Network computer algorithm operating system

Abstract

The invention relates to a network computer algorithm operating system which comprises a directional establishing device, a signal receiving device, a data collecting device, an image analyzing device, a pre-judging executing mechanism and a ratio identifying mechanism. According to the method, the AI pre-judgment model can be introduced to intelligently pre-judge the data volume of the coding data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image by adopting the reference image, so that pre-judgment of inter-frame coding compression ratios of different numerical values obtained by selecting different reference frames is completed, and an important basis is provided for selection of the reference frames; therefore, the most matched image processing mechanism is selected for the associated data.

Description

Technical Field

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

[0002] A network computer is an interactive information device under the client computing mode. The analysis of typical application behavior on it is of great significance for processor design and system development. A network computer is a computer used on the network, but it removes traditional hard disks, floppy disks and other components. It is a thin PC, and the server provides programs or storage on the network. A network computer has its own processing capabilities, but except for the core software, other software must be downloaded from the network server, which saves frequent software upgrades and maintenance and reduces costs. A network computer is a low-cost, upgrade-free, maintenance-free, easy-to-operate, right-manageable, highly secure, and highly reliable terminal client in a certain application field and network environment. It can meet the needs of managers and the public for information processing and information access, and is an inevitable product of the subdivision of information applications in various industries.

[0003] CN118541653A discloses a method for configuring a control network, wherein the control network includes a superior central unit for controlling and monitoring a facility system having multiple distributed facilities and a subordinate remote unit for directly controlling the distributed facilities, wherein in order to control the distributed facilities, planning data is stored in the remote unit, and the planning data includes facility-related objects and / or modules.

[0004] CN117955728A discloses a single blockchain system for different networks, including an external network computer, an internal network computer, a physically isolated computer A and a physically isolated computer B; the external network computer uses a serial port to Ethernet port serial port server to exchange network data with the physically isolated computer A; the physically isolated computer A and the physically isolated computer B use a serial bus port USB to perform non-network data transmission; the physically isolated computer B and the internal network computer use a serial port to USB port serial port server to perform network data interaction.

[0005] CN116204128A discloses a storage system of a network computer and a storage method thereof, and relates to the technical field of network computers. The storage system of a network computer includes a main storage group, which is configured to be connected to the network computer through a server network and store all the generated data. The main storage group includes a first storage group, a second storage group, a third storage group, a data analysis system, and a backup system. The data analysis system is configured to read the data of the first storage group and divide it into hot data, cold data, and read-only data. The data is divided into hot data, cold data, and read-only data by the data analysis system, and the hot data is stored using a data replication redundancy scheme, and the cold data and read-only data are stored using a distributed storage scheme based on erasure codes, which not only reduces the amount of data that needs to be stored in the replication redundancy scheme, but also avoids the problem of high data update cost of the distributed storage scheme based on erasure codes. Summary of the invention

[0006] In order to solve the technical problems in the prior art, the present invention provides a network computer algorithm operating system, which introduces a customized network computer algorithm operating system, performs multiple learning on the autoencoder neural network to obtain the autoencoder neural network after multiple learning and outputs it as an AI prediction model. The number of learning times performed by the autoencoder neural network is positively correlated with the number of lamp bodies of the traffic lights at the set traffic intersection, thereby realizing the structural customization of the AI ​​prediction model, obtaining the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, as well as obtaining various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image, wherein the various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels and the coordinate values ​​of each foreground pixel points of the image, thereby providing for subsequent intelligent prediction. It can provide valuable basic information for prediction, and also adopts an AI prediction model to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image according to the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. It also determines the compression ratio obtained by performing inter-frame encoding on the operating environment image using the reference image based on the data volume of the encoded data stream of the operating environment image and the original data volume of the operating environment image, and completes the prediction of inter-frame coding compression ratios of different values ​​obtained by selecting different reference frames, which provides an important basis for the selection of reference frames for subsequent specific scene monitoring images, thereby selecting the most matching reference frame for the associated data of different traffic lights.

