A multi-source remote sensing image automatic acquisition and release method based on parameter template

Abstract

The embodiment of the specification provides a multi-source remote sensing image automatic acquisition and release method based on a parameter template, relates to the technical field of remote sensing image processing, and comprises the following steps: acquiring business parameters input by a user, generating a structured parameter template according to the business parameters, and performing version solidification storage on the parameter template; reading the parameter template, instantiating an acquisition task, wherein the acquisition task comprises a task identification ID, a parameter snapshot and a state identification; performing automatic acquisition of multi-source remote sensing images according to the acquisition task, acquiring original image data; performing standardization processing on the original image data, generating standardized image tiles; releasing the image tiles, and providing a front-end interactive interface.

Description

Technical Field

[0001] This document relates to the field of remote sensing image processing technology, and in particular to a method for automated acquisition and publishing of multi-source remote sensing images based on parameter templates. Background Technology

[0002] With the rapid development of Earth observation technology, remote sensing data acquisition methods have become increasingly diversified. Remote sensing data exhibits characteristics such as multi-source, multi-scale, complex structure, diverse formats, and large volume, indicating that the remote sensing field has entered the era of big data. Remote sensing images acquired by different satellite platforms and sensors differ significantly in terms of spatial reference, resolution, band settings, and color characteristics. How to efficiently acquire, process, and publish these multi-source, heterogeneous remote sensing images as standardized services accessible via the web has become a pressing technical challenge for the geographic information industry.

[0003] Currently, the acquisition and publishing process of remote sensing imagery typically involves multiple independent stages. During the acquisition phase, personnel need to write separate download scripts or configure acquisition parameters for different data sources, target areas, and time windows. This process is highly manual, inefficient, and prone to data acquisition failures or acquiring data that does not meet quality requirements due to incorrect parameter configurations. In the processing phase, remote sensing images from different sources have different projection coordinate systems, requiring individual projection transformations, pixel alignment, and color correction. Traditional processing methods often use fragmented scripts or toolchains in series, lacking a unified, standardized processing pipeline, making it difficult to guarantee the consistency and reproducibility of processing results. In the publishing phase, the processed images need to be used to generate tile pyramids and provide services. Using the same tiling strategy for high-frequency and temporary access areas leads to wasted storage resources or poor response times. Furthermore, the entire acquisition, processing, and publishing chain lacks a unified auditing and traceability mechanism, making it difficult to quickly locate the cause and reproduce the process when a problem occurs at any stage.

[0004] Existing research has attempted to improve the efficiency of remote sensing image processing through automation technologies, such as using open-source graphics libraries like GDAL to automate functions like image preprocessing, cropping, registration, enhancement, and tiling. However, these solutions mostly focus on automating single steps, failing to integrate business parameter definition, acquisition execution, standardized processing, and service deployment into a complete automated chain. They also lack a systematic design for parameter template management and end-to-end auditing and traceability. Therefore, how to achieve end-to-end automation from parameter definition to service deployment while ensuring the quality of multi-source remote sensing image processing, and ensuring that the entire process is traceable, reproducible, and rollbackable, has become a pressing technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] This specification provides one or more embodiments of an automated acquisition and publishing method for multi-source remote sensing images based on parameter templates, including: Obtain the business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a fixed version. Read the parameter template and instantiate a data acquisition task. The data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier. The automated acquisition of multi-source remote sensing images is performed according to the acquisition task to obtain raw image data; The original image data is subjected to standardization processing to generate standardized image tiles; Publish the image tiles and provide a front-end interactive interface.

[0006] Furthermore, the specific steps of obtaining user-inputted business parameters, generating a structured parameter template based on the business parameters, and storing the parameter template in a version-fixed manner are as follows: Obtain the target area AOI, time window, data source type, cloud cover threshold, resolution constraint, and concurrency strategy input by the user; The obtained business parameters are serialized into parameter templates in JSON or YAML format; Record the template version number, creator information, and approver information of the parameter template, and then store the recorded parameter template in a fixed manner.

[0007] Furthermore, the step of reading the parameter template and instantiating it to generate a data acquisition task, wherein the data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier, specifically: After reading the parameter template, the task identifier ID is assigned to the acquisition task and the parameter snapshot is generated. At the same time, a status identifier is set for the acquisition task. The status identifier is used to mark the task execution status of the acquisition task. The task execution status includes pending execution, in execution, completed, and failed.

