Computer vision model operating system

Abstract

The invention discloses a computer vision model operating system, and relates to the technical field of computer vision models, and the technical scheme is that the computer vision model operating system comprises a data acquisition module used for acquiring and obtaining image and video data; the data encryption transmission module is used for encrypting and transmitting the image and video data acquired by the data acquisition module, and the data encryption transmission module is connected to the output end of the data acquisition module; the beneficial effects are that the data processing module composed of the data normalization processing module, the data de-noising processing module and the duplicated data removing module is designed, so that image and video data can be subjected to normalization processing, de-noising processing and duplicated data removing processing after being acquired; therefore, the system has the function of pre-processing the collected image and video data information, the functions are more comprehensive, the data information does not need to be pre-processed manually when the visual model is tested and evaluated, and the labor intensity of workers can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of computer vision models, and in particular to a computer vision model operating system. Background Art

[0002] Computer vision models understand and process visual information such as images and videos like humans. Computer vision models need to be tested and evaluated before use to ensure that the performance of the model can meet the expected goals. The computer vision model operating system is required during the test and evaluation.

[0003] The computer vision model operating system needs to collect image and video data information before using it. However, the existing operating information is found to be unable to pre-process the collected image and video data information when it is used, and the function is not comprehensive. Therefore, when testing and evaluating the visual model, it is necessary to pre-process the data information manually, which is labor-intensive and needs to be improved.

[0004] To this end, it is necessary to invent a computer vision model operating system. Summary of the invention

[0005] To this end, the present invention provides a computer vision model operating system to solve the problems in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a computer vision model operating system, comprising:

[0007] A data acquisition module, which is used to acquire image and video data;

[0008] A data encryption transmission module, which is used to encrypt and transmit the image and video data collected by the data acquisition module, and the data encryption transmission module is connected to the output end of the data acquisition module;

[0009] A data processing module, which is used to process the collected data, and includes a data normalization processing module, a data denoising processing module and a duplicate data removal module. The data normalization processing module is used to convert the data into a unified scale range to eliminate the dimensional differences between different variables, the data denoising processing module is used to remove noise in the video data, and the duplicate data removal module is used to remove duplicate parts in the data. The data processing module is connected to the output end of the data encryption transmission module;

[0010] The data intelligent labeling module is used to add corresponding label annotations to the data to indicate the target object, scene, and action information in the image and video data. The data intelligent labeling module is connected to the output end of the data processing module.

[0011] A data feature extraction module, which is used to extract feature information annotated by the data intelligent annotation module, and the data feature extraction module is connected to the output end of the data intelligent annotation module;

[0012] A feature transmission module, which is used to transmit the features extracted by the data feature extraction module, and the feature transmission module is connected to the output end of the data feature extraction module;

[0013] The model selection building block is used to select the appropriate model type from existing model architectures based on specific computer vision tasks and requirements;

[0014] A training optimization module, which is used to perform training optimization on the model constructed by the model selection construction module. The training optimization module includes a learning rate control module, a batch size control module and an iteration number recording module. The learning rate control module is used to control the speed of model optimization training, the batch size control module is used to control the number of samples input into the model during optimization training, and the iteration number recording module is used to record the number of iterations of the model during optimization training. The training optimization module is connected to the output end of the model selection construction module;

[0015] A model testing and evaluation module, which is used to use the collected data to comprehensively test and evaluate the performance of the model, and the model testing and evaluation module is connected to the output ends of the training optimization module and the feature transmission module;

[0016] A result analysis module, which is used to analyze the test evaluation results to determine whether the performance of the model meets the standards, and the result analysis module is connected to the output end of the model test evaluation module;

[0017] An analysis result display module, which is used to display the analysis result of the result analysis module, and the analysis result display module is connected to the output end of the result analysis module;

[0018] The model export module is used to export the model for use when its performance meets the requirements. The model export module is connected to the output ends of the training optimization module and the result analysis module.

[0019] Preferably, the data encryption transmission module is electrically connected to the data acquisition module, the data processing module is electrically connected to the data encryption transmission module, and the data intelligent labeling module is electrically connected to the data processing module.

[0020] Preferably, the data feature extraction module is electrically connected to the data intelligent labeling module, the feature transmission module is electrically connected to the data feature extraction module, and the training optimization module is electrically connected to the model selection and construction module.

