of the input control; and redraw the graphics based on user input.

[0117] 3D data visualization is achieved by using Three.js. Three.js is a library for creating and displaying animated 3D graphics in the browser. It provides very powerful functions for displaying geographic data in 3D space.

[0118] For data points in three-dimensional space, use Three.js to create a three-dimensional point cloud. Users can rotate and zoom to view the distribution of data. The specific steps are to create a Three.js scene, camera, and renderer; create a Three.Points object based on the data points; add the point cloud to the scene and render it. In addition, Three.js allows users to interact with the three-dimensional scene through the mouse or touch, such as rotating, zooming, and moving the perspective. The specific steps are to add the Three.OrbitControls control to achieve user interaction; dynamically update the data in the three-dimensional scene through user interaction events.

[0119] In some embodiments, the step of searching according to the time information and the grid index of the data to be searched includes the following steps:

[0120] Determine the corresponding grid index according to the time information of the data to be retrieved, wherein the time information of the data to be retrieved includes a time range, a time point and a time period;

[0121] The search is performed according to the grid index and the time search space of the time information of the data to be searched, and the initial data corresponding to the time information of the data to be searched is obtained.

[0122] In this step, the data matching the time condition specified by the user is quickly filtered through the index structure or direct search. First, the corresponding grid index is determined according to the time information of the data to be retrieved, wherein the time information of the data to be retrieved includes the time range, time point and time period. Then, the grid index of the time information of the data to be retrieved and the time retrieval space are searched to obtain the initial data corresponding to the time information of the data to be retrieved.

[0123] In this embodiment, refer to Figure 4 When the user searches for time information, the time search function is started. The time search function searches based on multi-source marine environment data, performs search processing according to the time range selected by the user, that is, the time information in the data to be searched, obtains the corresponding search results and outputs them.

[0124] In one implementation of this embodiment, time information input by the user is obtained, including a start time and an end time, and all data within the corresponding time period is retrieved through the input time information, or when the user enters a specific time point, all data near the time point is retrieved, which also includes retrieving a specific time period of each day (such as 8:00 to 10:00 every day).

[0125] Moreover, when the time information entered by the user is a specified time range t start , t end When the search is closest to t start and t end The index node of the t start , t end Assume that data d i The timestamp is t i , the retrieval target is all the initial data that meets the following conditions:

[0126] D result ={d i |t start ≤t end};

[0127] Use time index for fast positioning:

[0128] Index(t start )→Start Node, Index(t end )→End Node;

[0129] In the index nodes between the Start Node and the End Node, extract all data points that meet the time condition.

[0130] When the time information entered by the user is a time point t point and tolerance range Δt, find the closest to t point The initial data is extracted at t point -Δt to t point +Δt. For the input time point t point , retrieve all initial data that meets the following conditions:

[0131] D result ={d i ||t i -t point |≤Δt};

[0132] When the user inputs a time period (e.g. 8:00 to 10:00 every day) and a date range (e.g. within a month), for each day in the specified date range, retrieve the initial data within the time period and merge the results for output. Assume that the time period in a day is t start , t end , date range is d start , d end , then the final retrieved data set is:

[0133] D result =∪ dstart≤di≤dend {d i |t start ≤t end};

[0134] That is, on each date d i , retrieve the data within this time period.

[0135] In some embodiments, the step of searching according to the attribute information of the data to be searched and the grid index includes the following steps:

[0136] The search is performed according to the attribute information of the data to be searched and the attribute information of the initial data, and the initial data corresponding to the attribute information of the data to be searched is obtained.

[0137] In this step, you first need to classify the data according to the attribute information of the data to be retrieved, determine the initial data set of the type to be retrieved, and retrieve by combining the attribute information of the data to be retrieved with the attribute information of the initial data to obtain the initial data corresponding to the attribute information of the data to be retrieved. This allows users to query according to any combination of attributes, realize the logical combination of multiple attribute conditions (such as AND, OR operations), and then efficiently retrieve records that meet the conditions.

