High-speed railway bridge health monitoring method based on interference radar, medium and equipment

Abstract

The invention relates to the technical field of bridge track health monitoring, in particular to a high-speed railway bridge health monitoring method based on an interference radar, a medium and equipment. The method comprises the following steps: identifying a driving lane of a train on a bridge through an interference radar; on the basis of the driving lane, bridge data needed by health monitoring when the train passes through are obtained through an interference radar; processing the bridge data and extracting a product function; analyzing the product function, and selecting an effective product function; and performing time-frequency analysis on the effective product function to obtain a time-frequency graph of the bridge data, and performing health monitoring on the high-speed railway bridge according to the time-frequency graph. According to the method, the driving lane of the high-speed train is judged according to the bridge signal collected by the interference radar, the vibration characteristics of the bridge are further analyzed through processing of RLMD and SET, a clear time-frequency graph is obtained, the time-frequency characteristics of the bridge are judged according to the time-frequency graph, and a reliable method is provided for health monitoring in the driving process of the high-speed train.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge track health monitoring, and particularly relates to a high-speed rail bridge health monitoring method based on an interference radar, a medium and equipment. BACKGROUND

[0002] Bridges are the basic components of civil infrastructure and play a vital role in the transportation network. Over time, continuous exposure to environmental conditions and traffic loads can lead to gradual degradation of their structural performance, which can compromise their safety, functionality, and durability. To reduce these risks, bridge health monitoring systems have been widely applied to provide real-time insights into the condition and performance of bridge structures.

[0003] However, the existing health monitoring methods applied to high-speed rail bridges using time-frequency analysis are not clear and accurate enough in analyzing bridge data, and cannot well obtain the vibration characteristics of the bridge during train operation, and further analyze the health status of the bridge.

[0004] Therefore, it is urgent to propose a more accurate and clear high-speed rail bridge health monitoring method to solve the problems in the prior art. SUMMARY

[0005] The present application aims to provide a high-speed rail bridge health monitoring method based on an interference radar, a medium and equipment, and the specific technical solutions are as follows: A high-speed rail bridge health monitoring method based on an interference radar, comprising the following steps: S1, identifying the driving lane of the train on the bridge through the interference radar; S2, based on the driving lane, obtaining the bridge data required for health monitoring when the train passes through the bridge through the interference radar; S3, processing the bridge data using a robust local mean decomposition method to obtain its frequency modulation signal and amplitude signal, and then extracting the product function from the bridge data; S4, analyzing the product function using Hilbert transform and Fourier transform to select effective product functions; S5, using synchronous extraction transform to perform time-frequency analysis on the effective product functions selected in S4 to obtain the time-frequency pattern of the bridge data, and performing health monitoring on the high-speed rail bridge according to the time-frequency pattern.

[0006] Further, the S1 specifically comprises: placing the interference radar at a distance of 20-40m from the side of the bridge, and aiming the interference radar at the mid-span area of the bridge; When the high-speed rail passes through the bridge, the interference radar obtains the vibration data of the bridge; analyzing the vibration data to identify the driving lane of the high-speed rail.

[0007] Further, the interferometric radar is an IBIS-FS interferometric radar.

[0008] Further, the S3 specifically comprises: S3.1, preprocessing the bridge data, including boundary condition processing and signal extension; Boundary condition processing: determining boundary symmetry points; Signal extension: setting symmetric reflection points on the determined boundary symmetry points, extending the signal boundary, and obtaining an extended signal ; S3.2, iterative decomposition, extracting product function components from the extended signal one by one until the stopping rule is met; S3.3, separating the product function component from the current signal, repeating S3.2 until the residual is stable, and realizing the extraction of the product function; S3.4, post-processing: including bridge data signal reconstruction and truncation, and fitting all results back to the original signal.

