High-speed railway vehicle body acceleration normalization method and device

Abstract

The invention discloses a high-speed railway vehicle body acceleration normalization method and device, and the method comprises the steps: carrying out the frequency domain analysis and time domain feature analysis of the dynamic response measured data of a high-speed railway, and obtaining the envelope data of the dynamic response measured data; determining a first section meeting a principle of maximum envelope correlation from the dynamic response measured data section; according to the dynamic response actual measurement data corresponding to the first section, carrying out transfer relation fitting on the track height irregularity and the vehicle body acceleration to obtain a transfer coefficient calculation model; a transfer coefficient calculation model is adopted to calculate first transfer coefficients corresponding to different types of detection vehicles respectively; performing normalization processing on the first transfer coefficients corresponding to the different types of detection vehicles to obtain second transfer coefficients with unified dimensions; and according to the second transfer coefficient, carrying out normalization processing on the vehicle body acceleration data in the dynamic response actual measurement data of the high-speed rail. According to the invention, the accuracy and consistency of vehicle acceleration evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle body acceleration evaluation in the high - speed railway track specialty, and particularly to a method and device for normalizing the vehicle body acceleration of high - speed railways. Background Art

[0002] This section aims to provide background or context for the embodiments of the present invention described in the claims. The descriptions herein are not admitted to be prior art merely because they are included in this section.

[0003] To ensure the safety and efficiency of railway transportation, it is particularly important to scientifically evaluate and manage the maintenance of railway lines and vehicles. Among them, the vehicle body acceleration, as one of the important indicators for evaluating the running stability and riding comfort of vehicles, is widely used in railway maintenance. However, the inherent dynamic characteristics of vehicles of different types of inspection vehicles are different, and the influence of track vertical irregularities on the vertical acceleration of the vehicle body also varies. The detection data is different, and the evaluation results of the vehicle body acceleration vary greatly. Therefore, in order to improve the consistency of the vehicle body acceleration evaluation results and provide a scientific basis for railway maintenance, it is necessary to perform normalization analysis on the vehicle body acceleration. The normalization method of vehicle body acceleration is a technology for normalizing the vertical acceleration of the vehicle body by using the stable transfer relationship between the track elevation and the vehicle body acceleration under the action of periodic irregularities of the track geometry, aiming to eliminate the problem of inconsistent evaluation conclusions of vehicle body acceleration caused by detection differences.

[0004] The periodic vertical irregularities of the track, as a strong excitation source, can have a certain impact on the vertical vibration of the vehicle body, resulting in periodic changes in the vertical acceleration of the vehicle body. The transfer relationship between the two has always been the focus of researchers. The existing research on the transfer relationship between track geometry and vehicle body acceleration focuses on the construction of correlation models, conducts data simulation on track irregularities and vehicle responses, and uses methods such as spectral analysis and coherence analysis to study the influence of factors such as different wavelengths and speeds on the transfer relationship. However, the generality and stability of the relationship between the two have not been deeply explored, nor has the quantitative characterization of the transfer relationship and the normalization method of vehicle body acceleration been involved. The vehicle body acceleration is one of the important indicators for evaluating the running performance of trains, and its evaluation results are affected by various factors and may have large deviations.

[0005] How to eliminate the influence of these factors and improve the accuracy and consistency of vehicle body acceleration evaluation is an urgent problem to be solved. Summary of the Invention

[0006] Embodiments of the present invention provide a method for normalizing the vehicle body acceleration of high - speed railways, which is used to quantitatively characterize the transfer relationship between the vertical acceleration of the vehicle body and the vertical irregularities of the track, normalize the vehicle body acceleration, and further improve the accuracy and consistency of the vehicle body acceleration evaluation. The method includes:

[0007] Perform frequency-domain analysis on the measured dynamic response data of high-speed trains to obtain the strong excitation band of track irregularities;

[0008] According to the strong excitation band of track irregularities, perform time-domain feature analysis on the measured dynamic response data to obtain the envelope data of the measured dynamic response data;

[0009] According to the envelope data of the measured dynamic response data, determine the first section that satisfies the principle of maximum envelope correlation from the measured dynamic response data section;

[0010] According to the measured dynamic response data corresponding to the first section, perform fitting on the transfer relationship between track vertical irregularities and car body acceleration to obtain a transfer coefficient calculation model;

[0011] According to the measured data of different types of inspection vehicles on the same inspection line, use the transfer coefficient calculation model to calculate the first transfer coefficients corresponding to different types of inspection vehicles respectively;

[0012] Normalize the first transfer coefficients corresponding to different types of inspection vehicles respectively to obtain the second transfer coefficients with unified dimensions;

[0013] According to the second transfer coefficient, normalize the car body acceleration data in the measured dynamic response data of high-speed trains.

[0014] An embodiment of the present invention further provides a high-speed railway car body acceleration normalization device, which is used to quantitatively characterize the transfer relationship between the vertical acceleration of the car body and the track vertical irregularities, normalize the car body acceleration, and further improve the accuracy and consistency of the car body acceleration evaluation. The device includes:

[0015] The first processing module is used to perform frequency-domain analysis on the measured dynamic response data of high-speed trains to obtain the strong excitation band of track irregularities;

[0016] The second processing module is used to perform time-domain feature analysis on the measured dynamic response data according to the strong excitation band of track irregularities to obtain the envelope data of the measured dynamic response data;

[0017] The third processing module is used to determine the first section that satisfies the principle of maximum envelope correlation from the measured dynamic response data section according to the envelope data of the measured dynamic response data;

[0018] The fourth processing module is used to perform fitting on the transfer relationship between track vertical irregularities and car body acceleration according to the measured dynamic response data corresponding to the first section to obtain a transfer coefficient calculation model;

[0019] The fifth processing module is configured to calculate, according to the measured data of different types of inspection vehicles on the same inspection line, the first transfer coefficients corresponding to different types of inspection vehicles respectively by using a transfer coefficient calculation model;

[0020] The sixth processing module is configured to perform normalization processing on the first transfer coefficients corresponding to different types of inspection vehicles respectively to obtain second transfer coefficients with a unified dimension;

[0021] The seventh processing module is configured to perform normalization processing on the car body acceleration data in the measured dynamic response data of the high-speed rail according to the second transfer coefficient.