[0138] In this embodiment, refer to Figure 5 When the user searches for attribute information, the attribute search function is started. The spatial search function searches based on multi-source marine environmental data. According to the attribute information conditions selected by the user, that is, the attribute information in the data to be searched, the search process is performed to obtain the corresponding search results and output them.

[0139] In one implementation of this embodiment, classification is performed according to data type: such as temperature, salinity, ocean current speed, etc. Classification is performed according to marine elements: specific environmental elements, such as sea surface temperature (SST), ocean acidity (pH), chlorophyll concentration, etc. Classification is performed according to observation platform: the source platform of the observation data, such as buoys, satellites, remote sensors, etc. Classification is performed according to geographic location: the longitude and latitude coordinates of the data point. Therefore, users can combine multiple attribute conditions for retrieval as needed. For example: query all sea surface temperature data observed by satellite in 2024; query buoy observation data within a specific longitude and latitude range, and the data type is salinity.

[0140] When the information to be retrieved is a single attribute, such as data type, observation platform, or specific geographical location, the records that meet the conditions are filtered out from the initial data set according to the attribute type.

[0141] For attribute A (such as data type), the search condition with value V retrieves all initial data that meets the following conditions:

[0142] D result ={d i |A(d i )≤V};

[0143] If the search condition involves geographic location, such as longitude and latitude range L min , L max ,but:

[0144] D result ={d i |L mid ≤Longitude(d i )≤L max}and L min ≤Latitude(d i )≤L max ;

[0145] When the user inputs multiple attributes and their corresponding search conditions, such as data type, platform name, latitude and longitude range, etc., the user can select the logical relationship (AND / OR) between the search conditions of multiple attributes to construct a search expression under the combined conditions. Perform a combined search to return all data points that meet the combined conditions. For the combination of multiple attribute conditions, such as the values ​​of attributes A and B are V A , V B , and the user selects AND logical combination, then:

[0146] D result ={d i |A(d i )≤V A AND B(d i )≤V B};

[0147] If the user selects the OR logical combination, then:

[0148] D result ={d i |A(d i )≤V A OR B(d i )≤V B};

[0149] When the user inputs a combination of geographic location, the geographic location (longitude and latitude range) is usually used in combination with other attribute conditions. The user can specify the longitude and latitude range and combine it with other attribute conditions for query. The user specifies the longitude and latitude range and other attribute conditions. Filter data points based on the specified longitude and latitude range. Further filter based on other attribute conditions. Return data points that meet the geographic location and other attribute conditions.

[0150] For the specified latitude and longitude range L min , L max and attribute A value V A , the search conditions are:

[0151] D result ={d i |L min ≤Longitude(d i )≤L max}and L min ≤Latitude(d i )≤L max and A(d i )=V A .

[0152] In some embodiments, the step of searching according to the association relationship information of the data to be searched and the grid index includes the following steps:

[0153] Searching is performed according to the association relationship information and the relationship model of the data to be retrieved, and initial data corresponding to the association relationship information of the data to be retrieved is obtained;

[0154] The initial data corresponding to the association relationship information of the data to be retrieved is visualized and outputted through a second visualization algorithm.

[0155] In this step, the second visualization algorithm is preferably D3.js technology. The ideal visualization technology needs to be determined through different result outputs to achieve the best effect. First, the key entities in the initial data and the relationship between the entities are determined, such as monitoring sites, marine biological species, pollutant types and other key entities, such as "monitoring site A detected pollutant B", "species C and species D coexist in the same ecosystem", and then the nodes (representing entities) and edges (representing relationships) in the relationship model are used to represent the data. Each node and edge can have attributes, such as weight, type, etc. Then, by searching according to the association relationship information of the data to be retrieved and the relationship model, the association between the data is identified and processed, and the initial data corresponding to the association relationship information of the data to be retrieved is obtained.

