Image data management system based on cloud computing

Abstract

The invention relates to an image data management system based on cloud computing. The system comprises a cloud computing service node, a content acquisition device, a model conversion device, a code prediction mechanism and a parameter display mechanism. The image data management system based on cloud computing is wide in application and intelligent in control.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing, and in particular to an image data management system based on cloud computing. Background Art

[0002] There are many types of cloud computing, mainly including public cloud, private cloud and hybrid cloud.

[0003] ‌Public cloud‌: Owned and operated by a third-party cloud service provider, providing computing resources over the Internet, such as Microsoft Azure. The hardware, software, and other supporting infrastructure in the public cloud are managed and maintained by the cloud provider.

[0004] ‌Private Cloud‌: Dedicated to a single business or organization, it can be located in a company's on-site data center or hosted by a third-party service provider. A private cloud maintains services and infrastructure on a dedicated network.

[0005] ‌Hybrid cloud‌: A combination of public and private clouds, allowing data and applications to be shared between the two, providing more flexible business processing and optimizing existing infrastructure, security and compliance.

[0006] CN118470340A relates to the field of image processing technology, which is an image processing method, device, storage medium and electronic equipment based on cloud computing, including: outputting corresponding dual-temporal differential fusion images; extracting features in the dual-temporal differential fusion images to construct difference images; using clustering algorithms and Gaussian functions to obtain corresponding univariate potential functions and binary potential functions; combining univariate potential functions and binary potential functions to calculate the posterior probability of pixel changes. It generates corresponding dual-temporal differential fusion images from multiple dual-temporal image data, speeds up the data parallel reading speed, and uses clustering algorithms and Gaussian functions to obtain corresponding univariate potential functions and binary potential functions, optimizes the calculation process in a distributed environment, reduces repeated calculations, combines univariate potential functions and binary potential functions, and uses a mean field approximation algorithm to calculate the posterior probability of pixel changes, while ensuring the image processing accuracy, and realizes efficient processing of image pixel change detection.

[0007] CN111724451B relates to a tomographic image reconstruction acceleration method, system, terminal and storage medium based on cloud computing. It includes: obtaining the original scan data of an object, uploading the original scan data to a cloud computing system; wherein the cloud computing system includes a Master and a set number of Workers; configuring a parallel computing environment on the cloud computing system by the Master using a parallel computing framework, dividing the tomographic image reconstruction task of the original scan data into a set number of subtasks, assigning the subtasks to the set number of Workers for reconstruction calculation, and integrating the reconstruction calculation results of all Workers to generate a tomographic reconstruction image of the original scan data. The embodiment of the present application is compatible with multiple computing frameworks such as MapReduce and MPI, realizes cloud-based parallel computing, is easy to use, flexible in computing, low in cost, and not subject to geographical restrictions.

[0008] CN118135376A discloses an image data processing method and processing system based on cloud computing, belonging to the field of image processing technology. The method performs pre-clustering initialization on the image to be processed, and determines the starting position and the initial distribution area according to the result generated by the pre-clustering initialization; deploys the simulated particles of the ant colony algorithm in the initial distribution area, and performs pheromone initialization to obtain an initial pheromone matrix; uses the simulated particles of the ant colony algorithm to perform iterative optimization processing on the image to be processed, and divides the simulated particles into elite particles and ordinary particles, and updates the initial pheromone matrix to obtain an updated pheromone matrix; stops iteration according to a preset improvement amplitude threshold, and outputs the segmentation result of the image to be processed. The method combines heuristic initialization and elite ant strategy to process the image, reduces blindness in the exploration process, and reduces unnecessary exploration, thereby reducing the number of iterations as a whole and improving calculation efficiency. Summary of the invention

[0009] In order to overcome the technical problems in the prior art, the present invention proposes an image data management system based on cloud computing, which can, on the basis of cloud computing, for the same intra-frame coding block, use an artificial intelligence model with a customized structure to intelligently predict the coding performance of other colors that have not performed intra-frame coding based on the coding data of each color component that has been intra-frame encoded, so that before executing the intra-frame coding of other colors, its coding performance can be determined, creating space for the adjustment of coding parameters, thereby improving the speed and performance of image data processing.

