A distributed optical fiber vibration high signal-to-noise ratio measurement system and method for long-span bridges

Abstract

The present invention discloses a distributed optical fiber vibration high signal-to-noise ratio measurement system and method for long-span bridges. The system includes a sensing transmission and acquisition module, a fading noise suppression module, a vibration time history calibration module, and a modal parameter automatic identification module. Among them: The sensing transmission and acquisition module collects digital signals containing bridge structure deformation information based on the principle of optical time domain reflectometry and the principle of coherent optical phase detection; the fading noise suppression module is used to obtain the initial distributed phase vibration time history after noise suppression; the vibration time history calibration module is used to obtain the phase vibration time history of each spatial sampling point of the bridge structure; the modal parameter automatic identification module is used to calculate the phase modal parameter set and optimize it. The present invention can realize distributed vibration measurement with a high measurement point density in the whole domain of long-span bridges while ensuring the accuracy of modal parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of structural health monitoring, and particularly to a distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system and method. Background Art

[0002] Long-span bridges are important infrastructure for long-distance cross-regional transportation, characterized by long spans, huge structural volumes, and large traffic flows. They generally include forms such as cable-stayed bridges and suspension bridges. Therefore, their bridge health monitoring (BHM) is particularly important. Vibration measurement, as a core component of BHM, can provide detailed structural dynamics response parameters as an important basis for structural state assessment. Traditional long-span bridge vibration measurement systems are mostly composed of several independent accelerometers. However, due to various limitations such as deployment and maintenance costs, long-distance signal synchronization and transmission, and installation conditions for most bridges, the number of vibration sensors is often insufficient, making it difficult to obtain ideal distributed vibration parameters. For this reason, distributed optical fiber acoustic sensing technology (DAS) can be used as an effective alternative to measure the distributed vibration response of long-span bridges.

[0003] Distributed optical fiber acoustic sensing technology can globally sense structural vibrations by monitoring the optical phase changes along the fiber, and can measure parameters such as amplitude and frequency, which is suitable for BHM. However, most traditional DAS systems are applied in fields such as perimeter security intrusion detection and pipeline and cable safety monitoring. Their functions are mostly single vibration positioning or frequency identification, and there are few product achievements developed for long-span bridge vibration measurement. In addition, most of the algorithms relied on by traditional DAS vibration measurement come from the field of digital signal processing, ignoring the vibration information contained in the dynamic characteristics of the bridge itself, which will lead to and is difficult to distinguish a large number of false / deteriorated modes. On the other hand, since the vibration measurement distance of long-span bridges usually exceeds 1 kilometer, there is a very high probability of signal fading when using traditional DAS measurement, resulting in serious noise in the vibration time history and making it impossible to extract modal parameters of nearby positions. Most of the signal fading suppression schemes proposed in the field of optoelectronic sensing are based on multi-frequency source pulse transmitting-receiving devices, which have the disadvantages of too high cost, inapplicability to engineering service environments, and incompatibility with mainstream commercial DAS devices. Some signal fading suppression methods based on digital domain demodulation have phenomena such as phase mode mismatch and random phase delay when processing bridge vibration signals, resulting in the failure of the method. In addition, error transmission during long-distance high-resolution sensing and digital demodulation will lead to a decrease in the accuracy of the identified modal parameters.

[0004] Based on the defects of the existing technology, the present invention proposes a distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system and method. Summary of the Invention

[0005] The main object of the present invention is to provide a distributed optical fiber vibration high signal-to-noise ratio measurement system and method for long-span bridges, which can realize distributed vibration measurement with a high measurement point density in the whole range of long-span bridges while ensuring the accuracy of modal parameters.

[0006] The technical solution adopted by the present invention is: a distributed optical fiber vibration high signal-to-noise ratio measurement system for long-span bridges, including a sensing transmission and acquisition module, a fading noise suppression module, a vibration time history calibration module, and a modal parameter automatic identification module; wherein:

[0007] The design of the sensing transmission and acquisition module is based on the principle of optical time domain reflectometer and the principle of coherent optical phase detection. The optical fiber laid on the bridge structure senses the vibration of the bridge structure and deforms, causing the emitted detection light to be modulated inside the optical fiber. The reflected light after modulation is subjected to polarization diversity, and then the two orthogonally polarized states of light obtained by polarization diversity are converted into digital signals;

[0008] The fading noise suppression module is used to receive the digital signals and obtain the initial distributed phase vibration time history after noise suppression according to the first untwisting algorithm;

[0009] The vibration time history calibration module is used to receive the initial distributed phase vibration time history and obtain the phase vibration time history of each spatial sampling point of the bridge structure according to the second untwisting algorithm;

[0010] The modal parameter automatic identification module is used to receive the phase vibration time history of each spatial sampling point of the bridge structure, calculate the phase modal parameter set according to the automatic phase modal parameter optimization identification algorithm and optimize it.

[0011] According to the above technical solution, the sensing transmission and acquisition module includes a detection light source end, an optical fiber, a demodulation end, and a signal conversion end; wherein: the detection light source end is used to generate detection light; the optical fiber is laid in the bridge structure and is used to modulate the detection light transmitted inside it and form a reflected light to return; the demodulation end is used to perform polarization diversity on the reflected light; the signal conversion end is used to convert the orthogonally polarized states of light that have completed polarization diversity into digital signals;

[0012] The fading noise suppression module includes a first filter end. The first filter end performs band-pass filtering on the orthogonally polarized light converted into digital signals to obtain a plurality of equivalent detection signals. Further, the equivalent detection signals correspond to the central lobe and the left and right side lobes of the signal; the fading noise suppression module also includes a first algorithm execution end, which is used to receive the plurality of equivalent detection signals, perform the operation of the first untwisting algorithm, and output the initial distributed phase vibration time history after noise suppression;

[0013] The vibration time history calibration module includes a second algorithm execution end for receiving the initial distributed phase vibration time history after noise suppression, executing a second untwisting algorithm, and outputting the phase vibration time history of each spatial sampling point of the bridge structure; the vibration time history calibration module further includes a second filter end for extracting the signal components corresponding to the frequency band to be analyzed during the execution of the second untwisting algorithm;

[0014] The modal parameter automatic identification module includes a third algorithm execution end for inputting, executing, and outputting an automatic phase modal parameter optimization identification algorithm.