[0022] An embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned high-speed railway car body acceleration normalization method is implemented.

[0023] An embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned high-speed railway car body acceleration normalization method is implemented.

[0024] An embodiment of the present invention further provides a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, the above-mentioned high-speed railway car body acceleration normalization method is implemented.

[0025] In an embodiment of the present invention, frequency domain analysis is performed on the measured dynamic response data of the high-speed rail to obtain a strong excitation band of track irregularity; according to the strong excitation band of track irregularity, time domain feature analysis is performed on the measured dynamic response data to obtain envelope data of the measured dynamic response data; according to the envelope data of the measured dynamic response data, a first section that satisfies the principle of maximum envelope correlation is determined from the measured dynamic response data section; according to the measured dynamic response data corresponding to the first section, fitting of the transfer relationship between track vertical irregularity and car body acceleration is performed to obtain a transfer coefficient calculation model; according to the measured data of different types of inspection vehicles on the same inspection line, the first transfer coefficients corresponding to different types of inspection vehicles respectively are calculated by using the transfer coefficient calculation model; the first transfer coefficients corresponding to different types of inspection vehicles respectively are subjected to normalization processing to obtain second transfer coefficients with a unified dimension; according to the second transfer coefficient, normalization processing is performed on the car body acceleration data in the measured dynamic response data of the high-speed rail. In this way, the transfer relationship between the vertical acceleration of the car body and the track vertical irregularity can be quantitatively characterized, the car body acceleration is normalized, and thus the accuracy and consistency of the car body acceleration evaluation are improved. Description of the Drawings

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings:

[0027] Figure 1 It is a flowchart of a method for normalizing the carbody acceleration of a high-speed railway provided in an embodiment of the present invention;

[0028] Figure 2 It is an example diagram of a method for performing frequency-domain analysis on the measured dynamic response data of a high-speed train to obtain the strong excitation band of track irregularities;

[0029] Figure 3 It is an example diagram of the overall implementation logic of a method for normalizing the carbody acceleration provided in an embodiment of the present invention;

[0030] Figure 4 It is a power spectral density diagram provided in an embodiment of the present invention;

[0031] Figure 5 It is an example diagram of the coherence function between the measured data of track vertical irregularities and the measured data of carbody vertical acceleration provided in an embodiment of the present invention;

[0032] Figure 6 It is an example diagram of the measured data envelope based on Hilbert transform provided in an embodiment of the present invention;

[0033] Figure 7 It is an example diagram of time-delay processing based on the principle of maximum envelope coherence provided in an embodiment of the present invention;

[0034] Figure 8 It is an example diagram of fitting the transfer relationship between the measured data of track vertical irregularities and the measured data of carbody vertical acceleration provided in an embodiment of the present invention;

[0035] Figure 9 It is an example diagram of comparing the transfer coefficients of different lines provided in an embodiment of the present invention;

[0036] Figure 10 It is an example diagram of comparing the transfer coefficients on different detection dates in the same section provided in an embodiment of the present invention;

[0037] Figure 11 It is an example diagram of normalizing the carbody acceleration based on strong periodic geometric irregularity excitation provided in an embodiment of the present invention

[0038] Figure 12It is an exemplary diagram of a high - speed railway vehicle body acceleration normalization device provided in an embodiment of the present invention;

[0039] Figure 13 It is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed implementation manners

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

[0041] In the technical solutions of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0042] The term "and / or" in this article merely describes an association relationship, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C can represent any one or more elements selected from the set composed of A, B, and C.

[0043] In the description of this specification, the terms "comprising", "including", "having", "containing", etc. are all open - ended terms, that is, they are intended to include but not limited to. The description with reference to terms such as "an embodiment", "a specific embodiment", "some embodiments", "for example", etc. means that the specific features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. The step sequences involved in each embodiment are used to schematically illustrate the implementation of this application, and the step sequences are not limited and can be adjusted appropriately as needed.

[0044] It has been found through research that in order to ensure the safety and efficiency of railway transportation, it is particularly important to conduct scientific evaluation and management of the maintenance of railway lines and vehicles. Among them, the car body acceleration, as one of the important indicators for evaluating the running stability and riding comfort of vehicles, is widely used in railway maintenance. However, the inherent dynamic characteristics of vehicles of different types of inspection cars are different, and the influence of track vertical irregularities on the vertical acceleration of the car body also varies. The detection data is different, and the evaluation results of the car body acceleration vary greatly. Therefore, in order to improve the consistency of the evaluation results of the car body acceleration and provide a scientific basis for railway maintenance, it is necessary to conduct a normalization analysis of the car body acceleration. The normalization method of the car body acceleration is a technology for normalizing the vertical acceleration of the car body by using the stable transfer relationship between the track verticality and the car body acceleration under the excitation of periodic irregularities of the track geometry, aiming to eliminate the problem of inconsistent evaluation conclusions of the car body acceleration caused by detection differences.

