Intelligent analysis computer algorithm operating system

Abstract

The invention relates to an intelligent analysis computer algorithm operating system which comprises a block acquisition device, a content identification device, a data extraction device, an object generation device and an analysis processing mechanism. The intelligent analysis computer algorithm operating system is intelligent in control and reliable in operation.

Description

Technical Field

[0001] The present invention relates to the field of computer systems, and more particularly, to an intelligent parsing computer algorithm operating system. Background Art

[0002] Computer systems are updated approximately every 3 to 5 years, with the performance-price ratio increasing by a factor of ten and the volume decreasing significantly. Ultra-large-scale integrated circuit technology will continue to develop rapidly and have a huge and profound impact on various computer systems. 32-bit microcomputers have emerged, and 64-bit microcomputers have also come out. It won't be long before 10 million components can be made on a single chip. Research on devices that are 10 to 100 times faster than semiconductor integrated circuits, such as gallium arsenide, high electron mobility devices, Josephson junctions, and optical components, will yield important results. Microassembly technology, which improves the packaging density and shortens the interconnecting wires, is one of the key technologies for the new generation of computers. Optical fiber communication will be widely used. Various high-speed intelligent external devices are emerging continuously. The advent of optical discs will make the auxiliary mass storage look brand new. Multiprocessor systems, multi-computer systems, and distributed processing systems will be eye-catching system architectures.

[0003] CN119474469A discloses an optimization method, system, and computer device for the HNSW indexing algorithm based on a graph database, including: obtaining the current heat gradient table, comparing the current heat gradient table with the snapshot of the heat gradient table recorded in the previous optimization, and obtaining the relative difference of the heat gradient table; only when the relative difference of the heat gradient table is greater than the set threshold, perform the following optimization: randomly place the data not in the current heat gradient table into the lower levels of the HNSW index; insert the data in the current heat gradient table into the upper levels of the HNSW index and update the current heat gradient table; after the optimization is completed, record the snapshot of the current heat gradient table; this technology optimizes the data hierarchy of the HNSW indexing algorithm by introducing the heat gradient table, making the retrieval speed of high-frequency data faster, the accuracy higher, and applicable to more business scenarios.

[0004] CN118317449B discloses an active user detection method based on the decorrelation approximate message passing algorithm, which relates to the field of signal processing technology. The method includes: receiving preamble sequences sent by multiple random access users; performing an approximate message passing algorithm to iteratively calculate the estimated sequence of each preamble sequence from the received signal; in each iterative calculation, design a noise reducer using the minimum mean square error of the estimated signal in the previous iteration. The filtering matrix in the noise reducer uses the decorrelation matrix of the preamble sequence, and perform a log-likelihood ratio test on the estimated signal to obtain the detection result of the active state of the user. This technology introduces a decorrelation matrix into the noise reducer, making the estimation errors uncorrelated and achieving the orthogonality of the errors, thereby improving the accuracy of the detection results in scenarios of large-scale antennas and high signal-to-noise ratios.

[0005] CN119398289A discloses a path optimization method, device, computer device and storage medium based on a hybrid quantum algorithm. The method includes: obtaining a weight adjacency matrix of an undirected complete weighted graph of a path to be optimized; inputting the weight adjacency matrix into a preset quantum circuit for optimization to obtain a first candidate solution and a corresponding first weight. In the optimization process of encoding and pruning of the quantum circuit, all candidate solutions are divided into multiple steps; the choices of the traveling salesman in each step are encoded into the quantum circuit, and corresponding pruning is performed to form a uniform superposition state including all candidate solutions; the first candidate solution is updated according to the first weight and a preset first threshold to obtain a target path. This technology solves the problem of low efficiency in path planning in related technologies, effectively reduces the quantum resources required for encoding candidate solutions, and realizes improving the path planning efficiency while accurately planning the path. Summary of the Invention