[0015] According to the above technical solution, the first algorithm execution end is specifically used for:

[0016] Performing Hilbert transform on the multiple equivalent detection signals to obtain corresponding multiple initial vector detection signals;

[0017] Rotating the multiple initial vector detection signals in multiple stages until the multiple equivalent detection signals are in phase;

[0018] Summing the in-phase multiple equivalent detection signals, obtaining the phase angle of each spatial measurement point according to the summation result, and obtaining the phase distribution of the entire optical fiber arranged on the bridge structure through deconvolution;

[0019] Obtaining the initial distributed phase vibration time history of each spatial measurement point of the bridge structure according to the phase distribution, where the spatial measurement points correspond to the sampling points for continuously sampling the electrical signal at a preset rate.

[0020] According to the above technical solution, the rotation of the initial vector detection signals includes:

[0021] The original stage rotation according to the first formula, and the first formula is The first stage rotation according to the second formula, and the second formula is where the superscript number represents the processing stage number, i represents the serial number of different spatial measurement points, j represents different moments, is the reference vector, and p represents the serial number of the position where the reference vector is located;

[0022] The second stage rotation according to the third formula, the fourth formula, and the fifth formula, and the third formula is The fourth formula is The fifth formula is where, is the target matrix, where the superscript letters represent different polarization states, and N is the number of finite impulse response filters. Further, if there are already two or more vectors in the reference matrix that are very close to the corresponding elements of the target matrix, the calculation steps of the fifth formula can be skipped to improve the calculation efficiency of this algorithm.

[0023] According to the above technical solution, perform the third-stage rotation based on the relationship between the signal vectors and the composite vectors corresponding to the current spatial measurement points; wherein, the composite vector is determined based on the sixth formula, and the sixth formula is The judgment process specifically includes:

[0024] Preset a threshold, which is determined by the standard deviation of the phase angles of the initial vector detection signals; if the maximum interior angle between the signal vector and the composite vector of the spatial measurement point exceeds the threshold, rotate the initial vector detection signal that has completed the second-stage rotation according to the seventh formula until the current signal vectors coincide with the vector difference coincide, is the vector detection signal corresponding to the vector with the maximum interior angle, and the seventh formula is Otherwise, replace in the seventh formula with and perform the third-stage rotation.

[0025] According to the above technical solution, the method for obtaining the initial distributed phase vibration time history of each spatial measurement point of the bridge structure based on the phase distribution of the entire optical fiber includes: taking the difference of the phases of two spatial measurement points at each moment according to the phase distribution along the optical fiber.

[0026] According to the above technical solution, the specific execution end of the second algorithm is used for:

[0027] Eliminate the signal steps and fluctuations caused by environmental and system disturbances according to the eighth formula, the ninth formula, and the tenth formula, where the eighth formula includes The ninth formula is The tenth formula is where d0 is a preset step threshold, is the phase average value from time τ j to τ j+p ; is the phase average value from time τ j-p to τ j ; P is the preset signal segment duration, is the central trend line of the phase time history from time τ j to τ j+p , and the acquisition method of includes the nonlinear least squares method;

[0028] After filtering out low-frequency and high-frequency clutter caused by the environment and system operation at the second filter end, the outliers are replaced by multiple spline interpolations. The method for judging outliers includes that if the absolute value of a certain measurement value exceeds a preset measurement threshold, it is determined as an outlier, and the measurement threshold is determined according to the average Hilbert amplitude in its neighborhood.

[0029] According to the above technical solution, the third algorithm execution end is specifically used for:

[0030] Calculate the phase vibration time history of each spatial sampling point of the bridge structure through the time-domain analysis operation kernel, continuously obtain the initial phase modal parameter set through iteration, and delete the false or deteriorated modes in the initial phase modal parameter set;

[0031] Compare the modes in adjacent initial phase modal parameter sets pairwise. If the two are of the same order of mode, only retain the mode with a larger consistent mode index value in the latter set; if the two are orthogonal modes, retain both of them in the latter set;

[0032] Change the model order and monitoring data set in the time-domain analysis operation kernel, and repeat the above process until the final mode set under the current number of rows of the Hankel matrix is obtained. The Hankel matrix is a mathematical operator in the time-domain analysis process;

[0033] Delete the similar modes in the final mode set, use the final mode set obtained from the previous row size of the Hankel matrix as the initial mode set corresponding to the new row size, and iteratively optimize this initial mode set according to the above operations using the new Hankel matrix row size to obtain a new final mode set;

[0034] Repeat the above steps until the number of eigenmodes in the final set no longer increases or the Hankel matrix row size is greater than or equal to the preset value when the preset number of iterations is reached. Further, at this time, the modal parameter set converges, and the included eigenfrequencies and modal shapes can be used as the final vibration parameters.

[0035] According to the above technical solution, the time-domain analysis operation kernel includes the eigensystem realization algorithm, the data-driven or covariance-driven stochastic subspace method, the Hilbert-Huang transform, and the least squares complex exponential method; further, the time-domain analysis operation kernel can be used in combination with the natural excitation technique or the multi-reference point natural excitation technique.

[0036] The phase modal parameter set includes eigenfrequencies, mode shapes, damping ratios, and consistent mode indices;

[0037] The method for judging false or deteriorated modes is specifically as follows: if a mode meets any of the following conditions, it is judged as a false or deteriorated mode, including: Among them, CMI is the consistent modal index, the subscript i represents the i-th mode in the set, k1 and k2 are preset values, is the damping ratio, is the characteristic frequency.

[0038] On the other hand, the present invention provides a method for measuring the vibration of a long-span bridge with high signal-to-noise ratio using distributed optical fiber, including:

[0039] Laying optical fibers on the bridge structure, emitting detection light in the optical fibers and receiving reflected light, performing polarization diversity on the reflected light, and converting it into digital signals; further, the optical fibers are mainly laid on the main girder at the bottom of the bridge deck, the key cables to be measured, and the entire length of the bridge tower of the bridge structure.