[0021] Preferably, the model testing and evaluation module is electrically connected to the training optimization module and the feature transmission module, the result analysis module is electrically connected to the model testing and evaluation module, the analysis result display module is electrically connected to the result analysis module, and the model export module is electrically connected to the training optimization module and the result analysis module.

[0022] Preferably, the analysis result display module includes a display screen, a shell is provided on the rear side of the display screen, two connecting blocks are fixedly connected to the front side of the shell, a U-shaped rod is embedded in the two connecting blocks, the U-shaped rod is fixedly connected to the rear side of the display screen, an arc rod is slidably embedded in the top of the shell, the arc rod is fixedly connected to the rear side of the display screen, a through groove is opened on the arc rod, an arc-shaped tooth plate is fixedly connected to one side of the through groove, a rotating shaft 1 is embedded in the interior of the shell, the rotating shaft 1 passes through the through groove, a gear is fixedly sleeved on the outside of the rotating shaft 1, and the gear is meshingly connected to the arc-shaped tooth plate.

[0023] Preferably, a knob is fixedly connected to the rear end of the rotating shaft, a through groove is provided on the front side of the shell, a rotating block is fixedly sleeved on the outside of the U-shaped rod, a plurality of slots evenly distributed in a circular shape are provided on the rotating block, a fixed block is fixedly connected to the front side of the shell, a positioning rod is slidably embedded in the fixed block, the top end of the positioning rod is slidably embedded in one of the slots, a pulling block is fixedly connected to the bottom end of the positioning rod, a spring is fixedly connected between the pulling block and the fixed block, the spring is sleeved on the outside of the positioning rod, and the spring is in a stretched state.

[0024] Preferably, the bottom of the outer shell is fixedly connected to a bottom shell, support rods are provided on both sides of the bottom shell, two movable plates are slidably embedded in the bottom shell, the two movable plates are respectively fixedly connected to the inner sides of the two support rods, the inner sides of the two movable plates are fixedly connected to magnets, and the two magnets are in contact with each other.

[0025] Preferably, the U-shaped rod is connected to the connecting block via a bearing, and the rotating shaft 1 is connected to the housing via a bearing.

[0026] The beneficial effects of the present invention are:

[0027] 1. The present invention designs a data processing module composed of a data normalization processing module, a data denoising processing module and a duplicate data removal module, so that after the image and video data are collected, they can be normalized, denoised and de-duplicated. In this way, the system has the function of pre-processing the collected image and video data information, and the function is more comprehensive. In this way, when testing and evaluating the visual model, there is no need to pre-process the data information manually, which can reduce the labor intensity of manual labor;

[0028] 2. The present invention is designed with a knob, a rotating shaft, a gear and an arc-shaped toothed plate. The rotating shaft and the gear can be rotated by rotating the knob. The rotating gear can move the arc-shaped toothed plate, and then the arc-shaped plate can be moved. The movement of the arc-shaped toothed plate can make the display screen rotate with the U-shaped rod as the rotation axis. In this way, the pitch angle of the display screen can be adjusted when the display screen is in use, and the adjustment is simple and convenient, and only the knob needs to be turned;

[0029] 3. The present invention designs a positioning rod, a rotating block, a slot, a pulling block and a spring. After the pitch angle of the display screen is adjusted, the positioning rod can be stuck in one of the slots through the spring rebound, so that the rotating block and the position can be restricted, thereby restricting the position of the U-shaped rod, so that after the pitch angle is adjusted, the angle can be prevented from being changed arbitrarily;

[0030] 4. The present invention designs two support rods, a movable plate and a magnet. When the display screen is in use, the two support rods can be pulled outward, thereby increasing the distance between the support rods and the bottom shell, thereby increasing the supporting area of ​​the display screen, so that the display screen will be more stable and will not tip over when in use. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings described below are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0032] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0033] Figure 1 A schematic diagram of the overall system provided by the present invention;

[0034] Figure 2 A schematic diagram of a training optimization module system provided by the present invention;

[0035] Figure 3 A schematic diagram of the structure of the analysis result display module provided by the present invention;

[0036] Figure 4 The present invention provides Figure 3Front view, sectional view;

[0037] Figure 5 The present invention provides Figure 3 side cross-sectional view;

[0038] Figure 6 The present invention provides Figure 5 A in the enlarged view;

[0039] Figure 7 The present invention provides Figure 5 The enlarged view of point B in the figure;

[0040] Figure 8 The present invention provides Figure 3 Rear view;