[0008] Furthermore, the step of automatically acquiring multi-source remote sensing images according to the acquisition task to obtain raw image data includes: A download request is constructed based on the parameter snapshot, and the download is executed concurrently using multi-threading; During the download process, breakpoint resume is performed based on the HTTP Range protocol, the offset of downloaded bytes is recorded, and the download continues from the offset of downloaded bytes after the network is restored; Monitor network status, and when HTTP status code 429 or network timeout is detected, trigger the exponential backoff retry strategy to re-initiate the download request; After the download is complete, an integrity check is performed on the downloaded original image data. If the check fails, the corresponding original image data is marked as dirty data and a degradation process is triggered. Furthermore, the exponential backoff retry strategy is specifically defined as follows: the initial waiting time is set to 1 second, the waiting time for each retry increases by a factor of 2, and the maximum number of retries is set to 5.

[0009] Furthermore, the standardization process performed on the original image data to generate standardized image tiles includes: Read the spatial reference information of the original image data, determine whether the projection of the original image data is Web Mercator projection, and if not, use the GDAL library to reproject the original image data to the Web Mercator coordinate system. The pixel grids of the reprojected images from different sources are aligned to ensure that the pixel boundaries of each image are consistent at the same resolution. Perform color band normalization processing on the pixel grid aligned image to unify the color band mapping rules of each image; After color band normalization processing, cloud cover detection, hole detection and outlier detection are performed on the images. Based on the detection results, substandard products are downgraded or marked as unusable according to preset strategies. The images that have passed detection and undergone downgrade processing are used to construct a multi-level pyramid. Based on the multi-level pyramid, image tiles in XYZ or TMS format are generated, and the generated image tiles are stored in object storage according to the / zoom / x / y directory structure.

[0010] Furthermore, the process of publishing the image tiles and providing a front-end interactive interface includes: The image tile is associated with and stored with its metadata, which includes image source, acquisition time, projection information, and resolution. Construct a RESTful interface, receive zoom level and coordinate range parameters sent by the front end through the RESTful interface, and return the corresponding image tile to the front end according to the parameters; The system identifies target area areas (AOIs) with access frequencies higher than a preset threshold, performs pre-slicing processing on the target area AOIs, and performs on-demand real-time slicing processing on temporary target area AOIs with access frequencies lower than or equal to the preset threshold.

[0011] Furthermore, the method also includes: After the data collection task is completed, an audit report is generated. The audit report includes the task identifier ID, parameter snapshot, execution time, execution status, output file path, and quality indicators. The audit report is bound and stored with the corresponding file object, metadata, and version information, and supports retrieval and backtracking based on the task identifier ID, time range, and target area AOI dimension. This specification provides one or more embodiments of an automated acquisition and publishing system for multi-source remote sensing images based on parameter templates, including: Template generation module: used to obtain business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a version-fixed manner; Task generation module: used to read the parameter template and instantiate and generate a data acquisition task, wherein the data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier; Image acquisition module: used to automatically acquire multi-source remote sensing images according to the acquisition task and obtain raw image data; Image processing module: used to perform standardization processing on the raw image data to generate standardized image tiles; Image publishing module: Used to publish the image tiles and provide a front-end interactive interface.

[0012] This specification provides one or more embodiments of an electronic device, including: Processor; and, A memory is configured to store computer-executable instructions, which, when executed, cause the processor to implement the steps of the above-described method for automated acquisition and distribution of multi-source remote sensing images based on parameter templates.

[0013] This specification provides one or more embodiments of a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the above-described method for automated acquisition and publishing of multi-source remote sensing images based on parameter templates.

[0014] By encapsulating business parameters into reusable structured parameter templates and separating the definition of business rules from the engineering process of data acquisition and execution, the present invention achieves parameter-driven acquisition tasks. The entire acquisition and publishing process has good traceability and reproducibility, effectively improving the recoverability of acquisition failures in complex network environments. It ensures the consistency of multi-source images in terms of spatial reference, pixel boundaries, and color features. Pre-slicing of high-frequency access areas and on-demand slicing of temporary areas control storage costs while ensuring response speed. By combining business parameters with engineering processing to form an automated link, the efficiency and reliability of multi-source remote sensing images from data acquisition to service publishing are greatly improved.

