Convex lens imaging experiment method and system based on machine vision and data cooperation
By using a convex lens imaging experimental method that combines machine vision and data collaboration, the optimal imaging position can be automatically identified and data can be shared in real time. This solves the problems of human eye judgment error and data isolation in convex lens imaging experiments, and achieves more efficient and accurate imaging analysis and sharing of multiple sets of data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 泉州五中桥南校区
- Filing Date
- 2026-04-10
- Publication Date
- 2026-06-26
AI Technical Summary
Existing convex lens imaging experiments suffer from problems such as errors introduced by subjective judgment of the human eye, inefficient data recording, imprecise experimental models, and isolated data, making it difficult to achieve objective and accurate imaging analysis and sharing of multiple sets of data.
By employing a machine vision and data collaboration approach, the system acquires images in real time through a camera, evaluates sharpness using a processor, and automatically identifies the optimal imaging position by combining a one-dimensional precision guide rail and a planar ultra-thin light source. The data is then uploaded to the cloud in real time for sharing and visualization analysis.
It eliminates the subjectivity of human eye judgment, ensures the objective accuracy of imaging position and real-time sharing of experimental data, enhances the scientific nature and depth of the experiment, and supports the induction of patterns from multiple sets of data.
Smart Images
Figure CN122290416A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of physics experiments, and in particular to a method and system for convex lens imaging experiments based on machine vision and data collaboration. Background Technology
[0002] The convex lens imaging experiment is one of the core experiments in optics teaching. Its core purpose is to observe the laws of convex lens imaging by adjusting the object distance and image distance, and to understand the correspondence between the image size, virtual and real images, and the object distance and image distance.
[0003] Currently, most convex lens imaging experiments involve manually adjusting the object distance and image distance, and then manually recording the experimental data by observing the image sharpness on the screen with the naked eye. However, this method has the following problems: 1. Subjective judgment method: Relying on the human eye to subjectively judge the position of the clearest image introduces individual errors and violates the principle of objectivity in scientific measurement; 2. Inefficient inquiry process: Data recording and processing rely on manual methods, which takes too long. Students get bogged down in mechanical operations and find it difficult to compare multiple sets of data and conduct in-depth pattern analysis.
[0004] 3. Inaccurate experimental model: The use of a stereo light source leads to ambiguity in the imaging model itself, affecting the accurate definition and measurement of key physical quantities such as object distance and image distance.
[0005] 4. Data is isolated: In the traditional classroom model, data from different groups cannot be shared and integrated in real time, making it difficult to form sufficient data samples to summarize universal laws. Summary of the Invention
[0006] Therefore, in order to address the above-mentioned shortcomings, the present invention provides an experimental method and system for convex lens imaging based on machine vision and data collaboration, in order to solve the above-mentioned technical problems.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: a convex lens imaging experimental method based on machine vision and data collaboration, comprising the following steps: S1. Fix the relative position of the light source and the convex lens to obtain the current object distance; S2. The screen is continuously moved along the optical axis by a one-dimensional precision guide rail, so that the imaging spot after refraction by the convex lens is projected onto the screen. S3. Use a camera to capture images on the screen in real time and transmit the images to the processor in real time; S4. The processor runs a sharpness evaluation algorithm to calculate the sharpness of each frame of the image, obtains the sharpness evaluation value of each frame, and continuously compares the sharpness evaluation values to determine the maximum value. S5. When the sharpness evaluation value reaches its maximum, determine that the position of the screen at this time is the optimal imaging position, and record the image distance corresponding to this position to form a data pair of object distance and image distance.
[0008] Furthermore, the light source is a planar luminescent object, and all its luminescent patterns are located in the same optical plane with a thickness of less than 1 mm, so as to eliminate the imaging blur caused by the thickness difference of the light source and establish a rigorous physical model basis for accurate measurement of object distance and image distance.
[0009] Furthermore, the object distance and image distance data pairs are uploaded to the cloud server in real time through the data communication module in the processor to construct an experimental dataset shared by multiple terminals.
[0010] Furthermore, the cloud server processes the received full set of data, generates and updates a scatter plot of the relationship between object distance (u) and image distance (v) in real time, and divides and marks the following regions in the scatter plot according to the imaging rules of convex lenses: u>2f region, f<u<2f region, and u<f region.
[0011] Furthermore, the terminal provides a visual interactive interface, allowing users to select any data point in the relational scatter plot and view its corresponding imaging image, object distance, image distance, and related imaging information.
[0012] Furthermore, based on the data distribution characteristics in the relational scatter plot, corresponding imaging pattern prompts are generated and pushed to the terminal.