[0041] In the figure: 1. Data acquisition module; 2. Data encryption and transmission module; 3. Data processing module; 4. Data normalization processing module; 5. Data denoising processing module; 6. Duplicate data removal module; 7. Data intelligent labeling module; 8. Data feature extraction module; 9. Feature transmission module; 10. Model selection and construction module; 11. Training optimization module; 12. Learning rate control module; 13. Batch size control module; 14. Iteration number recording module; 15. Model testing Evaluation module; 16. Result analysis module; 17. Analysis result display module; 18. Model export module; 19. Display screen; 20. Shell; 21. Connecting block; 22. U-shaped rod; 23. Arc rod; 24. Arc tooth plate; 25. Rotating shaft 1; 26. Gear; 27. Knob; 28. Rotating block; 29. ​​Slot; 30. Fixed block; 31. Positioning rod; 32. Pull block; 33. Spring; 34. Bottom shell; 35. Support rod; 36. Moving plate; 37. Magnet. DETAILED DESCRIPTION

[0042] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0043] See attached Figure 1 -Attached Figure 8 The present invention provides a computer vision model operating system, comprising:

[0044] Data acquisition module 1, which is used to acquire image and video data;

[0045] The data encryption transmission module 2 is used for encrypting and transmitting the image and video data collected by the data acquisition module 1. The data encryption transmission module 2 is connected to the output end of the data acquisition module 1;

[0046] A data processing module 3 is used to process the collected data. The data processing module 3 includes a data normalization processing module 4, a data denoising processing module 5 and a duplicate data removal module 6. The data normalization processing module 4 is used to convert the data into a uniform scale range to eliminate the dimensional differences between different variables. The data denoising processing module 5 is used to remove noise from the video data. The duplicate data removal module 6 is used to remove duplicate parts from the data. The data processing module 3 is connected to the output end of the data encryption transmission module 2;

[0047] The data intelligent labeling module 7 is used to add corresponding label annotations to the data to indicate the target object, scene, and action information in the image and video data. The data intelligent labeling module 7 is connected to the output end of the data processing module 3.

[0048] A data feature extraction module 8, which is used to extract feature information annotated by the data intelligent annotation module 7, and the data feature extraction module 8 is connected to the output end of the data intelligent annotation module 7;

[0049] A feature transmission module 9, which is used to transmit the features extracted by the data feature extraction module 8, and the feature transmission module 9 is connected to the output end of the data feature extraction module 8;

[0050] A model selection building module 10, which is used to select a suitable model type from the existing model architecture according to specific computer vision tasks and requirements;

[0051] A training optimization module 11 is used to perform training optimization on the model constructed by the model selection construction module 10. The training optimization module 11 includes a learning rate control module 12, a batch size control module 13 and an iteration number recording module 14. The learning rate control module 12 is used to control the speed of model optimization training, the batch size control module 13 is used to control the number of samples input into the model during optimization training, and the iteration number recording module 14 is used to record the number of iterations of the model during optimization training. The training optimization module 11 is connected to the output end of the model selection construction module 10;

[0052] A model test and evaluation module 15, which is used to use the collected data to comprehensively test and evaluate the performance of the model, and the model test and evaluation module 15 is connected to the output end of the training optimization module 11 and the feature transmission module 9;

[0053] A result analysis module 16 is used to analyze the test evaluation results to determine whether the performance of the model meets the standards. The result analysis module 16 is connected to the output end of the model test evaluation module 15;

[0054] An analysis result display module 17, which is used to display the analysis result of the result analysis module 16, and the analysis result display module 17 is connected to the output end of the result analysis module 16;

[0055] A model export module 18, which is used to export the model for use when its performance meets the standard, and the model export module 18 is connected to the output end of the training optimization module 11 and the result analysis module 16;

[0056] In this embodiment, a data processing module 3 consisting of a data normalization processing module 4, a data denoising processing module 5 and a duplicate data removal module 6 is designed, so that after the image and video data are collected, they can be normalized, denoised and de-duplicated. In this way, the system has the function of pre-processing the collected image and video data information, and the function is more comprehensive. In this way, when testing and evaluating the visual model, there is no need to pre-process the data information manually, which can reduce the labor intensity of manual labor;

