Universe ultra-precision strain measurement method

Abstract

The invention discloses a global ultra-precision strain measurement method, and relates to the technical field of strain measurement. The method comprises the following steps: continuously shooting moving images of a target area by using a high-frame-rate high-definition camera, and recording accurate time; and carrying out image correction, feature extraction and tracking and data normalization. And after Gaussian white noise is added, singular value decomposition and reconstruction are carried out. And a Lucas-Kanade optical flow method is adopted to calculate the displacement of the feature points in the reconstruction matrix. And a displacement smoothing stage: smoothing the displacement data by using an LOESS method. And carrying out global strain calculation by adopting a window sliding method, and calculating the strain through the spatial derivative of the displacement field. According to the method, the quantization effect can be relieved, the continuity of displacement calculation is improved, low-energy noise and irrelevant features are removed, main motion information is reserved, meanwhile, smoothness is enhanced, local features are captured in a high-precision mode, and measurement sensitivity is improved. The method is suitable for strain measurement of large-scale structures such as bridges and buildings.

Description

Technical Field

[0001] The present invention belongs to the technical field of strain measurement, and in particular relates to a full-domain ultra-precision strain measurement method. Background Art

[0002] The importance of strain measurement is not only reflected in risk prevention and design optimization, but also in its long-term contribution to structural health monitoring and scientific research. Whether in bridges, tunnels, aerospace, or geology and bioengineering, accurate strain data provides reliable support for safety, economy, and innovation.

[0003] Existing technologies include contact measurement methods and non-contact measurement methods. The contact measurement methods include: strain gauges have the advantages of high precision and fast dynamic response, but the disadvantages are sensitivity to environmental conditions, strong dependence on installation, and limited to local measurement. Non-contact measurement methods include: digital image correlation (DIC), which has the advantages of non-contact, full-domain, low precision, and the disadvantage of complex data processing. Due to the interpolation method used by DIC, the accuracy problem is not solved from the physical essence, and the effect is limited.

[0004] The existing imaging method has too low accuracy and is limited in practical application. There is an urgent need to develop ultra-precision strain measurement methods.

[0005] The present invention aims to solve the problem of high-precision measurement of inter-story displacement of building structures under earthquake excitation, especially the problem of insufficient precision caused by low sensitivity, large noise interference and complex installation in existing measurement methods. Based on optical flow analysis, the present invention introduces ultra-sensitive displacement measurement technology, extracts key motion features through singular value decomposition, and obtains high-precision displacement solutions. Summary of the invention

[0006] In order to overcome the shortcomings and deficiencies of the above-mentioned prior art, the present invention adopts the following technical solutions: A full-domain ultra-precision strain measurement method, the process is as follows: Step 1: Data collection: Use a high-frame-rate HD camera, determine the appropriate model and parameters according to the size and shape of the target area, firmly install the camera to reduce vibration interference, use the camera to continuously capture the moving images of the target area and enable the timestamp synchronization function; Step 2: Data preprocessing: Use camera calibration technology to correct the image, select the research area of ​​interest, obtain the grayscale value spatiotemporal data matrix of the research area, and standardize the data; Step 3, analysis process: add Gaussian white noise to the space-time matrix, construct the space-time matrix and perform SVD decomposition, extract the main motion features and reconstruct the space-time matrix; Step 4: Displacement calculation: Use the LK optical flow method to calculate the displacement of all feature points in the reconstructed matrix; Step 5: Displacement smoothing: Use LOESS to smooth the displacement data; Step 6: Strain calculation: Use the window sliding method to calculate the global strain.

[0007] Preferably, in the data collection step: When selecting a camera, ensure that the camera's field of view completely covers the target area to avoid line of sight obstruction; When installing the camera, use a shock-absorbing bracket to fix the camera on a stable platform; When acquiring images, taking bridge strain measurement as an example, images are continuously acquired while vehicles are passing.

[0008] Preferably, in the data preprocessing step: During image correction, the camera's intrinsic and extrinsic parameters are obtained by shooting the calibration plate image, and the original image is corrected so that each pixel accurately corresponds to the actual spatial position; When extracting and tracking features, the Lucas-Kanade optical flow method is used to track the motion trajectory of feature points between adjacent frame images; When normalizing data, the mean and standard deviation of the feature point displacement data are calculated, and the data is standardized according to the formula.