[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in 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 recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating an automated acquisition and publishing method for multi-source remote sensing images based on parameter templates, provided for one or more embodiments of this specification. Figure 2 A schematic diagram illustrating the composition of a multi-source remote sensing image automated acquisition and publishing system based on parameter templates, provided in one or more embodiments of this specification. Figure 3 This is a schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0019] Method Implementation Examples According to embodiments of the present invention, an automated acquisition and publishing method for multi-source remote sensing images based on parameter templates is provided. Figure 1 A flowchart illustrating an automated acquisition and publishing method for multi-source remote sensing images based on parameter templates, provided for one or more embodiments of this specification, is shown below. Figure 1 As shown, the automated acquisition and publishing method for multi-source remote sensing images based on parameter templates according to an embodiment of the present invention specifically includes: S1. Obtain the business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a version-fixed manner.

[0020] Specifically, the system receives business parameters input by users based on project requirements. These parameters include the target area of ​​interest (AOI), time window, data source type, cloud cover threshold, resolution constraint, and concurrency strategy. The target area of ​​interest (AOI) refers to the geographic area of ​​interest to the user, typically defined as a polygon coordinate system, used to limit the spatial scope of image acquisition. The time window limits the time interval for image acquisition. The data source type specifies which satellite platform or data provider to acquire images from. The cloud cover threshold filters images with a cloud cover ratio below a set value. The resolution constraint limits the ground sampling distance for images. The concurrency strategy controls the number of concurrent users and the maximum download speed. These business parameters are serialized into a structured parameter template in JSON or YAML format. During template generation, the template version number, creator information, and approver information are recorded and stored permanently. The version number ensures that each template change is traceable, and the creator and approver information meet the project governance requirements for operational traceability.

[0021] S2. Read the parameter template and instantiate it to generate a data acquisition task. The data acquisition task includes a task identifier ID, parameter snapshot, and status identifier.

[0022] Specifically, after reading the stored parameter template, the task orchestration engine assigns a unique task identifier ID to the collection requirement corresponding to that parameter template, and generates a parameter snapshot for the task based on the template content. A parameter snapshot refers to making a solid copy of all parameter values ​​in the current parameter template and storing them independently, ensuring that even if the template changes later, the parameters on which the task execution was based can still be completely traced back. Simultaneously, a status flag is set for the collection task, marking its current task execution status, including pending, executing, completed, and failed.

[0023] S3. Perform automated acquisition of multi-source remote sensing images according to the acquisition task to obtain raw image data.

[0024] The data acquisition process includes several sub-stages such as download request construction, breakpoint resumption and retries, and integrity verification. First, the acquisition engine constructs download requests for each data source based on the parameter snapshots in the task, and executes the downloads using a multi-threaded concurrent approach to improve acquisition efficiency. During multi-threaded concurrent downloads, the download process is controlled according to the concurrency strategies defined in the parameter template, such as the maximum number of concurrent connections and the rate limit, to avoid affecting the normal operation of other services due to excessive network resource consumption.

[0025] During the download process, interrupted downloads are resumed based on the HTTP Range protocol. The HTTP Range protocol allows clients to specify the range of resource bytes they wish to acquire in their requests. The acquisition engine utilizes this feature to continuously record the offset of downloaded bytes during the download process. If a network interruption or connection failure causes the download to be interrupted, the download resumes from the recorded byte offset when the network is restored, without having to retransmit the entire file from the beginning.

[0026] During the download process, network status is continuously monitored. When an HTTP status code 429 (too many requests, server requires client to limit speed) or a network timeout is detected, an exponential backoff retry strategy is triggered to re-initiate the download request. This strategy is implemented as follows: an initial wait time of 1 second is set, and the wait time for each retry increases by a factor of 2; that is, 1 second before the first retry, 2 seconds before the second, 4 seconds before the third, and so on, with a maximum of 5 retries. By adopting an exponentially increasing wait time retry strategy, sufficient recovery time is given to the server and network, improving the retry success rate while avoiding secondary impact on the server.

[0027] After downloading, an integrity check is performed on the downloaded raw image data. Integrity checks typically use hash algorithms such as MD5 or SHA256, comparing the calculated hash value with the official hash value provided by the data source to confirm whether the file has been corrupted or tampered with during transmission. If the check fails, it indicates that the downloaded image data is incomplete or corrupted, and the image data is marked as dirty data, triggering a degradation process. The degradation process can perform operations such as re-downloading, using it at a lower resolution, or directly marking it as unusable, depending on a preset strategy.

