A coastal zone map service real-time publishing and dynamic loading method, device, medium and product

Abstract

This application discloses a method, device, medium, and product for real-time publishing and dynamic loading of coastal zone map services, relating to the field of remote sensing image map service technology. The method first unifies multi-source remote sensing images of the coastal zone into COG format to obtain a COG image set; then, it constructs the mosaic dataset metadata of the COG image set and stores it in the `mosic` collection of a MongoDB database; it constructs a quadtree spatial index of the COG image set and stores it in the `tiles_set` collection of a MongoDB database; finally, it generates a service endpoint for publishing. This application transforms the traditional model of "pre-generating and storing all tiles" into an approach of "storing raw data + lightweight index + real-time computation," achieving lightweight, automated, and high-performance publishing of coastal zone remote sensing image services through a technical route of "dynamic indexing, on-demand reading, and real-time mosaicking."

Description

Technical Field

[0001] This application relates to the field of remote sensing image map service technology, and in particular to a method, device, medium and product for real-time publishing and dynamic loading of coastal zone map services. Background Technology

[0002] As a crucial area for land-sea interaction, the coastal zone faces immense ecological pressure due to frequent human activities and environmental damage. Remote sensing imagery, with its wide coverage, timely data, and high information integration, is increasingly widely used in coastal zone surveys, monitoring, ecological protection, and development. However, with the continuous increase in remote sensing imagery data, traditional map service publishing and loading methods suffer from drawbacks such as long production cycles, severe storage redundancy, complex data updates, poor multi-source data fusion, and low reading efficiency, failing to support the demand for rapid, real-time map service publishing and access. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, medium, and product for real-time publishing and dynamic loading of coastal zone map services, so as to achieve lightweight, automated, and high-performance publishing and loading of remote sensing image services for coastal zones.

[0004] To achieve the above objectives, this application provides the following solution.

[0005] Firstly, this application provides a method for real-time publishing of coastal zone map services, including: The multi-source remote sensing images of the coastal zone were unified into COG (Cloud Optimized Geospatial Tagged Image File Format, also known as Cloud Optimized GeoTIFF, or COG for short) format to obtain a COG image set; Construct the mosaic dataset metadata of the COG image set, and store the mosaic dataset metadata into the collection mosic of the MongoDB (Mongo Database) database; Construct a quadtree spatial index for the COG image set and store the quadtree spatial index in the tiles_set collection of the MongoDB database; the tiles_set collection is specified by the tiles_set field in the mosaic dataset metadata; A service endpoint is generated based on a mosaic dataset of COG image sets in a map service format, and the mosaic dataset is published; the mosaic dataset includes at least mosaic dataset metadata and a quadtree spatial index.

[0006] Optionally, the mosaic dataset metadata includes: mosaic_id, crs, data_type, bands_count, nodata, bounds, min_zoom, max_zoom, quadkey_zoom, tiles_set, and url; Wherein, mosaic_id is the identifier of the mosaic dataset metadata; crs, data_type, bands_count, and nodata are the spatial coordinate reference, data type, band number, and nodata value of the COG image set; bounds is the minimum bounding rectangle of the area covered by all COG images in the COG image set; min_zoom and max_zoom are the minimum and maximum visualization levels of the COG image set, respectively; quadkey_zoom is the basic zoom level; tiles_set is the name of the set of quadtree spatial indexes of the COG image set stored in the MongoDB database; and url is the address of the map service to be published.

[0007] Optionally, the quadtree spatial index includes quadtree index records for each slice of the base scaling level in the COG image set; The quadtree index record is: {quadkey: [asset1, asset2, ...]}; Where quadkey is the quadtree key value of the target slice, [asset1, asset2, ...] is the list of file paths of COG images in the COG image set that intersect with the target slice, and asset1 and asset2 are two file paths in the file path list; the target slice is any slice in the basic scaling level of the COG image set.

[0008] Optionally, the quadtree key value of the target slice is obtained by using a Python function to transform the ZXY tile coordinates of the target slice, where Z is the scaling level of the slice, and X and Y are the column number and row number of the slice, respectively.