[0010] According to the present invention, there is provided an image data management system based on cloud computing, the system comprising: A cloud computing service node is used to obtain a red-green component image block, a black-and-white component image block, and a yellow-and-blue component image block corresponding to a current image block, wherein the red-and-green component image block and the black-and-white component image block corresponding to the current image block are both image blocks that have been intra-frame encoded, and the yellow-and-blue component image block corresponding to the current image block is an image block that has not been intra-frame encoded; a content acquisition device connected to the cloud computing service node, and used to obtain the red and green component reconstruction blocks and the black and white component reconstruction blocks respectively corresponding to the red and green component image blocks and the black and white component image blocks corresponding to the current image blocks, and simultaneously obtain the encoding speed, encoding operation amount and encoding compression ratio corresponding to the red and green component image blocks, and obtain the encoding speed, encoding operation amount and encoding compression ratio corresponding to the black and white component image blocks; A model conversion device, for performing multi-level conversion processing on a deep neural network to obtain an intelligent prediction model, wherein the multi-level conversion processing on the deep neural network to obtain the intelligent prediction model includes: each level of conversion processing performed on the deep neural network is each training action performed on the deep neural network, and the number of levels of conversion processing performed on the deep neural network is positively correlated with the number of pixels occupied by the image block; A coding prediction mechanism connected to the content acquisition device, the model conversion device and the cloud computing service node, for intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block by using an intelligent prediction model; A parameter display mechanism, connected to the coding prediction mechanism, for receiving and displaying the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image blocks; Among them, an intelligent prediction model is used to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block, red-green component image block, black-white component image block, red-green component reconstruction block, black-white component reconstruction block, and red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block corresponding to the current image block, including: the yellow-blue component image block, red-green component image block and black-white component image block corresponding to the current image block respectively select the yellow-blue component image block, red-green component image block and black-white component image block of the same reference coding block as their respective intra-frame coding reference frames.

[0011] It can be seen that the present invention has at least the following three important inventive features: Important invention point A: A cloud computing service node is used to obtain the red and green component image blocks, black and white component image blocks and yellow and blue component image blocks corresponding to the current image block, the red and green component image blocks and black and white component image blocks corresponding to the current image block are both image blocks that have been intra-frame encoded, and the yellow and blue component image blocks corresponding to the current image block are image blocks that have not been intra-frame encoded, and the red and green component image blocks and black and white component image blocks corresponding to the current image block are also obtained. The red and green component reconstruction blocks and black and white component reconstruction blocks corresponding to the red and green component image blocks and black and white component image blocks are obtained, and the encoding speed, encoding operation amount and encoding compression ratio corresponding to the red and green component image blocks are obtained at the same time, and the encoding speed, encoding operation amount and encoding compression ratio corresponding to the black and white component image blocks are obtained, so as to provide basic data for the subsequent prediction of the encoding performance of other colors that have not been intra-frame encoded; Important Invention Point B: Performing multi-level conversion processing on the deep neural network to obtain an intelligent prediction model, wherein performing multi-level conversion processing on the deep neural network to obtain the intelligent prediction model includes: each level of conversion processing performed on the deep neural network is each training action performed on the deep neural network, and the number of levels of conversion processing performed on the deep neural network is positively correlated with the number of pixels occupied by the image block, thereby providing an artificial intelligence model for the subsequent prediction of the encoding performance of other colors that are not intra-frame encoded; Important invention point C: An intelligent prediction model is used to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block according to the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block, thereby realizing intelligent prediction of the coding performance of other colors that have not performed intra-frame coding based on the coding data of each color component that has been intra-frame encoded.

[0012] The cloud computing-based image data management system of the present invention has wide applications and intelligent operation. Based on cloud computing, for the same intra-frame coding block, an artificial intelligence model with a customized structure can be used to intelligently predict the coding performance of other colors that have not been intra-coded based on the coded data of each color component that has been intra-coded, thereby improving the speed and performance of image data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1It is a block diagram of the internal structure of an image data management system based on cloud computing according to Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0014] At present, there is still a large application space for cloud computing. For example, in image data processing, for the same intra-frame coding block, it is expected to intelligently predict the coding performance of other colors that have not performed intra-frame coding based on the coding data of each color component that has been intra-frame encoded, so that the coding performance of other colors can be determined before executing intra-frame coding, creating space for adjusting the coding parameters. However, the existing technology lacks corresponding solutions using cloud computing.

