Distributed optical fiber long and large bridge vibration high signal-to-noise ratio measurement system and method

Abstract

The invention discloses a distributed optical fiber long and large bridge vibration high signal-to-noise ratio measurement system and method. The system comprises a sensing transmission acquisition module, a fading noise suppression module, a vibration time history calibration module and a modal parameter automatic identification module. Wherein the sensing transmission acquisition module is used for acquiring a digital signal containing bridge body structure deformation information based on an optical time domain reflectometer principle and a coherent optical phase detection principle; the fading noise suppression module is used for obtaining an initial distributed phase vibration time history after noise suppression; the vibration time history calibration module is used for obtaining the phase vibration time history of each space sampling point of the bridge body structure; and the modal parameter automatic identification module is used for calculating and optimizing a phase modal parameter set. According to the invention, on the premise of ensuring the modal parameter precision, the distributed vibration measurement of the long and large bridge under the global range high measuring point density is realized.

Description

Technical Field

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

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

[0003] Distributed fiber optic acoustic sensing technology can sense structural vibration from a global perspective by monitoring the phase change of light along the optical fiber, and can measure parameters such as amplitude and frequency, which is suitable for BHM. However, traditional DAS systems are mostly used in the fields of perimeter security intrusion detection, pipeline cable safety monitoring, etc., and their functions are mostly single vibration positioning or frequency identification, and there are few product results developed for vibration measurement of long bridges. In addition, the algorithms relied on by traditional DAS vibration measurement mostly 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 make it difficult to distinguish a large number of false / degraded modes. On the other hand, since the vibration measurement distance of long bridges is usually more than 1 km, there is a high probability of signal fading when using traditional DAS measurements, resulting in serious noise in the vibration time history, and it is even more impossible to extract modal parameters at nearby locations; some signal fading suppression schemes that have been proposed in the field of optoelectronic sensing are mostly based on multi-frequency source pulse transmitting-receiving devices, which have the disadvantages of being too expensive, not suitable for engineering service environments, and not matching mainstream commercial DAS devices. Some signal fading suppression methods based on digital domain demodulation have phase pattern mismatch and random phase delay when processing bridge vibration signals, which leads to the failure of the method. In addition, the 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 prior art, the present invention proposes a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system and method. Summary of the invention

[0005] The main purpose of the present invention is to provide a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system and method, which can realize distributed vibration measurement of the long bridge with high measurement point density over the entire domain while ensuring the accuracy of modal parameters.

[0006] The technical solution adopted by the present invention is: a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system, 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:

[0007] The design of 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.

[0008] 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 de-twisting 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 detorsion 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 and optimize the phase modal parameter set according to the automatic phase modal parameter optimization identification algorithm.

[0011] According to the above technical solution, 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 to 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;

[0012] The fading noise suppression module includes a first filter end, and the first filter end performs bandpass filtering on the orthogonal polarization state light converted into the digital signal to obtain a plurality of equivalent detection signals. Furthermore, the equivalent detection signal corresponds to the central lobe and 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 detwist 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, 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;

[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] The multiple equivalent detection signals are subjected to Hilbert transformation 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 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;

[0019] 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.

[0020] According to the above technical solution, the rotation performed by the initial vector detection signal includes:

[0021] 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 processing stage numbers, i represents the sequence numbers of different spatial measurement points, and j represents different moments. is the reference vector, and p represents the number of the position where the reference vector is located;

[0022] 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, and N is the number of finite-length unit impulse response filters. Furthermore, if there are two or more vectors in the reference matrix that are very close to the corresponding elements of the target matrix, the calculation step of the fifth formula can be skipped to improve the calculation efficiency of this algorithm.

[0023] According to the above technical solution, the third stage rotation is performed according to the relationship between the signal vector and the synthetic vector corresponding to each current spatial measurement point; wherein the synthetic vector is determined based on the sixth formula, which is: The judgment process specifically includes:

[0024] 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.

[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 according to the phase distribution of the entire optical fiber includes: according to the phase distribution along the optical fiber, differentiating the phases of two spatial measurement points at each moment.

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

[0027] 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 phase average 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;

[0028] 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.