[0045] As a strong excitation source, the periodic vertical irregularity of the track can have a certain impact on the vertical vibration of the car body, resulting in periodic changes in the vertical acceleration of the car body. The transfer relationship between the two has always been the focus of researchers. In 2010, relevant scholars studied the track geometry evaluation method based on vehicle response prediction. The results showed that factors such as different vehicle types, speed grades, and curvatures should be considered to more accurately quantify the correlation between track geometry and vehicle response. In 2013, relevant scholars studied the impact of track geometry on the ride comfort of high-speed railways. They carried out simulation modeling on the safety and comfort indicators of high-speed trains with a speed of 300 km / h for track irregularities of various wavelengths and wave depths to study the impact of vertical irregularities on ride comfort. In the same year, some scholars studied the coherence between the car body response and track irregularities. Combining the analysis of the car body acceleration power spectrum and the analysis of the natural vibration frequency of the car body system, they studied the relationship between track irregularities and vehicle dynamic response in the frequency domain, and determined the main vibration sources causing the car body vibration and their corresponding irregularity wavelengths. In 2015, relevant scholars studied the correlation model between long-wave vertical irregularities and car body vertical acceleration. They used the spectral analysis method to analyze the measured data of the No. 0 high-speed comprehensive inspection train, and used non-parametric models and ARX models to establish the correlation between long-wave vertical irregularities and car body vertical acceleration. The results showed that both models could better reflect the transfer relationship between long-wave vertical irregularities and car body vertical acceleration, and the ARX model was more accurate than the non-parametric model. In 2018, relevant scholars studied the correlation analysis of track irregularities and vehicle response based on dynamic simulation and modeling. They used measured and simulation data respectively, gradually decomposed the wheel-rail relationship, and considered factors such as track vertical stiffness and unsprung mass of the vehicle to complete a detailed analysis of the correlation between track geometry and vehicle response. In 2021, relevant scholars carried out a simulation study on the vertical dynamics of high-speed maglev trains under the excitation of track irregularities, and deeply analyzed the influence of factors such as driving speed, wavelength and amplitude of track irregularities, and vehicle weight on vehicle ride quality. The results showed that different vehicle speeds corresponded to different sensitive wavelength ranges. The greater the vehicle speed, the longer the main vertical response wavelength of the car body, the greater the amplitude of vertical irregularities, and the vertical ride quality index showed a non-linear upward trend. In addition, the greater the vehicle weight, the better the ride quality of the train. In the same year, some other scholars studied the multivariate statistical representation of railway track irregularities based on the ARMA model and modeled the spatial autocorrelation characteristics between track irregularities of different wavelengths. The results showed that the fitting of the transfer relationship between track irregularities and car body response based on the multivariate random process was better than the univariate model

[0046] That is, the existing research on the relationship between track geometry and carbody acceleration transmission focuses on the construction of correlation models, conducts data simulation on track irregularities and vehicle responses, and uses methods such as spectral analysis and coherence analysis to study the influence of different waves, speeds, and other factors on the transmission relationship. However, the generality and stability of the relationship between the two have not been deeply explored, nor have the quantitative characterization of the transmission relationship and the normalization method of carbody acceleration been involved. Carbody acceleration is one of the important indicators for evaluating train operation performance, and its evaluation results are affected by various factors, which may lead to large deviations. How to eliminate the influence of these factors and improve the accuracy and consistency of carbody acceleration evaluation is an urgent problem to be solved.

[0047] In view of the above research, as Figure 1 shown, an embodiment of the present invention provides a method for normalizing carbody acceleration of high-speed railways, including:

[0048] S101: Perform frequency-domain analysis on the measured dynamic response data of high-speed railways to obtain the strong excitation band of track irregularities;

[0049] S102: According to the strong excitation band of track irregularities, perform time-domain feature analysis on the measured dynamic response data to obtain the envelope data of the measured dynamic response data;

[0050] S103: According to the envelope data of the measured dynamic response data, determine the first section that satisfies the principle of maximum envelope correlation from the measured dynamic response data section;

[0051] S104: Fit the transmission relationship between track vertical irregularities and carbody acceleration according to the measured dynamic response data corresponding to the first section to obtain a transmission coefficient calculation model;

[0052] S105: According to the measured data of different types of inspection vehicles on the same inspection line, use the transmission coefficient calculation model to calculate the first transmission coefficients corresponding to different types of inspection vehicles respectively;

[0053] S106: Normalize the first transmission coefficients corresponding to different types of inspection vehicles respectively to obtain a second transmission coefficient with a unified dimension;

[0054] S107: Normalize the carbody acceleration data in the measured dynamic response data of high-speed railways according to the second transmission coefficient.

[0055] In the embodiment of the present invention, the measured dynamic response data of the high-speed rail is analyzed in the frequency domain to obtain the strong excitation band of track irregularity; according to the strong excitation band of track irregularity, the time-domain characteristic analysis is carried out on the measured dynamic response data to obtain the envelope data of the measured dynamic response data; according to the envelope data of the measured dynamic response data, the first section that satisfies the principle of the maximum envelope correlation is determined from the measured dynamic response data section; according to the measured dynamic response data corresponding to the first section, the fitting of the relationship between track vertical irregularity and car body acceleration transfer is carried out to obtain the transfer coefficient calculation model; according to the measured data of different types of inspection vehicles on the same inspection line, the first transfer coefficients corresponding to different types of inspection vehicles are calculated by using the transfer coefficient calculation model; the first transfer coefficients corresponding to different types of inspection vehicles are normalized to obtain the second transfer coefficients with a unified dimension; according to the second transfer coefficients, the car body acceleration data in the measured dynamic response data of the high-speed rail is normalized. This can quantitatively characterize the transfer relationship between the vertical acceleration of the car body and the track vertical irregularity, normalize the car body acceleration, and thus improve the accuracy and consistency of the car body acceleration evaluation.

