Algorithm operating system of monitoring computer

Abstract

The invention relates to a monitoring computer algorithm operating system which comprises a multi-time learning device, a picture receiving device, a first detection device, a second detection device, a time prediction mechanism and a block selection mechanism. According to the invention, the processing efficiency and performance can be improved.

Description

Technical Field

[0001] The present invention relates to the field of computer systems, and in particular to a monitoring computer algorithm operating system. Background Art

[0002] The characteristics of computer systems are that they can perform accurate and fast calculations and judgments, and they are versatile, easy to use, and can be connected into a network. The specific characteristics are as follows.

[0003] (1) Calculation: Almost all complex calculations can be performed by computers through arithmetic and logical operations.

[0004] (2) Judgment: Computers have the ability to distinguish different situations and choose different treatments, so they can be used in management, control, confrontation, decision-making, reasoning and other fields.

[0005] (3) Storage: Computers can store huge amounts of information.

[0006] (4) Accuracy: As long as the word length is sufficient, the calculation accuracy is theoretically unlimited.

[0007] (5) Fast: The time required for a computer operation is now measured in nanoseconds.

[0008] (6) General: Computers are programmable, and different programs can realize different applications.

[0009] (7) Ease of use: Rich high-performance software and intelligent human-computer interface greatly facilitate use.

[0010] (8) Networking: Multiple computer systems can transcend geographical boundaries and share remote information and software resources with the help of communication networks.

[0011] CN119226182A provides a main control chip, a data processing method, a solid state hard disk and a computer system, wherein the main control chip comprises: a flash memory host interface controller, comprising an allocation address acquisition module and a command execution module; the allocation address acquisition module is used to acquire a first cache address and a second cache address corresponding to a data write command, the first cache address is a cache space address located inside the main control chip, and the second cache address is a cache space address located outside the main control chip; the command execution module is used to write write data to the first cache address and the second cache address; the main control chip performs a programming operation based on the write data in the first cache address, and releases the write data in the second cache address after the main control chip confirms the data consistency of the write data in the first cache address.

[0012] CN119226201A discloses a firmware processing method, a retiming card, a computer system and a readable storage medium, the method comprising: obtaining level information of two single-ended signals of a high-speed expansion slot by connecting a wire of the high-speed expansion slot; determining the target bandwidth of an external device based on the level information according to a mapping relationship between the level and the bandwidth; determining the target memory to be connected to the retiming component based on the target bandwidth according to a mapping relationship between the bandwidth and the firmware; wherein firmware of different bandwidths are stored in different memories; and a control signal for connecting to the target memory is sent to a selection switch so that the retiming component performs signal adjustment based on the target firmware. The technical effect of the present application: no longer relying on a baseboard management controller, nor relying on bandwidth information stored in other devices, the firmware switching of the retiming card can be completed without turning on the computer, realizing a senseless operation.

[0013] CN119201357A relates to a computer system, including a simulation virtual machine, which includes a translator and an instruction executor for translating the simulation virtual machine instructions into instructions executable by a test platform, and the simulation virtual machine uses shared memory to send instructions to the test platform; the instruction translator includes a CPU simulator, the simulator receives and sends instructions to BIOS and OS, and the instruction translator also includes multiple instruction grabbers for grabbing corresponding instructions from the simulator; the instruction executor includes: a request filtering module for filtering and distributing various instructions received from the instruction translator, and sending the filtered remaining instructions required for the test platform to execute the test to a request control module; the request control module sends the filtered remaining instructions to a transmission module, and receives the test execution results from the test platform from the transmission module; the transmission module uses shared memory to send the instructions required by the test platform to the test platform. Summary of the invention