[0009] Further, S3.2 specifically is: 1) Find local extrema: Let the current signal be , initially ; find all local maxima and local minima on ; 2) Calculate the local mean function : For each group of adjacent extreme points, calculate the average , ; Extend all average values between the extreme points to form a continuous local mean function , representing time; Apply moving average filtering to to obtain a smooth result; 3) Calculate the envelope function : Calculate the local amplitude , based on adjacent extreme points; Extend all to obtain an initial envelope signal; Robust local mean decomposition method is adopted for robust processing: Obtain the step local amplitude signal or step local average signal; According to the step, calculate the probability density function where is a bin in the histogram, is the number of bins; the center of the step size and the standard deviation σ : ; ; where, is the th bin in the histogram; the optimal subset size : where, is the function that rounds an input value up to the nearest odd integer; using a control envelope smoothing procedure, resulting in a smoothly varying continuous envelope function , i.e. the amplitude signal; 4) Signal separation and demodulation: separating the local mean function from the current signal , resulting in a mean-removed signal : ; demodulating with the envelope function , resulting in a demodulated signal , i.e. the frequency modulation signal; ; 5) Check stopping criterion: defining a zero baseline envelope signal ; computing the objective function : ; where, is the signal length, is the zero baseline envelope value at the th sample point; if is smaller than a predetermined threshold or the envelope is sufficiently flat, stop the screening process of the current product function; otherwise, take as the new input signal and go back to 1) repeat the iterative decomposition; 6) Generate product function components: Once the stopping criterion is met, multiply the final demodulated signal with the envelope function, resulting in the th product function component : .

[0010] Further, S3.3 is specified as: Separating the product function component from the current signal results in a new residual signal : ; The new residual signal is considered as the new signal, and ; The iterative decomposition process of S3.2 is repeated to extract the next product function component; The decomposition is stopped when becomes constant or free of significant oscillations; The final residual is denoted as , where is the number of extracted product functions.

[0011] Further, S3.4 is specified as: Truncation of all components: The product function components and the residual obtained on the extended signal are truncated back to the original signal length; Signal reconstruction: The original signal is represented as the sum of all product function components and the final residual: .

[0012] Further, S5 is specified as: The is represented in short-time Fourier transform notation as: ; where is the Fourier transform notation; K is a finite number; denotes the amplitude of the effective product function, denotes the amplitude function; denotes the Fourier transform of the window function; denotes the angular frequency of the effective product function, is a function of the angular frequency; is the natural constant; is the time; is the imaginary unit; The instantaneous frequency IF is represented as: ; where is the instantaneous frequency; The Dirac δThe function is used for time-frequency display of the signal and key instantaneous frequency extraction, and in this case, the time-frequency function expression is as follows by using the synchronous extraction transform method: ; Among them, The time-frequency function is as follows: The synchronous extraction operator is as follows: .

[0013] The application further provides a readable storage medium, which stores computer program instructions, and the computer program instructions realize the high-speed rail bridge health monitoring method based on the interference radar when executed by a processor.

[0014] The application further provides an electronic device, which comprises at least one processor, at least one memory and computer program instructions stored in the memory, and the computer program instructions realize the high-speed rail bridge health monitoring method based on the interference radar when executed by the processor.

[0015] The application has the following beneficial effects: The high-speed rail bridge health monitoring method based on the interference radar provided by the application can analyze and judge the driving lane of the high-speed train according to the bridge signal collected by the interference radar, further analyze the vibration characteristics of the bridge through the processing of the RLMD and the SET, obtain a clear time-frequency graph, and judge the time-frequency characteristics of the bridge according to the time-frequency graph, thereby providing a reliable method for health monitoring in the driving process of the high-speed train.

[0016] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be further described below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the application, and are incorporated herein for purposes of explanation and not of limitation. In the drawings: Figure 1 FIG. 1 is a flowchart of the high-speed rail bridge health monitoring method based on the interference radar in the application; Figure 2 FIG. 2 is a schematic diagram of the interference radar arranged on the side close to the train; Figure 3 FIG. 3 is vibration data; Figure 1 FIG. 3 is vibration data, wherein (a) is a vibration data graph from the 2482nd second to the 2502nd second, and (b) is a vibration data graph from the 4188th second to the 4208th second; Figure 4A schematic diagram showing the interferometric radar deployed on the side furthest from the train; Figure 5 For vibration data Figure 2 Among them, (a) is the vibration data graph from 6113 seconds to 6133 seconds, and (b) is the vibration data graph from 8202 seconds to 8222 seconds; Figure 6 For vibration data Figure 3 (2.5 hours); Figure 7(a) is Figure 6 Figure 7(b) is an enlarged view of a certain segment of data (selected by a rectangular box), and it is a spectrum analysis diagram based on the data in Figure 7(a). Figure 8 The time-frequency characteristic map is obtained by processing the data in Figure 7(a) using the short-time Fourier transform method, and the curved part is the spectrum distribution. Figure 9 The frequency modulation signal diagram and amplitude signal diagram are obtained by processing the data in Figure 7(a) using the RLMD of this invention, wherein (a) is the frequency modulation signal diagram and (b) is the amplitude signal diagram; Figure 10(a) is a schematic diagram of the product function, Figure 10(b) is a schematic diagram of the product function after performing a Hilbert transform, and Figure 10(c) is a schematic diagram of the product function after performing a Fourier transform. Figure 11 The results are shown in the comparison charts of time-frequency analysis using the method of this invention and the traditional analysis method. (a) is the result chart of time-frequency analysis using continuous wavelet transform, (b) is the result chart of time-frequency analysis using short-time Fourier transform, and (c) is the result chart of time-frequency analysis using synchronous extraction transform. Detailed Implementation