[0056] The method for normalizing the car body acceleration of the high-speed railway will be described in detail below.

[0057] Regarding the above S101, first, based on the measured dynamic response data of the high-speed rail, the power spectrum analysis and coherence analysis methods are used to identify the energy distribution at different frequencies, and then the interaction mechanism between vertical irregularity and vehicle vertical acceleration is revealed, which can effectively determine the band of periodic irregularity as the strong excitation source for the periodic vibration of the car body vertical acceleration. Secondly, the fast Fourier transform and inverse transform and band-pass filtering processing are respectively carried out on the measured data of vertical irregularity and car body vertical acceleration, and then the continuous multi-wave section, that is, the section with periodic irregularity fluctuation, is analyzed and located as the data basis for subsequent research, which helps to improve the stability of the transfer relationship fitting.

[0058] For example Figure 2 As shown, it is an example diagram of a method for analyzing the measured dynamic response data of the high-speed rail in the frequency domain to obtain the strong excitation band of track irregularity provided by the embodiment of the present invention, including:

[0059] S201: Perform power spectrum analysis on the measured dynamic response data of the high-speed rail to obtain the power spectrum analysis result.

[0060] In an embodiment of the present invention, the measured dynamic response data of the high-speed rail includes: the measured track irregularity data and the measured vertical acceleration data of the car body; performing power spectrum analysis on the measured dynamic response data of the high-speed rail to obtain the power spectrum analysis result. For example, performing discrete Fourier transform on the measured track irregularity data and the measured vertical acceleration data of the car body to obtain the discrete Fourier transform result of the measured track irregularity data and the discrete Fourier transform result of the measured vertical acceleration data of the car body; dividing the square of the modulus of the discrete Fourier transform result of the measured track irregularity data by the square of the sampling frequency to obtain the energy spectral density of the measured track irregularity data; dividing the square of the modulus of the discrete Fourier transform result of the measured vertical acceleration data of the car body by the square of the sampling frequency to obtain the energy spectral density of the measured vertical acceleration data of the car body; dividing the energy spectral density of the measured track irregularity data by the total time to obtain the power spectral density of the measured track irregularity data; dividing the energy spectral density of the measured vertical acceleration data of the car body by the total time to obtain the power spectral density of the measured vertical acceleration data of the car body; windowing the power spectral density of the measured track irregularity data and the power spectral density of the measured vertical acceleration data of the car body to obtain the auto-power spectrum of the measured track vertical irregularity data and the auto-power spectrum of the measured vertical acceleration data of the car body; obtaining the cross-power spectrum between the measured track vertical irregularity data and the measured vertical acceleration data of the car body according to the auto-power spectrum of the measured track vertical irregularity data and the auto-power spectrum of the measured vertical acceleration data of the car body; taking the auto-power spectrum of the measured track vertical irregularity data, the auto-power spectrum of the measured vertical acceleration data of the car body, and the cross-power spectrum between the measured track vertical irregularity data and the measured vertical acceleration data of the car body as the power spectrum analysis result.

[0061] Among them, the process of performing power spectrum analysis on the measured track irregularity data and the measured vertical acceleration data of the car body is similar. The following takes the power spectrum analysis process of the measured track irregularity data as an example for introduction.

[0062] Exemplarily, for the measured track irregularity data x(n), the energy spectral density is the square of the modulus of the discrete Fourier transform of x(n) divided by the square of the sampling frequency, that is

[0063]

[0064] where E(k) is the energy density spectrum, is the discrete Fourier transform of the measured track irregularity data x(n), and f s is the sampling frequency.

[0065] The power spectral density of the measured track irregularity data x(n) is the energy spectral density divided by the total time T (which is also obtained by multiplying the number of sampling points by the sampling period), that is,

[0066]

[0067] Among them, P(k) is the power spectral density of the measured track irregularity data x(n), E(k) is the energy spectral density, N is the number of sampling points, and Δt is the sampling period.

[0068] After windowing x(n) with w(n), in order to keep its average power unchanged, it is necessary to multiply by an energy recovery coefficient K. Among them,

[0069] The power spectral density after windowing is:

[0070]

[0071] Among them, P(k)' is the power spectral density of the measured track irregularity data x(n) after windowing (i.e., the auto-power spectrum of the measured track vertical irregularity data), K is the energy recovery coefficient, E(k) is the energy spectral density, N is the number of sampling points, Δt is the sampling period, w is the window function, is the discrete Fourier transform of the measured track irregularity data x(n), f s is the sampling frequency.

[0072] S202: According to the power spectrum analysis result, perform coherence analysis to obtain the coherence analysis result.

[0073] Among them, the power spectrum analysis result includes: the auto-power spectrum of the measured track vertical irregularity data, the auto-power spectrum of the measured car body vertical acceleration data, and the cross-power spectrum of the measured track vertical irregularity data and the measured car body vertical acceleration data.

[0074] In an embodiment of the present invention, according to the power spectrum analysis result, perform coherence analysis to obtain the coherence analysis result. For example, it includes: calculating the coherence analysis result according to the auto-power spectrum of the measured track vertical irregularity data, the auto-power spectrum of the measured car body vertical acceleration data, and the cross-power spectrum of the measured track vertical irregularity data and the measured car body vertical acceleration data.

[0075] Exemplarily, when the two data of the track vertical irregularity and the car body vertical acceleration are x(n) and y(n) respectively, the coherence function of these two data is expressed as

[0076]

[0077] Among them: Pxx(w) is the auto-power spectrum of the measured track vertical irregularity data, Pyy(w) is the auto-power spectrum of the measured car body vertical acceleration data, and Pxy(w) is the cross-power spectrum of the measured track vertical irregularity data and the measured car body vertical acceleration data.