[0014] In order to solve the technical problems in the prior art, the present invention provides a monitoring computer algorithm operating system, which can obtain multiple scene information of the work scene in a specific scene, including the internal space volume of the work scene inside the office building, the area of ​​the background board, the distance from the background board to the fisheye camera mechanism, the number of employees and the number of desks, and also obtain various picture parameters of the work scene picture, including the horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetition of the work scene picture, so as to provide sufficient and comprehensive basic data for the intelligent prediction of different encoding times of the work scene picture in different sizes of encoding blocks in a specific scene, and adopt the encoding method to realize intelligent prediction of different encoding times of the work scene picture in a specific scene. The code prediction model intelligently predicts the predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, so as to obtain the predicted values ​​of the various coding times required to perform intra-frame coding on the working scene picture under various coding block sizes, and outputs the coding block size corresponding to the predicted value of the coding time with the smallest value as the optimal coding block size with coding rate priority, so as to effectively predict the coding time required before performing intra-frame coding on the working scene picture in a specific scene, thereby providing an important basis for the selection of the size of the intra-frame coding block.

[0015] According to the present invention, there is provided a monitoring computer algorithm operating system, the system comprising: A multiple learning device, used for performing multi-layer learning on the Hoffet neural network to obtain a coding prediction model output, wherein performing each layer of learning on the Hoffet neural network is performing each learning process on the Hoffet neural network; The picture receiving device is connected to the fisheye camera mechanism and is used to obtain a work scene picture, wherein the work scene picture is a picture obtained by the fisheye camera mechanism disposed inside the office building performing wide-view shooting of the work scene inside the office building; A first detection device is connected to the picture receiving device and is used to detect various picture parameters of the working scene picture, wherein the various picture parameters of the working scene picture are horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetitiveness of the working scene picture; The second detection device is used to detect multiple scene information of the working scene inside the office building, wherein the multiple scene information of the working scene inside the office building is the internal space volume of the working scene inside the office building, the background board area, the distance from the background board to the fisheye camera mechanism, the number of employees, and the number of desks; A time prediction mechanism is connected to the multiple learning device, the first detection device and the second detection device respectively, and is used to adopt a coding prediction model to intelligently predict a predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building; A block selection mechanism is connected to the time prediction mechanism, and is used to obtain predicted values ​​of the encoding time required to perform intra-frame encoding on the working scene screen under various encoding block sizes, and output the encoding block size corresponding to the predicted value of the encoding time with the smallest value as the optimal encoding block size with priority on encoding rate; The picture receiving device is connected to the fisheye camera mechanism and is used to obtain a work scene picture, wherein the work scene picture is a picture obtained by the fisheye camera mechanism disposed inside the office building performing wide-view shooting of the work scene inside the office building, and includes: the shooting view angle of the fisheye camera mechanism is greater than or equal to a set view angle threshold; Among them, the multiple learning device is used to perform multi-layer learning on the Hoffitt neural network to obtain the coding prediction model output, and performing each layer of learning on the Hoffitt neural network is to perform each learning process on the Hoffitt neural network, including: the number of learning layers performed on the Hoffitt neural network is proportional to the total number of pixels of the working scene screen.

[0016] It can be seen that the present invention has at least the following three main inventive concepts: First: for the work scene picture in a specific scene, multiple scene information of the work scene is obtained, including the internal space volume of the work scene inside the office building, the background board area, the distance from the background board to the fisheye camera mechanism, the number of employees and the number of desks, and various picture parameters of the work scene picture, including the horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetition of the work scene picture, so as to provide sufficient and comprehensive basic data for intelligent prediction of different encoding times of different size encoding blocks of the work scene picture in a specific scene; Second, the artificial intelligence model based on which the intelligent prediction of different encoding times of the working scene picture under different sizes of encoding blocks in a specific scene is a coding prediction model with a customized structure is customized in that the Hoffitt neural network is subjected to multi-layer learning to obtain the coding prediction model output, and each layer of learning performed on the Hoffitt neural network is performed for each learning process on the Hoffitt neural network, and the number of layers of learning performed on the Hoffitt neural network is proportional to the total number of pixels of the working scene picture; Third: a coding prediction model is used to intelligently predict the predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, so as to obtain the predicted values ​​of the various coding times required to perform intra-frame coding on the working scene picture under various coding block sizes, and output the coding block size corresponding to the predicted value of the coding time with the smallest value as the optimal coding block size with coding rate priority, so as to effectively predict the coding time required before performing intra-frame coding on the working scene picture in a specific scene, thereby providing an important basis for the selection of the size of the intra-frame coding block. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 It is a structural block diagram of a monitoring computer algorithm operating system according to the primary embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the prior art, it is impossible to utilize a computer system for monitoring to obtain multiple pieces of scene information and various picture parameters of the working scene picture in a specific scenario, and thus it is impossible to provide sufficient and comprehensive basic data for intelligent prediction of different encoding times of the working scene picture in a specific scenario under different sizes of coding blocks, and naturally it is impossible to parse out the optimal coding block size with coding rate priority.

