Structural vibration signal extraction method under adverse environment interference

Abstract

The invention relates to the technical field of vision measurement and vibration signal processing, in particular to a structure vibration signal extraction method under adverse environment interference. The method comprises the steps of collecting a structure vibration image through a camera, preprocessing the image and constructing a space-time matrix, and realizing noise reduction by using singular value decomposition. And compressing the space-time matrix into a one-dimensional vibration time sequence through displacement field estimation and space averaging. According to a delay embedding method, a one-dimensional vibration time sequence is reconstructed into a two-dimensional phase space matrix, singular value decomposition is applied again, and effective separation of a real vibration signal and a camera motion interference signal is achieved. By introducing the singular value decomposition and embedding reconstruction technology, accurate and efficient structural vibration signal extraction is realized, and the requirements for accuracy and real-time performance of vibration data in engineering application are met.

Description

Technical Field

[0001] The present invention relates to the technical field of visual measurement and vibration signal processing, and particularly relates to a method for extracting structural vibration signals under adverse environmental interference. Background Art

[0002] The structural vibration response is one of the key indicators in the safety assessment and health monitoring of engineering structures, and it can intuitively reflect the dynamic characteristics and stability of the structure under external excitation. The current structural vibration monitoring technologies can be roughly divided into two categories: contact type and non-contact type. Contact vibration measurement includes accelerometers, laser vibrometers, etc. Although the measurement accuracy is high, the number of measurement points is limited and the installation process of sensors may cause disturbance to the structure; the laser vibrometer measures the surface vibration velocity or displacement in a non-contact manner, with good measurement sensitivity, but it is usually only applicable to small-area or single-point measurement and has high requirements for environmental stability. Non-contact visual vibration measurement, including methods for extracting the structural surface motion trajectory based on video image sequences, can achieve wide-area, multi-point, and non-invasive monitoring, but its measurement accuracy is limited by image quality, the stability of the acquisition device, and the robustness of subsequent processing algorithms.

[0003] Although the existing visual measurement technologies have achieved non-contact and multi-point vibration data acquisition to a certain extent, there are the following main deficiencies: during the image acquisition process, the camera is easily interfered by environmental factors such as wind and ground vibration, resulting in the camera motion signal being superimposed on the image sequence and affecting the extraction of the true vibration signal; traditional methods mostly assume that the camera is stable or only rely on post-processing filtering, and it is difficult to effectively distinguish structural vibration from camera motion in complex environments; the data processing algorithm is sensitive to noise, especially in low signal-to-noise ratio scenarios, where the vibration frequency and modal information are easily confused or masked, affecting the accuracy of the monitoring results.