[0078] S203: Obtain the strong excitation band of track irregularity according to the coherence analysis result.

[0079] In the embodiment of the present invention, the measured dynamic response data includes: the measured vertical acceleration data of the car body; obtaining the strong excitation band of track irregularity according to the coherence analysis result includes: determining that the spatial frequency of the measured vertical acceleration data of the car body is within a preset spatial frequency range according to the coherence analysis result, and there is a peak spectrum in the multiple frequency of the spatial frequency, and the band where the coherence coefficient is greater than the preset coherence coefficient is the strong excitation band of track irregularity.

[0080] For the above S102 - S104, first, based on the Hilbert transform, extract the envelope of the measured dynamic response data, maximize the matching of the envelope coherence of the strong excitation section of the measured dynamic response data, and find the optimal time delay point. This method helps to calculate the transfer coefficient with strong generality and high stability. Then, use the least squares method to fit the relationship between the two variables, determine that there is a linear relationship between them under the strong excitation of periodic irregularity, and find the optimal parameter estimation as the quantitative characterization of the transfer relationship between track elevation and car body acceleration. The embodiment of the present invention can effectively reflect the transfer relationship between track geometry and car body response, measure the influence degree of track elevation on the vertical acceleration of the car body, with fast calculation speed, high stability of calculation results, and strong interpretability.

[0081] Specifically, for the above S102, perform time-domain feature analysis on the measured dynamic response data corresponding to the strong excitation band of track irregularity to obtain the envelope data of the strong excitation band of track irregularity. For example, it includes: determining the upper limit and lower limit of the filter of the band-pass filter according to the strong excitation band of track irregularity; performing Fourier transform on the measured dynamic response data of high-speed rail to obtain the transformation result of the measured dynamic response data; filtering the transformation result of the measured dynamic response data according to the upper limit of the filter, lower limit of the filter, and the corresponding frequency of the band-pass filter to obtain the filtered data; performing inverse Fourier transform on the filtered data to obtain the filtered measured dynamic response data, and extracting the Hilbert envelope according to the filtered measured dynamic response data to obtain the envelope data of the measured dynamic response data.

[0082] Exemplarily, perform Fourier transform on the measured dynamic response data x(n) to obtain X(k):

[0083]

[0084] Among them, FFT is the Fourier transform, and N is the number of sampling points.

[0085] According to the coherence analysis results, the upper and lower frequency limits of the band-pass filter are obtained. For example, according to the horizontal axis wavelengths corresponding to the coherence values within the preset coherence threshold in the coherence analysis results, the upper and lower frequency limits of the band-pass filter are obtained, such as 1 / 35 to 1 / 30. Then, the measured dynamic response data after band-pass filtering is obtained through inverse Fourier transform according to the following formula:

[0086]

[0087] where x′(n) is the measured dynamic response data after filtering, N is the number of sampling points, and X′(k) is the filtered data.

[0088] Exemplarily, according to the measured dynamic response data x′(n) after filtering, an analytic signal is constructed where is the Hilbert transform of

[0089] For example, x′(n) = A(n)cos(w0n + θ(tn)) Formula (7)

[0090] Substituting into the analytic signal gives:

[0091]

[0092]

[0093] Taking the absolute value of the analytic signal gives the required envelope signal:

[0094]

[0095] where A(n) is the amplitude and θ(n) is the phase.

[0096] For the above S103, according to the envelope data of the strong excitation band of track irregularities, a first section that satisfies the principle of maximum envelope correlation is determined from the strong excitation band of track irregularities. For example, it includes: according to the envelope data of the measured dynamic response data, as well as a preset detection value and a preset number of continuous waves, a section in the measured dynamic response data section where the peak detection value exceeding the preset number of continuous waves is greater than the preset detection value is determined as the first section.

[0097] Exemplarily, for the envelope data, screening of continuous multi-wave sections is performed, which includes two important parameters, namely the preset detection value and the preset number of continuous waves. For example, the parameter settings are: the preset detection value is 0.1 and the preset number of continuous waves is 10, then a section with 10 consecutive peak detection values exceeding 0.1 is determined from the measured dynamic response data section as the first section.

[0098] Regarding the above S104, the envelope data includes: track vertical irregularity envelope data and car body vertical acceleration envelope data; the measured dynamic response data includes: measured track vertical irregularity data and measured car body vertical acceleration data; based on the measured dynamic response data corresponding to the first section, fitting the relationship between track vertical irregularity and car body acceleration transmission to obtain a transmission coefficient calculation model. For example, it includes: determining the correlation coefficient of the first section according to the track vertical irregularity envelope data and car body vertical acceleration envelope data corresponding to the first section; displacing the measured track vertical irregularity data and measured car body vertical acceleration data corresponding to the first section according to the correlation coefficient of the first section to adjust the correlation coefficient of the first section to be greater than the preset correlation coefficient; using the least squares method to perform linear fitting on the adjusted measured track vertical irregularity data and measured car body vertical acceleration data of the first section to find the minimum error between the estimated value and the observed value, and obtaining the transmission coefficient calculation model.

[0099] Exemplarily, the track vertical irregularity envelope data and car body vertical acceleration envelope data corresponding to the first section are x ′ and y ′ , and their correlation coefficient is:

[0100]

[0101] where n is the data length. Displace the envelopes of the measured track vertical irregularity data and measured car body vertical acceleration data corresponding to the first section by 50 points before and after to find the time delay point with the maximum correlation, and given a threshold of 0.95 to ensure that the envelope correlation is greater than 0.95 to ensure the best correlation level between the two.