[0040] Obtaining the initial distributed phase time history of the bridge structure according to the first untwisting algorithm and the digital signals;

[0041] Obtaining the phase vibration time history of each spatial measurement point of the bridge structure according to the second untwisting algorithm and the initial distributed phase time history after suppressing noise of the electrical signals;

[0042] Obtaining a set of phase modal parameters and optimizing them according to the automatic phase modal parameter optimization and identification algorithm and the phase vibration time history of each spatial measurement point of the bridge structure.

[0043] On the other hand, the present invention provides a device for measuring the vibration of a long-span bridge with high signal-to-noise ratio using distributed optical fiber, including: a bridge structure, a sensing optical fiber, a narrow-linewidth laser, an optical coupler, an acousto-optic modulator, an erbium-doped fiber amplifier, an optical circulator, a polarization beam splitter, a balanced photodetector, a data acquisition card, and a data acquisition and storage device; further, the sensing optical fiber is arranged on the bridge structure and the return path as a sensor and a signal transmission channel; the tail end of the sensing optical fiber is connected to the demodulation end, constituting the sensing and transmission hardware foundation of the sensing transmission acquisition module; the narrow-linewidth laser is used to emit incident light, and the incident light is modulated by the acousto-optic modulator to form pulsed light, and an arbitrary function generator acts on the acousto-optic modulator to generate a pulsed signal and synchronously triggers the data acquisition card for analog-to-digital conversion; the optical circulator and the optical coupler are light guiding devices for forming an optical path with a specific path, and the fiber optic flange is also a light guiding device for docking two fiber optic connectors to form a continuous optical path; the erbium-doped fiber amplifier is used to amplify the optical pulse signal; the polarization beam splitter is used to separate the local light and the detection light into an orthogonal state, generating orthogonal polarization state optical signals I x(t) and I y(t) ; the balanced photodetector converts the optical signal into an electrical signal and inputs it to a two-channel high-frequency data acquisition card for realizing the acquisition function of the sensing transmission acquisition module.

[0044] The incident light emitted by the narrow linewidth laser is divided into two paths by an optical coupler. One path serves as the local light and enters a polarization beam splitter, while the other path serves as the detection light and sequentially passes through an acousto-optic modulator and an erbium-doped fiber amplifier, and then enters the sensing fiber deployed on the bridge structure through an optical circulator. The detection light forms Rayleigh backscattered light in the sensing fiber and also enters the polarization beam splitter through the optical circulator. The two beams of light after polarization diversity by the polarization beam splitter are converted into electrical signals by an optoelectronic balanced detector. The electrical signals are input into a data acquisition card to be continuously sampled and converted into digital signals, and finally enter the acquisition and storage device to participate in the execution of various algorithms.

[0045] Another aspect of the present invention provides a computer storage medium, which when executed by a processor implements the above-mentioned distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement method.

[0046] The beneficial effects of the present invention are as follows: The present invention provides a distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system and method. By suppressing the fading noise and distortion of the detection signal inside the fiber deployed on the bridge structure, a high signal-to-noise ratio phase vibration time history is obtained. Based on this distributed phase vibration time history, an automatic phase modal parameter optimization identification algorithm is designed to control error transmission and screen out false or deteriorated modes, so as to accurately obtain parameters such as the natural vibration frequency and modal vibration mode of the bridge structure.

[0047] Furthermore, the present invention designs a first untwisting algorithm and a second untwisting algorithm, which effectively suppress the fading noise in the spatial dimension and the signal distortion in the time dimension during the vibration measurement of long-span bridges in digital demodulation form.

[0048] Furthermore, the present invention designs an automatic phase modal parameter optimization identification algorithm, which can automatically identify and optimize the modal parameters based on the phase time history, and at the same time efficiently eliminate false modes and distinguish true modes.

[0049] Furthermore, the present invention adopts a digital demodulation method with high adaptability, convenience and efficiency, avoiding introducing additional modulation modules and increasing the hardware cost.

[0050] Furthermore, the present invention improves the reliability and recognition rate of modal recognition based on optical phase, which helps to avoid the influence caused by phase measurement error and demodulation instability.

[0051] In summary, compared with the prior art, the present invention has the advantages of wide sensing range, stable long-distance transmission signal, simple sensor deployment, large measurement point density, wide response frequency band, good automatic monitoring performance, etc., and is especially suitable for large-scale distributed monitoring of long distance, multiple objects and multiple components. On the premise of ensuring the accuracy of modal parameters, the distributed vibration measurement of long-span bridges with a high measurement point density in the whole domain range is realized.

[0052] Of course, it is not necessary for any product implementing the present invention to achieve all of the above-described advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order 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 use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 is a schematic diagram of the architecture of a distributed vibration high signal-to-noise ratio measurement system for long-span bridges using distributed fiber optic acoustic sensing according to an embodiment of the present invention;

[0055] Figure 2 is a flowchart of the operation of the first untwisting algorithm in a distributed vibration high signal-to-noise ratio measurement system for long-span bridges using distributed fiber optic acoustic sensing according to an embodiment of the present invention;

[0056] Figure 3 is a flowchart of the second untwisting algorithm in a distributed vibration high signal-to-noise ratio measurement system for long-span bridges using distributed fiber optic acoustic sensing according to an embodiment of the present invention;

[0057] Figure 4 is a flowchart of the automatic phase modal parameter optimization identification algorithm in a distributed vibration high signal-to-noise ratio measurement system for long-span bridges using distributed fiber optic acoustic sensing according to an embodiment of the present invention;

[0058] Figure 5 is a flowchart of the method for measuring the vibration of a distributed fiber optic long-span bridge with high signal-to-noise ratio according to an embodiment of the present invention;

[0059] Figure 6 is an operation logic diagram of the untwisting algorithm in another distributed vibration high signal-to-noise ratio measurement system for long-span bridges using distributed fiber optic acoustic sensing according to another embodiment of the present invention;

[0060] Figure 7 is an operation logic diagram of the automatic phase modal parameter optimization identification algorithm in another distributed vibration high signal-to-noise ratio measurement system for long-span bridges using distributed fiber optic acoustic sensing according to an embodiment of the present invention;

[0061] Figure 8 is a schematic diagram of a distributed fiber optic long-span bridge vibration high signal-to-noise ratio measurement device according to an embodiment of the present invention.