[0009] Secondly, this application provides a method for dynamically loading coastal zone map services. This method is used to dynamically load data published using the aforementioned real-time coastal zone map service publishing method. The method includes: Obtain a slice request; the slice request includes a query identifier and the ZXY tile coordinates of the query slice; The quadtree key value of the corresponding slice in the basic scaling level is determined based on the ZXY tile coordinates of the query slice, and used as the query key value; In the collection mosic in the MongoDB database, determine the mosaic dataset metadata corresponding to the query identifier, and use it as the target mosaic dataset metadata. Determine the tiles_set set specified by the tiles_set field of the target mosaic dataset metadata as the target tiles_set set; Search the list of file paths corresponding to the query key value in the quadtree spatial index of the target tiles_set collection; The pixel data representing the geographic range indicated by the ZXY tile coordinates of the query slice is read from the list of file paths corresponding to the query key value and then output.

[0010] Optionally, the pixel data representing the geographic area indicated by the ZXY tile coordinates of the query tile is read from the file path list corresponding to the query key value and output, specifically including: When the file path list corresponding to the query key value contains only one file path, the pixel data of the geographic range represented by the ZXY tile coordinates of the query tile in the target COG image is read and output in image format; the target COG image is the COG image stored at the file path in the file path list corresponding to the query key value. When the file path list corresponding to the query key contains multiple file paths, the fused pixel data of the geographic range represented by the ZXY tile coordinates of the query tile in each target COG image is read and output in image format; each target COG image is a COG image stored at each file path in the file path list corresponding to the query key, and the fused pixel data is obtained by fusing the pixel data of the geographic range represented by the ZXY tile coordinates of the query tile in each target COG image.

[0011] Optionally, the pixel data representing the geographic range of the query tile in each target COG image can be fused by taking the latest value and / or taking the average value.

[0012] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for real-time publishing of coastal zone map services or the above-described method for dynamic loading of coastal zone map services.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for real-time publishing of coastal zone map services or the above-described method for dynamic loading of coastal zone map services.

[0014] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for real-time publishing of coastal zone map services or the above-described method for dynamic loading of coastal zone map services.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects.

[0016] This application provides a method, device, medium, and product for real-time publishing and dynamic loading of coastal zone map services. First, multi-source remote sensing images of the coastal zone are unified into COG format to obtain a COG image set. Then, mosaic dataset metadata of the COG image set is constructed and stored in the `mosic` collection of a MongoDB database. A quadtree spatial index of the COG image set is constructed and stored in the `tiles_set` collection of a MongoDB database; the `tiles_set` collection is specified by the `tiles_set` field in the mosaic dataset metadata. Then, a service endpoint is generated based on the mosaic dataset of the COG image set in a map service format, and the mosaic dataset is published. This application transforms the traditional model of "pre-generating and storing all tiles" into a "storing raw data + lightweight index + real-time computation" approach. Through a technical route of "dynamic indexing, on-demand reading, and real-time mosaicking," it achieves lightweight, automated, and high-performance publishing of remote sensing image services for coastal areas. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for real-time publishing of a coastal zone map service, as provided in an embodiment of this application.

[0019] Figure 2 This is a flowchart illustrating a method for dynamically loading coastal zone map services according to an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] This application provides a method, device, medium, and product for real-time publishing and dynamic loading of coastal zone map services, which can realize the real-time publishing and dynamic loading of massive multi-source remote sensing images of the coastal zone. The core is to utilize the internal tiling characteristics of the COG format and the quadtree spatial index to achieve "dynamic indexing", "on-demand reading", and "real-time loading".

[0024] In one exemplary embodiment, a method for real-time publishing of coastal zone map services is provided, such as... Figure 1 As shown, it includes the following steps 101-104.

[0025] Step 101: Unify the multi-source remote sensing images of the coastal zone into COG format to obtain the COG image set.

[0026] Step 102: Construct the mosaic dataset metadata of the COG image set and store the mosaic dataset metadata in the collection mosic of the MongoDB database.

[0027] Step 103: Construct a quadtree spatial index for the COG image set and store the quadtree spatial index in the tiles_set collection of the MongoDB database; the tiles_set collection is specified by the tiles_set field in the mosaic dataset metadata.

[0028] Step 104: Generate a service endpoint based on the mosaic dataset of the COG image set in map service format, and publish the mosaic dataset; the mosaic dataset includes at least mosaic dataset metadata and a quadtree spatial index.