[0018] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Example: See Figure 1 This embodiment provides a method for health monitoring of high-speed railway bridges based on interferometric radar, including the following steps: S1. Identify the train's travel lane on the bridge using interferometric radar; specifically: The interferometric radar is placed 20-40m from the side of the bridge and aimed at the mid-span area of ​​the bridge; in this embodiment, the interferometric radar is the IBIS-FS interferometric radar. When the high-speed train passes over the bridge, the interferometric radar acquires the bridge's vibration data; The vibration data was analyzed to identify the high-speed train's operating lane. Specifically: Because the high-speed train has great impact energy and great load when passing the bridge from a single lane, the bridge produces asymmetric deformation, so when viewed from the side of the bridge, the data will show positive and negative, some data changes along the negative direction, and some data changes along the positive direction; as shown in Figure 2 , the interferometric radar is placed on the right side of the bridge, when the train travels in the right lane close to the interferometric radar, the data of the interferometric radar shows negative value, as shown in Figure 3 ; when the train travels in the left lane far from the interferometric radar, the data of the interferometric radar shows positive value, as shown in Figure 4 and Figure 5 ; According to the change of the above data, it can be judged that the high-speed train travels in the lane.

[0020] S2, based on the driving lane, the bridge data (such as the acceleration time history data and the displacement time history data of the bridge) required for health monitoring when the train passes through is obtained by the interferometric radar; It should be noted that when the high-speed train travels in the right lane, the signal obtained by the interferometric radar will be disturbed, because the interferometric radar determines the deformation response of the bridge under the action of the train load by analyzing the phase difference between the echo signals received at different times, when working in the right lane, the detection area of the radar captures the reflection signal of the high-speed train while capturing the echo signal of the bridge, and this interference to the extraction of the echo signal of the bridge will reduce the signal quality, therefore, the obtained bridge data needs to be further analyzed and processed.

[0021] S3, the bridge data is processed using a robust local mean decomposition method (RLMD) to obtain its frequency modulation signal and amplitude signal, and then the product function is extracted from the bridge data; specifically including: S3.1, pre-processing the bridge data, including boundary condition processing and signal extension, to solve the local extreme value problem at the signal boundary and avoid decomposition distortion; specifically including: 1) boundary condition processing: determining the boundary symmetry point; Left boundary processing: ① If the first local extreme value is the maximum value ( ): Check if the first point is less than the first local minimum value ( ), if so, regard the first point as the minimum value and designate it as the symmetry point; if not, designate the first maximum value as the left symmetry point; ② If the first local extreme value is the minimum value ( ): Check if the first point is greater than the first local maximum value ( If yes, treat the first point as the maximum value and designate it as the symmetric point; otherwise, designate it as the left symmetric point.

[0022] Right boundary processing: Similar to the left boundary, but for the end of the signal (check the relationship between the last point and the last local extremum, specify the right symmetric point).

[0023] 2) Signal extension: By setting symmetrical reflection points at defined boundary symmetrical points, the signal boundary is extended to obtain an extended signal. ; The extension range is determined based on actual needs, but it must be ensured that the extended signal length is sufficient to cover boundary effects.