[0062] Reference numerals: 1, bridge structure; 2, sensing optical fiber; 3, narrow linewidth laser; 4, optical coupler; 5, acousto-optic modulator; 6, erbium-doped fiber amplifier; 7, optical circulator; 8, polarization beam splitter; 9, photoelectric balanced detector; 10, acquisition card; 11, acquisition and storage device. Detailed implementation mode

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] It should be noted that the drawings provided in the embodiments of the present invention only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0065] In the present invention, it should also be noted that when terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present application. In addition, when terms such as "first" and "second" appear, they are only used for descriptive and distinguishing purposes and cannot be understood as indicating or implying relative importance.

[0066] Embodiment 1

[0067] This embodiment provides a distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system, as Figure 1 shown, including a sensing transmission acquisition module, a fading noise suppression module, a vibration time history calibration module, and a modal parameter automatic identification module; wherein:

[0068] The sensing transmission acquisition module is used to generate detection light inside the optical fiber laid on the bridge structure, receive the reflected light and perform polarization diversity, and then convert the light in the orthogonal polarization states obtained by polarization diversity into digital signals and input them into the fading noise suppression module.

[0069] Specifically, the perception transmission acquisition module realizes the acquisition and transmission of signal light based on the principle of optical time domain reflectometry and the principle of coherent optical phase detection, including a detection light source end, a demodulation end, and a signal conversion end; wherein: the detection light source end is used to generate detection light; the demodulation end is used to perform polarization diversity on the reflected light; the signal conversion end is used to convert the light of the orthogonal polarization states that have completed polarization diversity into digital signals.

[0070] Transmit pulsed detection light into the optical fiber arranged in the bridge structure. The optical fiber deforms due to structural vibration, and then the detection light inside the optical fiber is modulated. Receive the modulated reflected light, perform polarization diversity after the demodulation end, and then convert the two orthogonally polarized lights obtained by diversity into electrical signals through a balanced detector. After continuous sampling by the acquisition card, it is converted into digital signals and input into the fading noise suppression module.

[0071] Further, the process of continuous sampling by the acquisition card is as follows: The acquisition card continuously samples the electrical signals output by the photodetector at a certain rate. Each sampling point corresponds to a spatial measurement point in the actual bridge structure. The order of continuous sampling is the sequence of these spatial measurement points in the spatial dimension. According to the principle of optical time domain reflectometry, the position of each spatial measurement point on the optical fiber can be calculated.

[0072] The fading noise suppression module is used to obtain the initial distributed phase vibration time history after noise suppression according to the first untwisting algorithm and the digital signal.

[0073] Specifically, the fading noise suppression module includes a first algorithm execution end and a first filter end required for input, execution, and output of the first untwisting algorithm. A finite impulse response filter (FIR) is selected in the first filter end. The first untwisting algorithm specifically includes the following formulas (1) to (10).

[0074] The fading noise suppression module first performs band-pass filtering on the original optical intensity beat frequency signals I x(t) and I y(t) of the two orthogonal polarization states through a finite impulse response filter (FIR) to obtain six equivalent detection signals x1, x2, x3, y1, y2, y3 corresponding to the central lobe and the left and right side lobes. Then, perform the operation of the first untwisting algorithm on the obtained equivalent detection signals, as Figure 2 shown, including the steps:

[0075] S1. Obtain the initial vector detection signal h'(t) through Hilbert transform. The calculation formula is as follows,

[0076] I x ′(t) = f x (t) * I x (t), I y ′(t) = f y(t)*I y (t) (1)

[0077] h′(t) = Hilbert[I′(t)] (2)

[0078] Wherein, I'(t) represents each equivalent detection signal after filtering, corresponding to the specific equivalent detection signal through the subscript, f(t) is the FIR filter operator, t is the reflection light time delay of each spatial measurement point, and Hilbert(·) is the Hilbert operator. Through the operation of this step, a complex signal is derived with the original signal as the real part and its Hilbert transform pair as the imaginary part.

[0079] S2. Rotate the multiple initial vector detection signals in multiple stages until the multiple equivalent detection signals are in phase.

[0080] S201. Initial stage rotation: Rotate all h'(t) according to formula (3) to eliminate the initial phase difference between adjacent spatial measurement points.

[0081]

[0082] Where the superscript number represents the processing stage number, the same below, i represents the spatial measurement point serial number, and j represents different moments.

[0083] S202. First stage rotation: Rotate again with the reference vector as the reference.

[0084]

[0085] Where p is the position number of the reference vector. Preferably, the reference vector is selected as the vector with a larger amplitude among the positions before the interval to be analyzed.

[0086] S203. Second stage rotation: Use the reference vector and the subsequent continuous m vectors to form a reference matrix, which is in the form shown in the square brackets of formula (5), and then use as the target matrix. Compare the differences between the reference matrix and the target matrix, and rotate . The calculation formula for the above process is as follows,

[0087]

[0088] Where the superscript letter represents different polarization states, N is the number of FIR filters, and by taking the optimal value for θ j ', the sum of the inner products of the corresponding elements of the target matrix and the reference matrix can reach the maximum.

[0089] Preferably, the optimal value can be quickly obtained through the univariate particle swarm optimization algorithm.

[0090] Preferably, to improve the calculation efficiency, if there are already two or more vectors in the reference matrix that are very close to the corresponding elements of the target matrix, the calculation of θ j ' and the steps of formula (6) can be skipped.

[0091] S204. Third-stage rotation: For each spatial measurement point position q, set a threshold to determine whether the signal vectors corresponding to each frequency source at the current position deviate from the synthetic vector This threshold can be estimated with reference to the standard deviation of the phase angles of each vector detection signal. The formula for the synthetic vector is as follows.

[0092]

[0093] If the maximum interior angle among each exceeds the threshold, then at the current moment, all 6 vector detection signals are rotated from position q to the end position according to formula (8) so that each coincides, where s represents the vector detection signal with the largest interior angle; otherwise, replace in formula (8) with for calculation. The reason for the replacement is that when it is determined that the maximum interior angle among each does not exceed the threshold, all detection signal vectors are considered to have no obvious phase angle error, and even the signal represented by s, which has the largest angle deviation, does not need to be excluded.