[0029] Steps 101-104 above constitute the publishing process, used for index building and service deployment. In this process, the source data is first standardized and converted into COG format; then the metadata and quadtree spatial index of the mosaic dataset are calculated and built; then the above information is stored in the MongoDB database and published as a standard map service.

[0030] The aforementioned publishing process involves organizing the scattered COG imagery into a logically unified, efficiently queryable virtual mosaic dataset and publishing it as a map service.

[0031] In another exemplary embodiment, step 101 above is a data standardization preprocessing procedure, in which massive amounts of multi-source remote sensing images are uniformly converted into Cloud Optimized GeoTIFF (COG) format. During the conversion process, tile partitioning and resolution pyramid functions are used to obtain tiles of different resolutions (i.e., different scaling levels), as well as the ZXY tile coordinates of each tile.

[0032] The key prerequisite for this step is that all input multi-source remote sensing images must have a consistent spatial coordinate reference (CRS), data type, number of bands, and nodata value. Inconsistent data must be manually corrected before it can be used to generate the COG image mosaic dataset.

[0033] This step leverages the characteristics of COG imagery to logically embed massive amounts of data into a dataset, enabling dynamic tile publishing similar to a single COG image, while improving performance in multiple ways.

[0034] In another exemplary embodiment, step 102 above is used to generate a global metadata document (i.e., mosaic dataset metadata) for the entire COG image set, defining the basic attributes of the COG image set as a logical dataset.

[0035] In another exemplary embodiment, the above-mentioned mosaic dataset metadata includes: mosaic_id, crs, data_type, bands_count, nodata, bounds, min_zoom, max_zoom, quadkey_zoom, tiles_set, and url; Wherein, mosaic_id is the identifier of the mosaic dataset metadata; in this embodiment, mosaic_id serves as the unique identifier (UUID) of the mosaic dataset metadata. crs, data_type, bands_count, and nodata are the spatial coordinate reference, data type, band number, and nodata value of the COG image set. In this embodiment, the values ​​of crs, data_type, bands_count, and nodata can be taken from (and must be consistent with) the first COG image. bounds is the smallest bounding rectangle of the area covered by all COG images in the COG image set. min_zoom and max_zoom are the minimum and maximum visualization levels of the COG image set, respectively. In this embodiment, min_zoom and max_zoom are calculated based on the image resolution and the Web Mercator tiling scheme. quadkey_zoom is the base scaling level, which is usually equal to min_zoom; tiles_set is the name of the set in the MongoDB database that stores the quadtree spatial index of COG image sets; The URL is the address of the map service to be published.

[0036] In another exemplary embodiment, the terms "mosaic dataset", "mosaic dataset metadata", "logical dataset", "document", etc., used in the above embodiments will be explained.

[0037] A1. Mosaic dataset (hereinafter also called virtual mosaic dataset or COG image mosaic dataset, which is obtained by logically organizing the original COG images that conform to the specification) refers to generating an index of basic scaling level slices for a set of COG images as a whole based on their features. The purpose of constructing this mosaic dataset is: 1) Because the front end sends a tile request to a single COG image, the system responds to the request and can directly obtain the tile from the COG image and return the tile data; 2) If the front end sends a tile request to the COG image mosaic dataset, the system responds by first determining which COG image the tile is in. The process is as follows: based on the tile index at the quadkey_zoom level, the system finds the COG image containing the tile (which may be one or multiple images), retrieves the tile from the COG image, and returns it directly if it is retrieved from one image. If it is retrieved from multiple images, it undergoes mosaicking and other processing to be processed into a single tile and returned.

[0038] A2. Mosaic dataset metadata refers to the description of a mosaic dataset, represented by a set of attributes, including mosaic_id, crs, data_type, bands_count, nodata, bounds, etc.

[0039] A3. Logical dataset is a modifier for mosaic dataset. It means that the mosaic dataset is logically treated as a whole based on the spatial location and other features of the COG images, rather than physically mosaicking a group of COG images into a large image file.