[0007] According to the present invention, there is provided a network computer algorithm operating system, the system comprising: Directed establishment equipment is provided in the system, and is used to perform multiple learning on the autoencoder neural network to obtain the autoencoder neural network after multiple learning and output it as an AI prediction model; A signal receiving device, arranged in the system, for receiving an operating environment image captured by a remote network capturing device using a network transmission component, wherein the network capturing device is arranged opposite to a traffic light at a set traffic intersection, and for performing an image capturing operation on the operating environment of the traffic light at the set traffic intersection; Data acquisition equipment, used to obtain the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection; an image analysis device connected to the signal receiving device, and used to obtain various image data of the operating environment image and various image data of a reference image used when performing inter-frame coding on the operating environment image, wherein the various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels, and the coordinate value of each foreground pixel of the image; A prediction execution mechanism is connected to the orientation establishment device, the data acquisition device and the image analysis device respectively, and is used to use an AI prediction model to intelligently predict the data volume of the coded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image; a ratio identification mechanism connected to the prediction execution mechanism, and used to determine a compression ratio obtained by performing inter-frame coding on the operating environment image using the reference image based on the data volume of the coded data stream of the operating environment image and the original data volume of the operating environment image; Wherein, determining the compression ratio value obtained by performing inter-frame coding on the operating environment image using the reference image based on the data volume of the coded data stream of the operating environment image and the original data volume of the operating environment image comprises: dividing the data volume of the coded data stream of the operating environment image by the original data volume of the operating environment image to obtain the compression ratio value obtained by performing inter-frame coding on the operating environment image using the reference image; Wherein, obtaining various image data of the operating environment image and various image data of a reference image used when performing inter-frame coding on the operating environment image, wherein the various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels and the coordinate value of each foreground pixel of the image, includes: the reference image used when performing inter-frame coding on the operating environment image is an image captured by the network capture device at a past time before the time of capturing the operating environment image; Among them, a device is established in a targeted manner for performing multiple learning of the autoencoder neural network to obtain the autoencoder neural network after multiple learning and outputting it as an AI prediction model, including: the number of learning times performed by the autoencoder neural network is positively correlated with the number of light bodies of the traffic lights set at the traffic intersection.

[0008] It can be seen that the present invention has at least the following three outstanding substantive features: Substantive feature A: A network computer algorithm operating system with a customized structure is introduced to conduct multiple learning of the autoencoder neural network to obtain the autoencoder neural network after multiple learning and output it as the AI ​​prediction model. The number of times the autoencoder neural network has been learned is positively correlated with the number of light bodies of the traffic lights at the set traffic intersection, thereby achieving structural customization of the AI ​​prediction model; Substantive feature B: Obtaining the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, as well as obtaining various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image. The various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels and the coordinate value of each foreground pixel of the image, thereby providing valuable basic information for subsequent intelligent prediction; Substantive Feature C: An AI prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image according to the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. The compression ratio obtained by performing inter-frame encoding on the operating environment image using the reference image is also determined based on the data volume of the encoded data stream of the operating environment image and the original data volume of the operating environment image, thereby completing the prediction of inter-frame coding compression ratios of different values ​​obtained by selecting different reference frames, providing an important basis for the subsequent selection of reference frames. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 A structural block diagram of a network computer algorithm operating system according to embodiment A of the present invention. DETAILED DESCRIPTION

[0010] In the prior art, network computers are often used to monitor the operating environment of traffic lights at set traffic intersections. Therefore, it is hoped that the processing of the monitoring screen can be matched with the imaging characteristics of the key components in the monitoring screen, namely the traffic lights, so as to improve the flexibility and specificity of image processing. Obviously, the prior art lacks a corresponding solution mechanism.

[0011] The implementation scheme of the network computer algorithm operating system of the present invention will be described in detail below with reference to the accompanying drawings.

