Intelligent prediction system for meter reading data of volumetric water meter

Abstract

The invention relates to an intelligent prediction system for meter reading data of a volumetric water meter, and the system comprises a network shooting mechanism which is used for carrying out the front shooting operation of a current volumetric water meter, so as to obtain a real-time shooting image; and the prediction execution device is used for predicting whether a reference image block required for executing intra-frame coding on the target image block exists in each adjacent image block or not by adopting an intelligent prediction model. According to the invention, intelligent prediction before intra-frame coding can be directly carried out on whether the reference image blocks required by intra-frame coding of the target image blocks exist in the adjacent image blocks of the target image blocks, and an important reference basis is provided for selection of the reference image blocks; therefore, the intra-frame coding effect of each image block in the remote meter reading picture does not need to be verified by trying various image blocks as reference image blocks, and the defect that a large amount of operation cost is consumed due to the fact that the determination process of the reference image blocks is long is overcome.

Description

Technical Field

[0001] The invention relates to the field of water meters, and in particular to an intelligent prediction system for meter reading data of a volumetric water meter. Background Art

[0002] Volumetric water meters measure the actual volume of fluid passing through the water meter. Volumetric water meters are installed in pipes and consist of some chambers of known volume that are filled and discharged successively and a mechanism driven by the fluid, or simply quantitative discharge water meters. Volumetric water meters generally use piston structures. Volumetric water meters are different from velocity water meters. The most vivid metaphor is like the revolving door in front of a large supermarket or hotel, which can only rotate in a fixed direction. Every time it rotates a certain angle, the fluid passes through the water meter to the other side. Therefore, volumetric water meters are more accurate than velocity water meters. The measurement error of velocity water meters will have an error of ±2% depending on the speed of the passing fluid, while the error of volumetric water meters can be controlled at ±0.5% or even lower. In terms of use, volumetric water meters are generally used in precision enterprises or test and testing places because of their accurate measurement effect; while velocity water meters are generally used for normal life industrial water, etc.

[0003] In the prior art, the image processing for remote meter reading of volumetric water meters is conventional. For example, the intra-frame coding of each image block is still verified by trying various image blocks as reference image blocks, which results in a long process of determining the reference image blocks and consumes a lot of computing costs, and cannot meet the high requirements of real-time and intelligent transformation of image processing for remote meter reading. Summary of the invention

[0004] In order to solve the technical problems in the related fields, the present invention provides a volumetric water meter reading data intelligent prediction system, which uses a network shooting mechanism to perform a front shooting operation on the current volumetric water meter under the control of a remote meter reading command sent by a remote water meter reading manager to obtain and output a corresponding real-time shooting image, and obtain various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image, and use the image block to be intra-frame encoded in the real-time shooting image as the target image block, obtain the target image block and each adjacent image block adjacent to the target image block, and detect multiple visual parameters of the target image block and multiple visual parameters of each adjacent image block, wherein the multiple visual parameters of each image block are the horizontal coordinate value, vertical coordinate value and depth of field value of each edge pixel point of the image block, and the length and width ... The degrees are fixed length values ​​and fixed width values, respectively, so as to selectively screen various basic information that is sufficient and comprehensive for subsequent intelligent prediction. An intelligent prediction model is also used to intelligently predict whether there is a reference image block required for intra-frame coding of the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image. wherein, a content difference between a reconstructed image block of the target image block obtained after intra-frame coding of the target image block using the reference image block and the target image block is lower than or equal to a set difference threshold, so as to perform intelligent prediction before intra-frame coding on whether there is a reference image block required for intra-frame coding of the target image block in each adjacent image block of the target image block, thereby providing an important reference basis for selecting the reference image block required for intra-frame coding of the target image block.