[0028] S4. Perform standardization processing on the original image data to generate standardized image tiles.

[0029] Because remote sensing images acquired by different satellite platforms and sensors differ in spatial reference, pixel grid, and color features, they need to undergo standardized processing before publication. Standardization processing specifically includes sub-steps such as projection unification, pixel alignment, color normalization, quality inspection, and tile generation. First, the spatial reference information of the original image data is read to determine whether the image projection is Web Mercator projection. If the original image projection is not Web Mercator projection (EPSG:3857), the original image data is reprojected to the Web Mercator coordinate system using GDAL (Geospatial Data Abstraction Library). During the reprojection process, a bilinear interpolation algorithm is used to perform pixel resampling. Bilinear interpolation calculates the gray value of the new pixel by weighted averaging the gray values ​​of four adjacent pixels.

[0030] After projection unification is completed, the pixel grids of images from different sources are aligned to ensure that the pixel boundaries of each image are consistent at the same resolution. Pixel grid alignment ensures that each pixel of all images corresponds to the same ground position by resampling each image onto a unified spatial grid, eliminating the misalignment problem when multiple source images are superimposed.

[0031] Next, color band normalization is performed on the pixel-grid aligned images to unify the color band mapping rules of each image. Color band normalization analyzes the band statistical characteristics of each image to map the band values ​​of different sensors to a unified color space, eliminating color differences between multi-source data.

[0032] After color normalization, cloud cover detection, hole detection, and outlier detection are performed on the image. Cloud cover detection identifies areas in the image covered by clouds; when the cloud cover ratio exceeds the cloud cover threshold set in the parameter template, the image is deemed substandard. Hole detection identifies data holes in the image caused by sensor malfunction or missing data. Outlier detection identifies areas in the image where pixel values ​​significantly deviate from the normal range due to noise or sensor malfunction. Based on the detection results, substandard products are downgraded or marked as unusable according to a preset strategy.

[0033] Finally, a multi-level pyramid is constructed from the images that have passed inspection and undergone downgrade processing. Based on this multi-level pyramid, image tiles in XYZ or TMS format are generated. The tile size is set to 256×256 pixels, and the generated image tiles are stored in object storage (such as OSS or S3) according to the / zoom / x / y directory structure.

[0034] S5. Publish the image tiles and provide a front-end interactive interface.

[0035] The release process includes sub-steps such as metadata association storage, RESTful interface construction, and differentiated slicing strategies.

[0036] First, the generated image tiles are associated with and stored along with their metadata. Metadata is descriptive information about the image data, including image source, acquisition time, projection information, and resolution. By associating and storing the tile data with the metadata, the front-end can simultaneously obtain the source and attribute information when accessing the tiles, making it easier to understand the background and applicable scenarios of the data.

[0037] Secondly, a RESTful interface is constructed to receive zoom level and coordinate range parameters sent by the frontend, and to return the corresponding image tiles to the frontend based on these parameters. The RESTful interface uses the standard HTTP protocol, is platform-independent, and has good scalability. The frontend can directly request tile data for a specific location using a URL format such as GET / tiles / {z} / {x} / {y}.png.

[0038] Regarding the slicing strategy, target AOIs with access frequencies exceeding a preset threshold are identified. Pre-slicing is performed on these frequently accessed target AOIs, meaning that all level tiles are generated and persistently stored during data publishing. For temporary target AOIs with access frequencies below or equal to the preset threshold, the system performs on-demand real-time slicing, meaning that tiles of the corresponding level are only generated and cached upon the user's first request, avoiding the pre-occupation of large amounts of storage space.

[0039] The front-end interactive interface is built on OpenLayers and Vue3 to construct the rendering engine. OpenLayers is an open-source geographic information front-end rendering library that supports loading and displaying various data sources such as tile maps and vector maps; Vue3 is a progressive JavaScript front-end framework used to build the user interface. Based on the combination of the two, the rendering engine allows users to adjust the transparency of different image layers through the layer transparency control function, facilitating the overlay and comparison of multi-source images; it also supports a multi-temporal image slider comparison function, allowing users to compare images from different time windows side by side or overlaid in the same viewport by sliding the slider, intuitively observing changes in land cover.