[0012] Figure 1 It is a structural block diagram of a network computer algorithm operating system according to embodiment A of the present invention, and the system comprises: Directed establishment equipment is provided in the system, and is used to perform multiple learning on the autoencoder neural network to obtain the autoencoder neural network after multiple learning and output it as an AI prediction model; Specifically, the directional establishment device is arranged in the system, and is used to perform multiple learning on the autoencoder neural network to obtain the autoencoder neural network after multiple learning and output it as an AI prediction model, including: using an ASIC device to implement the directional establishment device, which is arranged in the system, and is used to perform multiple learning on the autoencoder neural network to obtain the autoencoder neural network after multiple learning and output it as an AI prediction model; A signal receiving device, arranged in the system, for receiving an operating environment image captured by a remote network capturing device using a network transmission component, wherein the network capturing device is arranged opposite to a traffic light at a set traffic intersection, and for performing an image capturing operation on the operating environment of the traffic light at the set traffic intersection; Data acquisition equipment, used to obtain the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection; an image analysis device connected to the signal receiving device, and used to obtain various image data of the operating environment image and various image data of a reference image used when performing inter-frame coding on the operating environment image, wherein the various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels, and the coordinate value of each foreground pixel of the image; A prediction execution mechanism is connected to the orientation establishment device, the data acquisition device and the image analysis device respectively, and is used to use an AI prediction model to intelligently predict the data volume of the coded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image; a ratio identification mechanism connected to the prediction execution mechanism, and used to determine a compression ratio obtained by performing inter-frame coding on the operating environment image using the reference image based on the data volume of the coded data stream of the operating environment image and the original data volume of the operating environment image; Wherein, determining the compression ratio value obtained by performing inter-frame coding on the operating environment image using the reference image based on the data volume of the coded data stream of the operating environment image and the original data volume of the operating environment image comprises: dividing the data volume of the coded data stream of the operating environment image by the original data volume of the operating environment image to obtain the compression ratio value obtained by performing inter-frame coding on the operating environment image using the reference image; Wherein, obtaining various image data of the operating environment image and various image data of a reference image used when performing inter-frame coding on the operating environment image, wherein the various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels and the coordinate value of each foreground pixel of the image, includes: the reference image used when performing inter-frame coding on the operating environment image is an image captured by the network capture device at a past time before the time of capturing the operating environment image; Wherein, the device is established to learn the autoencoder neural network multiple times to obtain the autoencoder neural network after completing the multiple learning and output it as the AI ​​prediction model, including: the number of times the autoencoder neural network learns is positively correlated with the number of light bodies of the traffic lights at the set traffic intersection; The data acquisition device is used to obtain the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, including: lamp bodies of different shapes have lamp body shape numbers with different values; Wherein, the data acquisition device for obtaining the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection also includes: the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection is the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection in a vertical cross section; Among them, an internal control instruction for using an AI prediction model to intelligently predict the data volume of the coded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image based on the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image is transmitted by the instruction control channel; Among them, the AI ​​prediction model is used to intelligently predict the data volume of the coded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image, and the internal control instruction is transmitted by the instruction control channel, including: the instruction control channel is operated and maintained by the SOC chip; Among them, the internal control instruction of using the AI ​​prediction model to intelligently predict the data volume of the coded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image is transmitted by the instruction control channel, including: the instruction control channel is operated and maintained by the ASIC chip; Among them, the AI ​​prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image, and various image data of the reference image used when performing inter-frame coding on the operating environment image, and the intermediate data is transmitted to the network transmission interface; Among them, the AI ​​prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, the various image data of the operating environment image and the various image data of the reference image used when performing inter-frame coding on the operating environment image, and the intermediate data is transmitted to the network transmission interface, including: the network transmission interface performs network data transmission and reception based on a time division duplex communication mechanism; And wherein, the AI ​​prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, the various image data of the operating environment image and the various image data of the reference image used when performing inter-frame coding on the operating environment image, and the intermediate data is transmitted to the network transmission interface, including: the network transmission interface performs the transmission and reception of network data based on the frequency division duplex communication mechanism; And wherein, an AI prediction model is used to intelligently predict the amount of data of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image according to the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image, and the intermediate data is transmitted to the network transmission interface, including: the network transmission interface performs network data reception and transmission based on the WIFI communication mechanism.

[0013] In addition, in the network computer algorithm operating system, an AI prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, various image data of the operating environment image, and various image data of the reference image used when performing inter-frame coding on the operating environment image, including: selecting and using the MATLAB toolbox to implement the test and simulation of the data processing process of using the AI ​​prediction model to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, various image data of the operating environment image, and various image data of the reference image used when performing inter-frame coding on the operating environment image.

[0014] The network computer algorithm operating system of the present invention aims to solve the technical problem in the prior art that image processing lacks flexibility and pertinence because the processing of the monitoring image cannot match the imaging characteristics of the key component in the monitoring image, namely the traffic light. By introducing an AI prediction model to intelligently predict the data amount of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using a reference image, the prediction of inter-frame encoding compression ratios of different values ​​obtained by selecting different reference frames is completed, which provides an important basis for the selection of reference frames and selects the most matching image processing mechanism for the associated data of different traffic lights, thereby solving the above technical problems.