[0004] The key to achieving high-precision extraction of structural vibration signals lies in improving the image signal processing ability, optimizing the signal separation algorithm, and performing data reconstruction by combining theories such as the embedding theorem to enhance the separability of vibration signal features and interference signals. In addition, developing a simple and efficient data processing flow and visualization interface is also the key to ensuring the feasibility and popularization of the method in practical engineering applications. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the purpose of the present invention is to provide a method for extracting structural vibration signals under adverse environmental interference, which overcomes the deficiencies of traditional visual vibration measurement methods in aspects such as insufficient processing of camera motion interference, low accuracy of vibration signal extraction, and poor environmental adaptability. Compared with the existing visual vibration monitoring methods, the present invention breaks through the limitations of traditional image processing methods in noise suppression and signal separation by introducing singular value decomposition and embedding reconstruction technologies. Especially under complex environments and low signal-to-noise ratio conditions, it can achieve high-precision extraction of structural vibration signals, meeting the requirements for the accuracy and real-time performance of vibration data in engineering applications.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A method for extracting structural vibration signals under adverse environmental interference, the process is as follows: Step 1. Configuration of the image acquisition system: Set the camera pixel resolution to 1920×1080 pixels and the frame rate to 50 frames per second to ensure that small dynamic motion details on the surface of the structure can be captured, while taking into account the real-time sampling requirements. Fix the camera in front of the free end of the cantilever beam to ensure that the detection area covers the entire vibration amplitude range, and apply an external excitation to the camera with a high-power fan to make the camera shake; Step 2. Image acquisition: Give an initial excitation to the cantilever beam to make it vibrate freely, and use the camera to continuously acquire vibration images for about 40 seconds to ensure a sufficient modal recognition time window; Step 3. Image preprocessing: Perform grayscale and normalization processing on the vibration images to enhance signal features and reduce the influence of background light changes; Step 4. Spatiotemporal noise reduction processing: Construct the vibration image sequence into a three-dimensional matrix, apply the singular value decomposition (SVD) method, and screen and retain the singular modes with an energy proportion of more than 90% to effectively remove random noise and improve the image quality; Step 5. Displacement field estimation and spatial averaging: Calculate the local block displacement field between adjacent frames through the cross-correlation method, and perform spatial averaging within the region of interest to compress it into a one-dimensional vibration time series; Step 6. Phase space reconstruction: According to the delay embedding method, reconstruct the one-dimensional vibration time series into a two-dimensional phase space matrix to fully reveal the dynamic characteristics of the signal; Step 7. Vibration signal extraction and processing: Apply SVD again to retain 2-3 main singular modes (usually the main vibration modes of the structure) to effectively separate the real vibration signal of the structure from the camera motion interference signal; Step 8. Signal reconstruction and analysis: Inverse transform the extracted main vibration modes into time-domain signals, perform a reference modal test on the cantilever beam with a laser vibrometer, and compare and verify the time-domain vibration signals with the results of the laser vibrometer; Step 9: Data Output and Visualization: Intuitively display the dynamic response of the cantilever beam through vibration mode animation; export the processed vibration data and generate an analysis report containing modal frequencies, mode shape characteristics, and signal quality assessment.

[0007] Furthermore, in the data preprocessing step, image grayscale conversion and normalization correction are performed. The specific method is as follows: Use the formula to calculate the pixel value size of the grayscale image, where represent the color intensities of the red, green, and blue channels respectively, ; Use the formula to calculate the normalized pixel value, where is the pixel value of the grayscale image, min(Gray) and max(Gray) are the minimum and maximum pixel values of the grayscale image, is the target normalization range.

[0008] Furthermore, in the spatio-temporal noise reduction processing step, a spatio-temporal matrix is constructed. The specific process includes: The image sequence captured by the camera contains K frames of images, and the size of each frame of image is H×W, where H is the height and W is the width. For each frame of image, use the formula to flatten the two-dimensional image into a one-dimensional vector, where represents the pixel intensity vector of the k-th frame of the image, represents the k-th frame of the image.

[0009] Stack the vectors of all frames in columns in sequence to form a spatio-temporal matrix M, , where the rows represent the spatial dimension (each pixel position) and the columns represent the time series (different frames).

[0010] Furthermore, perform singular value decomposition on the spatio-temporal matrix M, which specifically includes the following process: Use the formula to transform the matrix, where represents the left singular vector (time basis), represents the diagonal matrix of singular values, represents the right singular vector (modal direction); Take the first 2 singular values and the corresponding vectors ; Use the formula to reconstruct the low-rank matrix ; Taking the cumulative energy ratio as the criterion, retain the main modal components corresponding to the first r singular values with an energy contribution rate greater than 90%, and filter out environmental noise and low-amplitude perturbations. Use the formula Calculate the cumulative energy contribution rate of the first r singular values, where p is the number of diagonal elements in the diagonal matrix in SVD , that is, the number of singular values; Find the smallest r such that , where is a preset energy threshold.

[0011] Furthermore, the specific method for displacement field estimation is as follows: Select a reference sub-window from the image grayscale , and use the formula to select a reference sub-window centered at the coordinate with a size of ; The recommended size is pixels (empirical value, depending on the image resolution and texture scale).