[0040] A4. "Document" is a specific term in MongoDB databases, equivalent to the concept of "record" in relational databases. A collection in a MongoDB database is equivalent to a table in a relational database; this means that a record of mosaic dataset metadata is generated and stored in the `mosaic` table in the MongoDB database. The generated tile index is stored in the table associated with that record. The table name is automatically generated by the system; in this embodiment, the table name is automatically stored in the `tiles_set` attribute of the `cog` image mosaic dataset metadata record.

[0041] In another exemplary embodiment, step 103 above is the process of establishing a quadtree spatial index. This process is used to calculate the intersection of the dataset range and the Web Mercator tile grid at the base scaling level, obtain the ZXY tile coordinates of all tiles covering the region, and generate a quadtree index record for each tile covering the region.

[0042] In another exemplary embodiment, the quadtree index record described above is: {quadkey: [asset1,asset2, ...]}; Wherein, quadkey is the quadtree key value of the target slice. The construction process of this quadtree key value is a way of encoding the ZXY tile coordinates of the slice into a string. That is, quadkey is a string composed of numbers from 0 to 3, where Z is the scaling level of the slice, and X and Y are the column number and row number of the slice, respectively.

[0043] [asset1, asset2, ...] is a list of file paths for COG images in the COG image set that intersect with the target tile. `asset1` and `asset2` are two file paths in this list. The target tile is any tile at the base zoom level in the COG image set. This file path list is obtained by searching all COG images based on the geographic extent of the target tile.

[0044] In another exemplary embodiment, the quadtree key values ​​of the target slice are obtained by using Python functions to transform the ZXY tile coordinates of the target slice.

[0045] In another exemplary embodiment, the mosaic dataset metadata generated in step 102 is stored in the mosaic collection of the MongoDB database. All quadtree index records generated in step 103 are stored in a separate MongoDB database collection, the name of which is specified by the tiles_set field in the mosaic dataset metadata.

[0046] In another exemplary embodiment, step 104 above is the process of publishing a map service. This process generates a service endpoint (Uniform ResourceLocator, URL) based on the stored mosaic dataset (including at least mosaic dataset metadata and a quadtree spatial index) according to standard map service formats such as WMTS (Web Map Tile Service), and completes the publication. The service endpoint URL contains a mosaic_id to identify the specific mosaic dataset.

[0047] In one exemplary embodiment, a method for dynamically loading coastal zone map services is provided, used to dynamically load data published using the aforementioned method for real-time publishing of coastal zone map services, such as... Figure 2 As shown, the dynamic loading method for the coastal zone map service includes the following steps 201-206.

[0048] Step 201: Obtain a slice request; the slice request includes a query identifier and the ZXY tile coordinates of the query slice.

[0049] Step 202: Determine the quadtree key value of the corresponding slice in the basic scaling level based on the ZXY tile coordinates of the query slice, and use it as the query key value.

[0050] Step 203: Determine the mosaic dataset metadata corresponding to the query identifier in the collection mosic in the MongoDB database, and use it as the target mosaic dataset metadata.

[0051] Step 204: Determine the tiles_set set specified by the tiles_set field of the target mosaic dataset metadata as the target tiles_set set.

[0052] Step 205: Search for the list of file paths corresponding to the query key value in the quadtree spatial index of the target tiles_set collection.

[0053] Step 206: Read and output the pixel data of the geographic range represented by the ZXY tile coordinates of the query slice according to the file path list corresponding to the query key value.

[0054] Steps 201-206 above constitute the loading process, which includes: responding to the front-end tile request and converting it into the corresponding quadtree key value; querying the database for a list of COG images covering the current area based on the key value; dynamically reading the internal tiles of the relevant COG images and performing real-time mosaicking and rendering as needed, and finally returning the results to the front-end.

[0055] In another exemplary embodiment, the above step 201 is used to obtain a slice request from the front end and parse the front-end request parameters, including a query identifier and the ZXY tile coordinates of the query slice.

[0056] In another exemplary embodiment, since the index is established at a fixed base zoom level, the system needs to convert the level Z in the slice request into the corresponding parent slice or child slice of the query slice at the base zoom level. The above step 202 first determines the corresponding parent slice or child slice of the query slice at the base zoom level, and then obtains the quadtree key value of the corresponding slice or a set of slices as the query key value.

