Monocular vision multi-parameter vibration measurement digital metering system and method

Abstract

The invention relates to the technical field of vibration measurement and metering, and discloses a digital metering system and method for monocular vision multi-parameter vibration measurement, and the system comprises a display screen which is connected with a digital video library and is used for outputting a sinusoidal vibration video in the digital video library. The visual imaging unit is fixed to the digital video library through a fixing frame, the visual imaging unit is connected with the processing display unit and sends collected motion sequence images to the processing display unit, and the processing display unit establishes an uncertainty evaluation model according to the motion sequence images and evaluates the measurement uncertainty of the motion sequence images. And comparing the output standard quantity value with the measurement quantity value, establishing an uncertainty propagation chain according to each uncertainty component, obtaining a measurement error and expansion uncertainty of monocular vision, and storing and displaying a traceability result. The method can be applied to performance verification and metering of monocular vision measurement systems formed by visible light imaging units of different models and manufacturers; metering is simple, flexible and efficient, and the system cost is low.

Description

Technical Field

[0001] This invention relates to the field of vibration measurement and metrology. Background Technology

[0002] Multi-parameter vibration measurement is widely used in inertial navigation, mechanical motion control and pose estimation, aerospace equipment manufacturing, and intelligent manufacturing of automotive parts. Monocular vision methods have attracted significant attention in vibration measurement and calibration due to their advantages such as flexibility, efficiency, long measurement range, portable equipment, and low cost, gradually becoming a novel measurement and calibration technology. However, performance verification and uncertainty assessment of monocular vision multi-parameter vibration measurement are challenging, and the traceability chain is incomplete, making it difficult to fully utilize its inherent advantages in vibration measurement and metrology. Therefore, researching digital metrology methods for monocular vision multi-parameter vibration measurement to verify its measurement performance and improve its traceability uncertainty chain has significant theoretical and engineering value.

[0003] Currently, monocular vision multi-parameter measurement methods lack unified metrological standards and systems. Their performance can only be verified by comparing results with traditional measurement methods that have complete traceability chains. However, this approach is affected by various factors, such as different operating conditions and the limited frequency range of traditional measurement methods, leading to significant differences between laboratory comparison results and on-site online calibration. Furthermore, in laboratory measurement scenarios, the uncertainty assessment of monocular vision multi-parameter vibration measurements is influenced by the accuracy and uncertainty of standard vibration generators, often requiring multiple value transfers.

[0004] Therefore, in view of the shortcomings of current monocular vision measurement systems, such as high difficulty in performance verification, weak versatility, and lack of unified metrological standards and systems, this invention proposes an efficient, flexible, and low-cost digital metrological method for monocular vision multi-parameter vibration measurement. It improves the uncertainty chain of monocular vision measurement traceability, realizes digital metrology and flattened value transfer, and helps monocular vision on-site online calibration and traceability, thereby further promoting the digital transformation of vibration metrology. Summary of the Invention

[0005] The purpose of this invention is to provide a digital metrology method for monocular vision multi-parameter vibration measurement, which can effectively solve the problems of high performance verification difficulty, weak universality, and lack of unified metrology standards and systems in existing technologies.

[0006] To address the aforementioned technical problems, this invention provides a digital metrology system for monocular vision multi-parameter vibration measurement, including a display screen connected to a digital video library for outputting sinusoidal vibration videos from the digital video library. A visual imaging unit is fixed to the digital video library via a mounting bracket. The visual imaging unit is connected to a processing and display unit and sends the acquired motion sequence images to the processing and display unit. The processing and display unit establishes an uncertainty assessment model based on the motion sequence images and assesses its measurement uncertainty. It compares the output standard value with the measured value, establishes an uncertainty propagation chain based on each uncertainty component, obtains the measurement error and expanded uncertainty of monocular vision, and saves and displays the traceability results.

[0007] The sinusoidal vibration video is a standard multi-parameter sinusoidal vibration video with a specific frequency and amplitude.

[0008] The refresh rate of the display screen is no less than 60Hz.