[0013] This invention also provides a convex lens imaging experimental system based on machine vision and data collaboration, which is used to implement the above method. The system includes: guide; A convex lens, which is mounted on top of the guide rail; Two sliding brackets are movably mounted on the guide rail, and the two sliding brackets are located on both sides of the convex lens. One sliding bracket is equipped with a light source, and the other sliding bracket is equipped with a support bracket. Two stepper motors are installed at both ends of the guide rail. The output end of each stepper motor is connected to a lead screw. The lead screw is rotatably connected to the inside of the guide rail, and the two lead screws are threadedly connected to the two sliding frames one-to-one. The light screen is mounted on a support frame, and the light screen, convex lens, and light source are arranged coaxially. An extension arm is mounted on top of the support frame; A camera is mounted on an extension arm, with the camera's image-facing end pointing towards the screen. The guide rail is equipped with a controller and an independent processor. The controller is electrically connected to the stepper motor. The controller has a circuit board installed inside, which integrates a 4-channel optocoupler isolation board, an ESP32 control module, a driver, a TTL signal conversion module, and a power supply. The input and output terminals of the 4-channel optocoupler isolation board are electrically connected to the ESP32 control module and the driver, respectively, and the output terminal of the driver is electrically connected to the input terminal of the stepper motor. The ESP32 control module is electrically connected to the camera and the TTL signal conversion module respectively; the TTL signal conversion module is electrically connected to the processor; the ESP32 control module receives image data captured by the camera in real time and transmits it to the processor through the TTL signal conversion module. The power supply provides power to the 4-channel optocoupler isolation board, ESP32 control module, driver, and TTL signal conversion module.
[0014] Furthermore, the extension arm is located outside the guide rail, and the extension arm is offset from the light source.
[0015] The beneficial effects of this invention are: 1. This invention automatically identifies the optimal imaging position by combining a camera and a processor, eliminating the subjectivity of human judgment, improving the objectivity and reliability of experimental conclusions, and uploading experimental data to the cloud in real time to realize the collection, sharing and visualization analysis of multiple sets of data. This not only ensures the objectivity and accuracy of a single measurement, but also supports the summarization of imaging patterns from multiple sets of data, thereby enhancing the scientific nature and depth of the experiment.
[0016] 2. This invention uses a planar ultrathin light source combined with a one-dimensional precision guide rail to construct a rigorous optical model, ensuring accurate definition of object distance and image distance, which helps to improve the accuracy of experimental results.
[0017] 3. This invention uses a 4-way optocoupler isolation board to electrically isolate the control signal from the motor drive circuit, effectively suppressing electromagnetic interference generated when the stepper motor is working. At the same time, image processing is completed by an independent processor, avoiding mutual interference between the control process and the data calculation process, thereby ensuring stable and reliable judgment of image clarity, and enabling precise control of object distance and image distance. This makes the experimental system more suitable for teaching and scientific research experimental scenarios with high requirements for repeatability and reliability. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the experimental method of the present invention; Figure 2 This is a schematic diagram of the experimental system structure of the present invention; Figure 3 This is a frontal cross-sectional view of the controller of the present invention.
[0019] The components include: guide rail-1, convex lens-2, sliding frame-3, light source-4, support frame-5, stepper motor-6, lead screw-7, light screen-8, extension arm-9, camera-10, controller-11, processor-12, circuit board-13, power supply-14, 4-channel optocoupler isolation board-15, ESP32 control module-16, driver-17, and TTL signal conversion module-18. Detailed Implementation
[0020] To further explain the technical solution of the present invention, a detailed description is provided below through specific embodiments.