[0019] The following is a detailed description of an embodiment of the monitoring computer algorithm operating system of the present invention with reference to the accompanying drawings.

[0020] Figure 1 The structure block diagram of the monitoring computer algorithm operating system according to the primary embodiment of the present invention is shown, and the system includes: A multiple learning device, used for performing multi-layer learning on the Hoffet neural network to obtain a coding prediction model output, wherein performing each layer of learning on the Hoffet neural network is performing each learning process on the Hoffet neural network; Specifically, the multiple learning device is used to perform multi-layer learning on the Hoffet neural network to obtain the coding prediction model output, and performing each layer of learning on the Hoffet neural network is to perform each learning process on the Hoffet neural network, including: MATLAB toolbox can be used to complete the simulation and testing of the multi-layer learning of the Hoffet neural network; The picture receiving device is connected to the fisheye camera mechanism and is used to obtain a work scene picture, wherein the work scene picture is a picture obtained by the fisheye camera mechanism disposed inside the office building performing wide-view shooting of the work scene inside the office building; A first detection device is connected to the picture receiving device and is used to detect various picture parameters of the working scene picture, wherein the various picture parameters of the working scene picture are horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetitiveness of the working scene picture; The second detection device is used to detect multiple scene information of the working scene inside the office building, wherein the multiple scene information of the working scene inside the office building is the internal space volume of the working scene inside the office building, the background board area, the distance from the background board to the fisheye camera mechanism, the number of employees, and the number of desks; A time prediction mechanism is connected to the multiple learning device, the first detection device and the second detection device respectively, and is used to adopt a coding prediction model to intelligently predict a predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building; A block selection mechanism is connected to the time prediction mechanism, and is used to obtain predicted values ​​of the encoding time required to perform intra-frame encoding on the working scene screen under various encoding block sizes, and output the encoding block size corresponding to the predicted value of the encoding time with the smallest value as the optimal encoding block size with priority on encoding rate; The picture receiving device is connected to the fisheye camera mechanism and is used to obtain a work scene picture, wherein the work scene picture is a picture obtained by the fisheye camera mechanism disposed inside the office building performing wide-view shooting of the work scene inside the office building, and includes: the shooting view angle of the fisheye camera mechanism is greater than or equal to a set view angle threshold; Among them, the multiple learning device is used to perform multi-layer learning on the Hoffet neural network to obtain the coding prediction model output, and performing each layer of learning on the Hoffet neural network is to perform each learning process on the Hoffet neural network, including: the number of learning layers performed on the Hoffet neural network is proportional to the total number of pixels of the working scene screen; Among them, the first detection device is connected to the picture receiving device and is used to detect various picture parameters of the working scene picture, and the various picture parameters of the working scene picture are the horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetitiveness of the working scene picture, including: the maximum pixel depth of field of the working scene picture is the maximum value of each depth of field value corresponding to each pixel in the working scene picture; Wherein, a coding prediction model is used to intelligently predict the coding time required to perform intra-frame coding on the work scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the work scene picture, and multiple scene information of the work scene inside the office building. The input data of the predicted value is numerically normalized to obtain the input data after the numerical normalization processing is performed; Among them, the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is numerically normalized to obtain the input data after the numerical normalization is performed, including: the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building is numerically normalized based on hexadecimal numerical conversion to obtain the input data after the numerical normalization is performed; And wherein, the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building by using the coding prediction model to be numerically normalized to obtain the input data after the numerical normalization is performed, including: the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building is numerically normalized based on octal value conversion to obtain the input data after the numerical normalization is performed; And wherein, the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is numerically normalized to obtain the input data after the numerical normalization is performed, including: the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building is numerically normalized based on binary value conversion to obtain the input data after the numerical normalization is performed; And wherein, the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and numerical normalization processing is performed to obtain the input data after the numerical normalization processing is performed, including: the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size is intelligently predicted based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building is numerically normalized based on decimal value conversion to obtain the input data after the numerical normalization processing is performed; And wherein, the output data of the predicted value of the coding time required to perform intra-frame coding on the work scene picture under the coding block size is in the form of numerical representation after numerical normalization processing, using a coding prediction model to intelligently predict the coding block size used for intra-frame coding, various picture parameters of the work scene picture, and multiple scene information of the work scene inside the office building; And wherein, the output data of the predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size is intelligently predicted based on the coding prediction model used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, is a numerical representation after numerical normalization processing, including: the output data of the predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size is intelligently predicted based on the coding prediction model used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, is a numerical representation after numerical normalization processing based on hexadecimal numerical conversion, a numerical representation after numerical normalization processing based on octal numerical conversion, a numerical representation after numerical normalization processing based on binary numerical conversion, and a numerical representation after numerical normalization processing based on decimal numerical conversion.