[0009] This invention also provides a digital metrology method for monocular vision multi-parameter vibration measurement, employing the digital metrology system for monocular vision multi-parameter vibration measurement as described above, and including the following steps: Step S1: Make a preset specific visual mark reciprocate along a standard multi-parameter sinusoidal vibration trajectory and compile it into a video, establish a digital video library with specific frequency and amplitude, and output the standard vibration value as a video playback through a high refresh rate display. Step S2: The visual imaging unit acquires the motion sequence image of the visual sign output by the display, and measures the linear and angular vibration displacement at different acquisition times using monocular vision method. The measurement trajectory is corrected and sine fitting is performed by adopting the rasterization rounding error correction model and the vibration value spatiotemporal alignment method based on timestamp. Step S3: Based on the uncertainty components introduced by the measurement uncertainty source, establish an uncertainty assessment model and evaluate its measurement uncertainty; Step S4: Compare the output standard value with the measured value, establish an uncertainty propagation chain based on each uncertainty component, obtain the measurement error and expanded uncertainty of monocular vision, and save and display the traceability results.

[0010] The preset specific visual marker is a visual encoder with an embedded checkerboard pattern. The checkerboard pattern contains 25 X-shaped corner points with high contrast. The sub-pixel coordinates of the X-shaped corner points in the checkerboard pattern can be extracted to calculate the linear vibration displacement.

[0011] The digital video library with specific frequencies and amplitudes is a database of motion videos of visual markers moving along standard multi-parameter sinusoidal vibration trajectories in the range of 0.01 to 10 Hz; each motion video contains at least 5 vibration cycles, and the number of images per cycle is the ratio of the video frame rate to the motion frequency.

[0012] In step S2, measuring the line and angular vibration displacement at different acquisition times using monocular vision specifically involves: capturing motion sequence images of visual markers; using a high-precision corner detection algorithm to extract the pixel coordinates of the X-shaped corner points in the motion sequence images; calculating the average value of the corner point movement relative to the zero position of the first frame at each time point; extracting feature edges from the shapes in the motion sequence images; filtering the detected effective edges; obtaining the length and slope of the longest feature line and the length of the side edge of the coding ring; matching the length of the side edge of the coding ring with the preset code to obtain the absolute coding angle and vibration direction at the current acquisition time; and thus calculating the angular vibration displacement at each acquisition time.

[0013] The spatiotemporal alignment method for vibration values ​​based on timestamps in step S2 is as follows: extract the frame number and the corresponding timestamp from the image, and perform sinusoidal approximation fitting on the corrected linear and angular vibration displacements according to the timestamps corresponding to the frame number, thereby calculating the amplitude and phase of the linear and angular vibration displacements.

[0014] In step S2, the rasterization rounding error correction model calculates the feature line compensation amount based on the rounding error of the horizontal axis, and then calculates the feature line slope and angular vibration displacement.

[0015] The sources of measurement uncertainty include the visual imaging unit, the external environment, and the accuracy of standard data.

[0016] Compared with existing technologies, this invention can be applied to the performance verification and metrology of monocular vision measurement systems composed of visible light imaging units of different models and manufacturers. The metrology is simple, flexible, efficient, and low-cost, requiring only a set of standard digital video and a high refresh rate display to achieve monocular vision multi-parameter vibration measurement. The proposed rasterization rounding error correction model and the time-stamp-based spatiotemporal alignment method for vibration values ​​can greatly improve the effectiveness and reliability of traceability. It improves the traceability uncertainty chain of monocular vision multi-parameter sinusoidal vibration measurement, realizes the flattened transmission of values, and provides key technical support for on-site online calibration and traceability of monocular vision.

[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0019] Figure 1 This is a schematic diagram illustrating the principle of at least one embodiment of the present invention; Figure 2 This is a flowchart illustrating at least one embodiment of the present invention; Figure 3 This is a schematic diagram of the spatiotemporal alignment of vibration values ​​in one embodiment of the present invention; Figure 4 yes Figure 3 A schematic diagram of the signal parameters; Figure 5 This is a diagram showing the results of standard multi-parameter vibration in one embodiment of the present invention.

[0020] In the diagram: 1-Display screen, 2-Digital video library, 3-Visual imaging unit, 4-Processing and display unit, 5-Motion sequence image. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the embodiments of this invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of this invention to facilitate a better understanding of this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this invention. The embodiments can be combined with and referenced by each other without contradiction.