[0012] Furthermore, the specific method for spatial averaging is as follows: Use the normalized cross-correlation calculation formula to calculate the similarity between the current window and the candidate region in the target frame , where is the search offset vector, is the mean value of the target frame , is the mean value of the target frame ; Manually or automatically select the region with significant structural vibration as the spatial averaging domain, and finally obtain a one-dimensional vibration time series .

[0013] Furthermore, the phase space reconstruction specifically includes the following process: According to the delay embedding method, the delay time , the embedding dimension d = 10 - 10000, construct the vibration time series into a two-dimensional phase space matrix. According to the frame rate of the camera being 50 frames per second and the sampling rate being relatively low, the delay time is selected to reveal the dynamic characteristics of the signal to the greatest extent; the embedding dimension d = 10 - 10000 to construct a sufficiently large dimensional matrix for SVD to separate the mixed signal.

[0014] For the one-dimensional vibration time series , determine its delay time and embedding dimension d, and form a two-dimensional matrix of as follows: .

[0015] Furthermore, the vibration signal extraction and processing specifically include the following process:​ Perform SVD on the reconstructed phase space matrix again. According to experience and experimental results, the cumulative contribution of the first two singular values often exceeds 90% of the total energy, corresponding to the singular modes of the main structural vibration signals. Therefore, only the first two singular modes are retained, corresponding to the true vibration signal of the cantilever beam and the micro-motion interference signal of the camera respectively, to achieve effective signal separation.

[0016] Furthermore, for signal reconstruction, the specific method is as follows: Based on the first-order and second-order modal signals of the cantilever beam separated by SVD, the time-domain vibration signals are deduced inversely. The time-domain vibration signal of each mode corresponds to the amplitude change of that mode over time. The time-domain vibration signal of the i-th mode can be expressed as the i-th row of the right singular matrix That is, where represents the time point.

[0017] Furthermore, for data output and visualization, it specifically includes the following processes: <* [[ID=*17]]Intuitively display the dynamic response of the cantilever beam through vibration mode animations, which is convenient for subsequent dynamic modeling and simulation analysis; export the processed vibration data, and automatically generate an analysis report including modal frequencies, mode shape characteristics, and signal quality assessment, which is convenient for engineering applications and decision support.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. Through non-contact visual measurement combined with singular value decomposition and extraction technology, the present invention effectively realizes the high-precision separation of the true vibration signal of the structure and the camera interference signal in a complex environment. Especially at the construction site with strong wind disturbances, ground vibrations, or equipment interferences, it can still significantly improve the reliability and accuracy of vibration signal extraction; 2. By introducing two-stage singular value decomposition processing (spatiotemporal noise reduction + phase space separation), the present invention can retain the main vibration modes of the structure under a high-noise background, suppress random noise and pseudo-signals, and ensure the complete extraction of vibration characteristics. At the same time, the system supports real-time visualization analysis of vibration data, generates detailed modal frequency and dynamic response diagrams, and supports data export and automatic generation of standardized analysis reports, which is convenient for subsequent engineering evaluation and decision support; 3. During the signal transformation process, no complex operations are involved, optimizing the data processing flow, taking into account both algorithm efficiency and accuracy, and meeting the requirements of real-time and large-scale data processing in engineering applications; 4. The method has a wide range of applications, covering vibration monitoring tasks of different types of structures (such as buildings, bridges, tunnels, mechanical equipment) and various excitation conditions (such as wind vibration, traffic loads, seismic actions). BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 It is a flowchart of a method for extracting structural vibration signals under adverse environmental interference; Figure 2 and Figure 3 It is a data comparison chart of the test results in the embodiments of the present invention. Detailed implementation manners Embodiment

[0021] Referring to Figure 1 as shown, a method for extracting structural vibration signals under adverse environmental interference in this embodiment is as follows: Step 1: Configuration of the image acquisition system.