[0156] Finally, a suitable visualization library is selected according to the needs. In this embodiment, for association relationship retrieval, it is preferred to use the D3.js algorithm to visualize the initial data corresponding to the association relationship information of the data to be retrieved. Through rich charts and data-driven visualization, the retrieval results of the association relationship can be displayed to the user intuitively and clearly, which not only helps data analysis, but also improves user understanding and decision-making efficiency.

[0157] In this embodiment, refer to Figure 6 When the user searches for association information, the association search function is started. The association search function searches based on multi-source marine environmental data and performs search processing according to the association selected by the user, that is, the relationship information in the data to be searched, including the association between data and products or the association between products and time periods. According to the relationship information in the data to be searched, the corresponding association is extracted from the relationship model, thereby obtaining the corresponding search results and outputting them.

[0158] In one implementation of this embodiment, in the relational model, each node represents an entity (such as a product, a data type). Each edge represents a certain relationship between two entities (such as a product depends on a certain type of data). A graph is created based on entities and relationships, where nodes are entities and edges are relationships; the attributes of each node may include information such as data type and time period; based on the user's query conditions, a path search is performed in the graph to find an associated path that meets the conditions, and then the retrieved associated paths and related entities are displayed through visualization means.

[0159] For example, in a relationship graph, graph G consists of a node set V and an edge set E, denoted as G = (V, E), where node v∈V represents an entity and edge e = (v i ,v j )∈E represents entity v i With v j Find the path set P that meets the conditions, and the route length is n:

[0160] P={p=(v1→v2→…→v n )|v i ∈V,e i,i+1 ∈E};

[0161] When the attribute information entered by the user is a complex combined search based on multiple relationships, such as searching for the relationship between products and time periods, and data types at the same time, first determine multiple relationship conditions (such as the relationship between products and time periods, data types and products), combine these conditions into a complex query request, search in the relationship model, find all paths or relationship chains that meet the conditions, and then display the search results in a visual way, such as an association graph or relationship table.

[0162] For combined search, suppose the user queries the relationship between products, data types, and time periods:

[0163] R combined =R1∩R2∩…∩R n ;

[0164] Among them, R i It is the retrieval result under a single relationship condition.

[0165] The beneficial effect of this embodiment is to provide a method for retrieving marine environmental spatial data, which significantly improves the retrieval efficiency and accuracy of marine environmental data. By integrating the multi-dimensional retrieval function of space, time, attributes and association relationships, the present invention can support users to quickly locate required information in massive marine data, and is particularly suitable for combined queries under complex conditions. The graphical interactive interface and the visual display of association relationships enhance the user's operating experience and data comprehension ability, further explore the potential value of marine environmental data, and provide strong support for marine scientific research and applications.

[0166] This embodiment obtains the initial data to be processed of the marine environment, and pre-processes and stores the initial data to be processed in a unified data format required in the network mapping process to obtain the initial data; performs network mapping processing based on the initial data to obtain a corresponding grid index; obtains the data to be retrieved, and performs retrieval based on the data to be retrieved and the grid index to obtain a corresponding retrieval result, thereby achieving comprehensive coverage and fine division of marine environment spatial data, providing a solid foundation for the rapid retrieval of marine environment spatial data, and being able to realize conversion with information systems under various existing technical systems, facilitating cross-industry and cross-departmental information sharing and efficient services.

[0167] Reference Figure 7 Some embodiments of the present application provide a load balancing scheduling system, the system includes an acquisition module 710, a mapping module 720, and a retrieval module 730, specifically:

[0168] The acquisition module 710 is used to acquire the to-be-processed initial data of the marine environment, and pre-process the to-be-processed initial data and store it in a unified data format required in the network mapping process to obtain the initial data;

[0169] A mapping module 720 is used to perform network mapping processing according to the initial data to obtain a corresponding grid index;

[0170] The retrieval module 730 is used to obtain the data to be retrieved, perform retrieval based on the data to be retrieved and the grid index, and obtain corresponding retrieval results. The data to be retrieved includes spatial information, time information, attribute information, and association relationship information.