[0006] To solve the technical problems in the prior art, the present invention provides an intelligent parsing computer algorithm operating system, which can obtain a target image block to be encoded with intra green component in the computer algorithm operating system, use the reference image block used when the target image block has been encoded with intra red component as the reference red image block, use the reference image block used when the target image block has been encoded with intra blue component as the reference blue image block, obtain the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the reconstructed red component image block, and the block content of the reconstructed blue component image block. The block content of each image block is the signal-to-noise ratio, contrast, and content repetition degree of the image block, so as to provide comprehensive and sufficient basic data for the intelligent parsing of the intra-frame coding reference image block across color components. Extract the number of noise types, the maximum noise amplitude, and the number of pixel points in the image frame where the target image block is located, and output them as multiple image data of the image frame where the target image block is located, so as to further enrich the basic data for the intelligent parsing of the intra-frame coding reference image block across color components. Perform multiple learning actions on the convolutional neural network to obtain the convolutional neural network after completing multiple learning actions and output it as an AI parsing model. The number of times the convolutional neural network completes the learning action is monotonically and positively correlated with the number of noise types in the image frame where the target image block is located, so as to construct an artificially intelligent model with targeted design for intelligent parsing, and in the computer algorithm operating system, use the AI parsing model to intelligently parse the maximum coding compression ratio obtained by using the reference red image block or the reference blue image block as the reference image block used for intra green component coding of the green component image block corresponding to the target image block based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the reconstructed red component image block, and the block content of the reconstructed blue component image block, so as to select a more suitable reference image block for coding from the reference image blocks of other colors that have been encoded for the color image block that has not been encoded.

[0007] According to the present invention, there is provided an intelligent parsing computer algorithm operating system, the system comprising: A block acquisition device, which is set in a computer algorithm operating system and is used to acquire a target image block to be encoded intra-frame with green components. The reference image block used when the target image block has been encoded intra-frame with red components is used as a reference red image block, and the reference image block used when the target image block has been encoded intra-frame with blue components is used as a reference blue image block; A content identification device, which is set in a computer algorithm operating system and is connected to the block acquisition device, and is used to acquire the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block. The block content of each image block is the signal-to-noise ratio, contrast, and content repeatability of the image block; A data extraction device, which is set in a computer algorithm operating system and is used to extract the number of noise types, the maximum noise amplitude, and the number of pixel points in the image frame where the target image block is located, so as to output multiple pieces of image data of the image frame where the target image block is located; An object generation device, which is set in a computer algorithm operating system and is used to perform multiple learning actions on a convolutional neural network to obtain the convolutional neural network after completing multiple learning actions and output it as an AI analysis model. The number of times the convolutional neural network completes the learning action is monotonically and positively correlated with the number of noise types in the image frame where the target image block is located; An analysis processing mechanism, which is set in a computer algorithm operating system and is respectively connected to the content identification device, the data extraction device, and the object generation device, and is used to use the AI analysis model to intelligently analyze and represent the reference red image block or the reference blue image block based on multiple pieces of image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block, and obtain the selection identifier with the largest coding compression ratio when the reference image block is used for intra-frame encoding of the green component image block corresponding to the target image block.

[0008] Therefore, the present invention has at least the following four beneficial technical effects: Technical effect 1: In a computer algorithm operating system, obtain a target image block to be intra-coded for the green component. Use the reference image block used when the target image block has been intra-coded for the red component as the reference red image block, and use the reference image block used when the target image block has been intra-coded for the blue component as the reference blue image block. Obtain the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the reconstructed red component image block, and the block content of the reconstructed blue component image block. The block content of each image block is the signal-to-noise ratio, contrast, and content repeatability of the image block, thus providing comprehensive and sufficient basic data for the intelligent analysis of intra-coded reference image blocks across color components; Technical effect 2: Extract the number of noise types, the maximum noise amplitude, and the number of pixel points in the image frame where the target image block is located, and output them as multiple image data of the image frame where the target image block is located, so as to further enrich the basic data for the intelligent analysis of intra-coded reference image blocks across color components; Technical effect 3: Perform multiple learning actions on the convolutional neural network to obtain the convolutional neural network after completing multiple learning actions and output it as an AI analysis model. The number of learning actions completed by the convolutional neural network is monotonically and positively correlated with the number of noise types in the image frame where the target image block is located, thus constructing an artificially intelligent model designed specifically for intelligent analysis; Technical effect 4: In a computer algorithm operating system, use the AI analysis model to intelligently analyze the multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the reconstructed red component image block, and the block content of the reconstructed blue component image block to obtain the selection identifier with the largest coding compression ratio when the reference red image block or the reference blue image block is used as the reference image block for intra-coding the green component image block corresponding to the target image block. Thus, select a more suitable reference image block for coding from the reference image blocks of other colors that have been coded for the uncoded color image block.