[0022] The camera pixel resolution is set to 1920×1080 pixels, and the frame rate is 50 frames per second to ensure that minute dynamic motion details on the surface of the structure can be captured while taking into account the real-time sampling requirements. The camera is fixedly installed in front of the free end of the cantilever beam to ensure that the detection area covers the entire vibration amplitude range, and a high-power fan is used to apply an external excitation to the camera to make the camera shake.

[0023] Step 2: Image acquisition.

[0024] Give an initial excitation to the cantilever beam to make it vibrate freely, and continuously acquire vibration images for about 40 seconds with the camera to ensure a sufficient modal recognition time window.

[0025] Step 3: Image preprocessing (eliminating the interference caused by background light changes and enhancing signal features).

[0026] Grayscale processing: The weighted average method is adopted for grayscale processing, and the specific formula is as follows: ; where respectively represent the color intensities of the red, green, and blue channels, .

[0027] Normalization correction: Map the grayscale image pixel values to a specified interval, and the specific formula is as follows: ; where is the pixel value of the grayscale image, min(Gray) and max(Gray) are the minimum and maximum values of the grayscale image pixel value, is the target normalization range, in this embodiment .

[0028] Step 4: Spatiotemporal noise reduction processing.

[0029] Construct the space-time matrix: The vibration image sequence is constructed into a spatiotemporal matrix according to the following method: The image sequence captured by the camera contains K frames, each of which has a size of H×W, where H is the height and W is the width. For each frame, the two-dimensional image is flattened into a one-dimensional vector using the following formula: ; in, represents the pixel intensity vector of the kth frame of the image, Represents the k-th frame image.

[0030] The vectors of all frames are stacked column by column to form a The space-time matrix is as follows: ; Here, rows represent spatial dimensions (each pixel location) and columns represent temporal sequences (different frames).

[0031] Singular Value Decomposition (SVD): Perform singular value decomposition on the above space-time matrix M. The specific formula is as follows: ; : left singular vector (time basis); : singular value diagonal matrix; : right singular vector (modal direction); Take the first two singular values and the corresponding vector ; Reconstructing low-rank matrix , the formula is as follows: ; Based on the cumulative energy ratio, the main modal components corresponding to the first r singular values with an energy contribution rate greater than 90% are retained, and environmental noise and low-amplitude disturbances are filtered out. The cumulative energy contribution rate of the first r singular values is calculated as follows: ; Where p is the diagonal matrix in SVD The number of diagonal elements, that is, the number of singular values.

[0032] The goal is to find the minimum r such that , where , is a preset energy threshold. According to prior knowledge, r = 2 is taken in this embodiment.

[0033] Step Five: Displacement field estimation and spatial averaging.

[0034] Displacement field estimation: Select a reference sub-window from the image grayscale . The following is a formula for selecting a reference sub-window centered at the coordinate with a size of : ; The recommended size is pixels (empirical value, depending on the image resolution and texture scale); 50% overlapping sliding sampling can be used to form a dense displacement field.

[0035] Spatial averaging: Use normalized cross-correlation to calculate the similarity between the current window and the candidate region in the target frame . The formula is as follows: ; where is the search offset vector, is the mean of the target frame , is the mean of the target frame .

[0036] Manually or automatically select the region with significant structural vibration as the spatial averaging domain, and finally obtain the one-dimensional vibration time series .

[0037] Step Six: Phase space reconstruction.

[0038] According to the delay embedding method, with the delay time and the embedding dimension d = 10 - 10000, construct the vibration time series into a two-dimensional phase space matrix for subsequent extraction and analysis. In this embodiment, the frame rate of the camera is 50 frames per second, and the sampling rate is relatively low. Therefore, the delay time is selected to reveal the dynamic characteristics of the signal to the greatest extent; the embedding dimension d = 10 - 10000 is used to construct a sufficiently large dimensional matrix for SVD to separate the mixed signals. Specifically as follows: For the one-dimensional vibration time series , determine its delay time and embedding dimension , and form a two-dimensional matrix of :

[0039] ;

[0040] Step 7: Vibration signal extraction and processing.