[0094]

[0095] Among them, represents the part to be rotated, the superscript represents the processing stage, the subscript q:end represents rotating from position q to the end position, and j represents the moment.

[0096] S3. Sum the multiple in-phase equivalent detection signals, and obtain the phase angle of each spatial measurement point according to the summation result. The phase distribution of the entire optical fiber arranged on the bridge structure is obtained through deconvolution.

[0097] S4. Obtain the initial distributed phase vibration time history of each spatial measurement point of the bridge structure, where the spatial measurement points correspond to the sampling points that continuously sample the electrical signal at a preset rate.

[0098] The phase increment between two spatial measurement points shows a linear correspondence with the strain generated in the optical fiber between these two measurement points. Therefore, by differentiating the phase time series of the two spatial measurement points, the obtained phase time history corresponds to the strain time history caused by vibration. Based on this, the initial distributed phase vibration time history of the bridge structure can be obtained.

[0099]

[0100] θ ij =arctan[Im(V ij ) / Re(V ij )] (10)

[0101] The above-mentioned fading noise suppression module does not need to introduce additional hardware devices such as modulation modules, avoiding cost increase. Instead, it adopts a digital demodulation method with high adaptability, convenience and efficiency, and can construct multi-polarization state and multi-frequency source equivalent pulses to complement each other the information loss in the fading area. Finally, the influence of phase angle mismatch and random phase delay is effectively eliminated through four-stage signal attitude calibration.

[0102] The vibration time history calibration module is used to obtain the phase vibration time history of each spatial sampling point of the bridge structure according to the second untwisting algorithm and the initial distributed phase vibration time history.

[0103] Specifically, the vibration time history calibration module includes a second algorithm execution end required for inputting, executing, and outputting the second untwisting algorithm. The second untwisting algorithm calibrates various signal distortion phenomena prone to occur in the distributed fiber acoustic sensing technology (DAS) in the time dimension. As Figure 3 shown, the second algorithm execution end is specifically used to execute the following algorithm:

[0104] T1. Eliminate the signal steps and fluctuations caused by environmental and system disturbances according to equations (11) to (13):

[0105]

[0106]

[0107]

[0108] Among them, d0 is a preset step threshold, is the phase average value from time τ j to τ j+p , is the phase average value from time τ j-p to τ j , P is the preset signal segment duration, is the time from τ j to τ j+pThe central tendency line of the phase time course can be obtained by non - linear least squares method.

[0109] T2. Use a digital band - pass filter to extract the signal components corresponding to the frequency band to be analyzed, and filter out the low - frequency and high - frequency clutter caused by the environment and system operation.

[0110] T3. Replace the outliers by cubic spline interpolation.

[0111] Furthermore, if the absolute value of a certain measurement exceeds a given threshold, it can be determined as an outlier, and this threshold can be determined according to the average Hilbert amplitude in its neighborhood.

[0112] According to the operation processes of the above - mentioned first de - twist algorithm and second de - twist algorithm, the main operation logics of the de - twist algorithm including the first de - twist algorithm and the second de - twist algorithm are as Figure 6 shown, including:

[0113] (1) Input orthogonal signals: The execution device of the first de - twist algorithm receives the digital signals converted from two orthogonally polarized light.

[0114] (2) Equivalent pulse extraction: Perform band - pass filtering on the original optical intensity beat - frequency signals of two orthogonally polarized states through FIR to obtain 6 equivalent detection signals corresponding to the central lobe and the left and right side lobes.

[0115] (3) Construct vector signals: Perform Hilbert transform on the equivalent detection signals to obtain the initial vector detection signals.

[0116] (4) Signal attitude calibration: Perform multi - stage rotation on the initial vector detection signals, including:

[0117] a. Eliminate the original phase shift: Perform the original - stage rotation to eliminate the initial phase difference between adjacent spatial measurement points.

[0118] b. Set the reference direction: Set the reference vector and reference matrix

[0119] c. Correct the reference direction: Perform the first - stage rotation based on the reference vector, and perform the second - stage rotation by comparing the differences between the reference matrix and the target matrix.

[0120] d. Eliminate random phase delay: For each measuring point, set a threshold to determine whether the signal vectors corresponding to the frequency sources at the current position deviate from the synthetic vector. If the maximum inner angle between each signal vector and the synthetic vector exceeds the threshold, all six vector detection signals at the current moment are rotated from the current position to the end position and perform the third stage rotation so that the difference between each signal vector and the synthetic vector and the vector detection signal with the maximum inner angle coincides; otherwise, replace the difference between the synthetic vector and the vector detection signal with the maximum inner angle with the synthetic vector and perform the third stage rotation, so that the six groups of vector detection signals become in phase.

[0121] (5) Signal component superposition: sum the six groups of vector detection signals that are already in phase.

[0122] (6) Phase distribution unwrapping: Calculate the phase angle of each spatial measurement point and restore the phase distribution along the entire optical fiber through the unwrapping operation.

[0123] (7) Phase time history extraction: Differ the phases of the two measuring points at each moment to obtain the phase vibration time history of the initial distribution.

[0124] (8) Remove step drift: Use the formula to eliminate signal steps and fluctuations caused by environmental and system disturbances.

[0125] (9) Filter out environmental noise: Use a digital bandpass filter to extract the signal components corresponding to the frequency band to be analyzed and filter out low-frequency and high-frequency noise caused by the environment and system operation.

[0126] (10) Eliminate outliers: replace outliers through cubic spline interpolation.

[0127] (11) Complete the timeline reconstruction.

[0128] The modal parameter automatic identification module is used to calculate and optimize the phase modal parameter set according to the automatic phase modal parameter optimization identification algorithm and the phase vibration time history of each spatial sampling point of the bridge structure, and includes a third algorithm execution end for inputting, executing and outputting the automatic phase modal parameter optimization identification algorithm.

[0129] Specifically, Figure 4 As shown, the third algorithm execution end is specifically used to implement the following algorithm:

[0130] P1. The phase time history output by the vibration time history calibration module is used as input data through the time domain analysis operation kernel to calculate the phase modal parameter set n and delete the false or degraded modes in the mode.