[0015] The implementation scheme of the image data management system based on cloud computing of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Figure 1 The internal structure block diagram of the image data management system based on cloud computing according to Embodiment 1 of the present invention is shown, and the system includes: A cloud computing service node is used to obtain a red-green component image block, a black-and-white component image block, and a yellow-and-blue component image block corresponding to a current image block, wherein the red-and-green component image block and the black-and-white component image block corresponding to the current image block are both image blocks that have been intra-frame encoded, and the yellow-and-blue component image block corresponding to the current image block is an image block that has not been intra-frame encoded; For example, a cloud computing service node is used to obtain a red-green component image block, a black-white component image block, and a yellow-blue component image block corresponding to a current image block, wherein the red-green component image block and the black-white component image block corresponding to the current image block are both image blocks that have been intra-frame encoded, and the yellow-blue component image block corresponding to the current image block is an image block that has not been intra-frame encoded, including: each image block is in a square shape; a content acquisition device connected to the cloud computing service node, and used to obtain the red and green component reconstruction blocks and the black and white component reconstruction blocks respectively corresponding to the red and green component image blocks and the black and white component image blocks corresponding to the current image blocks, and simultaneously obtain the encoding speed, encoding operation amount and encoding compression ratio corresponding to the red and green component image blocks, and obtain the encoding speed, encoding operation amount and encoding compression ratio corresponding to the black and white component image blocks; A model conversion device, for performing multi-level conversion processing on a deep neural network to obtain an intelligent prediction model, wherein the multi-level conversion processing on the deep neural network to obtain the intelligent prediction model includes: each level of conversion processing performed on the deep neural network is each training action performed on the deep neural network, and the number of levels of conversion processing performed on the deep neural network is positively correlated with the number of pixels occupied by the image block; A coding prediction mechanism connected to the content acquisition device, the model conversion device and the cloud computing service node, for intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block by using an intelligent prediction model; A parameter display mechanism, connected to the coding prediction mechanism, for receiving and displaying the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image blocks; Wherein, the intelligent prediction model is used to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block corresponding to the current image block, including: the yellow-blue component image block, the red-green component image block and the black-white component image block corresponding to the current image block respectively select the yellow-blue component image block, the red-green component image block and the black-white component image block of the same reference coding block as their own intra-frame coding reference frames; Wherein, using the intelligent prediction model to intelligently predict the encoding speed, encoding operation amount and encoding compression ratio corresponding to the yellow-blue component image block according to the encoding speed, encoding operation amount and encoding compression ratio corresponding to the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the red-green component image block and the encoding speed, encoding operation amount and encoding compression ratio corresponding to the black-white component image block corresponding to the current image block also includes: each pixel value corresponding to each pixel point of each image block is used as input data of the image block input into the intelligent prediction model; And wherein, the input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block is numerically normalized to obtain the input data after the numerical normalization is performed; And wherein, the input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: using The intelligent prediction model performs numerical normalization processing based on hexadecimal numerical conversion on the input data of the step of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block, to obtain the input data after the numerical normalization processing is performed; And wherein, the input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: The input data of the step of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-and-white component image block by using an intelligent prediction model according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-and-white component image block, the red-and-green component reconstruction block, the black-and-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-and-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-and-white component image block are subjected to numerical normalization processing based on octal numerical conversion to obtain input data after the numerical normalization processing is performed; And wherein, the input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: The input data of the step of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-and-white component image block by using an intelligent prediction model according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-and-white component image block, the red-and-green component reconstruction block, the black-and-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-and-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-and-white component image block are subjected to numerical normalization processing based on binary numerical conversion to obtain input data after the numerical normalization processing is performed; And wherein, the input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: The input data of the step of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-and-white component image block by using an intelligent prediction model according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-and-white component image block, the red-and-green component reconstruction block, the black-and-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-and-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-and-white component image block are subjected to numerical normalization processing based on decimal numerical conversion to obtain input data after the numerical normalization processing is performed; And wherein, the output data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block is in the form of numerical representation after numerical normalization; And wherein, the output data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block is a numerical representation after numerical normalization processing, including: using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the reference coding block corresponding to the current image block, the yellow-blue component image block corresponding to the current image block The output data of the steps of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image blocks, the black and white component image blocks, the red and green component reconstruction blocks, the black and white component reconstruction blocks, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image blocks, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image blocks, and the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image blocks is one of the numerical representation forms after numerical normalization processing based on hexadecimal numerical conversion, the numerical representation forms after numerical normalization processing based on octal numerical conversion, the numerical representation forms after numerical normalization processing based on binary numerical conversion and the numerical representation forms after numerical normalization processing based on decimal numerical conversion.

[0017] In addition, in the cloud computing-based image data management system, a model conversion device is used to perform multi-level conversion processing on the deep neural network to obtain an intelligent prediction model, wherein the multi-level conversion processing performed on the deep neural network to obtain the intelligent prediction model includes: each level of conversion processing performed on the deep neural network is each training action performed on the deep neural network, and the number of conversion processing levels performed on the deep neural network is positively correlated with the number of pixels occupied by the image block, including: using a content conversion function to represent the content conversion relationship in which the number of conversion processing levels performed on the deep neural network is positively correlated with the number of pixels occupied by the image block.

[0018] Although specific embodiments of the present invention have been described in detail, the breadth and scope of the present invention should not be limited by the above exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents. All changes and modifications that fall within the spirit of the present invention are desired to be protected.