[0009] Preferably, in the analysis process steps: When adding random noise, Gaussian white noise with an intensity value between 0 and 0.5 is added to the space-time matrix to alleviate the quantization effect and improve the continuity of displacement calculation; Constructing a spatiotemporal matrix: Constructing a spatiotemporal matrix from feature point displacement data of a video or image sequence , whose dimensions are ;in, Indicates the number of spatial sampling points; Indicates the number of time frames; SVD decomposition: Perform SVD decomposition on the space-time matrix to obtain ;in is a left singular matrix, containing the main spatial features; is a singular value matrix whose diagonal elements reflect the importance of features; is the right singular matrix, containing the main dynamic modes in time; Extract the main motion features: Sort by singular value size, select the first r modes corresponding to the larger singular value, r is less than and , and obtain the simplified decomposition ; Space-time matrix reconstruction: Reconstruct the original space-time matrix using the retained r main eigenmodes ; Remove low-energy noise and irrelevant features and retain the main motion information.

[0010] Preferably, the displacement calculation step comprises: Determine feature points: Determine key points or feature areas on the target area that have obvious grayscale change characteristics and have been marked in the feature extraction and tracking steps based on the reconstruction matrix; Define neighborhood window: define a small neighborhood window of 3×3, 5×5 or 7×7 for each feature point, and assume that all pixels in the window move consistently; Establish the optical flow constraint equation: Based on the constant brightness assumption, let Represents the pixel at time t The gray value at adjacent time , the pixel moves to Position, its gray value remains unchanged, that is ;Will exist Perform a first-order Taylor expansion at: ; because , then the optical flow constraint equation can be obtained: ,in, and are the speeds of the pixel in the x and y directions, , are the gradients of the image in the x and y directions, respectively. is the gradient of the image in time; Solve the optical flow constraint equation: Combine the information of other pixels in the neighborhood window to establish the equation group and solve it using the least squares method; Iterative calculation: Update the position of the feature point according to the displacement obtained in the last calculation, recalculate the relevant parameters and solve the displacement until the displacement change is less than a certain threshold; Get displacement information: multiply the calculated speed by the time interval to get the displacement vector of the feature point between two frames.

[0011] Preferably, in the displacement smoothing step: Define a local neighborhood: For any point in the displacement field , define a circle around it of size The window is h, the radius is in the range of 3 - 7 pixels, and the points in the window are Smoothing calculation of participating point P; Select weighting function: calculate the distance from point Q in the neighborhood to the center point P , the weight function is defined according to the distance , the triangle weight function is , the Gaussian weight function is , is the standard deviation of weight distribution; Fitting a local polynomial: within a neighborhood window, based on weights , point The surrounding data are fitted with a first-order or second-order polynomial; the first-order polynomial is , the second-order polynomial is , using weighted least squares to calculate the fitting parameters ; Update center point value: Update the fitted polynomial at the center point The value of ; Traverse all points: Repeat the above steps for all points in the displacement field to generate a smooth displacement field .

[0012] Preferably, in the strain calculation step: According to the basic formula for strain calculation, the normal strain component , ; Shear strain component , by the displacement field The strain is calculated from the spatial derivative of Window sliding calculation: At each calculation point Around, take 30 to 50 points on the left and right sides, and use the displacement data of all points in the window to calculate the spatial derivative of the point by weighted fitting or difference method.

[0013] Preferably, this method can be applied to the strain measurement of structures such as bridges and buildings, providing data support for health monitoring and safety assessment of the structures.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention alleviates the quantization effect by adding Gaussian white noise, improves the continuity of displacement calculation, and provides a more stable data basis for subsequent analysis. In the feature extraction and tracking stage, the optical flow method is used to accurately track the key points or feature areas of the target area, further improving the measurement accuracy.

[0015] 2. The present invention uses singular value decomposition and reconstruction technology to extract the main motion features from complex spatiotemporal data, remove low-energy noise and irrelevant features, and retain the main motion information. This step not only simplifies the data model and reduces the computational complexity, but also enhances the robustness of data processing, so that the method can still maintain high-precision measurement in complex environments.

[0016] 3. The present invention uses a window sliding method to perform global strain calculation. This method can cover the entire target area and achieve global high-precision strain measurement. Around each calculation point, the spatial derivative (strain) is calculated using the displacement data of all points in the window, which increases the physical span of the calculation and reduces the impact of local noise. This method not only enhances smoothness, but also captures local features with high precision, while increasing the number of pixels involved in the calculation, significantly improving the measurement sensitivity. This is of great significance for the health monitoring of large structures such as bridges and buildings, and can promptly detect potential safety hazards and ensure structural safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a global ultra-precision strain measurement method of the present invention is shown; Figure 2 A flow chart of displacement smoothing of the present invention is shown. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure. Embodiment 1:

[0021] See also Figure 1 As shown, a global ultra-precision strain measurement method of this embodiment has the following process: Step 1: Data collection.