[0009] The intelligent parsing computer algorithm operating system of the present invention is intelligent in operation and reliable in running. Since in the computer algorithm operating system, the intelligent parsing represents the selection identifier with the largest coding compression ratio obtained by using the reference red image block or the reference blue image block as the reference image block corresponding to the green component image block when performing intra-frame coding of the green component, the pertinence and reliability of image processing are improved. Detailed implementation mode

[0010] In the prior art, a computer system can perform various algorithm operations. However, there are still gaps in the application fields in terms of specific algorithm operations. For example, it is necessary to determine the candidate order of multiple reference image blocks corresponding to multiple color components that have been intra-frame coded for the same image block as the reference image blocks of other color components to be intra-frame coded for the same image block. Obviously, there is a lack of corresponding subdivision algorithms in the prior art.

[0011] The implementation scheme of the intelligent parsing computer algorithm operating system of the present invention will be described in detail below.

[0012] The intelligent parsing computer algorithm operating system according to the first embodiment of the present invention includes: A block acquisition device, arranged in the computer algorithm operating system, is used to acquire the target image block to be subjected to intra-frame coding of the green component, take the reference image block used when the target image block has performed intra-frame coding of the red component as the reference red image block, and take the reference image block used when the target image block has performed intra-frame coding of the blue component as the reference blue image block; A content identification device, arranged in the computer algorithm operating system and connected to the block acquisition device, is used to acquire the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block. The block content of each image block is the signal-to-noise ratio, contrast, and content repeatability of the image block; A data extraction device, arranged in the computer algorithm operating system, is used to extract the number of noise types, the maximum noise amplitude, and the number of pixel points in the image frame where the target image block is located, so as to output multiple pieces of image data of the image frame where the target image block is located; An object generation device, which is set in a computer algorithm operating system and is used to perform multiple learning actions on a convolutional neural network to obtain the convolutional neural network after completing multiple learning actions and output it as an AI analysis model. The number of times the convolutional neural network completes the learning action is monotonically and positively correlated with the number of noise types in the image frame where the target image block is located; An analysis processing mechanism, which is set in a computer algorithm operating system and is respectively connected to the content identification device, the data extraction device, and the object generation device. It is used to use the AI analysis model to intelligently analyze and represent the reference red image block or the reference blue image block based on multiple image data in the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block, and obtain the selection identifier with the largest coding compression ratio when using it as the reference image block for intra-frame coding of the green component of the green component image block corresponding to the target image block; Wherein, when the selection identifier obtained by the intelligent analysis is 0B00, it means that the reference red image block has the largest coding compression ratio when used as the reference image block for intra-frame coding of the green component of the green component image block corresponding to the target image block; Wherein, when the selection identifier obtained by the intelligent analysis is 0B10, it means that the reference blue image block has the largest coding compression ratio when used as the reference image block for intra-frame coding of the green component of the green component image block corresponding to the target image block; Wherein, obtaining the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block includes: the red component reconstructed image block is a reconstructed image block obtained by performing intra-frame coding on the red component image block corresponding to the target image block and only includes each red component.

[0013] Different from the first embodiment of the present invention, the intelligent analysis computer algorithm operating system according to the second embodiment of the present invention may further include the following components: A data communication component for providing a parallel data bus for a processing step of using an AI parsing model to intelligently parse a selection identifier with the largest coding compression ratio obtained by representing a reference red image block or a reference blue image block as a reference image block used in performing green component intra-frame coding on a green component image block corresponding to the target image block based on multiple pieces of image data of an image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block; Among them, the data communication component for providing a parallel data bus for a processing step of using an AI parsing model to intelligently parse a selection identifier with the largest coding compression ratio obtained by representing a reference red image block or a reference blue image block as a reference image block used in performing green component intra-frame coding on a green component image block corresponding to the target image block based on multiple pieces of image data of an image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block includes: the data communication component is a programmable logic device.