[0131] Furthermore, the phase modal parameter set n includes eigenfrequency, vibration mode, damping ratio and consistent mode indicator (CMI).

[0132] The selection of the time-domain analysis operation kernel is not unique. Current mainstream algorithms such as the eigen-system realization algorithm, data-driven or covariance-driven stochastic subspace method, Hilbert-Huang transform, least squares complex exponential method, etc. can be used in combination with natural excitation technology. In this embodiment, the eigen-system realization algorithm (ERA) is selected as the time-domain analysis operation kernel.

[0133] The modes with poor parameter value evaluation in set n are regarded as false or degraded modes and are deleted. The specific evaluation criterion is that a certain mode satisfies any of the following conditions, including where the subscript i represents the i-th mode in the set, and the preset value k1 takes 0.5 - 0.7. is the damping ratio, and the preset value k2 takes 0.3. is the characteristic frequency.

[0134] P2. Compare set n with set n - 1. For the same-order modes, only the modes with higher CMI in n are retained. For the orthogonal modes, the modes in n - 1 that are orthogonal to all modes in n are directly added to n. The series of optimizations carried out in this step make the current set n gradually approach the true mode set.

[0135] P3. Change the model order and the monitoring data set in the calculation kernel respectively, and continuously repeat the above process to obtain the final mode set under the current number of rows of the Hankel matrix. The Hankel matrix is a mathematical operator in the time-domain analysis process, and the modal identification result based on the phase vibration time history is closely related to the row size of the Hankel matrix.

[0136] P4. After deleting the similar modes, use the final mode set obtained from the previous row size as the initial mode set corresponding to the new row size, and then use the new row size to iteratively optimize this initial mode set according to the above operations to obtain a new final mode set.

[0137] P5. Repeat the above steps until convergence. The convergence criterion is that the number of eigenmodes in the final set no longer increases within a certain number of iterations or the row size of the Hankel matrix reaches the upper limit. The eigenfrequencies and modal shapes contained in the modal parameter set at the convergence point can be used as the final vibration parameters.

[0138] Furthermore, the specific logic of the automatic phase modal parameter optimization and identification algorithm is as Figure 7 shown, including:

[0139] (1) Set the ERA parameters.

[0140] (2) Input the phase time history of all spatial measurement points of the bridge structure.

[0141] (3) Call ERA to calculate the modal parameter set n, which includes the characteristic frequency Damping ratio and modal shape.

[0142] (4) Determine whether each mode in the modal parameter set n satisfies any one of them. If it satisfies, delete this mode.

[0143] (5) Compare the adjacent (n - 1)-th modal parameter set and the n-th modal parameter set to determine whether they satisfy

[0144]

[0145] If it satisfies further determine whether it satisfies MAC ij > k4; if it satisfies MAC ij > k4, let S i,j = MAC ij ; if it does not satisfy MAC ij > k4, let S i,j = 0. If it does not satisfy let S i,j = 0. Where MAC ij is the modal assurance criterion value between two modes i and j for comparison, and S i,j is the temporary array of the comparison results of mode j with all modes in n.

[0146] (6) Find the maximum value S i,j in S r,j , and determine whether it satisfies S r,j = 0.

[0147] If it satisfies S r,j = 0, add the mode j in the (n - 1)-th modal parameter set to n;

[0148] If it does not satisfy S r,j = 0, further determine whether it satisfies If it satisfies replace the mode r in n with the mode j in the (n - 1)-th modal parameter set.

[0149] (7) Determine whether it satisfies and MAC i,i+1 > k6.

[0150] If it satisfies and MAC i,i+1 > k6, delete the mode with a smaller CMI value in mode i or i + 1; if it does not satisfy and MAC i,i+1If it is > k6, then increment i by 1 and continue the above comparison until all modes are traversed.

[0151] (8) Iteratively optimize this initial mode set according to the above operations with the new line size until the mode set converges.

[0152] The automatic phase mode parameter optimization and identification algorithm for the operation of this module fully considers the influence of the system model order, relevant dimension parameters, and different measurement data sets in the operation kernel. Then, an iterative optimization logic is used to screen out false or degraded modes, retain as many real modes as possible and make them approach the highest confidence level, effectively reducing the negative impact of error transmission in the measurement and demodulation stages on the frequency, especially the vibration mode. At the same time, it overcomes the shortcomings such as the loss of real modes, inaccurate order determination, and distorted vibration modes that may be caused by the traditional method based on the modal stability diagram.

[0153] Based on the above distributed optical fiber vibration high signal-to-noise ratio measurement system for long-span bridges, this embodiment also provides a distributed optical fiber vibration high signal-to-noise ratio measurement device based on DAS, as Figure 8 shown, including: bridge structure 1, sensing optical fiber 2, narrow linewidth laser 3, optical coupler 4, acousto-optic modulator 5, erbium-doped fiber amplifier 6, optical circulator 7, polarization beam splitter 8, photoelectric balanced detector 9, acquisition card 10, acquisition and storage device 11.

[0154] The incident light emitted by the narrow linewidth laser 3 is divided into two paths by the optical coupler 4. One path serves as the local light and enters the polarization beam splitter 8, and the other path serves as the detection light. After passing through the acousto-optic modulator 5 and the erbium-doped fiber amplifier 6 in sequence, it also enters the polarization beam splitter 8 through the optical circulator 7. The two beams of light after polarization diversity by the polarization beam splitter 8 are converted into electrical signals by the photoelectric balanced detector 9. The electrical signals are input into the acquisition card 10 for continuous sampling, and finally enter the acquisition and storage device 11 to participate in the execution of the algorithm.

[0155] The sensing optical fiber 2 is arranged on the bridge structure 1 and the return path as a sensor and a signal transmission channel; the tail end of the sensing optical fiber 2 is connected to the demodulation end, constituting the sensing and transmission hardware basis of the sensing transmission acquisition module; the narrow linewidth laser 3 is used to emit incident light, and the incident light is modulated by the acousto-optic modulator 5 to form pulsed light. The acousto-optic modulator 5 is used to generate pulsed signals with the help of an arbitrary function generator and synchronously trigger the acquisition card 10 for analog-to-digital conversion. The acquisition card 10 is a two-channel high-frequency acquisition card; the optical circulator 7 and the optical coupler 4 are light guiding devices used to form an optical path with a specific path; the erbium-doped fiber amplifier 6 is used to amplify the optical pulse signal; the polarization beam splitter 8 is used to separate the local light and the detection light into an orthogonal state, generating orthogonal polarization state optical signals I x(t) and I y(t); The balanced photodetector 9 converts the optical signal into an electrical signal and inputs it to the acquisition card 10. As described above, the acquisition card 10 converts the electrical signal into a digital signal, and finally enters the device 11 for acquisition and storage.