[0040] After the data acquisition task is completed, an audit report is generated. The audit report includes information such as task ID, parameter snapshot, execution time, execution status, output file path, and quality indicators. The parameter snapshot records the complete parameter configuration used for the task, ensuring accurate traceability even if the parameter template changes multiple times. The execution status records whether the task ultimately succeeded or failed. The output file path records the storage location of the generated tile data in object storage. Quality indicators record key quality parameters during image processing (such as cloud cover detection results and integrity verification results). The audit report is bound to the corresponding file object, metadata, and version information for storage, and interfaces are provided for retrieval and traceability based on task ID, time range, and target area AOI. When it is necessary to reproduce a processing result, users can retrieve the corresponding parameter snapshot using the task ID and re-trigger the data acquisition and processing task, achieving searchability, traceability, reproducibility, and rollback capability.

[0041] The beneficial effects of this invention are as follows: By encapsulating business parameters into reusable and versionable structured templates, this invention achieves parameter-driven acquisition tasks and full-process traceability. By introducing a robust acquisition engine that supports breakpoint resumption, exponential backoff retries, and integrity verification, the recovery capability of the acquisition process in complex network environments is significantly improved. By establishing a standardized production pipeline that includes projection unification, pixel alignment, color normalization, and quality inspection, high consistency in spatial reference, pixel boundaries, and color features of multi-source heterogeneous images is ensured. By adopting a dynamic and static service publishing strategy and a complete audit and traceability mechanism, full-link automation and traceability from parameter definition to service publishing are achieved. This effectively solves the technical problems of fragmented processes, strong reliance on manual labor, uncontrollable quality, and difficulty in traceability in traditional remote sensing image processing workflows, significantly improving the efficiency, quality, and reliability of multi-source remote sensing imagery from data acquisition to service publishing.

[0042] System Implementation Examples According to embodiments of the present invention, an automated acquisition and publishing system for multi-source remote sensing images based on parameter templates is provided. Figure 2 This specification provides a schematic diagram illustrating the composition of an automated acquisition and publishing system for multi-source remote sensing images based on parameter templates, as shown in one or more embodiments. Figure 2 As shown, the automated acquisition and publishing system for multi-source remote sensing images based on parameter templates according to an embodiment of the present invention specifically includes: Template generation module 20: used to obtain business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a version-fixed manner; Task generation module 22: used to read the parameter template and instantiate and generate a data acquisition task, wherein the data acquisition task includes a task identifier ID, a parameter snapshot and a status identifier; Image acquisition module 24: used to perform automated acquisition of multi-source remote sensing images according to the acquisition task, and obtain raw image data; Image processing module 26: used to perform standardization processing on the original image data to generate standardized image tiles; Image publishing module 28: Used to publish the image tiles and provide a front-end interactive interface.

[0043] The embodiments of the present invention are system embodiments corresponding to the above method embodiments. The specific operation of each module can be understood by referring to the description of the method embodiments, and will not be repeated here.

[0044] Device Example 1 This invention provides an electronic device, such as... Figure 3 As shown, it includes: a memory 30, a processor 32, and a computer program stored in the memory 30 and executable on the processor 32. When the computer program is executed by the processor 32, it performs the following method steps: S1. Obtain the business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a version-fixed manner; S2. Read the parameter template and instantiate it to generate a data acquisition task. The data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier. S3. Perform automated acquisition of multi-source remote sensing images according to the acquisition task to obtain raw image data; S4. Perform standardization processing on the original image data to generate standardized image tiles; S5. Publish the image tiles and provide a front-end interactive interface.

[0045] Device Example 2 This invention provides a computer-readable storage medium storing an information transmission implementation program. When executed by a processor 32, the program performs the following method steps: S1. Obtain the business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a version-fixed manner; S2. Read the parameter template and instantiate it to generate a data acquisition task. The data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier. S3. Perform automated acquisition of multi-source remote sensing images according to the acquisition task to obtain raw image data; S4. Perform standardization processing on the original image data to generate standardized image tiles; S5. Publish the image tiles and provide a front-end interactive interface.

[0046] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automated acquisition and publishing of multi-source remote sensing images based on parameter templates, characterized in that, include: Obtain the business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a fixed version. Read the parameter template and instantiate a data acquisition task. The data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier. The automated acquisition of multi-source remote sensing images is performed according to the acquisition task to obtain raw image data; The original image data is subjected to standardization processing to generate standardized image tiles; Publish the image tiles and provide a front-end interactive interface.

