Remote sensing image instant extraction and display method and system based on Beidou grid

Abstract

The invention provides a remote sensing image instant extraction and display method and system based on Beidou grids, and relates to the technical field of remote sensing, and the method comprises the steps: carrying out the layering division of a space region in a global range based on a Beidou geographic information network coding system, and determining a plurality of grid units and corresponding coding IDs; establishing an index mapping relationship between the standardized image file and the grid units, and storing the index mapping relationship in an image database; determining a target area to which an image requested by the request information belongs, and extracting instant image data corresponding to the target area from an image database; and carrying out image splicing on the instant image data to obtain first image information, and sending the first image information to a front end for dynamic display. Based on a Beidou geographic information network coding system, unified association and calling of multi-source image data are realized through the coding ID, a user can directly obtain a real-time complete image of a target area at a terminal, and dynamic updating and visual presentation of the image are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing, in particular to a remote sensing image instant extraction and display method and system based on a Beidou grid. BACKGROUND

[0002] As the core carrier of spatio-temporal information acquisition, remote sensing images play an irreplaceable role in many fields such as urban planning, ecological supervision, and disaster emergency. With the rapid development of remote sensing technology, image data is showing a trend of massive growth, and users' demand for multi-resolution and multi-scale visualization of image services is becoming increasingly urgent. To meet this demand, in the process of publishing remote sensing images, it is usually necessary to preprocess the original images before they are officially put into service, and multi-level slicing is a key link, that is, the original images are cut into standardized tiles according to fixed scales and stored in categories, thereby providing data support for subsequent visualization services.

[0003] In the existing remote sensing image publishing system, multi-level slicing preprocessing is the mainstream technical path to achieve multi-scale visualization services. Specifically, the technical process usually starts with the reception and verification of original image data, and then fixed tile scale parameters are set through professional slicing processing software, and the images are cut layer by layer according to the preset resolution level to generate multiple sets of tile data from low resolution to high resolution. These slicing results are indexed according to geographic coordinates or tile numbers and stored in corresponding databases or file systems. When a user initiates a visualization request, the system locates and calls the tile data of the corresponding scale for display through indexing.

[0004] However, the above-mentioned remote sensing image publishing technology based on fixed slicing has many defects that need to be solved: first, the data production cycle is long and the response delay is high. The slicing preprocessing process involves multiple data processing links, and the complete "image reception-slicing processing-service online" process usually takes several days to several weeks, which cannot meet the demand for instant visualization in disaster emergency, dynamic monitoring and other scenarios; second, the problem of data redundancy is prominent. In order to adapt to different resolution service needs, the same original image needs to generate multiple slicing results, resulting in a large amount of intermediate redundant data, significantly increasing the storage device investment and operation and maintenance cost; third, there is a disconnection between updating and display. When the original image data is updated or the user proposes new regional query requirements, the whole slicing process needs to be repeated, which makes it difficult to realize the real-time linkage of the front-end display layer, resulting in service lag and poor user experience; fourth, the spatial index efficiency is low. The traditional index method based on geographic coordinates or tile numbers has insufficient support capability when facing dynamic queries across scales and regions, especially in the application of large-scale, multi-temporal remote sensing data sets, the efficiency of data retrieval and splicing will be significantly reduced. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides a Beidou grid-based remote sensing image instant extraction and display method and system, which solves the problems of repeated slicing, serious data redundancy, low index mode efficiency, and difficulty in supporting cross-scale and cross-region dynamic queries in the prior art.

[0006] To achieve the above object, the application is implemented by the following technical solutions: In a first aspect, the application provides a Beidou grid-based remote sensing image instant extraction and display method, which comprises: based on a Beidou geographic information network coding system, dividing spatial regions in a global range into layers, determining a plurality of grid units and a unique coding ID corresponding to each grid unit; the coding ID comprises hierarchical information, latitude and longitude range information, and center point coordinate information of the grid unit; establishing an index mapping relationship between a standardized image file and a grid unit, associating each image block in the standardized image file with the coding ID, and storing the index mapping relationship in an image database; receiving request information sent by a user terminal, determining a target region to which an image requested by the request information belongs, extracting instant image data corresponding to the target region from the image database; performing image stitching on the extracted instant image data to obtain instant first image information and sending the instant first image information to a front end for dynamic display.

[0007] According to the first aspect of the application, the foregoing determining a target region to which an image requested by the request information belongs, and extracting instant image data corresponding to the target region from the image database can specifically include: parsing the request information to extract a latitude and longitude range parameter and a zoom level parameter; determining a corresponding display level according to the zoom level parameter; determining a grid set associated with the target region in combination with the latitude and longitude range parameter and the display level; the grid set comprises a plurality of grid units; based on an asynchronous IO and a parallel extraction strategy, retrieving and extracting image blocks in the image database that match the coding ID of the grid set to obtain instant image data.

[0008] According to the first aspect of the application, the foregoing performing image stitching on the extracted instant image data to obtain instant first image information and sending the instant first image information to a front end for dynamic display can specifically include: sending the extracted instant image data to a rendering engine; based on the position relationship corresponding to the coding ID associated with the instant image data, performing image stitching and rendering by the rendering engine to obtain instant first image information; and sending the first image information to the front end for dynamic display.