[0024] S3.2 Iterative decomposition: Extracting the product function from the extended signal one by one ( PF ) components, until the stopping rule is met; each PF Component extraction is a sub-iterative process (called the "screening process"); specifically: 1) Finding local extrema: Let the current signal be Initially ;exist Find all local maxima on the top and local minima ; 2) Calculate the local mean function : For each group of adjacent extreme points, calculate the average value. , ; Extend all averages Between the time of reaching the extreme point, a continuous local mean function is formed. (Achieved through linear interpolation between extreme points). Indicates time; right Apply a moving average filter to obtain smooth results and reduce the impact of noise. 3) Calculate the envelope function : Calculate local amplitude based on adjacent extreme points , ; Extend all Obtain the initial envelope signal; A robust Local Mean Decomposition (RLMD) method is used for robust processing: Obtain the local amplitude signal or local average signal of the step size; "step size" refers to the window or interval for signal processing; Calculate the probability density function based on the step size. ,in is the bin in the histogram, is the number of bins; calculating the center of the step and the standard deviation σ : ; ; where, is the bin in the histogram, is the bin number; calculating the optimal subset size : where, is the function that rounds the input value up to the nearest odd integer, for example, ; using control envelope smoothing process (such as applying a moving average filter with size ), get a smooth changing continuous envelope function , that is, the amplitude signal; 4) signal separation and demodulation: separate the local mean function from the current signal , get the mean-removed signal : ; demodulate with the envelope function , get the demodulation signal , that is, the frequency modulation signal; ; 5) check the stopping criterion: define the zero baseline envelope signal (the normalized or mean-removed form of the envelope function); calculate the objective function : ; where, is the signal length, is the zero baseline envelope value of the th sampling point; If is less than a predetermined threshold (the threshold is set according to the actual application) or the envelope is flat enough (that is, close to a pure frequency modulation signal), stop the screening process of the current product function; otherwise, as the new input signal, return 1) repeat the iteration decomposition; 6) generate product function components; Once the stop criterion is met, the final demodulated signal is multiplied with the envelope function (i.e. the frequency modulated signal is multiplied with the amplitude signal), resulting in a first product function component

[0025] S3.3, separate the product function component from the current signal, repeat S3.2 until the residual is stable, realize the extraction of the product function; specifically: separate the product function component from the current signal, get a new residual signal ; take the new residual signal as a new signal, let ; repeat the iterative decomposition process of S3.2 to extract the next product function component; when becomes a constant (no oscillation) or does not contain significant oscillation, stop the decomposition; the final residual is recorded as , where is the number of extracted product functions.

[0026] S3.4, post-processing: including bridge data signal reconstruction and truncation, adapt all results back to the original signal; specifically: truncate all components: truncate the product function components and the residual obtained on the extended signal back to the original signal length (remove the extended part); signal reconstruction: the original signal is expressed as the sum of all product function components and the final residual: ; where is the number of extracted product functions.

[0027] S4, use Hilbert transform and Fourier transform on the product function for analysis, select effective product functions; The product function contains multiple product function components, which contain different signals. By using Hilbert transform and Fourier transform on the product function for analysis, select the product function (i.e. effective product function) containing the bridge vibration characteristic signal (including vibration energy intensity, frequency distribution and time-frequency information, etc. Key information) for further analysis.

[0028] ​​​​​S5, the effective product function selected in S4 is analyzed by using a synchronous extraction transform method (SET) to obtain a clear time-frequency pattern of the bridge data, and the high-speed railway bridge is monitored according to the time-frequency pattern; specifically: Will The short-time Fourier transform expression is as follows: ; Wherein, The Fourier transform expression is as follows: K The finite number is: The amplitude of the effective product function is: The amplitude function is: The Fourier transform of the window function is: The angular frequency of the effective product function is: The angular frequency function is: The natural constant is: The time is: The imaginary unit is: The instantaneous frequency IF is expressed as: ; Wherein, The instantaneous frequency is: The Dirac δ Function (Dirac delta) is used for time-frequency display and key instantaneous frequency extraction of signals at the same time, and in this case, the time-frequency pattern function expression obtained by the synchronous extraction transform method is as follows: ; Wherein, The time-frequency pattern function is: The synchronous extraction operator is as follows: ; According to the time-frequency pattern function, a clear time-frequency pattern (the synchronous extraction operator is used to extract the main key frequency in the time-frequency pattern, so that the energy of the time-frequency display is more concentrated) can be obtained, and further the health monitoring of the high-speed railway bridge is realized.

[0029] The high-speed railway bridge health monitoring method based on the interference radar provided by the application can judge the driving lane of the high-speed train according to the bridge signal collected by the interference radar, further analyze the vibration characteristics of the bridge through the processing of RLMD and SET, obtain a clear time-frequency pattern, and judge the time-frequency characteristics of the bridge according to the time-frequency pattern, so as to provide a reliable method for health monitoring during the driving of the high-speed train.