[0014] Different from the first embodiment of the present invention, the intelligent parsing computer algorithm operating system according to the third embodiment of the present invention may further include the following components: A user access device for providing user instructions by providing a parallel data bus for a processing step of using an AI parsing model to intelligently parse a selection identifier with the largest coding compression ratio obtained by representing a reference red image block or a reference blue image block as a reference image block used in performing green component intra-frame coding on a green component image block corresponding to the target image block based on multiple pieces of image data of an image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block;

[0015] Next, the specific structure of the intelligent parsing computer algorithm operating system of the present invention will be further described.

[0016] In the intelligent parsing computer algorithm operating system according to any embodiment of the present invention: The user access device is used to provide a parallel data bus for the processing step of using the AI parsing model to intelligently parse the selection identifier with the largest coding compression ratio obtained by representing the reference red image block or the reference blue image block as the reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block. The user instructions provided include: the user access device is a touch screen.

[0017] In the intelligent parsing computer algorithm operating system according to any embodiment of the present invention: The user access device is used to provide a parallel data bus for the processing step of using the AI parsing model to intelligently parse the selection identifier with the largest coding compression ratio obtained by representing the reference red image block or the reference blue image block as the reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block. The user instructions provided include: the user access device is a keyboard array.

[0018] In the intelligent parsing computer algorithm operating system according to any embodiment of the present invention: A user access device for providing a parallel data bus for a processing step of intelligently analyzing a selection identifier with the largest coding compression ratio obtained by using the AI parsing model to represent a reference red image block or a reference blue image block as a reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block, and providing user instructions, including: the user access device is a voice input device.

[0019] And in the intelligent parsing computer algorithm operating system according to any embodiment of the present invention: A user access device for providing a parallel data bus for a processing step of intelligently analyzing a selection identifier with the largest coding compression ratio obtained by using the AI parsing model to represent a reference red image block or a reference blue image block as a reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block, and providing user instructions, including: the user access device is a gesture input device.

[0020] In addition, in the intelligent parsing computer algorithm operating system, obtaining the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block further includes: the blue component reconstructed image block is a reconstructed image block including only each blue component obtained after performing blue component intra-frame coding on the blue component image block corresponding to the target image block.

[0021] Obviously, those skilled in the art can make various modifications and improvements to the present invention. Therefore, the present invention is intended to cover all such modifications and improvements that fall within the scope of the appended claims and their equivalents.

Claims

1. An intelligent analytical computer algorithm operating system, characterized in that: The system comprises: A block acquisition device is provided in the computer algorithm operating system, and is used to acquire a target image block to be subjected to green component intra-frame coding, and to use a reference image block used when the target image block has been subjected to red component intra-frame coding as a reference red image block, and to use a reference image block used when the target image block has been subjected to blue component intra-frame coding as a reference blue image block; a content identification device, arranged in the computer algorithm operating system and connected to the block acquisition device, for acquiring the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block and the block content of the blue component reconstructed image block, the block content of each image block being the signal-to-noise ratio, contrast and content repetitiveness of the image block; A data extraction device, arranged in the computer algorithm operating system, is used to extract the number of noise types, the maximum noise amplitude and the number of pixels of the image frame where the target image block is located, so as to output multiple copies of image data of the image frame where the target image block is located; An object generation device is provided in a computer algorithm operating system, and is used to perform multiple learning actions on a convolutional neural network to obtain a convolutional neural network after completing multiple learning actions and output it as an AI analysis model, wherein the number of times the convolutional neural network completes the learning actions is monotonically positively correlated with the number of noise types in the image frame where the target image block is located; The analysis and processing mechanism is arranged in the computer algorithm operating system and is respectively connected to the content identification device, the data extraction device and the object generation device, and is used for adopting the AI ​​analysis model to intelligently analyze the selection mark with the largest coding compression ratio obtained by using the reference red image block or the reference blue image block as the reference image block used when the green component image block corresponding to the target image block performs green component intra-frame coding on the green component image block corresponding to the target image block based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block and the block content of the blue component reconstructed image block.