Claims

1. An image data management system based on cloud computing, characterized in that: The system comprises: A cloud computing service node is used to obtain a red-green component image block, a black-and-white component image block, and a yellow-and-blue component image block corresponding to a current image block, wherein the red-and-green component image block and the black-and-white component image block corresponding to the current image block are both image blocks that have been intra-frame encoded, and the yellow-and-blue component image block corresponding to the current image block is an image block that has not been intra-frame encoded; a content acquisition device connected to the cloud computing service node, and used to obtain the red and green component reconstruction blocks and the black and white component reconstruction blocks respectively corresponding to the red and green component image blocks and the black and white component image blocks corresponding to the current image blocks, and simultaneously obtain the encoding speed, encoding operation amount and encoding compression ratio corresponding to the red and green component image blocks, and obtain the encoding speed, encoding operation amount and encoding compression ratio corresponding to the black and white component image blocks; A model conversion device, for performing multi-level conversion processing on a deep neural network to obtain an intelligent prediction model, wherein the multi-level conversion processing on the deep neural network to obtain the intelligent prediction model includes: each level of conversion processing performed on the deep neural network is each training action performed on the deep neural network, and the number of levels of conversion processing performed on the deep neural network is positively correlated with the number of pixels occupied by the image block; A coding prediction mechanism is connected to the content acquisition device, the model conversion device and the cloud computing service node, and is used to adopt an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block, including: the yellow-blue component image block, the red-green component image block and the black-white component image block corresponding to the current image block respectively select the yellow-blue component image block, the red-green component image block and the black-white component image block of the same reference coding block as their own intra-frame coding reference frames; The parameter display mechanism is connected to the coding prediction mechanism and is used to receive and display the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image blocks.

2. The cloud computing-based image data management system according to claim 1, characterized in that: The intelligent prediction model is used to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block, red-green component image block, black-white component image block, red-green component reconstruction block, black-white component reconstruction block, and red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block corresponding to the current image block, and also includes: the pixel values ​​corresponding to each pixel point of each image block are respectively used as input data of the image block to the intelligent prediction model.

3. The cloud computing-based image data management system according to claim 2, characterized in that: The input data of the step of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block according to the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block using an intelligent prediction model is numerically normalized to obtain the input data after the numerical normalization processing is performed.

4. The cloud computing-based image data management system according to claim 3, characterized in that: The input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block. The prediction model intelligently predicts the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block according to the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block. The input data of the step of performing numerical normalization processing based on hexadecimal numerical conversion is obtained by performing the numerical normalization processing.

5. The cloud computing-based image data management system according to claim 3, characterized in that: The input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block. The prediction model intelligently predicts the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block according to the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block. The input data of the step of performing numerical normalization processing based on octal numerical value conversion is obtained by obtaining the input data after the numerical normalization processing.

6. The cloud computing-based image data management system according to claim 3, characterized in that: The input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block. The prediction model intelligently predicts the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block according to the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block. The input data of the step of performing numerical normalization processing based on binary numerical value conversion is obtained by performing the numerical normalization processing.

7. The cloud computing-based image data management system according to claim 3, characterized in that: The input data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized to obtain the input data after the numerical normalization processing is performed, including: using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block. The prediction model intelligently predicts the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block according to the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block. The input data of the step of performing numerical normalization processing based on decimal value conversion is obtained by obtaining the input data after the numerical normalization processing.

8. The cloud computing-based image data management system according to claim 2, characterized in that: The output data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image block based on the reference coding block corresponding to the current image block, the yellow and blue component image block, the red and green component image block, the black and white component image block, the red and green component reconstruction block, the black and white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red and green component image block, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image block is in the form of numerical representation after numerical normalization.

9. The cloud computing-based image data management system according to claim 8, characterized in that: The output data of the step of using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the yellow-blue component image block according to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component image block, the black-white component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block and the coding speed, coding operation amount and coding compression ratio corresponding to the black-white component image block are numerically normalized, including: using an intelligent prediction model to intelligently predict the coding speed, coding operation amount and coding compression ratio corresponding to the reference coding block corresponding to the current image block, the yellow-blue component image block, the red-green component reconstruction block, the black-white component reconstruction block, the coding speed, coding operation amount and coding compression ratio corresponding to the red-green component image block The output data of the steps of intelligently predicting the coding speed, coding operation amount and coding compression ratio corresponding to the green component image blocks, the black and white component image blocks, the red and green component reconstruction blocks, the black and white component reconstruction blocks, the red and green component image blocks, and the coding speed, coding operation amount and coding compression ratio corresponding to the black and white component image blocks, and the coding speed, coding operation amount and coding compression ratio corresponding to the yellow and blue component image blocks is one of the numerical representation forms after numerical normalization processing based on hexadecimal numerical conversion, the numerical representation form after numerical normalization processing based on octal numerical conversion, the numerical representation form after numerical normalization processing based on binary numerical conversion, and the numerical representation form after numerical normalization processing based on decimal numerical conversion.

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