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

[0030] 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;

[0031] Compare the modes in the adjacent initial phase modal parameter sets in pairs. 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;

[0032] 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;

[0033] 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;

[0034] Repeat the above steps 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 the preset value when the number of iterations is less than the preset number. Furthermore, at this time, the modal parameter set reaches convergence, and the eigenfrequencies and modal vibration shapes contained therein can be used as the final vibration parameters.

[0035] According to the above technical scheme, the time domain analysis operation kernel includes a characteristic system implementation algorithm, a data-driven or covariance-driven random subspace method, a Hilbert-Huang transform, and a least squares complex exponential method; further, the time domain analysis operation kernel can be used in combination with natural excitation technology or multi-reference point natural excitation technology.

[0036] The phase modal parameter set includes eigenfrequency, vibration mode, damping ratio and consistent modal index;

[0037] 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: 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, is the characteristic frequency.

[0038] Another aspect of the present invention provides a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement method, comprising:

[0039] Optical fibers are laid on the bridge structure, detection light is emitted in the optical fibers and reflected light is received, polarization diversity is performed on the reflected light, and the reflected light is converted into a digital signal; further, the optical fibers are mainly laid on the main beam at the bottom of the bridge deck of the bridge structure, the key cables to be tested, and the entire length of the bridge tower.

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

[0041] According to the second detwist algorithm and the initial distributed phase time history of the electrical signal after noise suppression, the phase vibration time history of each spatial measuring point of the bridge structure is obtained;

[0042] 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.

[0043] Another aspect of the present invention provides a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement device, comprising: 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 polarized light beam splitter, a photoelectric balance detector, an acquisition card, and an 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, forming the perception and transmission hardware foundation of the perception transmission acquisition module; the narrow linewidth laser is connected to the demodulation end, and ... The wide laser is used to emit incident light, which is modulated by the acousto-optic modulator to form pulse light. The arbitrary function generator acts on the acousto-optic modulator to generate a pulse signal and synchronously triggers the acquisition card to perform digital-to-analog conversion. The optical circulator and optical coupler are light-guiding devices used to form a light path of a specific path. The optical fiber flange is also a light-guiding device used to connect two sections of optical fiber connectors to form a continuous light 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 orthogonal states to generate orthogonal polarization state optical signals I x(t) and I y(t) The balanced photoelectric detector converts the optical signal into an electrical signal and then inputs it into a two-channel high-frequency acquisition card to realize the acquisition function of the perception transmission acquisition module.

[0044] The incident light emitted by the narrow linewidth laser is divided into two paths by an optical coupler. One path enters the polarization beam splitter as local light, and the other path enters the sensing optical fiber arranged on the bridge structure through an optical circulator after passing through an acousto-optic modulator and an erbium-doped fiber amplifier in sequence as detection light. The detection light forms Rayleigh backscattered light in the sensing optical 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 through a photoelectric balanced detector. The electrical signals are input into an acquisition card to receive continuous sampling and converted into digital signals, and finally enter an acquisition and storage device to participate in the execution of multiple 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 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 bridge vibration high signal-to-noise ratio measurement system and method, which obtains a high signal-to-noise ratio phase vibration time history by suppressing the fading noise and distortion of the internal detection signal of the optical fiber arranged in the bridge structure. 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 degraded modes, thereby accurately obtaining the natural frequency, modal vibration shape and other parameters of the bridge structure.

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

[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 identify real modes.

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

[0050] Furthermore, the present invention improves the reliability and recognition rate of modal recognition based on optical phase, and 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 layout, large measurement point density, wide response bandwidth, and good automatic monitoring performance. It is especially suitable for large-scale distributed monitoring of long distances, multiple objects, and multiple components. Under the premise of ensuring the accuracy of modal parameters, distributed vibration measurement of long bridges with high measurement point density over the entire domain is realized.