[0057] The above query key value is the quadtree key value of the corresponding slice of the query slice at the base zoom level in the COG image set, specifically: the quadtree key value of the query slice itself, or the quadtree key value of the parent slice of the query slice at the base zoom level, or the quadtree key value of one of the child slices of the query slice at the base zoom level. The file path list is obtained by looking up in the MongoDB database according to the query key value.

[0058] In another exemplary embodiment, the method for converting a slice request at any level into its corresponding slice at the base zoom level and obtaining the query key value is as follows: If Z > quadkey_zoom, calculate the quadtree key list of all child slices of the query slice; If Z < quadkey_zoom, calculate the quadtree key of the parent slice of the query slice; If Z = quadkey_zoom, directly use the quadtree key of the query slice.

[0059] In another exemplary embodiment, the above steps 203 - 204 are used to obtain an image file list. This process uses the identifier mosaic_id to locate the metadata of the mosaic dataset, and then uses the converted quadtree key list as the key (i.e., the query key value) to quickly query the relevant file path list (hereinafter referred to as the assets list) in the corresponding tiles_set collection.

[0060] In another exemplary embodiment, the process of dynamically reading and processing in the above step 205 is specifically divided into the following two cases: Single-image coverage: If the assets list contains only one COG image (i.e., only one file path), directly read the pixel data of the corresponding geographic range from the COG image; Multiple image coverage: If the assets list contains multiple COG images (i.e., only multiple file paths), the corresponding pixel blocks in each COG image are read in parallel, and pixel-level mosaicking and fusion processing is performed in memory (such as taking the latest value, average value, etc.). Finally, the processing result is converted into the image format requested by the front end (such as PNG or JPEG) and returned.

[0061] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned real-time coastal zone map service publishing method or the aforementioned dynamic coastal zone map service loading method.

[0062] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0063] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0064] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0065] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0066] According to the specific embodiments provided in this application, this application has the following technical effects.

[0067] This application addresses the problems of redundant data storage, long preprocessing time, and inflexible service deployment in the online publishing of massive multi-source remote sensing images of the coastal zone. It proposes a real-time publishing and loading method based on a custom mosaic dataset using COG (Cloud Optimized GeoTIFF). The core of this method lies in abandoning the traditional pre-tiling mode and achieving lightweight, automated, and high-performance publishing of remote sensing image services through a technical approach of "dynamic indexing, on-demand reading, and real-time mosaicking."

[0068] The technical solution of this application consists of three key points: B1. Custom mosaic dataset organization based on COG imagery: Organize the original COG imagery that conforms to the specifications into a logical virtual mosaic dataset, making full use of the internal tile structure of the COG format and its support for HTTP range requests, laying the foundation for subsequent dynamic data reading; B2. A unified spatial indexing mechanism based on quadtree key values: A global quadtree spatial index is established for the entire mosaic dataset, which can quickly map geographic tile requests at any level to the index key at the base level, achieving efficient and consistent data positioning across scaling levels; B3. Lightweight Metadata Management Based on No-Relational Databases: Leveraging the high performance and scalability of databases such as MongoDB, only lightweight metadata of the mosaic dataset and basic-level spatial index relationships are stored, rather than the image data itself. This completely eliminates redundant slice storage, greatly reduces the system storage burden, and improves management flexibility.

[0069] Compared with traditional geographic servers such as ArcGIS Server (a core, enterprise-level geographic information system server software developed by Esri Corporation in the United States) and GeoServer (an open-source map server), the core advantages of this application are: it completely avoids the huge storage space consumption and long preprocessing time caused by massive pre-tiling; by replacing static storage with dynamic calculation, it realizes true "on-demand service" and significantly improves the efficiency from data update to service availability; at the same time, the system architecture is lightweight and highly scalable, making it particularly suitable for managing the ever-growing ultra-large-scale remote sensing image database of coastal areas.

[0070] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0071] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0072] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for real-time publishing of coastal zone map services, characterized in that, include: The multi-source remote sensing images of the coastal zone were unified into COG format to obtain a COG image set. Construct the mosaic dataset metadata of the COG image set and store the mosaic dataset metadata into the collection mosic in the MongoDB database; Construct a quadtree spatial index for the COG image set and store the quadtree spatial index in the tiles_set collection of the MongoDB database; the tiles_set collection is specified by the tiles_set field in the mosaic dataset metadata; A service endpoint is generated based on a mosaic dataset of COG image sets in a map service format, and the mosaic dataset is published; the mosaic dataset includes at least mosaic dataset metadata and a quadtree spatial index.