[0005] According to the present invention, a volumetric water meter reading data intelligent prediction system is provided, the system comprising: A target extraction mechanism is used to parse the intelligent prediction model, wherein after performing each learning operation on the deconvolution neural network, a deconvolution neural network after performing each learning operation is obtained and output as the intelligent prediction model, and the number of learning operations of the deconvolution neural network is proportional to the number of adjacent image blocks adjacent to the target image block; The network shooting mechanism is used to perform a front shooting operation on the current volumetric water meter under the control of the remote meter reading command sent by the remote water meter reading manager to obtain and output a corresponding real-time shooting image; A content capture mechanism, connected to the network shooting mechanism, for acquiring various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image; A block detection mechanism is connected to the network shooting mechanism, and is used to use the image block to be intra-frame encoded in the real-time shot image as a target image block, obtain the target image block and each adjacent image block adjacent to the target image block, and detect multiple visual parameters of the target image block and multiple visual parameters of each adjacent image block, wherein the multiple visual parameters of each image block are the horizontal coordinate value, vertical coordinate value and depth of field value of each edge pixel point of the image block, and the length and width of each image block are respectively a fixed length value and a fixed width value; A prediction execution device, connected to the target extraction mechanism, the content capture mechanism and the block detection mechanism, respectively, for using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block and various image contents of the real-time captured image; The reference image block required for performing intra-frame coding on the target image block needs to meet the following conditions: the content difference between the reconstructed image block of the target image block obtained after performing intra-frame coding on the target image block using the reference image block and the target image block is lower than or equal to a set difference threshold; Wherein, the content capture mechanism is connected to the network shooting mechanism and is used to obtain various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition degree and the content mean square error of the real-time shooting image, including: the mean square error of each brightness value corresponding to each pixel point of the real-time shooting image is the mean square error of the real-time shooting image; Among them, the content capture mechanism is connected to the network shooting mechanism, and is used to obtain various image contents of the real-time shooting image. The various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image, and also include: performing deduplication processing on each brightness value corresponding to each pixel point of the real-time shooting image to obtain the remaining multiple brightness values, dividing the number of the multiple brightness values ​​by the number of each brightness value to obtain a reference ratio, and subtracting 1 from the reference ratio to obtain the content repetition of the real-time shooting image.

[0006] It can be seen that the present invention has at least the following four key invention points: Invention point A: After performing each learning operation on the deconvolution neural network, a deconvolution neural network after performing each learning operation is obtained and output as an intelligent prediction model, and the number of learning operations of the deconvolution neural network is proportional to the number of each adjacent image block adjacent to the target image block, thereby realizing targeted design of the intelligent prediction model; Invention point B: A network shooting mechanism is used to perform a front shooting operation on the current volumetric water meter under the control of a remote meter reading command sent by a remote water meter reading manager, so as to obtain and output a corresponding real-time shooting image, obtain various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image, take the image block to be intra-frame encoded in the real-time shooting image as the target image block, obtain the target image block and each adjacent image block adjacent to the target image block, detect multiple visual parameters of the target image block and multiple visual parameters of each adjacent image block, wherein the multiple visual parameters of each image block are the horizontal coordinate value, the vertical coordinate value and the depth of field value of each edge pixel point of the image block, and the length and width of each image block are respectively a fixed length value and a fixed width value, so as to selectively screen various basic information that is sufficient and comprehensive for subsequent intelligent prediction; Invention point C: An intelligent prediction model is used to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on a fixed length value, a fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image. The difference between the reconstructed image block of the target image block obtained after performing intra-frame coding on the target image block using the reference image block and the content of the target image block is lower than or equal to a set difference threshold, so as to perform intelligent prediction before intra-frame coding on whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block of the target image block, thereby providing an important reference basis for selecting the reference image block required for performing intra-frame coding on the target image block. Invention point D: The mean square error of each brightness value corresponding to each pixel point of the real-time captured image is the mean square error of the real-time captured image, and deduplication processing is performed on each brightness value corresponding to each pixel point of the real-time captured image to obtain the remaining multiple brightness values, the number of the multiple brightness values ​​is divided by the number of each brightness value to obtain a reference ratio, and 1 is subtracted from the reference ratio to obtain the content repetition of the real-time captured image. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 The figure is a structural block diagram of a volumetric water meter reading data intelligent prediction system according to implementation scheme A of the present invention. DETAILED DESCRIPTION

[0008] The implementation scheme of the volumetric water meter reading data intelligent prediction system of the present invention will be described in detail below with reference to the accompanying drawings.