[0052] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0055] Figure 2 It is a flow chart of the operation of the first detorsion algorithm in a distributed vibration high signal-to-noise ratio measurement system of a long bridge using distributed optical fiber acoustic sensing according to an embodiment of the present invention;

[0056] Figure 3 It is a flow chart of a second detorsion algorithm in a distributed vibration high signal-to-noise ratio measurement system of a long bridge using distributed optical fiber acoustic sensing according to an embodiment of the present invention;

[0057] Figure 4 It is a flow chart of an automatic phase modal parameter optimization and identification algorithm in a distributed vibration high signal-to-noise ratio measurement system of a long bridge using distributed optical fiber acoustic sensing according to an embodiment of the present invention;

[0058] Figure 5 This is a flow chart of a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement method according to an embodiment of the present invention;

[0059] Figure 6 It is an operation logic diagram of a detorsion algorithm in another distributed optical fiber acoustic sensing long bridge distributed vibration high signal-to-noise ratio measurement system according to another embodiment of the present invention;

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

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

[0062] Figure 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 DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 illustrations provided in the embodiments of the present invention are only used to illustrate the basic concept of the present invention in a schematic manner. Therefore, the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout type may also be more complicated.

[0065] In the present invention, it is also necessary to explain that, if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply 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 on the present application. In addition, if the terms "first" and "second" appear, they are only used for description and distinction purposes, and cannot be understood as indicating or implying relative importance.

[0066] Example 1

[0067] This embodiment provides a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system. Figure 1 As shown, 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; wherein:

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

[0069] Specifically, the sensing 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, and includes 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 in the orthogonal polarization state that completes polarization diversity into a digital signal.

[0070] Pulse detection light is emitted into the optical fiber arranged in the bridge structure. The optical fiber is deformed due to structural vibration, and then the detection light inside the optical fiber is modulated. The modulated reflected light is received and polarization diversity is performed after the demodulation end. The two orthogonal polarization states of light obtained by the diversity are converted into electrical signals through a balanced detector. After continuous sampling by the acquisition card, they are converted into digital signals and input into the fading noise suppression module.

[0071] Furthermore, the continuous sampling process of the acquisition card is as follows: the acquisition card continuously samples the electrical signal output by the photodetector at a certain rate, each sampling point corresponds to a spatial measurement point in the actual bridge structure, and the order of continuous sampling is the order 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 converted.

[0072] The fading noise suppression module is used to obtain an initial distributed phase vibration time history after noise suppression according to the first detwist 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 inputting, executing, and outputting a first detwisting algorithm. A finite impulse response filter (FIR) is selected from the first filter end. The first detwisting algorithm specifically includes the following equations (1) to (10).

[0074] The fading noise suppression module first uses a finite impulse response filter (FIR) to filter the original light intensity beat frequency signal I of the two orthogonal polarization states x and y. x(t) and I y(t) Bandpass filtering is performed to obtain 6 equivalent detection signals x1, x2, x3, y1, y2, y3 corresponding to the central lobe and the left and right side lobes. Then the first de-twist algorithm is performed on the obtained equivalent detection signals, such as Figure 2 As shown, the steps include:

[0075] S1, the initial vector detection signal h'(t) is obtained by Hilbert transform, and 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] Among them, I'(t) represents each equivalent detection signal after filtering, corresponds to the specific equivalent detection signal through the subscript, f(t) is the FIR filter operator, t is the time delay of the reflected light at each spatial measurement point, and Hilbert(·) is the Hilbert operator. Through the operation of this step, a complex signal consisting of the original signal as the real part and its Hilbert transform pair as the imaginary part is derived.

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

[0080] S201, original phase rotation: all h'(t) are rotated according to formula (3) to eliminate the initial phase difference between adjacent spatial measurement points.

[0081]

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

[0083] S202, first stage rotation: According to formula (4), the reference vector Rotate again based on the reference.

[0084]

[0085] Wherein, p is the position number of the reference vector. Preferably, the reference vector is a vector with a larger amplitude in the position before the interval to be analyzed.

[0086] S203, second stage rotation: The reference vector and the following m consecutive vectors form a reference matrix, the form of the reference matrix is ​​shown in the square brackets of formula (5), and then is the target matrix. Compare the difference between the reference matrix and the target matrix, and Rotate, the calculation formula for the above process is as follows:

[0087]

[0088] Among them, the superscript letters represent different polarization states, N is the number of FIR filters, and the pass is θ j 'Take the optimal value, 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 by using a univariate particle swarm optimization algorithm.

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

[0091] S204, third stage rotation: for each spatial measurement point position q, a threshold is set to determine the signal vector corresponding to each frequency source at the current position. Whether to deviate from the composite vector The threshold can be estimated by referring to the standard deviation of the phase angle of each vector detection signal. The synthetic vector formula is as follows:

[0092]

[0093] If each If the maximum inner angle between the two ends exceeds the threshold, all six vector detection signals at the current moment 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 internal angle; otherwise, replace Replace with The reason for replacement is that after determining the When the maximum inner angle between the two does not exceed the threshold, all detection signal vectors are considered to have no obvious phase angle error, and even the signal denoted by s with the largest angle deviation does not need to be eliminated.

