CN117992992B - A scalable satellite intelligence data cloud platform secure storage method and system - Google Patents

Abstract

The invention discloses a safe storage method and a safe storage system for an extensible satellite information data cloud platform, wherein the method comprises the following steps: extracting satellite remote sensing images in the satellite information data, cutting the satellite remote sensing images to generate a plurality of satellite remote sensing image blocks, and respectively storing the satellite remote sensing image blocks in a plurality of areas of a cloud platform; acquiring feature classification of each position in a satellite remote sensing image block, setting a satellite remote sensing image stitching model based on the feature classification, stitching images belonging to the same feature classification in a plurality of satellite remote sensing image blocks, setting a satellite remote sensing image stitching model based on color, stitching the plurality of satellite remote sensing image blocks according to color values of pixels, and mapping the satellite remote sensing image blocks to satellite remote sensing images with the feature stitching; setting a user security level, and distributing the number of blocks of the satellite remote sensing image blocks which can be spliced to the user according to the user security level, wherein the higher the user security level is, the more the number of blocks of the satellite remote sensing image blocks which can be spliced is.

Description

A scalable satellite intelligence data cloud platform secure storage method and system

Technical Field

The present invention belongs to the technical field of satellite intelligence security storage, and more specifically, relates to an expandable satellite intelligence data cloud platform security storage method and system.

Background technique

Image stitching is the process of combining multiple images into a larger image. This process has a wide range of applications in computer vision, photography, geographic information systems (GIS), and other fields. The following are some of the current status and common methods of image stitching technology:

Panorama-based methods: Panorama stitching: This method usually uses a panoramic camera or by taking multiple images and then stitching them into a panorama using panoramic stitching software (such as the Photomerge function in Adobe Photoshop).

Deep learning methods: Convolutional neural network (CNN): Deep learning methods have also achieved some success in image stitching. CNN can learn the features in the image and provide satisfactory results in the image stitching task.

Generative Adversarial Networks (GANs): GANs can generate realistic images and thus can be used to generate missing parts, thus achieving image stitching.

However, the image stitching technology in the prior art does not consider the issue of data security, and the stitching accuracy is not ideal.

Summary of the invention

In order to solve the above technical problems, the present invention proposes a scalable satellite intelligence data cloud platform security storage method, comprising:

Extracting satellite remote sensing images from the satellite intelligence data, cutting the satellite remote sensing images to generate multiple satellite remote sensing image blocks, storing them in multiple areas of the cloud platform respectively, and expanding the cloud platform according to the satellite remote sensing image blocks;

Obtain the feature classification of each position in the satellite remote sensing image block, set a satellite remote sensing image stitching model based on feature classification, and stitch images belonging to the same feature classification in multiple satellite remote sensing image blocks. At the same time, set a satellite remote sensing image stitching model based on color, stitch multiple satellite remote sensing image blocks according to the color value of the pixel, and map them to the satellite remote sensing image that has completed feature stitching;

The user security level is set, and according to the user security level, the number of satellite remote sensing image blocks that can be stitched is allocated to the user. The higher the user security level, the more satellite remote sensing image blocks that can be stitched.

Furthermore, the satellite remote sensing image stitching model based on feature classification includes:

,

in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image.

Furthermore, the color-based satellite remote sensing image stitching model includes:

,

in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function.

Furthermore, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for the color gradient.

Furthermore, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, Is a normal number.

The present invention also proposes an expandable satellite intelligence data cloud platform security storage system, comprising:

A cutting module is used to extract satellite remote sensing images from the satellite intelligence data, cut the satellite remote sensing images, generate multiple satellite remote sensing image blocks, store them in multiple areas of the cloud platform respectively, and expand the cloud platform according to the satellite remote sensing image blocks;

A stitching module is used to obtain the feature classification of each position in the satellite remote sensing image block, set a satellite remote sensing image stitching model based on feature classification, and stitch images belonging to the same feature classification in multiple satellite remote sensing image blocks. At the same time, a satellite remote sensing image stitching model based on color is set to stitch multiple satellite remote sensing image blocks according to the color value of the pixel, and map them to the satellite remote sensing image that has completed feature stitching;

The security module is used to set the user security level and allocate the number of satellite remote sensing image blocks that can be spliced to the user according to the user security level. The higher the user security level, the more satellite remote sensing image blocks that can be spliced.

Furthermore, the satellite remote sensing image stitching model based on feature classification includes:

,

in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image.

Furthermore, the color-based satellite remote sensing image stitching model includes:

,

in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function.

Furthermore, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for the color gradient.

Furthermore, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, Is a normal number.

Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

The present invention extracts satellite remote sensing images from the satellite intelligence data, cuts the satellite remote sensing images, generates multiple satellite remote sensing image blocks, and stores them in multiple areas of the cloud platform respectively; obtains the feature classification to which each position in the satellite remote sensing image block belongs, sets a satellite remote sensing image splicing model based on feature classification, splices images belonging to the same feature classification in multiple satellite remote sensing image blocks, and at the same time, sets a satellite remote sensing image splicing model based on color, splices multiple satellite remote sensing image blocks according to the color value of the pixel, and maps them to the satellite remote sensing image that has completed feature splicing; sets the user security level, and according to the user security level, allocates the number of satellite remote sensing image blocks that can be spliced to the user, the higher the user security level, the more satellite remote sensing image blocks that can be spliced. Through the above technical scheme, the present invention can splice satellite intelligence data, and set the user security level, and allocate the number of splicing blocks to users of different security levels, thereby achieving the purpose of data security.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method according to Embodiment 1 of the present invention;

FIG. 2 is a system structure diagram of Embodiment 2 of the present invention.

Detailed ways

In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

The display screen is used to display the user interface of each application.

All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

In addition, those skilled in the art will appreciate that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

Example 1

As shown in FIG1 , an embodiment of the present invention provides a scalable satellite intelligence data cloud platform secure storage method, including:

Step 101, extracting satellite remote sensing images from the satellite intelligence data, cutting the satellite remote sensing images to generate multiple satellite remote sensing image blocks, storing them in multiple areas of the cloud platform respectively, and expanding the cloud platform according to the satellite remote sensing image blocks;

Step 102, obtaining the feature classification of each position in the satellite remote sensing image block, setting a satellite remote sensing image stitching model based on the feature classification, stitching images belonging to the same feature classification in multiple satellite remote sensing image blocks, and at the same time, setting a satellite remote sensing image stitching model based on color, stitching multiple satellite remote sensing image blocks according to the color values of pixels, and mapping them to the satellite remote sensing image that has completed feature stitching;

Specifically, the satellite remote sensing image stitching model based on feature classification includes:

,

in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image.

Specifically, the color-based satellite remote sensing image stitching model includes:

in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for the color gradient.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, Is a normal number.

Step 103, setting the user security level, and allocating the number of satellite remote sensing image blocks that can be spliced to the user according to the user security level. The higher the user security level, the more satellite remote sensing image blocks that can be spliced.

Example 2

As shown in FIG. 2 , the embodiment of the present invention further proposes an expandable satellite intelligence data cloud platform security storage system, including:

A cutting module is used to extract satellite remote sensing images from the satellite intelligence data, cut the satellite remote sensing images, generate multiple satellite remote sensing image blocks, store them in multiple areas of the cloud platform respectively, and expand the cloud platform according to the satellite remote sensing image blocks;

A stitching module is used to obtain the feature classification of each position in the satellite remote sensing image block, set a satellite remote sensing image stitching model based on feature classification, and stitch images belonging to the same feature classification in multiple satellite remote sensing image blocks. At the same time, a satellite remote sensing image stitching model based on color is set to stitch multiple satellite remote sensing image blocks according to the color value of the pixel, and map them to the satellite remote sensing image that has completed feature stitching;

Specifically, the satellite remote sensing image stitching model based on feature classification includes:

,

in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image.

Specifically, the color-based satellite remote sensing image stitching model includes:

in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for the color gradient.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, Is a normal number.

The security module is used to set the user security level and allocate the number of satellite remote sensing image blocks that can be spliced to the user according to the user security level. The higher the user security level, the more satellite remote sensing image blocks that can be spliced.

Example 3

An embodiment of the present invention also proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the scalable satellite intelligence data cloud platform secure storage method.

Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, extracting a satellite remote sensing image from the satellite intelligence data, cutting the satellite remote sensing image to generate a plurality of satellite remote sensing image blocks, and storing the blocks in a plurality of areas of the cloud platform respectively;

Step 102, obtaining the feature classification of each position in the satellite remote sensing image block, setting a satellite remote sensing image stitching model based on the feature classification, stitching images belonging to the same feature classification in multiple satellite remote sensing image blocks, and at the same time, setting a satellite remote sensing image stitching model based on color, stitching multiple satellite remote sensing image blocks according to the color values of pixels, and mapping them to the satellite remote sensing image that has completed feature stitching;

Specifically, the satellite remote sensing image stitching model based on feature classification includes:

in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image.

Specifically, the color-based satellite remote sensing image stitching model includes:

in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for the color gradient.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, Is a normal number.

Step 103, setting the user security level, and allocating the number of satellite remote sensing image blocks that can be spliced to the user according to the user security level. The higher the user security level, the more satellite remote sensing image blocks that can be spliced.

Example 4

An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a scalable satellite intelligence data cloud platform security storage method.

Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

Among them, the storage medium can be used to store software programs and modules, such as an expandable satellite intelligence data cloud platform security storage method in an embodiment of the present invention, corresponding program instructions/modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned expandable satellite intelligence data cloud platform security storage method. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

The processor may call the information and application program stored in the storage medium through the transmission system to execute the steps: Step 101, extracting the satellite remote sensing image in the satellite intelligence data, cutting the satellite remote sensing image to generate a plurality of satellite remote sensing image blocks, and storing them in a plurality of areas of the cloud platform respectively;

Step 102, obtaining the feature classification of each position in the satellite remote sensing image block, setting a satellite remote sensing image stitching model based on the feature classification, stitching images belonging to the same feature classification in multiple satellite remote sensing image blocks, and at the same time, setting a satellite remote sensing image stitching model based on color, stitching multiple satellite remote sensing image blocks according to the color values of pixels, and mapping them to the satellite remote sensing image that has completed feature stitching;

Specifically, the satellite remote sensing image stitching model based on feature classification includes:

,

in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image.

Specifically, the color-based satellite remote sensing image stitching model includes:

,

in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for the color gradient.

Specifically, satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function include:

,

in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, Is a normal number.

Step 103, setting the user security level, and allocating the number of satellite remote sensing image blocks that can be spliced to the user according to the user security level. The higher the user security level, the more satellite remote sensing image blocks that can be spliced.

The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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

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

Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the protection scope of the invention.

Claims (2)

1. A scalable satellite intelligence data cloud platform secure storage method, characterized by comprising: Extracting satellite remote sensing images from the satellite intelligence data, cutting the satellite remote sensing images to generate multiple satellite remote sensing image blocks, storing them in multiple areas of the cloud platform respectively, and expanding the cloud platform according to the satellite remote sensing image blocks; The feature classification of each position in the satellite remote sensing image block is obtained, a satellite remote sensing image stitching model based on feature classification is set, and images belonging to the same feature classification in multiple satellite remote sensing image blocks are stitched. At the same time, a satellite remote sensing image stitching model based on color is set, and multiple satellite remote sensing image blocks are stitched according to the color value of the pixel, and mapped to the satellite remote sensing image that has completed feature stitching, wherein the satellite remote sensing image stitching model based on feature classification includes: , in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel value of the feature classification image; The color-based satellite remote sensing image mosaic model includes: , in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function; Satellite remote sensing image tiles In coordinates The color value of the pixel at The processing functions include: , in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for color gradient; Satellite remote sensing image tiles In coordinates The color value of the pixel at The processing function include: , in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, is a positive constant; The user security level is set, and according to the user security level, the number of satellite remote sensing image blocks that can be stitched is allocated to the user. The higher the user security level, the more satellite remote sensing image blocks that can be stitched. 2. A scalable satellite intelligence data cloud platform security storage system, characterized by comprising: A cutting module is used to extract satellite remote sensing images from the satellite intelligence data, cut the satellite remote sensing images, generate multiple satellite remote sensing image blocks, store them in multiple areas of the cloud platform respectively, and expand the cloud platform according to the satellite remote sensing image blocks; The stitching module is used to obtain the feature classification of each position in the satellite remote sensing image block, set a satellite remote sensing image stitching model based on feature classification, and stitch images belonging to the same feature classification in multiple satellite remote sensing image blocks. At the same time, a satellite remote sensing image stitching model based on color is set to stitch multiple satellite remote sensing image blocks according to the color value of the pixel, and map them to the satellite remote sensing image that has completed feature stitching, wherein the satellite remote sensing image stitching model based on feature classification includes: , in, For the coordinates Belongs to The pixel value of the image classified by each feature after feature splicing is completed. Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, Satellite remote sensing image blocks Central coordinates Belongs to The pixel value gradient of feature classification, is the gradient adjustment factor used to control the gradient similarity, Satellite remote sensing image blocks In coordinates Belongs to The pixel values of the feature classification image, Satellite remote sensing image blocks In coordinates Belongs to The pixel value of the feature classification image; The color-based satellite remote sensing image mosaic model includes: , in, For the coordinates The color value of the pixel after color splicing is completed. To adjust the weight, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function, Satellite remote sensing image blocks In coordinates The color value of the pixel at The processing function; Satellite remote sensing image tiles In coordinates The color value of the pixel at The processing function include: , in, is the direction of the color gradient, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks Adjustment factor for color gradient; Satellite remote sensing image tiles In coordinates The color value of the pixel at The processing function include: , in, Satellite remote sensing image blocks In coordinates The color value of the pixel at The gradient of Satellite remote sensing image blocks The average color value, Satellite remote sensing image blocks The adjustment factor for the color gradient, is a positive constant; The security module is used to set the user security level and allocate the number of satellite remote sensing image blocks that can be spliced to the user according to the user security level. The higher the user security level, the more satellite remote sensing image blocks that can be spliced.