[0009] Figure 1 The structure block diagram of the intelligent prediction system for volumetric water meter reading data according to the A embodiment of the present invention is shown, and the system includes: A target extraction mechanism is used to parse the intelligent prediction model, wherein after performing each learning operation on the deconvolution neural network, a deconvolution neural network after performing each learning operation is obtained and output as the intelligent prediction model, and the number of learning operations of the deconvolution neural network is proportional to the number of adjacent image blocks adjacent to the target image block; For example, a target extraction mechanism is used to parse an intelligent prediction model, wherein after performing each learning operation on a deconvolution neural network, a deconvolution neural network after performing each learning operation is obtained and output as an intelligent prediction model, and the number of learning operations of the deconvolution neural network is proportional to the number of each adjacent image block adjacent to the target image block, including: using a content transformation formula to represent a content transformation relationship in which the number of learning operations of the deconvolution neural network is proportional to the number of each adjacent image block adjacent to the target image block; The network shooting mechanism is used to perform a front shooting operation on the current volumetric water meter under the control of the remote meter reading command sent by the remote water meter reading manager to obtain and output a corresponding real-time shooting image; A content capture mechanism, connected to the network shooting mechanism, for acquiring various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image; A block detection mechanism is connected to the network shooting mechanism, and is used to use the image block to be intra-frame encoded in the real-time shot image as a target image block, obtain the target image block and each adjacent image block adjacent to the target image block, and detect multiple visual parameters of the target image block and multiple visual parameters of each adjacent image block, wherein the multiple visual parameters of each image block are the horizontal coordinate value, vertical coordinate value and depth of field value of each edge pixel point of the image block, and the length and width of each image block are respectively a fixed length value and a fixed width value; A prediction execution device, connected to the target extraction mechanism, the content capture mechanism and the block detection mechanism, respectively, for using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block and various image contents of the real-time captured image; The reference image block required for performing intra-frame coding on the target image block needs to meet the following conditions: the content difference between the reconstructed image block of the target image block obtained after performing intra-frame coding on the target image block using the reference image block and the target image block is lower than or equal to a set difference threshold; Wherein, the content capture mechanism is connected to the network shooting mechanism and is used to obtain various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition degree and the content mean square error of the real-time shooting image, including: the mean square error of each brightness value corresponding to each pixel point of the real-time shooting image is the mean square error of the real-time shooting image; Wherein, the content capture mechanism is connected to the network shooting mechanism and is used to obtain various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image, and further comprises: performing deduplication processing on each brightness value corresponding to each pixel point of the real-time shooting image to obtain a plurality of remaining brightness values, dividing the number of the plurality of brightness values ​​by the number of each brightness value to obtain a reference ratio, and subtracting the reference ratio from 1 to obtain the content repetition of the real-time shooting image; The content difference between a reconstructed image block of the target image block obtained after performing intra-frame coding on the target image block using the reference image block and the target image block is lower than or equal to a set difference threshold, comprising: performing a difference operation on pixel values ​​of pixels at the same position of the reconstructed image block and the target image block to obtain a difference image block, and accumulating the absolute values ​​of the differences corresponding to the respective positions in the difference image block to obtain a sum value as the content difference between the reconstructed image block and the target image block; Wherein, using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image, further comprises: using multiple different data processing channels to respectively implement multiple data processing processes corresponding to multiple decomposition steps of using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image; Among them, using a plurality of different data processing channels to respectively implement using the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block and various image contents of the real-time captured image to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each neighboring image block, and the plurality of data processing processes corresponding to the plurality of decomposition steps respectively also include: the plurality of different data processing channels occupy the same signal control channel; Among them, using a plurality of different data processing channels to respectively implement using the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block and various image contents of the real-time captured image to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block, and the plurality of data processing processes corresponding to the plurality of decomposition steps respectively also include: the plurality of different data processing channels occupying the same clock generation unit; Among them, using a plurality of different data processing channels to respectively implement using the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block and various image contents of the real-time captured image to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each neighboring image block, and the plurality of data processing processes corresponding to the plurality of decomposition steps respectively also include: the plurality of different data processing channels occupy different data processing processes; Among them, using a plurality of different data processing channels to respectively implement using the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block and various image contents of the real-time captured image to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block, and the plurality of data processing processes corresponding to the plurality of decomposition steps respectively also include: the plurality of different data processing channels occupy the same data storage space; Wherein, using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image, further comprises: using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image, and the type of operation fault of the reference image block required for performing intra-frame coding on the target image block is monitored and analyzed by the fault detection process; And wherein, using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image, further includes: using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image, and various intermediate data of the reference image block required for performing intra-frame coding on the target image block are captured and displayed in real time by the display process.