[0022] Camera selection and installation: Use a high-frame-rate HD camera, determine the appropriate model and parameters based on the size and shape of the target area, and ensure that the camera's field of view fully covers the target area to avoid line of sight obstruction. Take firm measures during installation to reduce vibration interference, such as using a shock-absorbing bracket to fix it on a stable platform.

[0023] Image acquisition: Use the selected camera to continuously capture moving images of the target area, and turn on the camera's timestamp synchronization function to ensure that each frame of the image records the exact shooting time. Taking bridge strain measurement as an example, images are continuously collected during vehicle traffic to provide a complete data sequence for subsequent analysis.

[0024] Step 2: Data preprocessing.

[0025] Image correction: Use camera calibration technology to remove lens distortion. By shooting the image of the calibration plate, the camera's intrinsic and extrinsic parameters are obtained, and the collected original image is corrected so that each pixel accurately corresponds to the actual spatial position, ensuring the accuracy of the measurement.

[0026] Feature extraction and tracking: Use the optical flow method to extract key points or feature areas on the target area (such as the bridge deck), track these feature points in each frame, record their displacement time series, and form a spatiotemporal data matrix. For example, use the Lucas-Kanade (LK) optical flow method to track the motion trajectory of feature points between adjacent frame images.

[0027] Data normalization (optional): If you need to eliminate the dimension effect, normalize the feature point displacement data. Calculate the mean of the feature point displacement data and standard deviation , the data is standardized according to the formula: .

[0028] Step 3: Analysis process.

[0029] S31. Add random noise: Add Gaussian white noise to the space-time matrix , noise intensity The value is between 0 and 0.5 to alleviate the quantization effect, improve the continuity of displacement calculation, and provide a more stable data basis for subsequent singular value decomposition.

[0030] S32. Singular value decomposition and reconstruction.

[0031] Constructing a spatiotemporal matrix: Constructing a spatiotemporal matrix from feature point displacement data of a video or image sequence , whose dimensions are ;in, Indicates the number of spatial sampling points (such as pixels or markers); Indicates the time frame number.

[0032] S33, SVD decomposition: Perform SVD decomposition on the space-time matrix to obtain ;in is a left singular matrix, containing the main spatial features; is a singular value matrix whose diagonal elements reflect the importance of features; is a right singular matrix, containing the main dynamic modes in time.

[0033] S34, extract the main motion features: sort by singular value size, select the first r modes corresponding to the larger singular value (r is much smaller than and ), and obtain the simplified decomposition .

[0034] S35, space-time matrix reconstruction: Reconstruct the original space-time matrix using the retained r main eigenmodes ; Remove low-energy noise and irrelevant features and retain the main motion information.

[0035] Step 4: Displacement calculation.

[0036] The LK optical flow method is used to calculate the displacement of all feature points in the reconstruction matrix. According to the grayscale changes of feature points in adjacent frame images, the displacement vector of feature points between two frames is obtained through iterative calculation to obtain the displacement information of each feature point.

[0037] Specifically, S41. Determine feature points.

[0038] Before calculating the displacement, the feature points for which the displacement needs to be calculated must first be determined based on the reconstruction matrix. These feature points are usually key points or feature areas on the target area (such as the bridge deck) that have been marked in the previous steps (such as feature extraction and tracking steps). These points should have obvious grayscale change characteristics so that they can be accurately tracked between adjacent frames.

[0039] S42. Define a neighborhood window.

[0040] Define a small neighborhood window for each feature point, such as the common 3×3, 5×5 or 7×7 windows. Within this window, it is assumed that the motion of all pixels is consistent. The choice of window size requires a trade-off between computational accuracy and computational efficiency. Smaller windows can provide more detailed local motion information, but may be more sensitive to noise; larger windows can enhance noise resistance, but may blur local motion details.

[0041] S43. Establish optical flow constraint equation.

[0042] According to the constant brightness assumption, Represents the pixel at time t The gray value at the adjacent time , the pixel moves to Position, its gray value remains unchanged, that is .Will exist Perform a first-order Taylor expansion at: ; because , then the optical flow constraint equation can be obtained: in and are the speed of the pixel in the x and y directions (i.e., the time rate of change of displacement), , are the gradients of the image in the x and y directions, respectively. is the temporal gradient of the image.