[0030] Select a certain bridge through which a high-speed train passes to perform an experiment, place an interferometric radar (IBIS-FS) on the ground at a side of the bridge, at a distance of 30 m from the side of the bridge, and aim the interferometric radar at a mid-span region of the bridge.

[0031] Referring to Figures 2-5 , by placing the interferometric radar at one side of the bridge, the driving lane of the train is determined in subsequent vibration data through positive and negative signs.

[0032] When the high-speed train passes through the bridge, the interferometric radar acquires vibration data of the bridge, the acquisition time is 2.5 hours, and the acquired data graph is shown in Figure 6 ; a certain segment of data (a segment selected by a rectangular frame) in Figure 6 is enlarged and subjected to fast Fourier transform (FFT) processing, as shown in Fig. 7(a) and Fig. 7(b), and it can be observed from Fig. 7(b) that low-frequency signals dominate, resulting in the frequency distribution of the bridge itself being masked.

[0033] The short-time Fourier transform method (STFT) is used to process the vibration signal of Fig. 7(a), and the time-frequency feature map is shown in Figure 8 , and it can be seen from Figure 8 that the time-frequency feature map is relatively fuzzy.

[0034] The vibration signal of Fig. 7(a) is processed by using the RLMD in the present application to obtain four pairs of frequency modulation signals and amplitude signals, which are then multiplied to obtain product functions PF 1 (frequency modulation signal 1 multiplied by amplitude signal 1), PF 2 (frequency modulation signal 2 multiplied by amplitude signal 2) , PF 3 (frequency modulation signal 3 multiplied by amplitude signal 3), and PF 4 (frequency modulation signal 4 multiplied by amplitude signal 4), as shown in Figure 9 ; Then, the product functions are analyzed by using Hilbert transform and Fourier transform, as shown in Fig. 10(a)-Fig. 10(c), and the effective product function PF 1 is extracted. PF 1 contains the main vibration characteristics and frequency domain information of the bridge, so the time-frequency analysis of PF 1 is further performed. The SET in the present application is used to perform time-frequency analysis on PF 1 to obtain a time-frequency feature map, as shown in (c) in Figure 11 ; and the results obtained by using the conventional time-frequency analysis method (short-time Fourier transform STFT, continuous wavelet transform CWT) are compared, and the comparison graph is shown in Figure 11 ; and Figure 11As can be seen from (a), (b), and (c), using SET can better concentrate time-frequency energy, which is more conducive to extracting the vibration characteristics of the bridge and obtaining a clearer time-frequency characteristic map. It provides a more accurate technical solution for the time-frequency analysis of the bridge, so as to accurately analyze the health status of the bridge.

[0035] This embodiment also includes an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor to provide a high-speed railway bridge health monitoring method based on interferometric radar as described above.

[0036] The electronic device can be a mobile phone, desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device may include, but is not limited to, processors and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-speed railway bridge health monitoring method based on interferometric radar, characterized in that, It comprises the following steps: S1, identifying the driving lane of the train on the bridge by the interferometric radar; S2, based on the driving lane, obtaining the bridge data at the train passing time required for health monitoring by the interferometric radar; S3, processing the bridge data using a robust local mean decomposition method to obtain its frequency chirp signal and amplitude signal, and then extracting the product function from the bridge data; S4, analyzing the product function using Hilbert transform and Fourier transform to select effective product functions; S5, using synchronous extraction transform to perform time-frequency analysis on the effective product functions selected in S4 to obtain the time-frequency pattern of the bridge data, and performing health monitoring on the high-speed railway bridge according to the time-frequency pattern.

2. The method of claim 1, wherein the method is based on interferometric radar. The S1 specifically comprises: The interferometric radar is placed at a distance of 20-40 m from the side of the bridge, and the interferometric radar is aimed at the mid-span area of the bridge; When the high-speed train passes through the bridge, the interferometric radar obtains the vibration data of the bridge; The vibration data is analyzed to identify the driving lane of the high-speed train.

3. The method of claim 2, wherein the method is based on interferometric radar. The interferometric radar is IBIS-FS interferometric radar.