[0102] For example, the measured car body vertical acceleration data is the dependent variable y i , and the measured track vertical irregularity data is the independent variable x i , and their functional relationship is:

[0103] y i = f(x i ) Formula (10)

[0104] From this functional relationship, a straight line can be fitted to find the best straight line parameters to minimize the objective function, that is:

[0105]

[0106] where is the error between the estimated value and the observed value. After the time delay processing, the track vertical and acceleration data already has a high coherence. At this time, use the least squares method to perform linear fitting on the two, that is:

[0107] y i = k i x i + c i Equation (12)

[0108] Wherein, y i is the measured data of the vertical acceleration of the car body, x i is the measured data of the track vertical irregularity, k i is the transfer coefficient, which is a quantitative characterization of the transfer relationship between the measured data of the track vertical irregularity and the measured data of the car body acceleration under the condition of continuous periodic irregularity. The normalization of the car body acceleration is to normalize different measured data of the car body acceleration through the stability characteristics of the transfer relationship.

[0109] For the above S105 - S106, the first transfer coefficients corresponding to different types of inspection vehicles are normalized to obtain the second transfer coefficient with a unified dimension. For example, it includes: taking the ratio of the first transfer coefficients corresponding to different types of inspection vehicles to obtain the second transfer coefficient.

[0110] Exemplarily, for two different types of inspection vehicles, the measured data of the same inspection line are different. Substitute them into the transfer model respectively to calculate the first transfer coefficients k i and k j , and calculate the normalized second transfer coefficient through the first transfer coefficients of the two measured data. The formula is:

[0111]

[0112] Wherein, k uni is the normalized second transfer coefficient. The formula for normalizing the measured data of the vertical acceleration of the car body in the measured data of the dynamic response of high - speed trains is:

[0113] y uni = k uni x i Equation (14)

[0114] In this way, the new normalized measured data of the vertical acceleration of the car body y uni is obtained. After normalization, the measured data of the vertical acceleration of the car body in the same continuous - period irregularity section are at the same level, which helps to improve the consistency of the evaluation conclusions of the car body acceleration.

[0115] In the embodiments of the present invention, the transfer coefficients under different detection scenarios are converted into a unified scale, and the measured data of the vehicle body acceleration is normalized, which helps to compare and evaluate the vehicle body acceleration. Moreover, the normalization coefficient is calculated based on a large amount of measured data of high-speed railway tracks, so that the data range is within a standard interval. In this way, the differences under different conditions are eliminated, and the evaluation results of the vehicle body acceleration have higher comparability and accuracy.

[0116] The following describes the vehicle body acceleration normalization method according to the embodiments of the present invention in conjunction with a specific embodiment. As Figure 3 shown, it is an overall implementation logic example diagram of a vehicle body acceleration normalization method provided by the embodiments of the present invention, including: analysis of the periodic characteristics of track vertical irregularities and vehicle body vertical acceleration, determination of the transfer model between periodic vertical irregularities and vehicle body vertical acceleration, and normalization of the vehicle body vertical acceleration under the excitation of periodic vertical irregularities.

[0117] Specifically, first, the periodic characteristics extraction method is used to analyze and extract the effective characteristics of the periodic characteristics of the measured data of the dynamic response of high-speed railways. This method combines power spectrum analysis and coherence analysis to reveal the interaction mechanism between track irregularities and vehicle vertical acceleration, and determine the band of periodic irregularities. For example, as Figure 4 known, there are spectral peaks in the track height and the vehicle body vertical acceleration at the spatial frequency of 0.03 -1 and its multiples, indicating that there are periodic irregularities in the track height caused by 32-meter simply supported beams. As Figure 5 known, the coherence coefficient is greater than 0.8, indicating that under the excitation of the track height irregularities caused by 32-meter simply supported beams, the vehicle body has vertical vibrations of the same frequency, and there is a high linear coherence, which is conducive to improving the goodness of fit of the transfer relationship.

[0118] Then, the Hilbert transform is used to extract the envelope of the measured data, which contains the main characteristics and change trends of the measured data of the dynamic response in the strong excitation section. As Figure 6 shown. Based on the envelope of the measured data of the dynamic response, the correlation is calculated. As Figure 7 shown, the envelope correlation is 0.979, and at this time the waveform correlation can reach 0.994. The embodiments of the present invention can process the measured data of the dynamic response efficiently and accurately. In addition, the embodiments of the present invention can greatly reduce the amount of correlation calculation, and have the advantages of fast calculation speed and high calculation efficiency. The embodiments of the present invention construct a calculation model for the transfer coefficient between periodic vertical irregularities and vehicle body vertical acceleration, and fit the relationship between the two. The effect is as Figure 8 shown. At this time, the mean square error is 0.000209, and the goodness of fit is 0.0996, indicating that the transfer coefficient calculated by the embodiments of the present invention is highly accurate, can effectively reflect the transfer relationship between track geometry and vehicle body response, and measure the influence degree of track height on vehicle body vertical acceleration.