[0171] In one embodiment, the mapping module 720 is further used to associate the grid index with the initial data, and construct a relational model of the association relationship of the initial data using a preset relational modeling model.

[0172] In one implementation, the mapping module 720 is further configured to construct a time retrieval space of the initial data according to the time information of the initial data and the grid index.

[0173] In one implementation, the mapping module 720 is further configured to: construct a retrieval space of a grid index by using a preset index structure.

[0174] In one embodiment, the retrieval module 730 is further used for: when the data to be retrieved is spatial information, performing retrieval according to the spatial information and grid index of the data to be retrieved.

[0175] In one embodiment, the retrieval module 730 is further used for: when the data to be retrieved is time information, performing retrieval according to the time information and the grid index of the data to be retrieved.

[0176] In one implementation, the search module 730 is further configured to: when the data to be searched is attribute information, search according to the attribute information and the grid index of the data to be searched.

[0177] In one implementation, the search module 730 is further configured to: when the data to be searched is association relationship information, search according to the association relationship information of the data to be searched and the grid index.

[0178] In one implementation, the search module 730 is further configured to: pre-process the spatial information of the data to be searched to obtain a grid index range to be searched.

[0179] In one implementation, the search module 730 is further configured to: calculate and obtain a first grid index set according to the grid index range to be searched.

[0180] In one embodiment, the search module 730 is further used to: perform a search based on the first grid index set using a regional search algorithm and a search space of the grid index to obtain initial data corresponding to the first grid index set.

[0181] In one implementation, the retrieval module 730 is further configured to: output the initial data of the first grid index set in a visualized manner using a first visualization algorithm.

[0182] In one embodiment, the search module 730 is further used to determine the corresponding grid index according to the time information of the data to be searched, wherein the time information of the data to be searched includes a time range, a time point and a time period.

[0183] In one embodiment, the search module 730 is further used to: perform a search according to the grid index and the time search space of the time information of the data to be searched, and obtain the initial data corresponding to the time information of the data to be searched.

[0184] In one implementation, the search module 730 is further used to: perform a search based on the attribute information of the data to be searched and the attribute information of the initial data, and obtain the initial data corresponding to the attribute information of the data to be searched.

[0185] In one implementation, the search module 730 is further used to: perform a search based on the association relationship information and the relationship model of the data to be searched, and obtain initial data corresponding to the association relationship information of the data to be searched.

[0186] In one implementation, the retrieval module 730 is further configured to: output the initial data corresponding to the association relationship information of the data to be retrieved visually by using a second visualization algorithm.

[0187] Reference Figure 8 The embodiment of the present application further provides an electronic device, the electronic device comprising:

[0188] at least one memory;

[0189] at least one processor;

[0190] at least one program;

[0191] At least one hydrogen fuel cell; the program is stored in the memory, and the processor executes at least one program to implement the above-mentioned marine environment spatial data retrieval method implemented in the present disclosure.

[0192] The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a vehicle-mounted computer, etc.

[0193] The electronic device according to the embodiment of the present application is described in detail below.

[0194] The processor 1600 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement a technical solution of a method for retrieving marine environment spatial data provided in an embodiment of the present disclosure;

[0195] The memory 1700 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1700 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1700, and the processor 1600 calls and executes the marine environment spatial data retrieval method of the embodiment of the present disclosure.

[0196] Input / output interface 1800, used to implement information input and output;

[0197] The communication interface 1900 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0198] Bus 2000 , which transmits information between various components of the device (e.g., processor 1600 , memory 1700 , input / output interface 1800 , and communication interface 1900 );

[0199] The processor 1600 , the memory 1700 , the input / output interface 1800 , and the communication interface 1900 are connected to each other in communication within the device via the bus 2000 .

[0200] The embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned marine environment spatial data retrieval method.