[0009] According to the first aspect of the embodiments of the present application, after the instant image data is extracted and image stitching is performed to obtain the first instant image information and send the first instant image information to the front end for dynamic display, the method for instant extraction and display of remote sensing images based on the Beidou grid can further include the following steps: when a user performs a zoom or pan operation on the map in the front end, the range information and the pixel resolution information of the current viewport are obtained; the display level and the corresponding target grid granularity are recalculated according to the range information and the pixel resolution information of the viewport; the image data corresponding to the target grid granularity is extracted from the image database; the image data corresponding to the target grid granularity is sent to the rendering engine, and image stitching and rendering are performed by the rendering engine to obtain the second instant image information; and the second instant image information is sent to the front end and displayed dynamically in the front end.

[0010] According to the first aspect of the embodiments of the present application, the method for instant extraction and display of remote sensing images based on the Beidou grid can further include the following steps: obtaining the original remote sensing image; performing standardization processing on the obtained original remote sensing image to output a standardized image file that can be indexed by a grid; the standardization processing at least includes projection conversion, radiation correction and geometric correction; the standardized image file is not subjected to a slicing operation and is stored in the form of a complete pixel matrix.

[0011] According to the first aspect of the embodiments of the present application, the front end is a WebGIS platform or a three-dimensional visualization platform, and supports the functions of two-dimensional browsing, three-dimensional rendering and spatial query of image data; the mapping between the display level and the grid granularity is realized by the level of detail (LOD) technology to establish a multi-scale grid granularity level, so that the image data of different display levels is matched with the grid granularity with the optimal matching accuracy.

[0012] According to the first aspect of the embodiments of the present application, each image block is associated with one or more encoding IDs, and the image database supports the parent-child relationship index between the grid levels and the cross-resolution retrieval.

[0013] In a second aspect, the embodiments of the present application provide a system for instant extraction and display of remote sensing images based on the Beidou grid, which includes a spatial division module, an index mapping module, a request analysis module and an image processing module.

[0014] In particular, the space division module is configured to divide space regions in a global range into multiple grid units and determine a unique corresponding code ID for each grid unit based on a Beidou geographic information network coding system; the code ID includes hierarchical information, latitude and longitude range information, and center point coordinate information of the grid unit; the index mapping module is configured to establish an index mapping relationship between a standardized image file and the grid unit, associate each image block in the standardized image file with the code ID, and store the index mapping relationship in an image database; the image database supports parent-child relationship index and cross-resolution retrieval between grid levels; the request analysis module is configured to receive request information sent by a user terminal, determine a target region to which an image requested by the request information belongs, and extract instant image data corresponding to the target region from the image database; and the image processing module is configured to perform image stitching on the extracted instant image data, obtain instant first image information, and send the instant first image information to a front end for dynamic display.

[0015] In a third aspect, an electronic device is provided, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and the program is executed by the processor to implement the Beidou grid-based remote sensing image instant extraction and display method in the first aspect.

[0016] In a fourth aspect, a computer readable storage medium is provided, which stores a program or instructions, and the program or instructions are executed by a processor to implement the Beidou grid-based remote sensing image instant extraction and display method in the first aspect.

[0017] The present application provides a Beidou grid-based remote sensing image instant extraction and display method and system. Compared with the prior art, the present application has the following beneficial effects: This application, based on the BeiDou geographic information network coding system, divides the global spatial area into hierarchical layers and associates each grid cell with a coding ID. This allows standardized image files from different sources, formats, and resolutions to be incorporated into a single management framework, achieving unified association and retrieval of multi-source image data through coding IDs. Each grid cell's coding ID integrates hierarchical information, latitude and longitude range, and center point coordinates, ensuring comprehensive coverage of the global spatial area without omissions while providing a unique identifier for each grid cell. This application strongly associates image blocks with coding IDs, forming a structured image data retrieval system. When a user's request is received, there is no need to traverse a massive number of image files; the associated image block can be quickly matched using only the coding ID corresponding to the target area, enabling accurate extraction of real-time image data. This application performs stitching processing on extracted real-time image data to generate complete first image information and pushes it to the front end for dynamic display. This breaks the limitation of the traditional remote sensing image process that requires downloading locally and then stitching for viewing. Users can directly obtain real-time complete images of the target area on the terminal, which avoids the equipment performance loss and time cost of local stitching, and also enables dynamic updating and visualization of the images. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary flowchart illustrating a method for real-time extraction and display of remote sensing images based on the BeiDou grid, provided in an embodiment of this application. Figure 2 This is an interactive schematic diagram of a method for real-time extraction and display of remote sensing images based on the BeiDou grid, provided in an embodiment of this application. Figure 3 This is an action flowchart of a method for real-time extraction and display of remote sensing images based on the BeiDou grid, provided in an embodiment of this application. Figure 4 This is a schematic diagram of the structure of a real-time extraction and display system for remote sensing images based on the BeiDou grid, provided in an embodiment of this application. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0021] This application provides a method and system for real-time extraction and display of remote sensing images based on the BeiDou grid, which solves the problems of repeated slicing, serious data redundancy, low indexing efficiency, and difficulty in supporting cross-scale and cross-regional dynamic queries in existing remote sensing image extraction and display methods.