[0021] In addition, in the monitoring computer algorithm operating system, a first detection device is connected to the picture receiving device, and is used to detect various picture parameters of the working scene picture. The various picture parameters of the working scene picture are the horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetition of the working scene picture, and also include: the maximum pixel depth of field of the working scene picture is the minimum value of the depth of field values ​​corresponding to each pixel in the working scene picture.

[0022] The monitoring computer algorithm operating system of the present invention is used to address the technical problem in the prior art that it is impossible to select the optimal coding block size for the working scene picture in a specific scene without giving priority to the coding rate. By obtaining multiple pieces of scene information of the working scene and various picture parameters of the working scene picture, different coding times of the working scene picture in the specific scene under coding blocks of different sizes are intelligently predicted, and the coding block size corresponding to the coding time with the smallest value is used as the optimal coding block size with giving priority to the coding rate, thereby improving the efficiency and performance of image processing and solving the above technical problems.

[0023] Having described the embodiments of the present invention and its advantages, it should be noted that various changes, substitutions and alterations may be made herein without departing from the spirit and scope of the present invention as defined in the appended claims. In addition, the use of terms such as "first", "second", etc. does not indicate any order or importance, but rather the terms "first", "second", etc. are used to distinguish elements from each other.

Claims

1. A monitoring computer algorithm operating system, characterized in that: The system comprises: A multiple learning device, used for performing multi-layer learning on the Hoffet neural network to obtain a coding prediction model output, wherein performing each layer of learning on the Hoffet neural network is performing each learning process on the Hoffet neural network; The picture receiving device is connected to the fisheye camera mechanism and is used to obtain a work scene picture, wherein the work scene picture is a picture obtained by the fisheye camera mechanism disposed inside the office building performing wide-view shooting of the work scene inside the office building; A first detection device is connected to the picture receiving device and is used to detect various picture parameters of the working scene picture, wherein the various picture parameters of the working scene picture are horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetitiveness of the working scene picture; The second detection device is used to detect multiple scene information of the working scene inside the office building, wherein the multiple scene information of the working scene inside the office building is the internal space volume of the working scene inside the office building, the background board area, the distance from the background board to the fisheye camera mechanism, the number of employees, and the number of desks; A time prediction mechanism is connected to the multiple learning device, the first detection device and the second detection device respectively, and is used to adopt a coding prediction model to intelligently predict a predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building; A block selection mechanism is connected to the time prediction mechanism, and is used to obtain predicted values ​​of the encoding time required to perform intra-frame encoding on the working scene screen under various encoding block sizes, and output the encoding block size corresponding to the predicted value of the encoding time with the smallest value as the optimal encoding block size with priority on encoding rate; The picture receiving device is connected to the fisheye camera mechanism and is used to obtain a work scene picture, wherein the work scene picture is a picture obtained by the fisheye camera mechanism disposed inside the office building performing wide-view shooting of the work scene inside the office building, and includes: the shooting view angle of the fisheye camera mechanism is greater than or equal to a set view angle threshold; Among them, the multiple learning device is used to perform multi-layer learning on the Hoffitt neural network to obtain the coding prediction model output, and performing each layer of learning on the Hoffitt neural network is to perform each learning process on the Hoffitt neural network, including: the number of learning layers performed on the Hoffitt neural network is proportional to the total number of pixels of the working scene screen.