[0057] Among them, in order to achieve the purpose of normal operation of the system, the present device is implemented by the following technical solutions: the data encryption transmission module 2 is electrically connected to the data acquisition module 1, the data processing module 3 is electrically connected to the data encryption transmission module 2, the data intelligent labeling module 7 is electrically connected to the data processing module 3, the data feature extraction module 8 is electrically connected to the data intelligent labeling module 7, the feature transmission module 9 is electrically connected to the data feature extraction module 8, the training optimization module 11 is electrically connected to the model selection and construction module 10, the model test and evaluation module 15 is electrically connected to the training optimization module 11 and the feature transmission module 9, the result analysis module 16 is electrically connected to the model test and evaluation module 15, the analysis result display module 17 is electrically connected to the result analysis module 16, and the model export module 18 is electrically connected to the training optimization module 11 and the result analysis module 16. The electrical connection between each module can ensure the normal operation of the system;

[0058] Among them, in order to achieve the purpose of adjusting the pitch angle of the display screen 19, the present device is implemented by the following technical scheme: the analysis result display module 17 includes a display screen 19, a shell 20 is provided on the rear side of the display screen 19, and two connecting blocks 21 are fixedly connected to the front side of the shell 20, and a U-shaped rod 22 is embedded in the two connecting blocks 21, and the U-shaped rod 22 is fixedly connected to the rear side of the display screen 19, and an arc rod 23 is slidably embedded in the top of the shell 20, and the arc rod 23 is fixedly connected to the rear side of the display screen 19. A through groove is provided on the arc rod 23, and an arc gear plate 24 is fixedly connected to one side of the through groove, and a rotating shaft 25 is embedded inside the shell 20, and the rotating shaft 25 passes through the through groove, and a gear 26 is fixedly sleeved on the outside of the rotating shaft 25, and the gear 26 is meshed with the arc gear plate 24, and a knob 27 is fixedly connected to the rear end of the rotating shaft 25, and a through groove is provided on the front side of the shell 20, and a rotating block 28 is fixedly sleeved on the outside of the U-shaped rod 22, and a plurality of circularly evenly distributed card plates are provided on the rotating block 28. The cam 32 is pressed against the top of the cam 32 to push the cam 32 out of the way and the cam 32 is released, so that the cam 32 can be easily moved out of the cam 32. The rotation of the gear 26 can move the arc-shaped toothed plate 24, and the movement of the arc-shaped toothed plate 24 can move the arc-shaped plate, so that the display screen 19 can be driven to rotate with the U-shaped rod 22 as the rotation axis. The rotation angle of the display screen 19 can be controlled by controlling the rotation direction of the knob 27, so that the pitch angle of the display screen 19 can be adjusted when the display screen 19 is in use;

[0059] In order to reduce wear, the device is implemented by the following technical solution: the U-shaped rod 22 is connected to the connecting block 21 through a bearing, and the rotating shaft 25 is connected to the housing 20 through a bearing. The bearing connection can reduce wear.

[0060] The use process of the present invention is as follows: when the operating system tests and evaluates the visual model, the model selection construction module 10 can select a suitable model type from the existing model architecture according to the specific computer vision tasks and requirements. The existing model architecture includes convolutional neural network CNN for image classification and target detection, recurrent neural network RNN ​​for video processing, etc. Then the training optimization module 11 can train and optimize the model constructed by the model selection construction module 10. During the training optimization, the learning rate control module 12 can control the speed of the model optimization training, because a larger learning rate may make the model converge quickly in the early stage of training, but it may also cause the model to be unstable, and a smaller learning rate may make the training process slower but more stable. The batch size control module 13 can control the number of samples input into the model during the optimization training. During the training optimization, the iteration number recording module 14 can record the number of iterative updates of the model during the optimization training. When the training optimization is completed, the model test evaluation module 15 can receive the trained and optimized model;

[0061] Then the data acquisition module 1 can acquire the image and video data. After the acquisition is completed, the data encryption transmission module 2 can transmit the data. Then the data processing module 3 receives the data and processes it. During the processing, the data normalization processing module 4 can convert the data into a uniform scale range to eliminate the dimensional differences between different variables. The data denoising processing module 5 can remove the noise in the video data, and the duplicate data removal module 6 can remove the duplicate parts in the data. In this way, the system has the function of preprocessing the collected image and video data information, and the function is more comprehensive. In this way, when testing and evaluating the visual model, there is no need to manually preprocess the data information, which can reduce the labor intensity of manual work.