[0022] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows: Remote sensing imagery, acting as a "sky eye" capturing dynamic changes in geospatial data, directly determines its application value in key areas such as urban planning, ecological monitoring, and disaster emergency response through its efficient dissemination and real-time service capabilities. Currently, with the widespread adoption of high-resolution remote sensing satellites, drones, and other data acquisition equipment, the volume of image data is growing exponentially, and users' visualization needs for multi-resolution switching and multi-scale scaling of image services are becoming increasingly sophisticated. However, the existing remote sensing imagery dissemination system, centered on "fixed-scale multi-level tiling," has gradually revealed a serious disconnect from actual application needs, giving rise to a series of technical bottlenecks that urgently need to be overcome, specifically involving the following four dimensions: First, the contradiction between the data production cycle and the demand for real-time services is prominent, and response delays have become a "fatal weakness" in emergency scenarios. In existing technologies, remote sensing imagery, from reception to final service deployment, requires multiple complex processes, including data verification, format conversion, hierarchical tiling, quality checks, index building, and storage deployment. Each step requires professional technical personnel or time-consuming backend computation. For example, a high-resolution remote sensing image covering thousands of square kilometers may take several days just for the multi-level tiling process, with the complete process typically taking several days to weeks. This lengthy production cycle is difficult to match users' expectations for data timeliness in conventional scenarios, and it has a more severe impact in special scenarios such as disaster emergencies. For instance, after disasters such as earthquakes and floods, rescue decisions urgently require a rapid understanding of the disaster distribution based on the latest imagery. However, delayed tiling processing prevents image services from being deployed in a timely manner, forcing rescue command to rely on old data or low-precision information, potentially missing the optimal rescue opportunity. Simultaneously, in scenarios such as dynamic monitoring of land violations and real-time ecological environment inspections, delayed image services cannot accurately capture real-time changes, leading to time lag loopholes in supervision.

[0023] Secondly, the surge in redundant data resulting from multi-resolution adaptation significantly increases storage and maintenance costs. To meet the visualization needs of different users—from mobile users requiring fast loading of low-resolution data to professional analysts demanding detailed viewing of high-resolution data—existing technologies require generating multiple independent tile results for the same original image according to different resolution levels. Taking a common 10-level resolution tile as an example, the same original image will be cut into 10 sets of tile data from level 1 (low resolution) to level 10 (high resolution), with the number of high-resolution tiles often being tens or even hundreds of times greater than the number of low-resolution tiles. This redundant data not only consumes a large amount of storage hardware resources, leading to a surge in data center storage equipment investment costs, but also increases the complexity of data management. Maintenance personnel need to perform synchronous backups, fault repairs, and version management for multiple sets of tile data, which not only consumes a lot of manpower but may also cause service errors due to data version inconsistencies.

[0024] Third, the lack of a linkage mechanism between data updates and service display results in extremely poor service flexibility and user experience. In practical applications, remote sensing image data has significant dynamic updating characteristics. Satellites periodically revisit and photograph the same area, generating updated image data. Simultaneously, user query needs are uncertain, with potential requests for visualization of specific areas at any time. However, current technologies require restarting the entire tile preprocessing workflow for both original image updates and changes in user needs: either re-slicing tiles at all resolution levels for the updated image, or performing separate tiling operations for the new query area. This process often takes hours to days. During this period, the front-end display layer cannot access the latest data and can only continue displaying the old tile results, leading to a disconnect where data has been updated but services are not synchronized. For example, when a city was conducting an investigation into illegal construction, it updated the latest remote sensing imagery of a certain area. However, due to the time-consuming tiling process, the front-end system only completed the service update three days later. During this period, staff could only rely on the old imagery for investigation, which not only reduced work efficiency but also could lead to regulatory errors due to the omission of newly added illegal constructions. When a user temporarily requests to view a high-resolution imagery of a remote area, they have to wait for the tiling to be completed before it can be displayed. The excessively long waiting time seriously affects the user experience.

[0025] Fourth, traditional spatial indexing mechanisms are inefficient and cannot support dynamic query needs across scales and regions. Current remote sensing image tile services primarily rely on geographic coordinate ranges or fixed tile numbers for indexing. While this approach can barely meet the needs of queries at a single scale and in a fixed region, it has significant shortcomings when dealing with complex queries across scales and regions. On the one hand, for cross-scale queries, the system needs to search separately in tile datasets of different resolutions, then perform data stitching and scale adaptation—a cumbersome and time-consuming process. On the other hand, for cross-regional queries, if the query area covers multiple tile number ranges, the index needs to match and locate them one by one. Especially in large-scale, multi-temporal remote sensing datasets, the time cost of data retrieval increases exponentially.