[0041] Perform SVD on the phase space matrix obtained in Step 6 again. According to experience and experimental results, the first two singular values often cumulatively contribute more than 90% of the total energy, corresponding to the singular modes of the main structural vibration signals. Therefore, only the first two singular modes are retained, corresponding to the true vibration signal of the cantilever beam and the micro-motion interference signal of the camera respectively, effectively realizing the extraction of vibration signals.

[0042] Step 8: Signal reconstruction and analysis.

[0043] Based on the first-order and second-order modal signals of the cantilever beam separated by SVD, the time-domain vibration signals are deduced backwards. The time-domain vibration signal of each mode corresponds to the amplitude change of that mode over time. The specific calculation method is as follows:

[0044] The time-domain vibration signal of the i-th mode is the i-th row of the right singular matrix :

[0045] ;

[0046] where , representing the time point.

[0047] To verify the accuracy of the extracted signals, a laser vibrometer is used to conduct a benchmark modal test on the cantilever beam, and the time-domain vibration signals are compared and verified with the results of the laser vibrometer. As shown in the figure, Figure 2 is the structural vibration signal measured by the camera under wind load, Figure 3 is the structural vibration signal measured by the camera under seismic waves. The normalized root mean square error (NRMSE) obtained by the traditional method and the method of the present invention is shown in Figure 2 and Figure 3 in the legend.

[0048] Step 9: Data output and visualization.

[0049] The time-domain vibration signals of the structure obtained through extraction can be input into Matlab or Python software, and the dynamic response of the cantilever beam can be intuitively displayed through vibration modal animation, facilitating subsequent dynamic modeling and simulation analysis; at the same time, the processed vibration data is exported, and an analysis report containing modal frequencies, vibration mode characteristics, and signal quality assessment can be generated, facilitating engineering applications and decision-making support.

[0050] Through the detailed introduction and comparative analysis of this embodiment, the vibration signal extraction method based on SVD spatio-temporal noise reduction and extraction of the present invention shows significant advantages over traditional visual measurement methods in many aspects, especially in the extraction accuracy of vibration signals, non-contact operation, and data processing efficiency in complex environments. This method provides a more reliable, economical, and efficient technical solution for the monitoring of the dynamic characteristics of results in practical engineering.

[0051] The above formulas are all dimensionless and take their numerical calculations. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0052] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using 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 processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0053] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0054] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0055] 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 foregoing method embodiments and will not be repeated here.

[0056] In several embodiments provided in this 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 merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0057] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0058] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application and should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for extracting structural vibration signals under adverse environmental interference, characterized in that, Including: Step 1, Image acquisition system configuration: Fix the camera in front of the structure to be measured, and apply an external excitation to the camera with a fan to make the camera shake; Step 2, Image acquisition: Apply an initial excitation to the structure to be measured to make it vibrate freely, and continuously acquire the vibration images of the structure to be measured with the camera; Step 3, Image preprocessing: Perform grayscale and normalization processing on the vibration images of the structure to be measured; Step 4, Spatiotemporal noise reduction processing: Construct a three-dimensional matrix from the vibration images of the structure to be measured, and apply the singular value decomposition method for noise reduction processing; Step 5, Displacement field estimation and spatial averaging: Calculate the local block displacement field between adjacent frames in the constructed three-dimensional matrix by the cross-correlation method, and perform spatial averaging to obtain the one-dimensional vibration time series of the structure to be measured; Step 6, Phase space reconstruction: According to the delay embedding method, reconstruct the one-dimensional vibration time series into a two-dimensional phase space matrix; Step 7, Extraction processing: Apply singular value decomposition to the reconstructed phase space matrix again, retain the main singular modes, and separate the true vibration signal of the structure to be measured from the camera motion interference signal; Step 8, Signal reconstruction and analysis: Inverse transform the main singular modes obtained by singular value decomposition into time-domain signals, perform a reference modal test on the structure to be measured with a laser vibrometer, and compare and analyze the measurement results with the vibration signals extracted by this method; Step 9, Data output and visualization: Intuitively display the dynamic response of the structure to be measured through a vibration modal animation, and export the frequency and modal shape data files.