[0062] After the data processing is completed, the data intelligent annotation module 7 can add corresponding label annotations to the data, indicating the target objects, scenes, and action information in the image and video data. Then the data feature extraction module 8 can extract the feature information annotated by the data intelligent annotation module 7. After the extraction is completed, the feature transmission module 9 can transmit the features extracted by the data feature extraction module 8. Then the model test evaluation module 15 can receive the features of the data. Then the model test evaluation module 15 can use the collected data to conduct a comprehensive test and evaluation of the performance of the model. Then the result analysis module 16 can analyze the results of the test evaluation to determine whether the performance of the model meets the standard. If the performance of the model meets the expected goals and performs well in various evaluation indicators, then the model can be considered to be effective and can be applied to actual scenarios. If the performance of the model is not ideal, the reasons need to be further analyzed. It may be data problems, unreasonable model structure, problems in the training process, etc. After the analysis is completed, the analysis result display module 17 can display the analysis results of the result analysis module 16. At the same time, the model export module 18 can export the model for use when the performance meets the standard;

[0063] When the analysis result display module 17 needs to adjust the pitch angle of the display screen 19 during use, the pull block 32 is pulled downward, so that the positioning rod 31 can be moved downward, so that the positioning rod 31 can be removed from the slot 29, and the spring 33 is compressed. Then, the knob 27 is turned to rotate the shaft 25, and the rotation of the shaft 25 drives the gear 26 to rotate. The rotation of the gear 26 can move the arc-shaped toothed plate 24, and the movement of the arc-shaped toothed plate 24 can move the arc-shaped plate, so that the display screen 19 can be driven to rotate with the U-shaped rod 22 as the rotation axis, and the rotation angle of the display screen 19 can be controlled by controlling the rotation direction of the knob 27, so that the pitch angle of the display screen 19 can be adjusted when the display screen 19 is in use, and the adjustment is simple and convenient, and only the knob 27 needs to be rotated. After the adjustment is completed, the pull block 32 is released, and then the positioning rod 31 will move upward under the action of the spring 33 rebounding, so that the positioning rod 31 will be stuck in one of the slots 29, so that the rotation block 28 and the position can be restricted, thereby limiting the position of the U-shaped rod 22, so that after the pitch angle is adjusted, the angle can be prevented from being changed arbitrarily;

[0064] When the display screen 19 is in use and the bottom shell 34 supports it, the two support rods 35 can be pulled outward during support, so that the distance between the support rods 35 and the bottom shell 34 can be increased, thereby increasing the supporting area of ​​the display screen 19, so that the display screen 19 will be more stable when in use and will not fall over.

[0065] The above are only preferred embodiments of the present invention. Any person skilled in the art may modify the present invention by using the above technical solutions or modify it into an equivalent technical solution. Therefore, any simple modification or equivalent replacement made according to the technical solution of the present invention belongs to the scope of protection claimed by the present invention.

Claims

1. A computer vision model operating system, characterized in that: include: A data acquisition module (1), which is used to acquire image and video data; A data encryption transmission module (2) is used for encrypting and transmitting the image and video data collected by the data collection module (1), wherein the data encryption transmission module (2) is connected to the output end of the data collection module (1); A data processing module (3) is used to process the collected data. The data processing module (3) comprises a data normalization processing module (4), a data denoising processing module (5) and a duplicate data removal module (6). The data normalization processing module (4) is used to convert the data into a uniform scale range to eliminate the dimensional differences between different variables. The data denoising processing module (5) is used to remove noise from the video data. The duplicate data removal module (6) is used to remove duplicate parts from the data. The data processing module (3) is connected to the output end of the data encryption transmission module (2); The data intelligent labeling module (7) is used to add corresponding label annotations to the data to indicate the target object, scene, and action information in the image and video data. The data intelligent labeling module (7) is connected to the output end of the data processing module (3).