[0026] These intertwined technical problems not only restrict the full release of the value of remote sensing image data, but also make it difficult to meet the development needs of various fields for "real-time, low-cost, and highly flexible" spatiotemporal information services in the new era. Therefore, it is urgent to build a new remote sensing image service technology system that breaks through the reliance on traditional slicing.

[0027] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0028] The following section first introduces a method for real-time extraction and display of remote sensing images based on the BeiDou grid, as provided in the embodiments of this application.

[0029] This application provides a flowchart illustrating a method for real-time extraction and display of remote sensing images based on a BeiDou grid, as shown in the embodiments below. Figure 1 As shown, the method for real-time extraction and display of remote sensing images based on the BeiDou grid may include the following steps S110-S140.

[0030] S110. Based on the BeiDou geographic information network coding system, the spatial area worldwide is divided into layers, and multiple grid units and a unique coding ID for each grid unit are determined. The coding ID includes the grid unit's hierarchical information, latitude and longitude range information, and center point coordinate information.

[0031] Understandably, this application, based on the BeiDou geographic information network coding system, divides the global spatial area into layers and associates each grid cell with a coding ID. Each grid cell's coding ID integrates hierarchical information, latitude and longitude range, and center point coordinates, ensuring both complete coverage of the global spatial area and a unique identifier for each grid cell. Compared to traditional remote sensing image management methods that rely on scattered latitude and longitude coordinates or region names, this method can directly pinpoint the precise spatial range corresponding to an image block through the coding ID, avoiding image positioning deviations caused by coordinate conversion errors or ambiguities in region names, and significantly improving the accuracy of the association between remote sensing images and geospatial data.

[0032] S120. Establish an index mapping relationship between standardized image files and grid cells, associate each image block in the standardized image file with a coding ID, and store the index mapping relationship in the image database.

[0033] Understandably, this application strongly associates image blocks with their coded IDs, forming a structured image data retrieval system. When a user's request is received, there's no need to fully traverse massive image files; the associated image block can be quickly matched using only the coded ID corresponding to the target area, enabling accurate extraction of real-time image data. Compared to traditional retrieval methods based on file directories or keywords, this method significantly reduces image retrieval time, especially in scenarios involving large-scale, multi-batch image retrieval, where the extraction response time can be reduced to the second level, meeting the business requirements for real-time extraction.

[0034] S130: Receive request information sent by the user terminal, determine the target area to which the requested image belongs, and extract real-time image data of the corresponding target area from the image database.

[0035] S140. Perform image stitching on the extracted real-time image data to obtain real-time first image information and send it to the front end for dynamic display.

[0036] Understandably, this application stitches together extracted real-time image data to generate complete first-image information and pushes it to the front end for dynamic display. This breaks the traditional limitation of remote sensing images requiring local download and stitching for viewing. Users can directly obtain real-time complete images of the target area on the terminal, avoiding the equipment performance loss and time cost of local stitching, while also enabling dynamic image updates and visualization. It is adaptable to scenarios with high requirements for image timeliness, such as emergency monitoring, geographic mapping, and resource exploration, thus improving the practical value and service response speed of remote sensing image data.

[0037] The above is a specific implementation method for real-time extraction and display of remote sensing images based on BeiDou grid provided in this application. Please refer to it as well. Figure 1 , Figure 2 and Figure 3 This application establishes an image index based on a unified BeiDou geographic information network coding system. This index can integrate standardized image files from different sources, in different formats, and at different resolutions into a single management framework, achieving unified association and retrieval of multi-source image data through coded IDs. Compared to the integration difficulties caused by inconsistent formats and positioning benchmarks in traditional image databases, this method eliminates data silos, improves the compatibility and scalability of the image database, and facilitates subsequent cross-regional and cross-time period image comparison and analysis. This provides technical support for the centralized management and efficient application of large-scale remote sensing image data.

[0038] It is important to emphasize that this application, relying on the global spatial coverage characteristics of the BeiDou geographic information network coding system, enables the extraction and display of images from any region globally, overcoming the limitations of some traditional remote sensing image services that are restricted by regional coverage. Whether it's land, ocean, or special regions such as the polar regions, image data can be retrieved through the corresponding grid coding ID, adapting to cross-regional business scenarios such as global geographic information monitoring and cross-border resource management, thus expanding the application boundaries and service scope of remote sensing image technology.

[0039] In some embodiments, determining the target area to which the requested image belongs and extracting real-time image data of the corresponding target area from the image database may specifically include the following steps: S210. Parse the request information to extract the latitude and longitude range parameters and the scaling level parameters.

[0040] S220. Determine the corresponding display level based on the scaling level parameter.

[0041] Understandably, this application associates display levels with scaling level parameters, automatically matching grid cells and image blocks of corresponding precision according to the user's terminal display needs: when the user only needs to view a large area of ​​imagery macroscopically, image data from a low-level, coarse-grained grid is retrieved, reducing data transmission volume; when the user needs to focus on small details, high-precision images from a high-level, fine-grained grid are retrieved to ensure display clarity. This on-demand precision matching design not only improves the flexibility of image display but also avoids unnecessary redundant retrieval of high-precision image data, reducing system data transmission bandwidth and database retrieval pressure, achieving a balance between display effect and resource consumption, and improving the long-term economic efficiency and stability of the system.