4. The method of claim 1, wherein the method is based on interferometric radar. The S3 specifically comprises: S3.1, preprocessing the bridge data, including boundary condition processing and signal extension; Boundary condition processing: determining the boundary symmetry point; Signal extension: setting a symmetric reflection point on a determined boundary symmetric point, extending the signal boundary to obtain an extended signal ; S3.2, iterative decomposition, extracting product function components from the extended signal one by one until the stop rule is met; S3.3, separate the product function component from the current signal, repeat S3.2 until the residual is stable, realize the extraction of the product function; S3.4, post-processing: including bridge data signal reconstruction and truncation, fitting all results back to the original signal.

5. The interferometric radar-based method for monitoring the health of a high-speed rail bridge according to claim 4, wherein, S3.2 specifically is: 1) find local extreme value: Let the current signal be Initially ;exist Find all local maxima on and local minima ; 2) Compute local mean function : For each set of adjacent extreme points, the average value is calculated , ; Extending all averages Forming a continuous local mean function between the times of the extrema , t denotes time; To Apply a moving average filter to obtain smoothed results; 3) Calculate envelope function : Calculating local amplitudes based on adjacent extreme points , ; extend all obtaining an initial envelope signal; Robust local mean decomposition method is used for robust processing: Obtain step local amplitude signal or step local average signal; Calculating a probability density function from step lengths wherein is a bin in the histogram, is the number of bins; center of the calculation step and standard deviation 4) signal separation and demodulation: : ； ； wherein is the bin number of the histogram; Computing the optimal subset size : where, is a function that rounds an input value up to the nearest odd integer. Using Controlling the envelope smoothing process to obtain a continuously smooth envelope function i.e. the amplitude signal; 5) check and select stop criterion: from the current signal separating a local mean function from the current signal : ； with an envelope function on demodulated to obtain a demodulated signal i.e. a frequency modulated signal; ； 6) generate product function component; Defining a zero baseline envelope signal ; Computing the objective function : ； wherein is the signal length, is the zero baseline envelope value for the th sample point; If If the product function is less than a predetermined threshold or the envelope is sufficiently flat, then stop the screening process for the current product function; otherwise, return to 1) repeat the iterative decomposition as a new input signal. If the product function is less than a predetermined threshold or the envelope is sufficiently flat, then stop the screening process for the current product function; otherwise, return to 1) repeat the iterative decomposition as a new input signal. S3.3 specifically is: Once the stop criterion is met, the final demodulated signal is multiplied with the envelope function, resulting in a first product function component i :​ 。 6. The interferometric radar-based method for monitoring the health of a high-speed rail bridge according to claim 5, wherein, Repeat the iterative decomposition process of S3.2 to extract the next product function component; separating the product function component from the current signal to obtain a new residual signal : ； The new residual signal is The new signal is considered as ; S3.4 specifically is: When The decomposition stops when it becomes constant or free of significant oscillations; The final residual is noted as where is the number of extracted product functions.

7. The interferometric radar-based method for monitoring the health of a high-speed rail bridge according to claim 6, wherein, Truncate all components: truncate the product function components and residuals obtained on the extended signal back to the original signal length; The S5 specifically comprises: Signal reconstruction: original signal is expressed as the sum of all product function components and the final residual: 。 8. The interferometric radar-based method for monitoring the health of a high-speed rail bridge according to claim 7, wherein, The instantaneous frequency IF is represented as: The following is expressed using the short-time Fourier transform representation as follows: ； wherein is the notation for the Fourier transform; K is a finite number; denotes the amplitude of the effective product function, denotes the amplitude function; denotes the Fourier transform of the window function; denotes the angular frequency of the effective product function, is a function of the angular frequency; is the natural constant; is the time; is the imaginary unit; Its computer program instructions are stored, and when the computer program instructions are executed by the processor, a high-speed railway bridge health monitoring method based on interferometric radar is realized according to any one of claims 1-8. ； wherein is the instantaneous frequency; Using Dirac It comprises: The function is used for both time-frequency display of the signal and extraction of key instantaneous frequencies. In this case, the time-frequency graph function expression obtained through the synchronous extraction transformation method is as follows: ； wherein is a time-frequency pattern function; is a synchronization extraction operator, expressed as follows: 。 9. A readable storage medium, characterized by, At least one processor, at least one memory, and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, a high-speed railway bridge health monitoring method based on interferometric radar is realized according to any one of claims 1-8.

10. An electronic device, comprising: ​ ​

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