2. A computer vision model operating system according to claim 1, characterized in that: Also includes: A data feature extraction module (8), which is used to extract feature information annotated by the data intelligent annotation module (7), and the data feature extraction module (8) is connected to the output end of the data intelligent annotation module (7); A feature transmission module (9), which is used to transmit the features extracted by the data feature extraction module (8), and the feature transmission module (9) is connected to the output end of the data feature extraction module (8); A model selection building block (10) is used to select a suitable model type from existing model architectures according to specific computer vision tasks and requirements; A training optimization module (11) is used to perform training optimization on the model constructed by the model selection construction module (10), the training optimization module (11) comprising a learning rate control module (12), a batch size control module (13) and an iteration number recording module (14), the learning rate control module (12) is used to control the speed of model optimization training, the batch size control module (13) is used to control the number of samples input into the model during optimization training, the iteration number recording module (14) is used to record the number of iterations of the model during optimization training, and the training optimization module (11) is connected to the output end of the model selection construction module (10); A model testing and evaluation module (15), which is used to use the collected data to perform a comprehensive test and evaluation on the performance of the model, and the model testing and evaluation module (15) is connected to the output end of the training optimization module (11) and the feature transmission module (9); A result analysis module (16) is used to analyze the test evaluation results to determine whether the performance of the model meets the standards, and the result analysis module (16) is connected to the output end of the model test evaluation module (15); An analysis result display module (17), which is used to display the analysis result of the result analysis module (16), and the analysis result display module (17) is connected to the output end of the result analysis module (16); A model export module (18) is used to export the model for use when the performance of the model meets the standard. The model export module (18) is connected to the output ends of the training optimization module (11) and the result analysis module (16).

3. A computer vision model operating system according to claim 2, characterized in that: The data encryption transmission module (2) is electrically connected to the data acquisition module (1), the data processing module (3) is electrically connected to the data encryption transmission module (2), and the data intelligent labeling module (7) is electrically connected to the data processing module (3).

4. A computer vision model operating system according to claim 2, characterized in that: The data feature extraction module (8) is electrically connected to the data intelligent labeling module (7), the feature transmission module (9) is electrically connected to the data feature extraction module (8), and the training optimization module (11) is electrically connected to the model selection and construction module (10).

5. A computer vision model operating system according to claim 2, characterized in that: The model test evaluation module (15) is electrically connected to the training optimization module (11) and the feature transmission module (9), the result analysis module (16) is electrically connected to the model test evaluation module (15), the analysis result display module (17) is electrically connected to the result analysis module (16), and the model export module (18) is electrically connected to the training optimization module (11) and the result analysis module (16).

6. A computer vision model operating system according to claim 2, characterized in that: The analysis result display module (17) comprises a display screen (19), a shell (20) is provided at the rear side of the display screen (19), two connecting blocks (21) are fixedly connected to the front side of the shell (20), a U-shaped rod (22) is embedded in the two connecting blocks (21), the U-shaped rod (22) is fixedly connected to the rear side of the display screen (19), an arc rod (23) is slidably embedded in the top of the shell (20), the arc rod (23) is fixedly connected to the rear side of the display screen (19), a through slot is provided on the arc rod (23), one side of the through slot is fixedly connected to an arc tooth plate (24), a rotating shaft (25) is embedded inside the shell (20), the rotating shaft (25) passes through the through slot, a gear (26) is fixedly sleeved on the outside of the rotating shaft (25), and the gear (26) is meshedly connected to the arc tooth plate (24).

7. A computer vision model operating system according to claim 6, characterized in that: The rear end of the rotating shaft (25) is fixedly connected with a knob (27); a through groove is provided on the front side of the housing (20); a rotating block (28) is fixedly sleeved on the outside of the U-shaped rod (22); a plurality of slots (29) evenly distributed in a circumferential shape are provided on the rotating block (28); a fixed block (30) is fixedly connected to the front side of the housing (20); a positioning rod (31) is slidably embedded on the fixed block (30); the top end of the positioning rod (31) is slidably embedded in one of the slots (29); a pulling block (32) is fixedly connected to the bottom end of the positioning rod (31); a spring (33) is fixedly connected between the pulling block (32) and the fixed block (30); the spring (33) is sleeved on the outside of the positioning rod (31); and the spring (33) is in a stretched state.

8. A computer vision model operating system according to claim 7, characterized in that: The bottom of the housing (20) is fixedly connected to a bottom shell (34), both sides of the bottom shell (34) are provided with support rods (35), two movable plates (36) are slidably embedded in the bottom shell (34), the two movable plates (36) are respectively fixedly connected to the inner sides of the two support rods (35), the inner sides of the two movable plates (36) are fixedly connected to magnets (37), and the two magnets (37) are in contact with each other.

9. A computer vision model operating system according to claim 8, characterized in that: The U-shaped rod (22) is connected to the connecting block (21) via a bearing, and the rotating shaft (25) is connected to the housing (20) via a bearing.

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