[0042] S230. Combining latitude and longitude range parameters with display level, determine the grid set associated with the target area; the grid set includes multiple grid cells.

[0043] S240. Based on asynchronous I / O and parallel extraction strategies, retrieve image blocks in the image database that match the encoded ID of the grid set and complete the extraction to obtain real-time image data.

[0044] In this embodiment, the request information is first parsed to extract the latitude and longitude range parameters and the zoom level parameters. Then, the corresponding display level is matched according to the zoom level. Finally, the associated grid set is locked by combining the latitude and longitude range. Compared with the traditional method of fuzzy matching based solely on a single latitude and longitude range for image requests, this solution achieves hierarchical and precise positioning of image retrieval through the association logic of zoom level, display level, and grid set. It can match grid units of corresponding precision according to the user's zoom requirements (e.g., a large zoom level corresponds to a high-precision fine-grained grid, and a small zoom level corresponds to a low-precision coarse-grained grid), and it can limit the boundary of the target area through latitude and longitude parameters. This avoids the image block retrieval range being too large or missing due to a single positioning dimension, ensuring that the extracted image data fully matches the user's actual needs.

[0045] Furthermore, for multi-grid unit image blocks associated with the target area, this application abandons the traditional serial retrieval and extraction mode and adopts an asynchronous I / O and parallel extraction strategy. On the one hand, asynchronous I / O enables non-blocking processing of database requests, avoiding overall process stagnation caused by waiting for single-grid image block retrieval. On the other hand, parallel extraction can simultaneously perform batch retrieval and data retrieval of multiple coded IDs within the grid set, transforming the extraction operation of multiple image blocks from sequential execution to synchronous progress. In multi-grid, large-area image retrieval scenarios, this strategy can significantly shorten data extraction time, compressing the minute-level response of the traditional serial mode to the second level, truly realizing the rapid acquisition of real-time image data, and meeting the business scenarios with stringent response time requirements such as emergency monitoring and real-time geographic surveys.

[0046] Furthermore, this application uses the unique coded ID of grid cells for image block matching, rather than relying on traditional file paths or fuzzy keyword searches, thus fundamentally avoiding the mis-selection or omission of image blocks due to non-standard file naming or ambiguous keywords. Simultaneously, batch matching of the grid set for the target area ensures coverage of all associated image blocks within the target area, guaranteeing the integrity of the final stitched image. Compared to the image gaps and overlapping areas that easily occur in traditional extraction methods, this application achieves zero-error and full-coverage image data extraction through precise association of coded IDs, providing a high-quality data foundation for subsequent image stitching and front-end dynamic display.

[0047] In some embodiments, the aforementioned image stitching of the extracted real-time image data to obtain real-time first image information and sending it to the front end for dynamic display may specifically include the following steps: S310: Send the extracted real-time image data to the rendering engine.

[0048] This solution directly sends the extracted image data to the rendering engine, where stitching and rendering are performed synchronously, forming an integrated workflow of data extraction, stitching rendering, and result output. Traditional image stitching typically requires local data integration followed by separate rendering, resulting in fragmented workflows and time-consuming intermediate data storage. This solution, through integrated processing within the rendering engine, eliminates intermediate data transfer steps, significantly shortening the image generation cycle. In scenarios such as emergency response and real-time monitoring, the time from data extraction to generating a complete image can be compressed to the second level, truly achieving rapid output of real-time image information and meeting the core business requirement for image timeliness.

[0049] S320: Based on the positional relationship corresponding to the encoded ID associated with real-time image data, the rendering engine stitches and renders the images to obtain real-time first image information.

[0050] This application uses the positional relationship corresponding to the coded IDs associated with image blocks as the basis for image stitching. The coded IDs themselves integrate the grid cell hierarchy, latitude and longitude range, and center point coordinate information, which can directly provide the rendering engine with a precise spatial positioning reference. Compared with the traditional image stitching method that relies on manual calibration of control points or matching of image edge features, this solution achieves data self-positioning through the spatial correlation of coded IDs. This avoids the stitching misalignment, overlap, or gap problems caused by similar image textures and lighting differences during edge feature matching, and eliminates the tedious process of manual calibration. It ensures that the stitched first image information can completely match the real geographic space, achieving sub-pixel-level stitching accuracy and providing high-fidelity geographic image data for front-end display.

[0051] S330: Send the first image information to the front end for dynamic display.

[0052] In this embodiment, the generated first image information can be directly pushed to the front end for dynamic display. On the one hand, the image data output by the rendering engine can adapt to the display resolution and interaction logic of different terminals (such as PC, mobile, and large-screen monitoring terminals) without the need for secondary format conversion for different terminals. On the other hand, the dynamic display mode supports users to perform interactive operations such as zooming, panning, and layer switching on the front end. During the interaction, image data of corresponding precision can be quickly retrieved based on the encoded ID, realizing on-demand loading and real-time rendering. Compared with the traditional static image download and viewing mode, this application breaks through the limitations of terminals and scenarios, which can not only meet the lightweight needs of daily geographic information queries, but also adapt to the high-interactivity visualization needs of professional scenarios such as emergency command and resource exploration.