[0094]

[0095] in, Refers to the rotated part, the superscript indicates the stage of processing, the subscript q:end indicates rotation from position q to the end position, and j refers to the time.

[0096] S3. Sum the multiple equivalent detection signals of the same phase, obtain the phase angle of each spatial measurement point according to the summation result, and obtain the phase distribution of the entire optical fiber laid by the bridge structure by unwinding.

[0097] S4. deriving the initial distributed phase vibration time history of each spatial measurement point of the bridge structure 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.

[0098] The phase increment between the two spatial measurement points is linearly related to the strain generated by the optical fiber between the two measurement points. Therefore, the phase time series of the two spatial measurement points are differentiated, and the obtained phase time history corresponds to the strain time history caused by vibration, based on which 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, thus 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's information missing in the fading area. Finally, the four-stage signal attitude calibration is used to effectively eliminate the influence of phase angle mismatch and random phase delay.

[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 detwist 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 a second detwist algorithm. The second detwist algorithm calibrates various signal distortion phenomena that are prone to occur in distributed optical acoustic sensing technology (DAS) in the time dimension. Figure 3 As shown, the second algorithm execution end is specifically used to execute the following algorithm:

[0104] T1, according to formula (11) ~ (13), eliminate the signal steps and fluctuations caused by environmental and system disturbances:

[0105]

[0106]

[0107]

[0108] 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+pThe central trend line of the phase time course can be obtained by nonlinear least squares method.

[0109] T2. Use a digital bandpass 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 outliers by cubic spline interpolation.

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

[0112] According to the operation flow of the first detwist algorithm and the second detwist algorithm, the main operation logic of the detwist algorithm including the first detwist algorithm and the second detwist algorithm is as follows: Figure 6 As shown, including:

[0113] (1) Inputting orthogonal signals: The execution device of the first detwist algorithm receives digital signals converted from two orthogonal polarization states of light.

[0114] (2) Equivalent pulse extraction: The original light intensity beat frequency signals of two orthogonal polarization states are bandpass filtered through FIR to obtain six equivalent detection signals corresponding to the central lobe and left and right side lobes.

[0115] (3) Constructing a vector signal: performing a Hilbert transform on the equivalent detection signal to obtain an initial vector detection signal.

[0116] (4) Signal attitude calibration: The initial vector detection signal is rotated in multiple stages, including:

[0117] a. Eliminate original phase shift: Perform original phase 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 difference 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. The current mainstream algorithms include the characteristic system realization algorithm, the data-driven or covariance-driven random subspace method, the Hilbert-Huang transform, the least squares complex exponential method, etc., and can be used in conjunction with the natural excitation technology. The time domain analysis operation kernel of this embodiment selects the characteristic system realization algorithm (Eigen-system realization algorithm, ERA).

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

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

[0135] P3, respectively change the model order and monitoring data set in the calculation kernel, and repeat the above process to obtain the final modal set under the current number of Hankel matrix rows. 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 Hankel matrix row size.

[0136] P4. After deleting similar modes, the final mode set obtained by the previous row size is used as the initial mode set corresponding to the new row size, and the new row size is used to iteratively optimize this initial mode set according to the above operation 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 does not increase within a certain number of iterations or the row size of the Hankel matrix reaches the upper limit. The eigenfrequency and mode shape 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 identification algorithm is as follows: Figure 7 As shown, including:

[0139] (1) Set 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 contains the characteristic frequency Damping ratio and mode shapes.

[0142] (4) Determine whether each mode in the modal parameter set n satisfies If any of these conditions are met, the mode is deleted.

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

[0144]

[0145] If satisfied Further determine whether MAC is satisfied ij >k4; if MAC is satisfied ij >k4, let S i,j =MAC ij ; If MAC is not satisfied ij >k4, let S i,j = 0. If not satisfied Let S i,j =0. Among them, MAC ij is the modal assurance criterion value between the two modes i and j to be compared, S i,j It is a temporary array of all modal comparison results between modal j and n.