[0156] Embodiment 2

[0157] This embodiment provides a method for measuring the vibration of a long-span bridge with high signal-to-noise ratio using a distributed optical fiber. The execution of this method is based on the distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system described in Embodiment 1. As Figure 5 shown, the process includes:

[0158] A1. Lay optical fibers on the bridge structure, emit detection light in the optical fibers and receive the reflected light, perform polarization diversity on the reflected light, and convert it into a digital signal.

[0159] The implementation of this step is based on the sensing transmission and acquisition module of the distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system. The components and corresponding functions of this module have been described in Embodiment 1 and will not be repeated here.

[0160] A2. Obtain the initial distributed phase time history of the bridge structure according to the first untwisting algorithm and the digital signal.

[0161] The implementation of this step is based on the fading noise suppression module of the distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system. The components and corresponding functions of this module have been described in Embodiment 1 and will not be repeated here.

[0162] A3. Obtain the phase vibration time history of each spatial measurement point of the bridge structure according to the second untwisting algorithm and the initial distributed phase time history after noise suppression of the electrical signal.

[0163] The implementation of this step is based on the vibration time history calibration module of the distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system. The components and corresponding functions of this module have been described in Embodiment 1 and will not be repeated here.

[0164] A4. Obtain the phase modal parameter set and optimize it according to the automatic phase modal parameter optimization and identification algorithm and the phase vibration time history of each spatial measurement point of the bridge structure.

[0165] The implementation of this step is based on the modal parameter automatic identification module of the distributed optical fiber long-span bridge vibration high signal-to-noise ratio measurement system. The components and corresponding functions of this module have been described in Embodiment 1 and will not be repeated here.

[0166] Based on the above-mentioned method for measuring the vibration of long-span bridges with high signal-to-noise ratio using distributed optical fibers, this embodiment also provides a non-volatile computer storage medium. The computer storage medium stores computer-executable instructions, which are executed by one or more processors and are used for the above-mentioned method for measuring the vibration of long-span bridges with high signal-to-noise ratio using distributed optical fibers.

[0167] In summary, the present invention provides a system and method for measuring the vibration of long-span bridges with high signal-to-noise ratio using distributed optical fibers. By designing an algorithm, it automatically and computerizedly realizes the suppression of fading noise and signal distortion in the spatial and temporal dimensions during the vibration measurement of long-span bridges, and completes the measurement of the modal parameter set. The present invention can achieve distributed vibration measurement with a high measurement point density in the entire domain of long-span bridges while ensuring the accuracy of modal parameters.

[0168] It should be noted that according to the needs of implementation, each step / component described in this application can be split into more steps / components, or two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0169] In the above embodiments, the magnitudes of the sequence numbers of the steps do not mean the order of execution. 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 this application.

[0170] It should be understood that those of ordinary skill in the art can make improvements or transformations according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system, characterized in that: It includes a sensing transmission acquisition module, a fading noise suppression module, a vibration time history calibration module, and a modal parameter automatic identification module; among which: The sensing transmission acquisition module is based on the principle of optical time domain reflectometry and coherent optical phase detection. The optical fiber arranged on the bridge structure senses the deformation of the bridge structure after vibration, so that the emitted detection light is modulated inside the optical fiber. The modulated reflected light is polarized and then the two orthogonal polarization states of light obtained by polarization diversity are converted into digital signals. The fading noise suppression module is used to receive the digital signal, and obtain the initial distributed phase vibration time history after noise suppression according to the first detwisting algorithm; the first detwisting algorithm is used to extract the equivalent pulse of the digital signal and construct it into a vector signal, perform attitude calibration, component superposition and deconvolution on the vector signal, and extract the initial distributed phase vibration time history; the attitude calibration includes performing an original phase rotation to eliminate the initial phase difference of adjacent spatial sampling points; presetting a reference vector and a reference matrix, performing a first phase rotation based on the reference vector, and performing a second phase rotation based on the difference between the target matrix and the reference matrix; performing a third phase rotation based on the degree of deviation between the vector signal and the synthetic vector; The vibration time history calibration module is used to receive the initial distributed phase vibration time history, and obtain the phase vibration time history of each spatial sampling point of the bridge structure according to the second detwist algorithm; the second detwist algorithm is used to remove the step drift, environmental noise and outliers in the initial distributed phase vibration time history, and obtain the phase vibration time history of each spatial sampling point of the bridge structure; The modal parameter automatic identification module is used to receive the phase vibration time history of each spatial sampling point of the bridge structure, calculate and optimize the phase modal parameter set according to the automatic phase modal parameter optimization identification algorithm.

2. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 1 is characterized in that: The sensing transmission acquisition module includes a detection light source end, an optical fiber, a demodulation end and a signal conversion end; wherein: the detection light source end is used to generate detection light; the optical fiber is arranged in the bridge structure, and is used to modulate the detection light transmitted inside it to form reflected light for return; the demodulation end is used to perform polarization diversity on the reflected light; the signal conversion end is used to convert the light in the orthogonal polarization state that completes polarization diversity into a digital signal; The fading noise suppression module includes a first filter end, which performs bandpass filtering on the orthogonal polarization state light converted into the digital signal to obtain a plurality of equivalent detection signals, and the fading noise suppression module also includes a first algorithm execution end, which is used to receive the plurality of equivalent detection signals, perform a first detwist algorithm operation, and output an initial distributed phase vibration time history after noise suppression; The vibration time history calibration module includes a second algorithm execution end, which is used to receive the initial distributed phase vibration time history after noise suppression, execute the second detwist algorithm, and output the phase vibration time history of each spatial sampling point of the bridge structure. The vibration time history calibration module also includes a second filter end, which is used to extract the signal component corresponding to the frequency band to be analyzed during the execution of the second detwist algorithm; The modal parameter automatic identification module includes a third algorithm execution end for inputting, executing and outputting an automatic phase modal parameter optimization identification algorithm.

3. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 2 is characterized in that: The first algorithm execution end is specifically used for: The multiple equivalent detection signals are subjected to Hilbert transformation to obtain corresponding multiple initial vector detection signals; Rotating the multiple initial vector detection signals in multiple stages until the multiple equivalent detection signals are in phase; Summing the multiple equivalent detection signals of the same phase, obtaining the phase angle of each spatial measurement point according to the summation result, and obtaining the phase distribution of the entire optical fiber laid by the bridge structure by unwinding; The initial distributed phase vibration time history of each spatial measurement point of the bridge structure is obtained according to the phase distribution, wherein the spatial measurement points correspond to sampling points at which the electrical signal is continuously sampled at a preset rate.

4. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 3 is characterized in that: The rotation performed by the initial vector detection signal includes: The original phase rotation is performed according to the first formula, which is The first stage rotation is performed according to the second formula, which is Among them, the superscript numbers represent the rotation stages, i represents the serial numbers of different spatial measurement points, and j represents different moments. is the reference vector, and p represents the number of the spatial measuring point where the reference vector is located; The second stage rotation is performed according to the third formula, the fourth formula and the fifth formula, wherein the third formula is The fourth formula is The fifth formula is in, is the target matrix, the superscript letters represent different polarization states, N is the number of finite length unit impulse response filters, θ j 'The value selected to maximize the sum of the inner products of the corresponding elements of the target matrix and the reference matrix, wherein the reference matrix is ​​a matrix consisting of the reference vector and m consecutive vectors thereafter.

5. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 4 is characterized in that: According to the relationship between the signal vector and the synthetic vector corresponding to each current spatial measurement point, the third stage rotation is performed; wherein the synthetic vector is determined based on the sixth formula, and the sixth formula is The third stage rotation specifically includes: A threshold is preset, and the threshold is determined by the phase angle standard deviation of each initial vector detection signal; if the maximum inner angle between the signal vector and the synthetic vector of the spatial measurement point exceeds the threshold, the initial vector detection signal that has completed the second stage rotation is rotated according to the seventh formula to the current phase angle standard deviation of each signal vector and the vector difference. coincide, is the vector detection signal with the largest internal angle, the seventh formula is Otherwise, replace Replace with Perform the third stage rotation.

6. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 3 is characterized in that: The method for obtaining the initial distributed phase vibration time history of each spatial measuring point of the bridge structure according to the phase distribution of the entire optical fiber includes: according to the phase distribution along the optical fiber, the phase of two spatial measuring points at each moment is differentiated.

7. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 2 is characterized in that: The second algorithm execution end is specifically used for: According to the eighth, ninth and tenth formulas, the signal steps and fluctuations caused by environmental and system disturbances are eliminated, wherein the eighth formula includes The ninth formula is The tenth formula is Where d0 is the preset step threshold, is the time τ j to τ j+p The average phase value of is the time τ j-p to τ j The phase average value, P is the preset signal segment duration, is the time τ j to τ j+p The central trend line of the phase time course is The methods for obtaining include nonlinear least squares method; After filtering out low-frequency and high-frequency clutter caused by the environment and system operation at the second filter end, outliers are replaced by multiple spline interpolations. The method for determining outliers includes: if the absolute value of a certain measurement value exceeds a preset measurement threshold, it is determined as an outlier, and the measurement threshold is determined according to the average Hilbert amplitude in its neighborhood.

8. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 2 is characterized in that: The third algorithm execution end is specifically used for: The phase vibration time history of each spatial sampling point of the bridge structure is calculated by the time domain analysis operation kernel, an initial phase modal parameter set is obtained by iteration, and false or degraded modes in the initial phase modal parameter set are deleted; Compare each mode in the adjacent initial phase modal parameter set. If the two modes are of the same order, only the mode with the larger consistent modal index value is retained in the latter set. If the two modes are orthogonal, both are retained in the latter set. Changing the model order and monitoring data set in the time domain analysis operation kernel, repeating the above process until the final mode set under the current number of Hankel matrix rows is obtained, wherein the Hankel matrix is ​​a mathematical operator in the time domain analysis process; Deleting similar modes in the final mode set, using the final mode set obtained by the previous row size of the Hankel matrix as the initial mode set corresponding to the new row size, and iteratively optimizing the initial mode set according to the above operation using the new Hankel matrix row size to obtain a new final mode set; The above steps are repeated until the number of eigenmodes in the final set no longer increases or the row size of the Hankel matrix is ​​greater than or equal to a preset value when the number of iterations is less than a preset value.

9. The distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to claim 8 is characterized in that: The time domain analysis operation kernel includes a characteristic system realization algorithm, a data-driven or covariance-driven random subspace method, a Hilbert-Huang transform, and a least squares complex exponential method; The phase modal parameter set includes eigenfrequency, vibration mode, damping ratio and consistent modal index; The method for judging the false or degraded mode is specifically that if the mode satisfies any of the following conditions, it is judged as a false or degraded mode, including: f i n =0; where CMI is the consistent modal index, the subscript i represents the i-th mode in the set, k1 and k2 are preset values, is the damping ratio, f i n is the characteristic frequency.

10. A distributed optical fiber long bridge vibration high signal-to-noise ratio measurement method, the method is executed based on the distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system according to any one of claims 1 to 9, characterized in that: include: An optical fiber is arranged on the bridge structure, a detection light is emitted in the optical fiber and a reflected light is received, polarization diversity is performed on the reflected light, and the reflected light is converted into a digital signal; Obtaining an initial distributed phase time history of the bridge structure according to the first detwist algorithm and the digital signal; According to the second detorsion algorithm and the initial distributed phase time history of the bridge structure, the phase vibration time history of each spatial measuring point of the bridge structure is obtained; According to the automatic phase modal parameter optimization identification algorithm and the phase vibration time history of each spatial measuring point of the bridge structure, a phase modal parameter set is obtained and optimized.

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