[0053] Furthermore, in this process, the image data only needs to go through two core stages from extraction to front-end display: rendering engine processing and front-end push. The rendering engine can directly complete spatial positioning and stitching based on the encoded ID, without the need for additional spatial coordinate transformation or data format adaptation modules. In traditional solutions, image stitching, coordinate correction, and format conversion need to be completed collaboratively by multiple independent modules. This not only results in a long data flow chain and is prone to inter-module interface compatibility issues, but also consumes a large amount of server computing resources. This application simplifies the chain through integrated processing, reduces the coupling between modules, reduces the server's computing and storage load, improves the stability of system operation, and significantly reduces the cost of subsequent operation, maintenance, and troubleshooting.

[0054] Furthermore, the front-end dynamic display can be linked with user operations in real time. When a user adjusts the viewing range or zoom level, the front-end can quickly request image data for the corresponding area based on the encoded ID. The rendering engine can simultaneously complete the stitching and rendering of the new data and push it to the front-end, achieving seamless image switching and dynamic updates. This real-time synchronization of operation, response, and display avoids the lag problem in traditional static image displays where switching areas requires reloading the entire image. This allows users to smoothly browse and explore the entire geospatial area, significantly improving the smoothness and convenience of user interaction and enhancing the practicality and ease of use of remote sensing image services.

[0055] In some embodiments, after the aforementioned image stitching of the extracted real-time image data to obtain real-time first image information and sending it to the front end for dynamic display, the method for real-time extraction and display of remote sensing images based on the BeiDou grid further includes: S150. When the user performs map zooming or panning operations on the front end, obtain the current viewport range information and pixel resolution information.

[0056] Understandably, the image extraction process triggered by user interaction is essentially a real-time data update for the current viewport requirements. Each time the system responds to an operation, it retrieves the latest image data of the corresponding granularity from the image database, rather than relying on locally cached historical data. This interaction-as-update mechanism ensures that users always see the latest remote sensing images in the database during dynamic operations.

[0057] S160. Recalculate the display level and the corresponding target grid granularity based on the viewport range information and pixel resolution information.

[0058] S170. Extract image data with the corresponding target grid granularity from the image database.

[0059] Understandably, the extraction of new image data still relies on the coded ID based on the BeiDou grid as the core association basis. The recalculated target grid granularity and viewport range essentially lock in the unique identifier of the corresponding spatial region through the coding system. Compared to the traditional method of positioning based on pixel coordinate conversion (which is prone to image misalignment due to coordinate offset), the BeiDou grid coding provides an absolutely accurate spatial reference for the positioning of new viewport images with its built-in hierarchical, latitude, longitude, and center point coordinate information. No matter how the user zooms or pans, the system can quickly match the grid set corresponding to the current viewport through the coded ID, ensuring that the extracted image blocks completely match the real geographic space. This positioning consistency not only avoids the offset and misalignment of images and geographic coordinates during interaction, but also ensures the continuity of image stitching after multiple interactions, providing reliable data support for professional needs such as accurate geographic analysis and on-site scene matching.

[0060] S180. Send the image data corresponding to the target grid granularity to the rendering engine. The rendering engine performs image stitching and rendering to obtain real-time second image information.

[0061] S190. Send the second image information to the front end and display it dynamically on the front end.

[0062] In the embodiments of this application, it is understood that this application rapidly recalculates the display layer and target grid granularity by capturing the changes in viewport range and pixel resolution triggered by user zooming and panning operations in real time, thereby completing the extraction, stitching, and display of new image data. Compared to the traditional remote sensing image interaction mode of waiting for the entire image to load after an operation, this process compresses the response chain into a closed loop of operation triggering, data updating, and display synchronization: when the user performs an interactive action, the system does not need to reload the complete area image, but only retrieves the grid image of the corresponding granularity for the current viewport requirement, realizing the instant matching of the second image information with the front-end operation; it solves the loading delay problem in dynamic interaction, allowing users to obtain a smooth experience without perceptible connection when zooming to browse details and panning to switch areas, especially suitable for scenarios such as lightweight operation on mobile devices and rapid access for emergency command.

[0063] In one example, the front end is a WebGIS platform or a 3D visualization platform, supporting 2D browsing, 3D rendering, and spatial querying of image data. The mapping between display levels and grid granularity can be achieved through Level of Detail (LOD) technology to establish multi-scale grid granularity levels, ensuring that image data at different display levels corresponds to the grid granularity with optimal matching accuracy. Each image block is associated with one or more coded IDs, and the image database supports parent-child relationship indexing between grid levels and cross-resolution retrieval.

[0064] In some embodiments, the method for real-time extraction and display of remote sensing images based on the BeiDou grid may further include: S101. Acquire raw remote sensing images; perform standardization processing on the acquired raw remote sensing images to output a grid-indexable standardized image file; the standardization processing includes at least projection transformation, radiometric correction and geometric correction; the standardized image file is not sliced ​​and is stored in the form of a complete pixel matrix.