[0022] Example 1 like Figure 1 The digital metrology system for monocular vision multi-parameter vibration measurement shown includes a display screen 1, which is connected to a digital video library 2 to output sinusoidal vibration videos from the digital video library 2. A visual imaging unit 3 is fixed to the digital video library 2 via a mounting bracket. The visual imaging unit 3 is connected to a processing and display unit 4 and sends the acquired motion sequence images 5 to the processing and display unit 4. The processing and display unit 4 establishes an uncertainty evaluation model based on the motion sequence images 5 and evaluates its measurement uncertainty. It compares the output standard value with the measured value, establishes an uncertainty propagation chain based on each uncertainty component, obtains the measurement error and expanded uncertainty of monocular vision, and saves and displays the traceability results.

[0023] Example 2 Based on Example 1, the sinusoidal vibration video is a standard multi-parameter sinusoidal vibration video with a specific frequency and amplitude.

[0024] Furthermore, the refresh rate of display screen 1 is no less than 60Hz.

[0025] Example 3 like Figure 2 The digital metrology method for monocular vision multi-parameter vibration measurement, as shown in Example 1 or Example 2, includes the following steps: Step S1: Make a preset specific visual mark reciprocate along a standard multi-parameter sinusoidal vibration trajectory and compile it into a video, establish a digital video library with specific frequency and amplitude, and output the standard vibration value as a video playback through a high refresh rate display. Step S2: The visual imaging unit acquires the motion sequence image of the visual sign output by the display, and measures the linear and angular vibration displacement at different acquisition times using monocular vision method. The measurement trajectory is corrected and sine fitting is performed by adopting the rasterization rounding error correction model and the vibration value spatiotemporal alignment method based on timestamp. Step S3: Based on the uncertainty components introduced by the measurement uncertainty source, establish an uncertainty assessment model and evaluate its measurement uncertainty; Step S4: Compare the output standard value with the measured value, establish an uncertainty propagation chain based on each uncertainty component, obtain the measurement error and expanded uncertainty of monocular vision, and save and display the traceability results.

[0026] Example 4 Based on Example 3, a specific visual marker is preset as a visual encoder with an embedded checkerboard pattern. The checkerboard pattern contains 25 X-shaped corner points with high contrast. The sub-pixel coordinates of the X-shaped corner points in the checkerboard pattern can be extracted and used to calculate the linear vibration displacement.

[0027] Furthermore, the digital video library with specific frequencies and amplitudes is a database of motion videos of visual markers moving along standard multi-parameter sinusoidal vibration trajectories in the range of 0.01 to 10 Hz; each motion video contains at least 5 vibration cycles, and the number of images per cycle is the ratio of the video frame rate to the motion frequency.

[0028] Furthermore, in step S2, measuring the line and angular vibration displacement at different acquisition times using monocular vision specifically involves: capturing motion sequence images of visual markers; using a high-precision corner detection algorithm to extract the pixel coordinates of the X-shaped corner points in the motion sequence images; calculating the average value of the corner point movement relative to the zero position of the first frame at each time point; extracting feature edges from the shapes in the motion sequence images; filtering the detected effective edges; obtaining the length and slope of the longest feature line and the length of the side edge of the coding ring; matching the length of the side edge of the coding ring with the preset code to obtain the absolute coding angle and vibration direction at the current acquisition time; and thus calculating the angular vibration displacement at each acquisition time.

[0029] Furthermore, the spatiotemporal alignment method for vibration values ​​based on timestamps in step S2 is as follows: extract the frame number and corresponding timestamp from the image, and perform sinusoidal approximation fitting on the corrected linear and angular vibration displacements according to the timestamp corresponding to the frame number, thereby calculating the amplitude and phase of the linear and angular vibration displacements.

[0030] Furthermore, in step S2, the rasterization rounding error correction model calculates the feature line compensation amount based on the rounding error of the horizontal axis, and thereby calculates the feature line slope and angular vibration displacement.

[0031] Furthermore, sources of measurement uncertainty include the visual imaging unit, the external environment, and the accuracy of standard data.

[0032] Example 5 Based on the above embodiments, the following steps are included: S1: The designed visual logo is made to reciprocate along a standard multi-parameter sinusoidal vibration trajectory through a program and compiled into a video. A standard digital video library with specific frequency and amplitude is established, and the standard multi-parameter sinusoidal vibration is output through a high refresh rate display in the form of video playback. S2: The visual imaging unit acquires motion sequence images of visual markers output by the display and measures the linear and angular vibration displacements at different acquisition times using monocular vision. A rasterization rounding error correction model and a time-stamp-based spatiotemporal alignment method for vibration values ​​are employed to correct the measurement trajectory and perform sine fitting. S3: Based on the uncertainty components introduced by measurement uncertainty sources such as visual imaging unit, external environment, and standard data accuracy, establish an uncertainty assessment model and evaluate its measurement uncertainty; S4: Compare the output standard value with the measured value, establish an uncertainty propagation chain based on each uncertainty component, obtain the measurement error and expanded uncertainty of monocular vision, and save and display the traceability results.