2. The method for real-time publishing of coastal zone map services according to claim 1, characterized in that, The mosaic dataset metadata includes: mosaic_id, crs, data_type, bands_count, nodata, bounds, min_zoom, max_zoom, quadkey_zoom, tiles_set, and url; Wherein, mosaic_id is the identifier of the mosaic dataset metadata; crs, data_type, bands_count, and nodata are the spatial coordinate reference, data type, band number, and nodata value of the COG image set; bounds is the minimum bounding rectangle of the area covered by all COG images in the COG image set; min_zoom and max_zoom are the minimum and maximum visualization levels of the COG image set, respectively; quadkey_zoom is the basic zoom level; tiles_set is the name of the set of quadtree spatial indexes of the COG image set stored in the MongoDB database; and url is the address of the map service to be published.

3. The method for real-time publishing of coastal zone map services according to claim 1, characterized in that, The quadtree spatial index includes quadtree index records for each slice of the basic scaling level in the COG image set. The quadtree index record is: {quadkey: [asset1, asset2, ...]}; Where quadkey is the quadtree key value of the target slice, [asset1, asset2, ...] is the list of file paths of COG images in the COG image set that intersect with the target slice, and asset1 and asset2 are two file paths in the file path list; the target slice is any slice in the basic scaling level of the COG image set.

4. The method for real-time publishing of coastal zone map services according to claim 3, characterized in that, The quadtree key values ​​of the target slice are obtained by using Python functions to transform the ZXY tile coordinates of the target slice, where Z is the scaling level of the slice, and X and Y are the column number and row number of the slice, respectively.

5. A method for dynamically loading coastal zone map services, characterized in that, The dynamic loading method for coastal zone map services is used to dynamically load data published using the real-time publishing method for coastal zone map services according to any one of claims 1-4. The dynamic loading method for coastal zone map services includes: Obtain a slice request; the slice request includes a query identifier and the ZXY tile coordinates of the query slice; The quadtree key value of the corresponding slice in the basic scaling level is determined based on the ZXY tile coordinates of the query slice, and used as the query key value; In the collection mosic in the MongoDB database, determine the mosaic dataset metadata corresponding to the query identifier, and use it as the target mosaic dataset metadata. Determine the tiles_set set specified by the tiles_set field of the target mosaic dataset metadata as the target tiles_set set; Search the list of file paths corresponding to the query key value in the quadtree spatial index of the target tiles_set collection; The pixel data representing the geographic range of the query slice's ZXY tile coordinates is read from the list of file paths corresponding to the query key value and then output.

6. The method for dynamically loading coastal zone map services according to claim 5, characterized in that, Based on the list of file paths corresponding to the query key, the pixel data representing the geographic area indicated by the ZXY tile coordinates of the query tile is read and output, specifically including: When the file path list corresponding to the query key value contains only one file path, the pixel data of the geographic range represented by the ZXY tile coordinates of the query tile in the target COG image is read and output in image format; the target COG image is the COG image stored at the file path in the file path list corresponding to the query key value. When the file path list corresponding to the query key contains multiple file paths, the fused pixel data of the geographic range represented by the ZXY tile coordinates of the query tile in each target COG image is read and output in image format; each target COG image is a COG image stored at each file path in the file path list corresponding to the query key, and the fused pixel data is obtained by fusing the pixel data of the geographic range represented by the ZXY tile coordinates of the query tile in each target COG image.

7. The method for dynamically loading coastal zone map services according to claim 6, characterized in that, The methods for fusing pixel data representing the geographic range of the query tile's ZXY tile coordinates in each target COG image include taking the latest value and / or taking the average value.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the real-time publishing method for coastal zone map services according to any one of claims 1-4 or the dynamic loading method for coastal zone map services according to any one of claims 5-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the real-time publishing method for coastal zone map services as described in any one of claims 1-4 or the dynamic loading method for coastal zone map services as described in any one of claims 5-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the real-time publishing method for coastal zone map services as described in any one of claims 1-4 or the dynamic loading method for coastal zone map services as described in any one of claims 5-7.

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