[0043] S44. Solve the optical flow constraint equation.

[0044] The optical flow constraint equation is an underdetermined equation (the unknown and More than the number of equations), it cannot be solved directly. Therefore, it is necessary to combine the information of other pixels in the neighborhood window. For each pixel in the neighborhood window, an optical flow constraint equation can be established to obtain a set of equations.

[0045] Assuming there are n pixels in the neighborhood window, the equation group can be expressed as:

[0046] The least squares method is usually used to solve this system of equations. The goal of the least squares method is to find and The value of makes the sum of squared errors on both sides of the equation system as small as possible. Specifically, it is to solve the following equation: ; in, ; .

[0047] S45. In actual calculations, iterative calculations may be required to improve the accuracy of the solution. In each iteration, the position of the feature point is updated according to the displacement obtained in the previous calculation, and then the image gradient and optical flow constraint equation in the neighborhood window are recalculated to solve the displacement again. The iterative process can be repeated multiple times until a certain convergence condition is met, such as the change in displacement is less than a certain threshold.

[0048] S46. Through the above steps, for each feature point in the reconstruction matrix, its velocity in the x and y directions can be calculated. and At known time intervals In the case of , multiply the speed by the time interval to get the displacement vector of the feature point between the two frames , thereby obtaining the displacement information of each feature point.

[0049] Step 5: Displacement smoothing.

[0050] LOESS (locally weighted scatter point smoothing) is used to smooth the displacement data.

[0051] See also Figure 2 As shown in the figure, the specific process of displacement smoothing is as follows: S51. Define local neighborhood: For any point in the displacement field , define a circle around it of size The point in the window is Smoothing calculation of participating point P.

[0052] S52, select weighted function: calculate the distance from point Q in the neighborhood to the center point P , the weight function is defined according to the distance The commonly used triangle weight function is , the Gaussian weight function is ( is the standard deviation of weight distribution), the triangular weight function is suitable for most scenarios, and the Gaussian weight function is more effective for noise-sensitive signals.

[0053] S53, Fitting local polynomial: In the neighborhood window, based on weights , point The surrounding data are fitted with a first-order or second-order polynomial. The first-order polynomial (local linear regression) is , the second-order polynomial (local quadratic regression) is , using weighted least squares to calculate the fitting parameters .

[0054] S54, update the center point value: update the fitting polynomial at the center point The value of .

[0055] S55. Traverse all points: Repeat the above steps for all points in the displacement field to generate a smooth displacement field .

[0056] Step 6: Strain calculation.

[0057] S61. Use the window sliding method to calculate the global strain.

[0058] Strain calculation principle: According to the basic formula of strain calculation, the positive strain component , Shear strain component , by the displacement field The strain is calculated from the spatial derivative of .

[0059] S62, Window sliding calculation: At each calculation point Around, take 30-50 points on each side (covering a certain physical length), use the displacement data of all points in the window, and calculate the spatial derivative (strain) of the point through weighted fitting or differential method. This method increases the physical span of the calculation, reduces the impact of local noise, suppresses high-frequency noise, enhances smoothness, can capture local features with high precision, and at the same time increases the number of pixels involved in the calculation and improves measurement sensitivity.

[0060] The beneficial effects of this embodiment are: high-precision displacement calculation is achieved by collecting data through a high-frame rate camera, combining image processing and SVD decomposition technology; LOESS smoothing processing improves data quality; window sliding strain calculation enhances measurement sensitivity and smoothness, effectively captures local features, and improves the global strain measurement accuracy.

[0061] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0062] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0064] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0065] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0066] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A global ultra-precision strain measurement method, characterized in that: The method flow is as follows: Step 1: Data collection: Use a high-frame-rate HD camera, determine the appropriate model and parameters according to the size and shape of the target area, firmly install the camera to reduce vibration interference, use the camera to continuously capture the moving images of the target area and enable the timestamp synchronization function; Step 2: Data preprocessing: Use camera calibration technology to correct the image, select the research area of ​​interest, obtain the grayscale value spatiotemporal data matrix of the research area, and standardize the data; Step 3: Analysis process: Add Gaussian white noise to the spatiotemporal matrix, construct the spatiotemporal matrix and perform SVD decomposition, extract the main motion features and reconstruct the spatiotemporal matrix; Step 4: Displacement calculation: Use the LK optical flow method to calculate the displacement of all feature points in the reconstruction matrix; Step 5: Displacement smoothing: Use the LOESS method to smooth the displacement data; Step 6: Strain calculation: Use the window sliding method to calculate the global strain.