[0201] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0202] The embodiments described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0203] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present disclosure and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0204] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0205] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0206] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0207] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0208] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0209] 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 on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0211] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store programs.

[0212] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A method for retrieving marine environment spatial data, characterized in that: The method comprises: Acquire the to-be-processed initial data of the marine environment, and pre-process and store the to-be-processed initial data in a unified data format required in the network mapping process to obtain initial data; Performing network mapping processing according to the initial data to obtain a corresponding grid index; Acquire the data to be retrieved, perform retrieval according to the data to be retrieved and the grid index, and obtain corresponding retrieval results, wherein the data to be retrieved includes spatial information, time information, attribute information, and association relationship information; The searching according to the data to be searched and the grid index includes: When the data to be retrieved is spatial information, searching is performed according to the spatial information of the data to be retrieved and the grid index; When the data to be retrieved is time information, performing retrieval according to the time information of the data to be retrieved and the grid index; When the data to be retrieved is attribute information, performing retrieval according to the attribute information of the data to be retrieved and the grid index; When the data to be retrieved is association relationship information, retrieval is performed according to the association relationship information of the data to be retrieved and the grid index.

2. The marine environment spatial data retrieval method according to claim 1, characterized in that: The unified data format is an initial calculation format used for conversion into grid units.

3. The marine environment spatial data retrieval method according to claim 2, characterized in that: Before performing network mapping processing according to the initial data to obtain a corresponding grid index, the method further includes: Associating the grid index with the initial data, and constructing a relational model of the association relationship of the initial data using a preset relational modeling model; Constructing a time retrieval space of the initial data according to the time information of the initial data and the grid index; The retrieval space of the grid index is constructed by a preset index structure.

4. The marine environment spatial data retrieval method according to claim 3, characterized in that: The searching according to the spatial information of the data to be searched and the grid index includes: Preprocessing the spatial information of the data to be retrieved to obtain a grid index range to be retrieved; Calculate and obtain a first grid index set according to the grid index range to be searched; According to the first grid index set, searching is performed using a regional search algorithm and a search space of the grid index to obtain initial data corresponding to the first grid index set; The initial data of the first grid index set is visualized and outputted using a first visualization algorithm.

5. The marine environment spatial data retrieval method according to claim 3, characterized in that: The searching according to the time information of the data to be searched and the grid index includes: Determine a corresponding grid index according to the time information of the data to be retrieved, wherein the time information of the data to be retrieved includes a time range, a time point, and a time period; A search is performed according to the grid index of the time information of the data to be searched and the time search space to obtain initial data corresponding to the time information of the data to be searched.

6. The marine environment spatial data retrieval method according to claim 3, characterized in that: The initial data includes attribute information, and the searching according to the attribute information of the data to be searched and the grid index includes: A search is performed according to the attribute information of the data to be searched and the attribute information of the initial data to obtain the initial data corresponding to the attribute information of the data to be searched.

7. The marine environment spatial data retrieval method according to claim 3, characterized in that: The searching according to the association relationship information of the data to be searched and the grid index includes: Performing a search based on the association relationship information of the data to be retrieved and the relationship model to obtain initial data corresponding to the association relationship information of the data to be retrieved; The initial data corresponding to the association relationship information of the data to be retrieved is visualized and outputted through a second visualization algorithm.

8. A marine environment spatial data retrieval system, characterized in that: The system comprises: An acquisition module is used to acquire the initial data to be processed of the marine environment, and pre-process the initial data to be processed and store it in a unified data format required in the network mapping process to obtain the initial data; A mapping module, used to perform network mapping processing according to the initial data to obtain a corresponding grid index; The retrieval module is used to obtain the data to be retrieved, perform retrieval according to the data to be retrieved and the grid index, and obtain corresponding retrieval results. The data to be retrieved includes spatial information, time information, attribute information and association relationship information.

9. An electronic device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute a method for retrieving marine environment spatial data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the marine environment spatial data retrieval method according to any one of claims 1 to 7.

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