2. The monitoring computer algorithm operating system according to claim 1, characterized in that: A first detection device is connected to the picture receiving device and is used to detect various picture parameters of the working scene picture. The various picture parameters of the working scene picture are the horizontal resolution, vertical resolution, maximum pixel depth of field, minimum pixel depth of field, signal-to-noise ratio and picture content repetition of the working scene picture, including: the maximum pixel depth of field of the working scene picture is the maximum value of the depth of field values ​​corresponding to each pixel in the working scene picture.

3. The monitoring computer algorithm operating system according to claim 2, characterized in that: A coding prediction model is used to intelligently predict the coding time required to perform intra-frame coding on the work scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the work scene picture, and multiple scene information of the work scene inside the office building. The input data of the predicted value is numerically normalized to obtain the input data after the numerical normalization processing.

4. The monitoring computer algorithm operating system according to claim 3, characterized in that: The method comprises: using a coding prediction model to intelligently predict the coding time required for performing intra-frame coding on the working scene picture at the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and performing numerical normalization processing on the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and performing numerical normalization processing based on hexadecimal value conversion to obtain the input data after the numerical normalization processing is performed.

5. The monitoring computer algorithm operating system according to claim 3, characterized in that: The method comprises: adopting a coding prediction model to intelligently predict the coding time required for performing intra-frame coding on the working scene picture at the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building, and performing numerical normalization processing on the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture at the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building, and performing numerical normalization processing based on octal value conversion to obtain the input data after the numerical normalization processing is performed.

6. The monitoring computer algorithm operating system according to claim 3, characterized in that: The method comprises: adopting a coding prediction model to intelligently predict the coding time required to perform intra-frame coding on the working scene picture at the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building, and performing numerical normalization processing on the input data of the predicted value of the coding time required to perform intra-frame coding on the working scene picture at the coding block size, and performing numerical normalization processing based on binary value conversion to obtain the input data after the numerical normalization processing is performed.

7. The monitoring computer algorithm operating system according to claim 3, characterized in that: The method comprises: adopting a coding prediction model to intelligently predict the coding time required for performing intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building, and performing numerical normalization processing on the input data of the predicted value of the coding time required for performing intra-frame coding on the working scene picture under the coding block size. The method comprises: adopting a coding prediction model to intelligently predict the coding time required for performing intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture and multiple scene information of the working scene inside the office building, and performing numerical normalization processing based on decimal value conversion to obtain the input data after the numerical normalization processing.

8. The monitoring computer algorithm operating system according to claim 2, characterized in that: A coding prediction model is used to intelligently predict the coding time required to perform intra-frame coding on the working scene picture under the coding block size based on the coding block size used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building. The output data of the predicted value is a numerical representation after numerical normalization.

9. The monitoring computer algorithm operating system according to claim 8, characterized in that: The output data of the predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size is intelligently predicted based on the coding prediction model used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and the predicted value is a numerical representation after numerical normalization processing, including: the output data of the predicted value of the coding time required to perform intra-frame coding on the working scene picture under the coding block size is intelligently predicted based on the coding prediction model used for intra-frame coding, various picture parameters of the working scene picture, and multiple scene information of the working scene inside the office building, and the predicted value is a numerical representation after numerical normalization processing based on hexadecimal numerical conversion, a numerical representation after numerical normalization processing based on octal numerical conversion, a numerical representation after numerical normalization processing based on binary numerical conversion, and a numerical representation after numerical normalization processing based on decimal numerical conversion.

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