[0065] In the embodiments of this application, please refer to Figure 3This application establishes a standardized processing flow through projection transformation, radiometric correction, and geometric correction, thereby improving the data consistency and reliability of original remote sensing imagery from the source. Projection transformation unifies original images from different coordinate systems to the geographic reference corresponding to the BeiDou grid code, avoiding positioning deviations caused by coordinate system confusion during subsequent grid association. Radiometric correction eliminates image brightness distortion caused by environmental factors such as illumination and atmosphere, ensuring grayscale consistency of images from different time periods in the same area, and guaranteeing visual continuity of image switching during interaction. Geometric correction corrects geometric deformation caused by sensor errors and terrain undulations, allowing image pixels to accurately match real geographic coordinates. Compared to the mode of directly using original images for grid association, standardized processing completely solves the problems of mixed formats, poor accuracy, and chaotic coordinates in multi-source images, providing a high-quality data foundation for subsequent accurate extraction, stitching, and interactive display based on grid codes, and reducing error correction costs in subsequent processes.

[0066] Furthermore, the standardized image files are stored as complete pixel matrices without slicing, overcoming the limitations of traditional pre-sliced ​​images, which suffer from fixed granularity and difficulty in adapting to dynamic needs. On one hand, the complete pixel matrix preserves the original precision information of the image. When user interaction triggers a request for grid images of different granularities, the system can generate image blocks of corresponding precision based on the complete data, avoiding precision loss due to granularity mismatch or storage redundancy caused by repeated slicing in the pre-slicing mode. On the other hand, this application does not rely on pre-generated slice files; the system can directly process the complete pixel matrix in real time according to the current viewport requirements, reducing the time cost and storage overhead of the pre-slicing process. This storage method is particularly suitable for interactive scenarios where users frequently switch precision, allowing the system to quickly generate adaptable data when facing diverse needs such as fine-grained detail viewing and coarse-grained overview browsing, further improving the flexibility and efficiency of interactive response.

[0067] In some embodiments, this application provides a real-time extraction and display system 400 for remote sensing images based on the BeiDou grid, such as... Figure 4 As shown, the BeiDou grid-based remote sensing image real-time extraction and display system 400 may include the following modules: The spatial division module 410 is used to divide the spatial region on a global scale based on the BeiDou geographic information network coding system, determine multiple grid units and the unique coding ID corresponding to each grid unit; the coding ID includes the grid unit's hierarchical information, latitude and longitude range information and center point coordinate information; The index mapping module 420 is used to establish an index mapping relationship between standardized image files and grid cells, associate each image block in the standardized image file with a coding ID, and store the index mapping relationship in the image database; wherein, the image database supports parent-child relationship indexing between grid levels and cross-resolution retrieval; The request analysis module 430 is used to receive request information sent by the user terminal, determine the target area to which the requested image belongs, and extract real-time image data of the corresponding target area from the image database. The image processing module 440 is used to stitch together the extracted real-time image data to obtain real-time first image information and send it to the front end for dynamic display.

[0068] According to embodiments of this application, any multiple modules among the spatial partitioning module 410, index mapping module 420, request analysis module 430, and image processing module 440 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module.

[0069] Figure 4 Each module in the system shown has the function of implementing each step in the aforementioned method for real-time extraction and display of remote sensing images based on the BeiDou grid, and can achieve the corresponding technical effects. For the sake of brevity, it will not be elaborated here.

[0070] In some embodiments, the BeiDou grid-based remote sensing image real-time extraction and display system 400 may further include an image adjustment module 450, which is specifically used for: When a user performs map zooming or panning operations on the front end, obtain the current viewport's range information and pixel resolution information; The display level and corresponding target grid granularity are recalculated based on the viewport range information and pixel resolution information. Extract image data corresponding to the target grid granularity from the image database; The image data corresponding to the target grid granularity is sent to the rendering engine, and the rendering engine performs image stitching and rendering to obtain real-time second image information; The second image information is sent to the front end and displayed dynamically on the front end.

[0071] In some embodiments, the BeiDou grid-based remote sensing image real-time extraction and display system 400 may further include a standardization module 401, which is specifically used for: acquiring original remote sensing images; performing standardization processing on the acquired original remote sensing images to output a grid-indexable standardized image file; the standardization processing includes at least projection transformation, radiometric correction and geometric correction; the standardized image file is not sliced ​​and is stored in the form of a complete pixel matrix.

[0072] In some embodiments, this application provides an electronic device, the structural schematic of which is shown below.Figure 5 As shown.

[0073] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0074] Specifically, the processor 510 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0075] Memory 520 may include mass storage for data or instructions. For example, and not limitingly, memory 520 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 520 may include removable or non-removable (or fixed) media. Where appropriate, memory 520 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 520 is non-volatile solid-state memory.

[0076] The memory 520 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, the memory 520 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the BeiDou grid-based remote sensing image real-time extraction and display methods in the above embodiments.

[0077] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any of the BeiDou grid-based remote sensing image real-time extraction and display methods in the above embodiments.

[0078] In one example, the electronic device may also include a communication interface 530 and a bus 500. Wherein, such as Figure 5 As shown, the processor 510, memory 520, and communication interface 530 are connected via bus 500 and communicate with each other.