[0033] Furthermore, the design of the visual logo follows the principle of: The designed visual logo is a visual encoder embedded with a checkerboard pattern containing 25 high-contrast X-shaped corner points. Accurate extraction of these X-shaped corner points and acquisition of their sub-pixel coordinates allows for the calculation of linear vibration displacement.

[0034] The center and radius of the black ring between the checkerboard pattern and the coding region are used to define the region of interest (ROI). The radius of the visual encoder is twice the radius of the outer edge of the central black ring. The external visual encoder consists of eight feature lines and their adjacent absolute coding regions. The extensions of the side edges of each absolute coding region converge at the center and are parallel to the adjacent feature lines. The center of the coding ring coincides with the center of the checkerboard pattern. At the zero position, within the ROI of the acquired image, the feature line with a slope of 1 serves as the zero-position reference line. The zero-position reference line and its extensions divide the visual encoder into two coding forms: three-ring and four-ring. The ring length ratios for each coding form are 4:2:1 and 8:4:2:1, respectively. Each absolute coding region can be assigned a unique binary code, with black coding rings representing "1" and white rings representing "0," and the coding order proceeding from the outside in. Using the zero-position reference line as the boundary, the clockwise direction from 0 to 180° is a three-ring code, with the corresponding binary codes of the four absolute coding regions being 000, 001, 010, and 011, matching absolute coding angles of 45°, 90°, 135°, and 180° respectively. The counter-clockwise direction from 0 to -180° is a four-ring code, with the corresponding codes of the four absolute coding regions being 0001, 0010, 0011, and 0100, matching absolute coding angles of -45°, -90°, -135°, and -180° respectively. By identifying the coding pattern within the region of interest of the visual encoder in the sequence image, the direction of angular vibration can be directly determined. Therefore, the entire visual encoder includes a total of 8* i ( i For a standard image frame count, the vibration angle is determined by the number of identifiable feature groups. θ The sum of the absolute coding angle and the angle between the feature line and the zero-position reference line is given by the following formula: (1) in, α For absolute encoding angle, For the first i The slope of the feature lines in the frame image. The slope of the zero-position reference line.

[0035] Furthermore, the standard digital video library with specific frequencies and amplitudes consists of motion videos of visual markers along standard multi-parameter sinusoidal vibration trajectories within the range of 0.01 to 10 Hz. The displacement amplitudes of the linear and angular vibration trajectories are 60 to 200 pixels and 15 to 90° at different frequencies, respectively. Each video contains at least 5 vibration cycles, and the number of images per cycle is the ratio of the video frame rate to the motion frequency.

[0036] Furthermore, the monocular vision method measures the linear and angular vibration displacements at different acquisition times. The principle is as follows: The visual imaging unit captures motion sequence images of visual signs. ,in i Indicates the number of frames. N The number of images is calculated, and the images are saved and transmitted to the processing and display unit. A high-precision corner detection algorithm is used to extract the pixel coordinates of the X-shaped corner points in the sequence images. The average value of the corner point movement relative to the zero position of the first frame at different times is calculated to obtain the linear vibration displacement at each acquisition time. The sequence images in the region of interest are subjected to high-precision feature edge extraction. The detected effective edges are filtered to obtain the length and slope of the longest feature line, as well as the side edge length of the coding loop. The detected coding loop edge length ratio is matched with the preset code to obtain the absolute coding angle and vibration direction at the current acquisition time. Combined with formula (1), the angular vibration displacement at each acquisition time can be determined.

[0037] Furthermore, the principle of the rasterization rounding error correction model is as follows: In standard digital video, visual markers are generated by rasterizing vector graphics into pixel images, and the video output is a sinusoidal vibration trajectory after coordinate rounding. Since the visual markers vibrate only in the horizontal direction of the display, the actual output checkerboard pattern is... i Liede j The rounding error of the x-coordinate of the row corner point is: (2) In the formula: "int" represents the integer operation. The first square on the chessboard i Liede j The standard vector coordinates of the corner points are as follows: (3) in, r The radius of the visual encoder, k This represents the number of feature lines in the visual encoder, which is 8 in this case. ω is the angular velocity.