[0119] In addition, the stability of the transfer relationship is a prerequisite for the normalization of the car body acceleration. The stability is verified by analyzing from the time and space dimensions, and the results are as Figure 9 shown in Figure 10 Figure []. Figure 9 It shows that for each single line, the transfer coefficient values in the sections with the same speed are at the same level. For the three experimental lines, under the same conditions, there is no significant difference in the transfer coefficients of different lines. As Figure 10 can be seen, with the change of time, the transfer coefficient in the same section remains unchanged, indicating that the transfer coefficient calculated in the embodiment of the present invention has high reliability and stability and can be applied to the normalization of the car body acceleration. The normalization of the car body acceleration is carried out, and the calculation results are shown in Table 1:

[0120] Table 1 Output of the calculation models of the transfer coefficients of two inspection cars

[0121] Detection vehicle A Detection vehicle B Transfer coefficient 0.00134 0.00214 Goodness of fit 0.996 0.996 Mean square error 0.000171 0.000436

[0122] The transfer coefficients of the two types of inspection cars are 0.00134 and 0.00214 respectively, and the normalization coefficient is 0.626. The normalization effect is as Figure 11 shown in Figure []. Before normalization, the fluctuation range of the measured car body acceleration data of inspection car B is between ±0.4, and after normalization, it is ±0.2, which is the same as that of inspection car A. The two groups of measured data can be evaluated and compared. Therefore, the embodiment of the present invention can eliminate the detection difference of the car body acceleration caused by the inspection car model, and improve the comparability and objectivity of the evaluation results of the car body acceleration.

[0123] In the embodiment of the present invention, a car body acceleration normalization device is also provided, as described in the following embodiment. Since the principle of solving problems by this device is similar to that of the car body acceleration normalization method, the implementation of this device can refer to the implementation of the car body acceleration normalization method, and the repeated parts will not be described again.

[0124] As Figure 12 shown in Figure []. This is an example diagram of a high-speed railway car body acceleration normalization device provided by the embodiment of the present invention, including: a first processing module 1201, a second processing module 1202, a third processing module 1203, a fourth processing module 1204, a fifth processing module 1205, a sixth processing module 1206, and a seventh processing module 1207; wherein,

[0125] The first processing module 1201 is used to perform frequency domain analysis on the measured dynamic response data of the high-speed railway to obtain the strong excitation band of track irregularities;

[0126] The second processing module 1202 is configured to perform time-domain feature analysis on the measured dynamic response data according to the strong excitation band of track irregularity, so as to obtain the envelope data of the measured dynamic response data;

[0127] The third processing module 1203 is configured to determine a first section that satisfies the principle of maximum envelope correlation from the measured dynamic response data section according to the envelope data of the measured dynamic response data;

[0128] The fourth processing module 1204 is configured to perform fitting on the relationship between track vertical irregularity and carbody acceleration transmission according to the measured dynamic response data corresponding to the first section, so as to obtain a transmission coefficient calculation model;

[0129] The fifth processing module 1205 is configured to calculate first transmission coefficients corresponding to different types of inspection vehicles respectively by using the transmission coefficient calculation model according to the measured data of different types of inspection vehicles on the same inspection line;

[0130] The sixth processing module 1206 is configured to perform normalization processing on the first transmission coefficients corresponding to different types of inspection vehicles respectively, so as to obtain second transmission coefficients with a unified dimension;

[0131] The seventh processing module 1207 is configured to perform normalization processing on the carbody acceleration data in the measured dynamic response data of high-speed railways according to the second transmission coefficients.

[0132] In a possible implementation manner, the first processing module is specifically configured to perform power spectrum analysis on the measured dynamic response data of high-speed railways to obtain a power spectrum analysis result; perform coherence analysis according to the power spectrum analysis result to obtain a coherence analysis result; and obtain the strong excitation band of track irregularity according to the coherence analysis result.

[0133] In a possible implementation, the measured dynamic response data of the high-speed rail includes: the measured track irregularity data and the measured vertical acceleration data of the car body; the first processing module is specifically configured to perform discrete Fourier transform on the measured track irregularity data and the measured vertical acceleration data of the car body to obtain the discrete Fourier transform results of the measured track irregularity data and the discrete Fourier transform results of the measured vertical acceleration data of the car body; divide the square of the modulus of the discrete Fourier transform result of the measured track irregularity data by the square of the sampling frequency to obtain the energy spectral density of the measured track irregularity data; divide the square of the modulus of the discrete Fourier transform result of the measured vertical acceleration data of the car body by the square of the sampling frequency to obtain the energy spectral density of the measured vertical acceleration data of the car body; divide the energy spectral density of the measured track irregularity data by the total time to obtain the power spectral density of the measured track irregularity data; divide the energy spectral density of the measured vertical acceleration data of the car body by the total time to obtain the power spectral density of the measured vertical acceleration data of the car body; window the power spectral density of the measured track irregularity data and the power spectral density of the measured vertical acceleration data of the car body to obtain the auto-power spectrum of the measured track vertical irregularity data and the auto-power spectrum of the measured vertical acceleration data of the car body; obtain the cross-power spectrum between the measured track vertical irregularity data and the measured vertical acceleration data of the car body according to the auto-power spectrum of the measured track vertical irregularity data and the auto-power spectrum of the measured vertical acceleration data of the car body; use the auto-power spectrum of the measured track vertical irregularity data, the auto-power spectrum of the measured vertical acceleration data of the car body, and the cross-power spectrum between the measured track vertical irregularity data and the measured vertical acceleration data of the car body as the power spectrum analysis result.

[0134] In a possible implementation, the power spectrum analysis result includes: the first processing module is specifically configured to calculate the coherence analysis result according to the auto-power spectrum of the measured track vertical irregularity data, the auto-power spectrum of the measured vertical acceleration data of the car body, and the cross-power spectrum between the measured track vertical irregularity data and the measured vertical acceleration data of the car body.

[0135] In a possible implementation, the measured dynamic response data includes: the measured vertical acceleration data of the car body; the first processing module is specifically configured to determine, according to the coherence analysis result, that the spatial frequency of the measured vertical acceleration data of the car body is within a preset spatial frequency range, and there is a peak spectrum in the multiple frequency of the spatial frequency, and the band with a coherence coefficient greater than the preset coherence coefficient is the strong excitation band of the track irregularity.