[0021] like Figures 1 to 3 As shown, the present invention provides a convex lens imaging experimental system based on machine vision and data collaboration, including a guide rail 1, a convex lens 2, a sliding frame 3, a light source 4, a support frame 5, a stepper motor 6, a lead screw 7, a light screen 8, an extension arm 9, a camera 10, a controller 11, and a processor 12. The guide rail 1 is a one-dimensional precision guide rail, with a convex lens 2 installed in the middle of its top surface; and two sliding frames 3 are slidably installed on the guide rail 1, with the two sliding frames 3 located on the left and right sides of the convex lens 2 respectively. A light source 4 with a thickness of less than 1mm is installed on the left sliding frame 3, and an L-shaped support frame 5 is installed on the right sliding frame 3. The light source 4 is a planar light-emitting object, and all its light-emitting patterns are located in the same optical plane to eliminate the imaging blur caused by the difference in the thickness of the light source, and to establish a rigorous physical model basis for the accurate measurement of object distance and image distance. The top front and rear ends of the support frame 5 are respectively fixedly installed with a light screen 8 and an extension arm 9. The light screen 8, the convex lens 2 and the light source 4 are coaxially arranged. A stepper motor 6 is installed at each of the left and right ends of the guide rail 1. The output end of each stepper motor 6 is connected to a lead screw 7 through a coupling. The lead screw 7 is rotatably connected to the inner side of the guide rail 1, and the two lead screws 7 are threadedly engaged with two sliding frames 3 respectively. When the stepper motor 6 drives the lead screw 7 to rotate, the lead screw 7 drives the corresponding sliding frame 3 to move horizontally along the guide rail 1, thereby adjusting the position of the light source 4 or the light screen 8. The extension arm 9 extends backward to the outer rear end of the guide rail 1, and the extension arm 9 and the light source 4 are offset in the left and right directions to avoid the extension arm 9 blocking the light. The camera 10 is mounted on the side surface of the extension arm 9, and the camera end of the camera 10 faces the light screen 8. The camera 10 is used to capture the imaging image on the light screen 8 in real time. The guide rail 1 is equipped with a controller 11 and an independent processor 12. The controller 11 is equipped with a circuit board 13. The circuit board 13 integrates a 4-channel optocoupler isolation board 15, an ESP32 control module 16, a driver 17, a TTL signal conversion module 18, and a power supply 14. The input and output terminals of the 4-channel optocoupler isolation board 15 are electrically connected to the ESP32 control module 16 and the driver 17, respectively. The 4-channel optocoupler isolation board 15 is used to isolate the control signal of the ESP32 control module 16 from the drive circuit of the stepper motor 6 and suppress the electromagnetic interference generated by the operation of the stepper motor 6. The output of driver 17 is electrically connected to the input of stepper motor 6. Driver 17 is used to drive stepper motor 6 to precisely adjust the position of light source 4 and screen 8. The ESP32 control module 16 is electrically connected to the camera 10 and the TTL signal conversion module 18, respectively. The TTL signal conversion module 18 is electrically connected to the processor 12 and is used to realize the level conversion and stable transmission of image data; The ESP32 control module 16 receives image data collected by the camera 10 in real time and transmits it to the processor 12 through the TTL signal conversion module 18. The processor 12 is loaded with an image processing software system to execute a sharpness evaluation algorithm, thereby completing the image sharpness analysis. Power supply 14 supplies power to the 4-channel optocoupler isolation board 15, ESP32 control module 16, driver 17, and TTL signal conversion module 18.
[0022] The specific implementation steps of the experimental method of the present invention are as follows: S1. Fix the relative position of the light source 4 and the convex lens 2. The ESP32 control module 16 calculates the displacement of the sliding frame 3 at the left end based on the number of pulse steps of the stepper motor 6 at the left end, thereby obtaining the current object distance. S2. By controlling the start of the stepper motor 6 on the right end, the corresponding lead screw 7 is driven to rotate, thereby driving the sliding frame 3 on the right end to move continuously and uniformly along the guide rail 1. The sliding frame 3 on the right end drives the screen 8 to move synchronously. The screen moves continuously along the optical axis through the one-dimensional precision guide rail, and the light source 4 is started. The light generated by the light source 4 is refracted by the convex lens 2 and the resulting imaging spot is projected onto the screen 8. S3. Start the camera 10 to capture the image on the screen 8 in real time and transmit the image data to the ESP32 control module 16; the ESP32 control module 16 forwards the image data to the processor 12 through the TTL signal conversion module 18. S4. The sharpness evaluation algorithm pre-installed in the processor 12 calculates the sharpness of each frame of the image, obtains the sharpness evaluation value of each frame, and continuously compares the sharpness evaluation values to determine the maximum value. S5. When the sharpness evaluation value reaches its peak, the processor 12 feeds back the optimal imaging position information to the ESP32 control module 16. The ESP32 control module immediately controls the stepper motor 6 to stop working and records the position of the screen 8 at this time as the image distance (v), forming a set of object distance (u) and image distance (v) data pairs.
[0023] In this embodiment, the data pairs of object distance and image distance are uploaded to the cloud server in real time through the data communication module (such as the wifi module) in the processor 12. The cloud server processes the data received from multiple experimental terminals, generates and updates the scatter plot of the relationship between object distance (u) and image distance (v) in real time, and divides and marks the following regions in the scatter plot according to the imaging law of convex lens: u>2f region, f<u<2f region, u<f region. Generate prompts corresponding to the imaging patterns and push the prompts to the terminal, providing a visual interactive interface through the terminal; Users can select any data point in the relational scatter plot to view its corresponding imaging image, object distance, image distance, and related imaging information, and obtain conclusions such as: when u > 2f, an inverted and reduced real image is formed, in which case f < v < 2f; when u = 2f, an inverted and same-size real image is formed, in which case v = 2f; when f < u < 2f, an inverted and magnified real image is formed, in which case v > 2f, etc.