2. The intelligent analysis computer algorithm operating system according to claim 1, characterized in that: When the selection identifier of the intelligent analysis is 0B00, it means that the coding compression ratio obtained by using the reference red image block as the reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block is the largest; Wherein, when the selection mark of the intelligent analysis is 0B10, it means that the coding compression ratio obtained by using the reference blue image block as the reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block is the largest; Among them, obtaining the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the blue component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block and the block content of the blue component reconstructed image block includes: the red component reconstructed image block is a reconstructed image block that only includes each red component and is obtained after performing red component intra-frame encoding on the red component image block corresponding to the target image block.

3. The intelligent analysis computer algorithm operating system according to claim 2, characterized in that: The system further comprises: A data communication component is used to provide a parallel data bus for the processing step of intelligently parsing the selection mark with the largest coding compression ratio obtained by using the AI ​​parsing model to represent the reference red image block or the reference blue image block as the reference image block used when performing green component intra-frame coding on the green component image block corresponding to the target image block, based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block.

4. The intelligent analysis computer algorithm operating system according to claim 3, characterized in that: A data communication component, which is used to provide a parallel data bus for the processing step of intelligently parsing the selection mark representing the maximum coding compression ratio obtained by using the AI ​​parsing model based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block as the reference red image block or the reference blue image block when performing green component intra-frame coding on the green component image block corresponding to the target image block, including: the data communication component is a programmable logic device.

5. The intelligent analysis computer algorithm operating system according to claim 2, characterized in that: The system further comprises: A user access device is used to provide a parallel data bus and user instructions for the processing step of using the AI ​​analysis model to intelligently parse the selection mark of the reference red image block or the reference blue image block as the reference image block used when performing green component intra-frame encoding on the green component image block corresponding to the target image block, based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block, to obtain the maximum coding compression ratio of the reference image block used when performing green component intra-frame encoding on the green component image block corresponding to the target image block.

6. The intelligent analysis computer algorithm operating system according to claim 5, characterized in that: A user access device, used for providing a parallel data bus and providing user instructions for the processing steps of intelligently parsing the selection mark with the largest coding compression ratio obtained by using the AI ​​parsing model based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block as the reference red image block or the reference blue image block when performing green component intra-frame coding on the green component image block corresponding to the target image block, including: the user access device is a touch screen.

7. The intelligent analysis computer algorithm operating system according to claim 5, characterized in that: A user access device, used for providing a parallel data bus and providing user instructions for the processing steps of intelligently parsing the selection mark representing the maximum coding compression ratio obtained by using the AI ​​parsing model based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block as the reference red image block or the reference blue image block when performing green component intra-frame coding on the green component image block corresponding to the target image block, including: the user access device is a keyboard array.

8. The intelligent analysis computer algorithm operating system according to claim 5, characterized in that: A user access device, used for providing a parallel data bus and providing user instructions for the processing steps of intelligently parsing the selection mark with the largest coding compression ratio obtained by using the AI ​​parsing model based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block as the reference red image block or the reference blue image block when performing green component intra-frame coding on the green component image block corresponding to the target image block, including: the user access device is a voice input device.

9. The intelligent analysis computer algorithm operating system according to claim 5, characterized in that: A user access device, used for providing a parallel data bus and providing user instructions for the processing steps of intelligently parsing the selection mark with the largest coding compression ratio obtained by using the AI ​​parsing model based on multiple image data of the image frame where the target image block is located, the block content of the green component image block corresponding to the target image block, the block content of the red component image block corresponding to the target image block, the block content of the reference red image block, the block content of the reference blue image block, the block content of the red component reconstructed image block, and the block content of the blue component reconstructed image block as the reference red image block or the reference blue image block when performing green component intra-frame coding on the green component image block corresponding to the target image block, including: the user access device is a gesture input device.