[0038] Then the collection time The corrected x-coordinate of the corner point at that location is the sum of the measured value and the rounding error. Since the absolute coded angle used to determine the vibration angle in visual marking is a fixed angle obtained by matching coded values, only the pixel coordinates of the endpoints of the feature lines detected by monocular vision need to be compensated throughout the entire measurement process. Taking the rounded feature line starting point coordinates as an example, the rasterization rounding error sequence generated by the feature line starting point coordinates within a single angular vibration cycle is as follows: (4) In the formula: These are the horizontal and vertical coordinates of the visual logo's rotation center. q This is the scaling factor between the vector image size and the rasterized image pixel size. The coordinates of the starting point of the feature line output at each time step. These are the coordinates of the starting point of the feature line after rounding.

[0039] The coordinates of the start and end points of the compensated feature line can be used to calculate The slope of the time-time characteristic line can be substituted into equation (1) to solve for the corrected sampling time. Angular vibration displacement.

[0040] Furthermore, the principle of the time-stamp-based vibration magnitude spatiotemporal alignment method is as follows: The sequence of images acquired by the visual imaging unit simultaneously contains video frames of standard digital video and the timestamp corresponding to the current image frame. The timestamp consists of standard-formatted numeric characters, with a structure and arrangement exhibiting certain regularities and characteristics. Using image digital recognition and classification technology, the frame number and corresponding timestamp in the image can be accurately extracted. Because the frame number in the image corresponds one-to-one with the data points of the standard multi-parameter sine trajectory, the standard phase of that frame image can be accurately obtained. Similarly, based on the timestamp corresponding to the frame number, precise alignment in the time domain can be achieved between the vibration signal acquired by the industrial camera and the standard signal output by the display. The corrected linear and angular vibration displacements are fitted using a sine approximation method, as shown in the following equation: (5) Among them, parameters It is obtained through the least squares principle, where the parameters... The parameters are related, while the other parameters are independent of each other.

[0041] Therefore, the amplitude and phase of the linear and angular vibration displacements can be further obtained, given by equations (6) and (7), respectively: (6) (7) Furthermore, the uncertainty assessment model is based on the following principle: The uncertainty sources for measuring standard multi-parameter sinusoidal vibrations using monocular vision mainly include the imaging unit, the external environment, and the accuracy of the standard data. The measurement model is given by the following equation: (8) In the formula: as well as These are the parameters used in the sinusoidal approximation fitting process. to This refers to the impact of other sources of uncertainty during the measurement process on the measurement results.

[0042] In the measurement model The uncertainty components introduced by the parameters can be estimated using the covariance matrix obtained during the least-squares fitting process. Other sources and components of uncertainty in the measurement process are as follows: Depend on N The repeatability error of the linear and angular displacement amplitude and phase obtained from the second measurement. and It can be given by the following formula: (9) Due to monitor refresh rate error Display pixel size error Standard vibration data accuracy Image resolution error Image pixel quantization error Image filtering error and camera exposure time Uncertainty introduced by factors such as The assessment will be conducted using the Category B assessment method, as given by the following formula: (10) Where, Δ a The half-width of the uncertainty source's error, precision, or confidence interval, here is... . It is the inclusion factor corresponding to the probability distribution of each source of uncertainty.

[0043] Illumination, photoelectric conversion, and external environmental noise inevitably affect the grayscale values ​​of image pixels, leading to random errors. This is especially true when acquiring images under the same illumination conditions. M A frame image, with a total of 100 pixels per frame. T The corresponding grayscale value is The uncertainties introduced by illumination, photoelectric conversion, and external environmental noise were assessed using Monte Carlo methods. u ( r ): (11) The residual distortion after camera calibration and the uncertainty introduced by the industrial camera's optical axis not being perfectly perpendicular to the monitor screen can be given by the following formula: (12) in, (13) In the formula: (14) in,( , () is the pixel coordinate of the checkerboard corner point in the image, calculated using the intrinsic and extrinsic parameters of the imaging unit and the distortion model. , ) are the pixel coordinates detected by the corner detection algorithm.