[0010] In addition, in the volumetric water meter reading data intelligent prediction system, the intelligent prediction model is used to intelligently predict whether there is a reference image block required for performing intra-frame encoding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block, and various image contents of the real-time captured image, including: outputting an existence identifier of different values ​​in the intelligent prediction model to respectively indicate whether there is a reference image block required for performing intra-frame encoding on the target image block in each adjacent image block.

[0011] The intelligent prediction system for volumetric water meter reading data of the present invention aims at the technical problem in the prior art that the intra-frame coding of each image block in the remote meter reading screen needs to verify the respective intra-frame coding effects by trying various image blocks as reference image blocks. By directly performing intelligent prediction before intra-frame coding on whether there are reference image blocks required for performing intra-frame coding on the target image block in each adjacent image block of the target image block, an important reference basis is provided for the selection of reference image blocks. Therefore, it is not necessary to verify the respective intra-frame coding effects by trying various image blocks as reference image blocks for the intra-frame coding of each image block in the remote meter reading screen. The defect that the determination process of the reference image blocks is long and consumes a lot of computing cost is overcome, and the above-mentioned technical problem is solved.

[0012] It will be appreciated by those skilled in the art that further variations are possible within the scope of the invention, which is defined in the appended claims.

Claims

1. A volumetric water meter reading data intelligent prediction system, characterized in that: The system comprises: A target extraction mechanism is used to parse the intelligent prediction model, wherein after performing each learning operation on the deconvolution neural network, a deconvolution neural network after performing each learning operation is obtained and output as the intelligent prediction model, and the number of learning operations of the deconvolution neural network is proportional to the number of adjacent image blocks adjacent to the target image block; The network shooting mechanism is used to perform a front shooting operation on the current volumetric water meter under the control of the remote meter reading command sent by the remote water meter reading manager to obtain and output a corresponding real-time shooting image; A content capture mechanism, connected to the network shooting mechanism, for acquiring various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image; A block detection mechanism is connected to the network shooting mechanism, and is used to use the image block to be intra-frame encoded in the real-time shot image as a target image block, obtain the target image block and each adjacent image block adjacent to the target image block, and detect multiple visual parameters of the target image block and multiple visual parameters of each adjacent image block, wherein the multiple visual parameters of each image block are the horizontal coordinate value, vertical coordinate value and depth of field value of each edge pixel point of the image block, and the length and width of each image block are respectively a fixed length value and a fixed width value; A prediction execution device, connected to the target extraction mechanism, the content capture mechanism and the block detection mechanism, respectively, for using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block and various image contents of the real-time captured image; The reference image block required for performing intra-frame coding on the target image block needs to meet the following conditions: the content difference between the reconstructed image block of the target image block obtained after performing intra-frame coding on the target image block using the reference image block and the target image block is lower than or equal to a set difference threshold; Wherein, the content capture mechanism is connected to the network shooting mechanism and is used to obtain various image contents of the real-time shooting image, wherein the various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition degree and the content mean square error of the real-time shooting image, including: the mean square error of each brightness value corresponding to each pixel point of the real-time shooting image is the mean square error of the real-time shooting image; Among them, the content capture mechanism is connected to the network shooting mechanism, and is used to obtain various image contents of the real-time shooting image. The various image contents of the real-time shooting image are the number of pixel columns, the number of pixel rows, the content repetition and the content mean square error of the real-time shooting image, and also include: performing deduplication processing on each brightness value corresponding to each pixel point of the real-time shooting image to obtain the remaining multiple brightness values, dividing the number of the multiple brightness values ​​by the number of each brightness value to obtain a reference ratio, and subtracting 1 from the reference ratio to obtain the content repetition of the real-time shooting image.