2. The global ultra-precision strain measurement method according to claim 1, characterized in that: In the data collection step: When selecting a camera, ensure that the camera's field of view completely covers the target area to avoid line of sight obstruction; When installing the camera, use a shock-absorbing bracket to fix the camera on a stable platform; When acquiring images, taking bridge strain measurement as an example, images are continuously acquired while vehicles are passing.

3. The global ultra-precision strain measurement method according to claim 1, characterized in that: In the data preprocessing step: During image correction, the camera's intrinsic and extrinsic parameters are obtained by shooting the calibration plate image, and the original image is corrected so that each pixel accurately corresponds to the actual spatial position; When normalizing data, the mean and standard deviation of the feature point displacement data are calculated, and the data is standardized according to the formula.

4. The global ultra-precision strain measurement method according to claim 1, characterized in that: In the analysis process steps: When adding random noise, Gaussian white noise with an intensity value between 0 and 0.5 is added to the space-time matrix to alleviate the quantization effect and improve the continuity of displacement calculation; Constructing a spatiotemporal matrix: Constructing a spatiotemporal matrix from feature point displacement data of a video or image sequence , whose dimensions are ;in, Indicates the number of spatial sampling points; Indicates the number of time frames; SVD decomposition: Perform SVD decomposition on the space-time matrix to obtain ;in is a left singular matrix, containing the main spatial features; is a singular value matrix whose diagonal elements reflect the importance of features; is the right singular matrix, containing the main dynamic modes in time; Extract the main motion features: Sort by singular value size, select the first r modes corresponding to the larger singular value, r is less than and , and obtain the simplified decomposition ; Space-time matrix reconstruction: Reconstruct the original space-time matrix using the retained r main eigenmodes ; Remove low-energy noise and irrelevant features and retain the main motion information.

5. The global ultra-precision strain measurement method according to claim 1, characterized in that: The displacement calculation step comprises: Determine feature points: Determine key points or feature areas on the target area that have obvious grayscale change characteristics and have been marked in the feature extraction and tracking steps based on the reconstruction matrix; Define neighborhood window: define a small neighborhood window of 3×3, 5×5 or 7×7 for each feature point, and assume that all pixels in the window move consistently; Establish the optical flow constraint equation: Based on the constant brightness assumption, let Represents the pixel at time t The gray value at adjacent time , the pixel moves to Position, its gray value remains unchanged, that is ;Will exist Perform a first-order Taylor expansion at: ;because , then the optical flow constraint equation can be obtained: in, and are the speeds of the pixel in the x and y directions, , are the gradients of the image in the x and y directions, respectively. is the gradient of the image in time; Solve the optical flow constraint equation: Combine the information of other pixels in the neighborhood window to establish the equation group and solve it using the least squares method; Iterative calculation: Update the position of the feature point according to the displacement obtained in the last calculation, recalculate the relevant parameters and solve the displacement until the displacement change is less than a certain threshold; Get displacement information: multiply the calculated speed by the time interval to get the displacement vector of the feature point between two frames.

6. The global ultra-precision strain measurement method according to claim 1, characterized in that: In the displacement smoothing step: Define a local neighborhood: For any point in the displacement field , define a circle around it of size The window is h, the radius is in the range of 3 - 7 pixels, and the points in the window are Smoothing calculation of participating point P; Select weighting function: calculate the distance from point Q in the neighborhood to the center point P , the weight function is defined according to the distance , the triangle weight function is , the Gaussian weight function is , is the standard deviation of weight distribution; Fitting a local polynomial: within a neighborhood window, based on weights , point The surrounding data are fitted with a first-order or second-order polynomial; the first-order polynomial is , the second-order polynomial is , using weighted least squares to calculate the fitting parameters ; Update center point value: Update the fitted polynomial at the center point The value of ; Traverse all points: Repeat the above steps for all points in the displacement field to generate a smooth displacement field .

7. The global ultra-precision strain measurement method according to claim 1, characterized in that: In the strain calculation step: According to the basic formula for strain calculation, the normal strain component , Shear strain component , by the displacement field The strain is calculated from the spatial derivative of ; Window sliding calculation: At each calculation point Around, take 30 to 50 points on the left and right sides, and use the displacement data of all points in the window to calculate the spatial derivative of the point by weighted fitting or difference method.

8. A global ultra-precision strain measurement method according to any one of claims 1 to 7, characterized in that: This method can be applied to strain measurement of structures such as bridges and buildings, providing data support for health monitoring and safety assessment of structures.

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