2. The method for extracting structural vibration signals under adverse environmental interference according to claim 1, wherein In the spatiotemporal noise reduction processing step, construct a spatiotemporal matrix, and the specific process includes: The image sequence collected by the camera contains K frames of images, and the size of each frame of image is H×W, where H is the height and W is the width; for each frame of image, use the formula , to flatten the two-dimensional image into a one-dimensional vector, where represents the pixel intensity vector of the k-th frame of the image, represents the k-th frame of the image; stack the vectors of all frames column by column in turn to form a spatiotemporal matrix M, , where the rows represent the spatial dimension and the columns represent the time series.

3. A method for extracting structural vibration signals under adverse environmental interference according to claim 1, characterized in that, Perform singular value decomposition on the spatiotemporal matrix M, specifically including the following process: Use the formula , transform the matrix, where represents the left singular vector, represents the diagonal matrix of singular values, represents the right singular vector; take the first 2 singular values and the corresponding vectors ; use the formula , reconstruct the low-rank matrix ; taking the cumulative energy ratio as the criterion, retain the main modal components corresponding to the first r singular values with an energy contribution rate greater than 90%; use the formula , calculate the cumulative energy contribution rate of the first r singular values, where p is the number of diagonal elements of the diagonal matrix in the singular value decomposition; find the smallest r such that , where is the preset energy threshold.

4. A method for extracting structural vibration signals under adverse environmental interference according to claim 1, characterized in that, The specific method of displacement field estimation and spatial averaging is: Select a reference sub-window from the image grayscale and use the formula to select a reference sub-window centered at the coordinate with a size of ; ​ Use the normalized cross - correlation calculation formula to calculate the similarity between the current window and the candidate region in the target frame , where is the search offset vector, is the mean value of the target frame , is the mean value of the target frame . Manually or automatically select the region with significant vibration of the structure to be measured as the spatial averaging domain, and finally obtain a one-dimensional vibration time series .

5. A method for extracting structural vibration signals under adverse environmental interference according to claim 1, characterized in that, Phase space reconstruction, specifically including the following process: According to the delay embedding method, the delay time , embedding dimension d = 10 - 10000, construct a one-dimensional vibration time series into a two-dimensional phase space matrix; for the one-dimensional vibration time series , determine its delay time and embedding dimension , and form the following two-dimensional matrix: 。 6. The method for extracting the structural vibration signal under the interference of a bad environment according to claim 1, wherein, Extraction processing, specifically including the following process: Perform singular value decomposition on the reconstructed phase space matrix again, retain the first two singular modes, which respectively correspond to the true vibration signal of the structure to be measured and the camera micro-motion interference signal.

7. A method for extracting structural vibration signals under adverse environmental interference according to claim 1, characterized in that, The specific method of signal reconstruction is: Based on the first-order and second-order modal signals of the structure to be measured separated by singular value decomposition, the time-domain vibration signal is inversely deduced; the time-domain vibration signal of the i-th mode can be expressed as the i-th row of the right singular matrix , that is , where represents the time point.

8. A method for extracting structural vibration signals under adverse environmental interference according to claim 1, characterized in that Data output and visualization specifically include: Intuitively display the dynamic response of the structure to be measured through a vibration modal animation; The processing system of this method supports exporting the processed vibration data, and can automatically generate an analysis report including modal frequency, vibration mode characteristics and signal quality evaluation.

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