[0024] This invention provides a convex lens imaging experimental method and system based on machine vision and data collaboration. By combining a camera 10 with a processor 12, the optimal imaging position is automatically identified, eliminating the subjectivity of human judgment, improving the objectivity and reliability of experimental conclusions, and uploading experimental data to the cloud in real time to realize the collection, sharing and visualization analysis of multiple sets of data. This ensures the objectivity and accuracy of a single measurement and supports the summarization of imaging patterns from multiple sets of data, thereby enhancing the scientific nature and depth of the experiment.
[0025] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. An experimental method for convex lens imaging based on machine vision and data collaboration, characterized in that, Includes the following steps: S1. Fix the relative position of the light source and the convex lens to obtain the current object distance; S2. The screen is continuously moved along the optical axis by a precision guide rail, so that the imaging spot after refraction by the convex lens is projected onto the screen. S3. Use a camera to capture images on the screen in real time and transmit the images to the processor in real time; S4. The processor runs a sharpness evaluation algorithm to calculate the sharpness of each frame of the image, obtains the sharpness evaluation value of each frame, and continuously compares the sharpness evaluation values to determine the maximum value. S5. When the sharpness evaluation value reaches its maximum, determine that the position of the screen at this time is the optimal imaging position, and record the image distance corresponding to this position to form a data pair of object distance and image distance.
2. The experimental method for convex lens imaging based on machine vision and data collaboration according to claim 1, characterized in that: The light source is a planar luminescent object, and all its luminescent patterns are located in the same optical plane with a thickness of less than 1 mm.
3. The experimental method for convex lens imaging based on machine vision and data collaboration according to claim 1, characterized in that: The precision guide rail is a one-dimensional precision moving platform.
4. The experimental method for convex lens imaging based on machine vision and data collaboration according to claim 1, characterized in that: The object distance and image distance data pairs are uploaded to the cloud server in real time through the data communication module in the processor to build an experimental dataset shared by multiple terminals.
5. The experimental method for convex lens imaging based on machine vision and data collaboration according to claim 4, characterized in that: The cloud server processes the received full set of data, generates and updates a scatter plot of the relationship between object distance (u) and image distance (v) in real time, and divides and marks the following regions in the scatter plot according to the imaging rules of convex lenses: u>2f region, f<u<2f region, and u<f region.
6. The experimental method for convex lens imaging based on machine vision and data collaboration according to claim 5, characterized in that: The terminal provides a visual interactive interface, allowing users to select any data point in the relational scatter plot and view its corresponding imaging image, object distance, image distance, and related imaging information.
7. The experimental method for convex lens imaging based on machine vision and data collaboration according to claim 6, characterized in that: Based on the data distribution characteristics in the relational scatter plot, corresponding imaging pattern prompts are generated and pushed to the terminal.
8. A convex lens imaging experimental system based on machine vision and data collaboration, used to implement the convex lens imaging experimental method based on machine vision and data collaboration as described in any one of claims 1-7, characterized in that, The system includes: guide; A convex lens, which is mounted on top of the guide rail; Two sliding brackets are movably mounted on the guide rail, and the two sliding brackets are located on both sides of the convex lens. One sliding bracket is equipped with a light source, and the other sliding bracket is equipped with a support bracket. Two stepper motors are installed at both ends of the guide rail. The output end of each stepper motor is connected to a lead screw. The lead screw is rotatably connected to the inside of the guide rail, and the two lead screws are threadedly connected to the two sliding frames one-to-one. The light screen is mounted on a support frame, and the light screen, convex lens, and light source are arranged coaxially. An extension arm is mounted on top of the support frame; A camera is mounted on an extension arm, with the camera's image-facing end pointing towards the screen. The guide rail is equipped with a controller and an independent processor. The controller is electrically connected to the stepper motor. The controller has a circuit board installed inside, which integrates a 4-channel optocoupler isolation board, an ESP32 control module, a driver, and a TTL signal conversion module. The input and output terminals of the 4-channel optocoupler isolation board are electrically connected to the ESP32 control module and the driver, respectively, and the output terminal of the driver is electrically connected to the input terminal of the stepper motor. The ESP32 control module is electrically connected to the camera and the TTL signal conversion module respectively; the TTL signal conversion module is electrically connected to the processor; the ESP32 control module receives image data captured by the camera in real time and transmits it to the processor through the TTL signal conversion module. The controller is equipped with a power supply, which is electrically connected to a 4-channel optocoupler isolation board, an ESP32 control module, a driver, and a TTL signal conversion module.
9. The convex lens imaging experimental system based on machine vision and data collaboration according to claim 8, characterized in that: The extension arm is located outside the guide rail and is offset from the light source.