[0044] Example 6 Based on the above embodiments, the system mainly includes: a calibrated high refresh rate display 1, a standard digital video library 2, a visual imaging unit 3, and a processing and display unit 4. Its features include: the calibrated high refresh rate display 1 outputting standard multi-parameter sinusoidal vibration videos from the standard digital video library 2; the visual imaging unit 3 being fixed to an imaging unit mounting bracket, with its optical axis perpendicular to the plane of the calibrated high refresh rate display 1; the visual imaging unit 3 acquiring motion sequence images 5 of the standard multi-parameter sinusoidal vibration videos from the standard digital video library 2; and the processing and display unit 4 analyzing and processing the acquired motion sequence images 5, evaluating uncertainties based on the uncertainty sources in the monocular vision measurement process, and saving and displaying the monocular vision multi-parameter sinusoidal vibration measurement and tracing results.

[0045] The method mainly includes the following steps: Step S1: The designed visual logo is made to reciprocate along a standard multi-parameter sinusoidal vibration trajectory through a program and compiled into a video. A digital video library with specific frequencies and amplitudes is established, and the standard vibration values ​​are output through a high refresh rate display in the form of video playback. Step S2: The visual imaging unit acquires the motion sequence images of the visual markers output by the display, and measures the linear and angular vibration displacements at different acquisition times using monocular vision. A rasterization rounding error correction model and a time-stamp-based vibration value spatiotemporal alignment method are employed to correct the measurement trajectory and perform sine fitting. Step S3: Based on the uncertainty components introduced by measurement uncertainty sources such as the visual imaging unit, external environment, and standard data accuracy, establish an uncertainty assessment model and evaluate its measurement uncertainty; Step S4: Compare the output standard value with the measured value, establish an uncertainty propagation chain based on each uncertainty component, obtain the measurement error and expanded uncertainty of monocular vision, and save and display the traceability results.

[0046] like Figure 3 , Figure 4 The diagram illustrates the principle of spatiotemporal alignment of vibration values ​​based on timestamps. The sequence of images captured by the industrial camera simultaneously contains standard digital video frames and the timestamp corresponding to the current frame. The timestamp consists of standard-formatted numeric characters, with a structure and arrangement exhibiting certain regularities and characteristics. Using image digital recognition and classification technology, the frame number and corresponding timestamp in the image can be accurately extracted. Because the frame number in the image corresponds one-to-one with the data points of the standard multi-parameter sine trajectory, the standard phase of that frame can be accurately obtained. Similarly, based on the timestamp corresponding to the frame number, precise time-domain alignment between the vibration signal captured by the industrial camera and the standard signal output by the display can be achieved.

[0047] like Figure 5 The figure shows the results of standard multi-parameter sinusoidal vibration output from a monocular vision measurement display. The relative deviations between the measured values ​​and the standard values ​​of linear and angular vibration amplitudes are respectively... , and the standard deviation are respectively The mean absolute error and standard deviation of the phase measurement are respectively, and .

[0048] The specific parameters of the system in this implementation are as follows: the standard digital video includes multiple standard multi-parameter sinusoidal vibration videos with a frequency range of 0.01~10 Hz, and linear and angular vibration displacement amplitudes of 60~200 pixels and 15~90°, respectively. The certified high refresh rate display model is Lenovo Y25-30, the visual imaging unit uses an industrial camera with a maximum resolution of 9 megapixels and a maximum frame rate of 1000 fps, a lens focal length of 20 mm, and the processing and display unit is Lenovo Y7000P.

[0049] To verify the effectiveness of this scheme, traceability of multi-parameter measurements using monocular vision within the frequency range of 0.01–10 Hz is achieved. For example... Figure 5 The results of the standard multi-parameter sinusoidal vibration measured by the monocular vision display are shown. Table 1 shows the uncertainty sources and corresponding uncertainty components of the displacement amplitude of the standard multi-parameter sinusoidal vibration measured by the monocular vision method. Since all uncertainty components are uncorrelated, the expanded uncertainties of the displacement amplitude and phase of the multi-parameter sinusoidal vibration are calculated to be 0.0735% and 0.0723°, respectively.

[0050] Table 1. Uncertainty Sources and Uncertainty Components of Standard Multi-parameter Sinusoidal Vibration Displacement in Monocular Vision Measurement

[0051] Those skilled in the art will understand that the above embodiments can be modified in form and detail in practical applications without departing from the spirit and scope of the invention.