[0015] It should be understood that the embodiments and examples disclosed herein are exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than by the preceding description, and the equivalent concepts of the meaning of the claims and all changes within the meaning are intended to be included by the claims.

Claims

1. A network computer algorithm operating system, characterized in that: The system comprises: A directional establishment device is provided in the system, and is used to perform multiple learning of the autoencoder neural network to obtain the autoencoder neural network after completing multiple learning and output it as an AI prediction model, including: the number of learnings performed by the autoencoder neural network is positively correlated with the number of lamp bodies of the traffic light set at the traffic intersection; A signal receiving device, arranged in the system, for receiving an operating environment image captured by a remote network capturing device using a network transmission component, wherein the network capturing device is arranged opposite to a traffic light at a set traffic intersection, and for performing an image capturing operation on the operating environment of the traffic light at the set traffic intersection; Data acquisition equipment, used to obtain the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection; an image analysis device connected to the signal receiving device, and used to obtain various image data of the operating environment image and various image data of a reference image used when performing inter-frame coding on the operating environment image, wherein the various image data of each image are the number of pixel columns, the number of pixel rows, the number of foreground pixels, and the coordinate value of each foreground pixel of the image, including: the reference image used when performing inter-frame coding on the operating environment image is an image captured by the network capture device at a past time before the capture time of the operating environment image; A prediction execution mechanism is connected to the orientation establishment device, the data acquisition device and the image analysis device respectively, and is used to use an AI prediction model to intelligently predict the data volume of the coded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image according to the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, various image data of the operating environment image and various image data of the reference image used when performing inter-frame coding on the operating environment image; A ratio identification mechanism is connected to the prediction execution mechanism, and is used to determine the compression ratio obtained by performing inter-frame coding on the operating environment image using the reference image based on the data volume of the coded data stream of the operating environment image and the original data volume of the operating environment image, including: dividing the data volume of the coded data stream of the operating environment image by the original data volume of the operating environment image to obtain the compression ratio obtained by performing inter-frame coding on the operating environment image using the reference image.

2. The network computer algorithm operating system according to claim 1, characterized in that: The data acquisition device is used to obtain the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, including: lamp bodies of different shapes have lamp body shape numbers with different values; Among them, the data acquisition equipment is used to obtain the lamp body shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection, and also includes: the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection is the cross-sectional area of ​​a single lamp body of the traffic light at the set traffic intersection in a vertical section.

3. The network computer algorithm operating system according to claim 2, characterized in that: An AI prediction model is used to intelligently predict the amount of data in the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. An internal control instruction is transmitted through the instruction control channel.

4. The network computer algorithm operating system according to claim 3, characterized in that: An AI prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame coding on the operating environment image. Internal control instructions are transmitted through an instruction control channel, including: the instruction control channel is operated and maintained by a SOC chip.

5. The network computer algorithm operating system according to claim 3, characterized in that: An AI prediction model is used to intelligently predict the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame coding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame coding on the operating environment image. Internal control instructions are transmitted through an instruction control channel, including: the instruction control channel is operated and maintained by an ASIC chip.

6. The network computer algorithm operating system according to claim 2, characterized in that: An AI prediction model is used to intelligently predict the intermediate data of the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. The data is transmitted to the network transmission interface.

7. The network computer algorithm operating system according to claim 6, characterized in that: An AI prediction model is used to intelligently predict the amount of data of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. The intermediate data of the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image is transmitted to the network transmission interface, including: the network transmission interface performs network data reception and transmission based on a time division duplex communication mechanism.

8. The network computer algorithm operating system according to claim 6, characterized in that: An AI prediction model is used to intelligently predict the amount of data of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. The intermediate data of the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image is transmitted to the network transmission interface, including: the network transmission interface performs network data reception and transmission based on a frequency division duplex communication mechanism.

9. The network computer algorithm operating system according to claim 6, characterized in that: An AI prediction model is used to intelligently predict the amount of data of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image based on the lamp shape number, the number of lamp bodies, the distance between adjacent lamp bodies and the cross-sectional area of ​​a single lamp body of the traffic lights at the set traffic intersection, the various image data of the operating environment image, and the various image data of the reference image used when performing inter-frame encoding on the operating environment image. The intermediate data of the data volume of the encoded data stream of the operating environment image obtained by performing inter-frame encoding on the operating environment image using the reference image is transmitted to the network transmission interface, including: the network transmission interface performs network data reception and transmission based on the WIFI communication mechanism.

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