2. The intelligent prediction system for volumetric water meter reading data according to claim 1, characterized in that: The content difference between a reconstructed image block of the target image block obtained after performing intra-frame encoding on the target image block using the reference image block and the target image block is lower than or equal to a set difference threshold, comprising: performing a difference operation on the pixel values ​​of pixels at the same position of the reconstructed image block and the target image block to obtain a difference image block, and accumulating the absolute values ​​of the differences corresponding to each position in the difference image block to obtain a sum value as the content difference between the reconstructed image block and the target image block.

3. The intelligent prediction system for volumetric water meter reading data according to claim 2, characterized in that: The intelligent prediction model is used to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image. The method also includes: using multiple different data processing channels to respectively implement multiple data processing processes corresponding to multiple decomposition steps of using the intelligent prediction model to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image.

4. The intelligent prediction system for volumetric water meter reading data according to claim 3, characterized in that: A plurality of different data processing channels are used to respectively implement the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block, and various image contents of the real-time captured image, and the plurality of data processing processes corresponding to the plurality of decomposition steps respectively corresponding to the plurality of decomposition steps of intelligently predicting whether there are reference image blocks required to perform intra-frame encoding on the target image block in each neighboring image block also include: the plurality of different data processing channels occupy the same signal control channel.

5. The intelligent prediction system for volumetric water meter reading data according to claim 3, characterized in that: A plurality of different data processing channels are used to respectively implement the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block, and various image contents of the real-time captured image, and the plurality of data processing processes corresponding to the plurality of decomposition steps of intelligently predicting whether there are reference image blocks required to perform intra-frame encoding on the target image block in each neighboring image block, and the plurality of different data processing channels also occupy the same clock generating unit.

6. The intelligent prediction system for volumetric water meter reading data according to claim 3, characterized in that: A plurality of different data processing channels are used to respectively implement the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block, and various image contents of the real-time captured image, and the plurality of data processing processes corresponding to the plurality of decomposition steps respectively corresponding to the plurality of decomposition steps of intelligently predicting whether there are reference image blocks required to perform intra-frame encoding on the target image block in each neighboring image block also include: the plurality of different data processing channels occupy different data processing processes.

7. The intelligent prediction system for volumetric water meter reading data according to claim 3, characterized in that: A plurality of different data processing channels are used to respectively implement the intelligent prediction model based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each neighboring image block adjacent to the target image block, and various image contents of the real-time captured image, and the plurality of data processing processes corresponding to the plurality of decomposition steps of intelligently predicting whether there are reference image blocks required to perform intra-frame encoding on the target image block in each neighboring image block also include: the plurality of different data processing channels occupy the same data storage space.

8. The intelligent prediction system for volumetric water meter reading data according to claim 2, characterized in that: The intelligent prediction model is used to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image. The intelligent prediction model is also used to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image. The operating fault type is monitored and analyzed by the fault detection process.

9. The intelligent prediction system for volumetric water meter reading data according to claim 2, characterized in that: The intelligent prediction model is used to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image. The intelligent prediction model is also used to intelligently predict whether there is a reference image block required for performing intra-frame coding on the target image block in each adjacent image block based on the fixed length value, the fixed width value, multiple visual parameters of the target image block, multiple visual parameters of each adjacent image block adjacent to the target image block, and various image contents of the real-time captured image, and various intermediate data of the reference image block required for performing intra-frame coding on the target image block are captured and displayed in real time by the display process.

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