Claims

1. A digital metrology system for monocular vision multi-parameter vibration measurement, characterized in that, The system includes a display screen (1), which is connected to a digital video library (2) for outputting sinusoidal vibration videos from the digital video library (2). A visual imaging unit (3) is fixed to the digital video library (2) via a mounting bracket. The visual imaging unit (3) is connected to a processing display unit (4) and sends the acquired motion sequence images (5) to the processing display unit (4). The processing display unit (4) establishes an uncertainty assessment model based on the motion sequence images (5) and assesses its measurement uncertainty. It compares the output standard value with the measured value, establishes an uncertainty propagation chain based on each uncertainty component, obtains the measurement error and expanded uncertainty of monocular vision, and saves and displays the traceability results.

2. The digital metrology system for monocular vision multi-parameter vibration measurement as described in claim 1, characterized in that, The sinusoidal vibration video is a standard multi-parameter sinusoidal vibration video with a specific frequency and amplitude.

3. The digital metrology system for monocular vision multi-parameter vibration measurement as described in claim 1, characterized in that, The refresh rate of the display screen (1) is not less than 60Hz.

4. A digital metrological method for monocular vision multi-parameter vibration measurement, characterized in that, The digital metrology system for monocular vision multi-parameter vibration measurement as described in any one of claims 1 to 3 includes the following steps: Step S1: Make a preset specific visual mark reciprocate along a standard multi-parameter sinusoidal vibration trajectory and compile it into a video, establish a digital video library with specific frequency and amplitude, and output the standard vibration value as a video playback through a high refresh rate display. Step S2: The visual imaging unit acquires the motion sequence image of the visual sign output by the display, and measures the linear and angular vibration displacement at different acquisition times using monocular vision method. The measurement trajectory is corrected and sine fitting is performed by adopting the rasterization rounding error correction model and the vibration value spatiotemporal alignment method based on timestamp. Step S3: Based on the uncertainty components introduced by the measurement uncertainty source, establish an uncertainty assessment model and evaluate its measurement uncertainty; Step S4: Compare the output standard value with the measured value, establish an uncertainty propagation chain based on each uncertainty component, obtain the measurement error and expanded uncertainty of monocular vision, and save and display the traceability results.

5. The digital metrology method for monocular vision multi-parameter vibration measurement as described in claim 4, characterized in that, The preset specific visual marker is a visual encoder with an embedded checkerboard pattern. The checkerboard pattern contains 25 X-shaped corner points with high contrast. The sub-pixel coordinates of the X-shaped corner points in the checkerboard pattern can be extracted to calculate the linear vibration displacement.

6. The digital metrology method for monocular vision multi-parameter vibration measurement as described in claim 4, characterized in that, The digital video library with specific frequencies and amplitudes is a database of motion videos of visual markers moving along standard multi-parameter sinusoidal vibration trajectories in the range of 0.01 to 10 Hz; each motion video contains at least 5 vibration cycles, and the number of images per cycle is the ratio of the video frame rate to the motion frequency.

7. The digital metrology method for monocular vision multi-parameter vibration measurement as described in claim 4, characterized in that, In step S2, measuring the line and angular vibration displacement at different acquisition times using monocular vision specifically involves: capturing motion sequence images of visual markers; using a high-precision corner detection algorithm to extract the pixel coordinates of the X-shaped corner points in the motion sequence images; calculating the average value of the corner point movement relative to the zero position of the first frame at each time point; extracting feature edges from the shapes in the motion sequence images; filtering the detected effective edges; obtaining the length and slope of the longest feature line and the length of the side edge of the coding ring; matching the length of the side edge of the coding ring with the preset code to obtain the absolute coding angle and vibration direction at the current acquisition time; and thus calculating the angular vibration displacement at each acquisition time.

8. The digital metrology method for monocular vision multi-parameter vibration measurement as described in claim 1, characterized in that, The spatiotemporal alignment method for vibration values ​​based on timestamps in step S2 is as follows: extract the frame number and the corresponding timestamp from the image, and perform sinusoidal approximation fitting on the corrected linear and angular vibration displacements according to the timestamps corresponding to the frame number, thereby calculating the amplitude and phase of the linear and angular vibration displacements.

9. The digital metrology method for monocular vision multi-parameter vibration measurement as described in claim 1, characterized in that, In step S2, the rasterization rounding error correction model calculates the feature line compensation amount based on the rounding error of the horizontal axis, and then calculates the feature line slope and angular vibration displacement.

10. The digital metrology method for monocular vision multi-parameter vibration measurement as described in claim 1, characterized in that, The sources of measurement uncertainty include the visual imaging unit, the external environment, and the accuracy of standard data.

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