[0146] (6) Find S i,j The maximum value S in r,j , determine whether S is satisfied r,j =0.

[0147] If S is satisfied r,j = 0, add mode j in the n-1th modal parameter set to n;

[0148] If S is not satisfied r,j =0, further determine whether it satisfies If satisfied Replace mode r in n with mode j in the n-1th modal parameter set.

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

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

[0151] (8) Use the new row size and iteratively optimize the initial mode set according to the above operation until the mode set converges.

[0152] The automatic phase modal parameter optimization and identification algorithm run by this module fully considers the influence of the system model order, related dimensional parameters, and different measurement data sets in the operation kernel, and then uses iterative optimization logic to screen out false or degraded modes, retaining as many true modes as possible and making them approach the highest confidence level, effectively reducing the negative impact of error transmission on frequency, especially vibration mode, in the measurement and demodulation stages. At the same time, it overcomes the shortcomings of traditional methods based on modal stability diagrams, such as loss of true modes, inaccurate order determination, and vibration mode distortion.

[0153] Based on the above-mentioned distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system, this embodiment also provides a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement device based on DAS, such as Figure 8 As shown, it includes: a bridge structure 1, a sensing optical fiber 2, a narrow linewidth laser 3, an optical coupler 4, an acousto-optic modulator 5, an erbium-doped fiber amplifier 6, an optical circulator 7, a polarization beam splitter 8, a photoelectric balanced detector 9, an acquisition card 10, and an 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 enters the polarization beam splitter 8 as local light, and the other path enters the polarization beam splitter 8 as detection light after passing through the acousto-optic modulator 5 and the erbium-doped fiber amplifier 6 in sequence. Then, it passes through the optical circulator 7 and also enters the polarization beam splitter 8. 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 signal transmission channel; the tail end of the sensing optical fiber 2 is connected to the demodulation end, forming the sensing and transmission hardware foundation of the sensing transmission acquisition module; the narrow linewidth laser 3 is used to emit the incident light, and the incident light is modulated by the acousto-optic modulator 5 to form a pulse light, and the acousto-optic modulator 5 is used to generate a pulse signal with the help of an arbitrary function generator, and synchronously trigger the acquisition card 10 to perform digital-to-analog conversion, and 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, which are used to form an optical path of 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, and generate an orthogonal polarization state optical signal I x(t) and I y(t)The balanced photodetector 9 converts the optical signal into an electrical signal and then inputs it into the acquisition card 10. As can be seen from the above, the acquisition card 10 converts the electrical signal into a digital signal, and finally enters the acquisition and storage device 11.

[0156] Example 2

[0157] This embodiment provides a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement method, the method is based on the distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system described in Example 1, such as Figure 5 As shown, the process includes:

[0158] A1. An optical fiber is laid 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.

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

[0160] A2. Obtaining an initial distributed phase history of the bridge structure according to the first detwist 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 bridge vibration high signal-to-noise ratio measurement system. The components and corresponding functions of this module have been described in Example 1 and will not be repeated here.

[0162] A3. According to the second detwist algorithm and the initial distributed phase time history of the electrical signal after noise suppression, the phase vibration time history of each spatial measuring point of the bridge structure is obtained.

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

[0164] A4. According to the automatic phase modal parameter optimization and 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.

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

[0166] Based on the above-mentioned distributed optical fiber long bridge vibration high signal-to-noise ratio measurement method, this embodiment also provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by one or more processors for the above-mentioned distributed optical fiber long bridge vibration high signal-to-noise ratio measurement method.

[0167] In summary, the present invention provides a distributed optical fiber long bridge vibration high signal-to-noise ratio measurement system and method, which realizes the suppression of fading noise and signal distortion in space and time dimensions during long bridge vibration measurement in an automated and computerized manner by designing an algorithm, and completes the measurement of modal parameter sets. The present invention can realize distributed vibration measurement of long bridges at high measurement point density over the entire domain while ensuring the accuracy of modal parameters.

[0168] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and 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] The order of execution of each step in the above embodiment does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0170] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection 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 de-twisting algorithm; 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 detorsion algorithm; 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 judgment process 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 phase average 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: 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, 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 detwist algorithm and the initial distributed phase time history of the electrical signal after noise suppression, 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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