2. The method according to claim 1, characterized in that, The specific steps of obtaining user-inputted business parameters, generating a structured parameter template based on the business parameters, and storing the parameter template in a version-fixed manner are as follows: Obtain the target area AOI, time window, data source type, cloud cover threshold, resolution constraint, and concurrency strategy input by the user; The obtained business parameters are serialized into parameter templates in JSON or YAML format; Record the template version number, creator information, and approver information of the parameter template, and then store the recorded parameter template in a fixed manner.

3. The method according to claim 1, characterized in that, The step of reading the parameter template and instantiating a data acquisition task, wherein the data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier, specifically: After reading the parameter template, the task identifier ID is assigned to the acquisition task and the parameter snapshot is generated. At the same time, a status identifier is set for the acquisition task. The status identifier is used to mark the task execution status of the acquisition task. The task execution status includes pending execution, in execution, completed, and failed.

4. The method according to claim 1, characterized in that, The automated acquisition of multi-source remote sensing images according to the acquisition task, and the acquisition of raw image data, includes: A download request is constructed based on the parameter snapshot, and the download is executed concurrently using multi-threading; During the download process, breakpoint resume is performed based on the HTTP Range protocol, the offset of downloaded bytes is recorded, and the download continues from the offset of downloaded bytes after the network is restored; Monitor network status, and when HTTP status code 429 or network timeout is detected, trigger the exponential backoff retry strategy to re-initiate the download request; After the download is complete, an integrity check is performed on the downloaded original image data. If the check fails, the corresponding original image data is marked as dirty data, and a degradation process is triggered.

5. The method according to claim 1, characterized in that, The exponential backoff retry strategy is as follows: the initial waiting time is set to 1 second, the waiting time for each retry increases by 2 times, and the maximum number of retries is set to 5.

6. The method according to claim 1, characterized in that, The standardization process performed on the original image data to generate standardized image tiles includes: Read the spatial reference information of the original image data, determine whether the projection of the original image data is Web Mercator projection, and if not, use the GDAL library to reproject the original image data to the Web Mercator coordinate system. The pixel grids of the reprojected images from different sources are aligned to ensure that the pixel boundaries of each image are consistent at the same resolution. Perform color band normalization processing on the pixel grid aligned image to unify the color band mapping rules of each image; After color band normalization processing, cloud cover detection, hole detection and outlier detection are performed on the images. Based on the detection results, substandard products are downgraded or marked as unusable according to preset strategies. The images that have passed detection and undergone downgrade processing are used to construct a multi-level pyramid. Based on the multi-level pyramid, image tiles in XYZ or TMS format are generated, and the generated image tiles are stored in object storage according to the / zoom / x / y directory structure.

7. The method according to claim 1, characterized in that, The process of publishing the image tiles and providing a front-end interaction interface includes: The image tile is associated with and stored with its metadata, which includes image source, acquisition time, projection information, and resolution. Construct a RESTful interface, receive zoom level and coordinate range parameters sent by the front end through the RESTful interface, and return the corresponding image tile to the front end according to the parameters; The system identifies target area areas (AOIs) with access frequencies higher than a preset threshold, performs pre-slicing processing on the target area AOIs, and performs on-demand real-time slicing processing on temporary target area AOIs with access frequencies lower than or equal to the preset threshold.

8. The method according to claim 1, characterized in that, The method further includes: After the data collection task is completed, an audit report is generated. The audit report includes the task identifier ID, parameter snapshot, execution time, execution status, output file path, and quality indicators. The audit report is bound and stored with the corresponding file object, metadata and version information, and supports retrieval and backtracking according to the task identifier ID, time range and target area AOI dimension.

9. A multi-source remote sensing image automated acquisition and publishing system based on parameter templates, characterized in that, include: Template generation module: used to obtain business parameters input by the user, generate a structured parameter template based on the business parameters, and store the parameter template in a version-fixed manner; Task generation module: used to read the parameter template and instantiate and generate a data acquisition task, wherein the data acquisition task includes a task identifier ID, a parameter snapshot, and a status identifier; Image acquisition module: used to automatically acquire multi-source remote sensing images according to the acquisition task and obtain raw image data; Image processing module: used to perform standardization processing on the raw image data to generate standardized image tiles; Image publishing module: Used to publish the image tiles and provide a front-end interactive interface.

10. An electronic device, characterized in that, include: processor; as well as, A memory configured to store computer-executable instructions, which, when executed, cause the processor to implement the steps of the method for automated acquisition and distribution of multi-source remote sensing images based on parameter templates as described in any one of claims 1 to 7.

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