[0079] The communication interface 530 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application. Bus 500 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 500 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0080] Furthermore, in conjunction with the BeiDou grid-based real-time extraction and display method for remote sensing images in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the BeiDou grid-based real-time extraction and display methods for remote sensing images in the above embodiments.

[0081] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0082] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0083] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0084] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time extraction and display of remote sensing images based on BeiDou grid, characterized in that, include: Based on the BeiDou geographic information network coding system, the spatial region worldwide is divided into layers, and multiple grid units and a unique coding ID for each grid unit are determined. The coding ID includes the grid unit's hierarchical information, latitude and longitude range information, and center point coordinate information. Establish an index mapping relationship between standardized image files and the grid cells, associate each image block in the standardized image file with a coding ID, and store the index mapping relationship in the image database; Receive request information sent by user terminal, determine the target area to which the requested image belongs, and extract real-time image data corresponding to the target area from the image database; The extracted real-time image data is stitched together to obtain real-time first image information, which is then sent to the front end for dynamic display.

2. The method for real-time extraction and display of remote sensing images based on BeiDou grid as described in claim 1, characterized in that, The step of determining the target region to which the requested image belongs and extracting real-time image data corresponding to the target region from the image database includes: The request information is parsed to extract the latitude and longitude range parameters and the scaling level parameters; The corresponding display level is determined based on the scaling level parameter; By combining the latitude and longitude range parameters with the display level, a grid set associated with the target area is determined; the grid set includes multiple grid cells. Based on asynchronous I / O and parallel extraction strategies, image blocks that match the encoded IDs of the grid set in the image database are retrieved and extracted to obtain real-time image data.

3. The method for real-time extraction and display of remote sensing images based on BeiDou grid as described in claim 1, characterized in that, The step of stitching together the extracted real-time image data to obtain real-time first image information and sending it to the front end for dynamic display includes: The extracted real-time image data is sent to the rendering engine; Based on the positional relationship corresponding to the encoded ID associated with real-time image data, the rendering engine performs image stitching and rendering to obtain real-time first image information. The first image information is sent to the front end for dynamic display.

4. The method for real-time extraction and display of remote sensing images based on BeiDou grid as described in claim 1, characterized in that, After stitching the extracted real-time image data to obtain real-time first image information and sending it to the front end for dynamic display, the real-time extraction and display method of remote sensing images based on the BeiDou grid further includes: When a user performs map zooming or panning operations on the front end, obtain the current viewport's range information and pixel resolution information; The display level and corresponding target grid granularity are recalculated based on the viewport range information and pixel resolution information. Extract image data corresponding to the target grid granularity from the image database; The image data corresponding to the target grid granularity is sent to the rendering engine, and the rendering engine performs image stitching and rendering to obtain real-time second image information; The second image information is sent to the front end and displayed dynamically on the front end.

5. The method for real-time extraction and display of remote sensing images based on BeiDou grid as described in claim 1, characterized in that, Also includes: Acquire raw remote sensing images; standardize the acquired raw remote sensing images and output a grid-indexable standardized image file; The standardization process includes at least projection transformation, radiometric correction, and geometric correction; the standardized image file is not sliced ​​and is stored in the form of a complete pixel matrix.

6. The method for real-time extraction and display of remote sensing images based on BeiDou grid as described in claim 2, characterized in that, The front end is a WebGIS platform or a 3D visualization platform, and supports 2D browsing, 3D rendering and spatial query functions of image data; The mapping between the display level and the grid granularity is achieved through the Level of Detail (LOD) technology to establish a multi-scale grid granularity hierarchy, so that the image data of different display levels correspond to the grid granularity with the optimal matching accuracy.

7. The method for real-time extraction and display of remote sensing images based on BeiDou grid as described in claim 1, characterized in that, Each image block is associated with one or more coded IDs, and the image database supports parent-child relationship indexing between grid levels and cross-resolution retrieval.

8. A real-time extraction and display system for remote sensing images based on the BeiDou grid, characterized in that, include: The spatial division module is used to divide the spatial region globally into layers based on the BeiDou geographic information network coding system, and to determine multiple grid units and a unique coding ID for each grid unit; the coding ID includes the grid unit's hierarchical information, latitude and longitude range information, and center point coordinate information; The index mapping module is used to establish an index mapping relationship between the standardized image file and the grid unit, associate each image block in the standardized image file with a coding ID, and store the index mapping relationship in the image database; wherein, the image database supports parent-child relationship indexing between grid levels and cross-resolution retrieval; The request analysis module is used to receive request information sent by the user terminal, determine the target area to which the requested image belongs, and extract real-time image data corresponding to the target area from the image database. The image processing module is used to stitch together the extracted real-time image data to obtain real-time first image information and send it to the front end for dynamic display.

9. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the method for real-time extraction and display of remote sensing images based on the BeiDou grid 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 a program or instructions, which, when executed by a processor, implement the method for real-time extraction and display of remote sensing images based on the BeiDou grid as described in any one of claims 1 to 7.

Images