[0136] In a possible implementation, the second processing module is specifically configured to determine the upper filter limit and the lower filter limit of the band-pass filter according to the strong excitation band of track irregularity; perform Fourier transform on the measured dynamic response data of the high-speed train to obtain the transformation result of the measured dynamic response data; filter the transformation result of the measured dynamic response data according to the upper filter limit, the lower filter limit of the band-pass filter, and the corresponding frequencies to obtain filtered data; perform inverse Fourier transform on the filtered data to obtain the measured dynamic response data after filtering, and perform Hilbert envelope extraction on the measured dynamic response data after filtering to obtain the envelope data of the measured dynamic response data.

[0137] In a possible implementation, the third processing module is specifically configured to determine, from the measured dynamic response data section, a section where the peak detection value exceeding the preset continuous wave number is greater than the preset detection value as the first section according to the envelope data of the measured dynamic response data, the preset detection value, and the preset continuous wave number.

[0138] In a possible implementation, the envelope data includes: the envelope data of track vertical irregularity and the envelope data of car body vertical acceleration; the measured dynamic response data includes: the measured data of track vertical irregularity and the measured data of car body vertical acceleration; the fourth processing module is specifically configured to determine the correlation coefficient of the first section according to the envelope data of track vertical irregularity corresponding to the first section and the envelope data of car body vertical acceleration; displace the measured data of track vertical irregularity and the measured data of car body vertical acceleration corresponding to the first section according to the correlation coefficient of the first section to adjust the correlation coefficient of the first section to be greater than the preset correlation coefficient; perform linear fitting on the adjusted measured data of track vertical irregularity and the measured data of car body vertical acceleration of the first section by using the least square method to find the minimum value of the error between the estimated value and the observed value, and obtain the transfer coefficient calculation model.

[0139] In a possible implementation, the sixth processing module is specifically configured to calculate the ratio of the first transfer coefficients corresponding to different types of inspection vehicles to obtain the second transfer coefficient.

[0140] Based on the foregoing inventive concept, as Figure 13 shown, the present invention further provides a computer device 1300, including a memory 1310, a processor 1320, and a computer program 1330 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1330, the foregoing high-speed railway car body acceleration normalization method is implemented.

[0141] The embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the foregoing high-speed railway car body acceleration normalization method is implemented.

[0142] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned high-speed railway car body acceleration normalization method is implemented.

[0143] In an embodiment of the present invention, frequency-domain analysis is performed on the measured dynamic response data of the high-speed railway to obtain the strong excitation band of track irregularity; according to the strong excitation band of track irregularity, time-domain feature analysis is performed on the measured dynamic response data to obtain the envelope data of the measured dynamic response data; according to the envelope data of the measured dynamic response data, a first section that satisfies the principle of maximum envelope correlation is determined from the measured dynamic response data section; according to the measured dynamic response data corresponding to the first section, fitting is performed on the transfer relationship between track vertical irregularity and car body acceleration to obtain a transfer coefficient calculation model; according to the measured data of different types of inspection vehicles on the same inspection line, the first transfer coefficients corresponding to different types of inspection vehicles are calculated by using the transfer coefficient calculation model; the first transfer coefficients corresponding to different types of inspection vehicles are normalized to obtain a second transfer coefficient with a unified dimension; according to the second transfer coefficient, the car body acceleration data in the measured dynamic response data of the high-speed railway is normalized. In this way, the transfer relationship between the vertical acceleration of the car body and the track vertical irregularity can be quantitatively characterized, the car body acceleration is normalized, and thus the accuracy and consistency of the car body acceleration evaluation are improved.

[0144] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0145] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0146] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0148] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for normalizing acceleration of a high-speed railway vehicle body, characterized in that: include: The frequency domain analysis of the measured data of dynamic response of high-speed railway is carried out to obtain the strong excitation band of track irregularity; According to the strong excitation band of track irregularity, the time domain characteristic analysis of the dynamic response measured data is carried out to obtain the envelope data of the dynamic response measured data; According to the envelope data of the dynamic response measured data, a first section satisfying the maximum envelope correlation principle is determined from the dynamic response measured data sections; According to the measured dynamic response data corresponding to the first section, the relationship between track height irregularity and vehicle acceleration transmission is fitted to obtain a transfer coefficient calculation model; According to the measured data of different types of inspection vehicles on the same inspection line, the first transfer coefficients corresponding to the different types of inspection vehicles are calculated by using the transfer coefficient calculation model; The first transfer coefficients corresponding to different types of inspection vehicles are normalized to obtain a second transfer coefficient with a unified dimension; According to the second transfer coefficient, the vehicle body acceleration data in the dynamic response measured data of the high-speed rail is normalized.

2. The high-speed railway vehicle body acceleration normalization method according to claim 1, characterized in that: The frequency domain analysis of the measured dynamic response data of the high-speed railway is carried out to obtain the strong excitation band of track irregularity, including: Perform power spectrum analysis on the measured data of dynamic response of high-speed rail and obtain the power spectrum analysis results; According to the power spectrum analysis results, coherence analysis is performed to obtain the coherence analysis results; According to the coherence analysis results, the strong excitation band of track irregularity is obtained.

3. The high-speed railway vehicle body acceleration normalization method according to claim 2, characterized in that: High-speed rail dynamic response measured data, including: track irregularity measured data, and vehicle vertical acceleration measured data; The power spectrum analysis is performed on the measured data of the dynamic response of the high-speed rail to obtain the power spectrum analysis results, including: Performing discrete Fourier transform on the track irregularity measured data and the vehicle body vertical acceleration measured data to obtain discrete Fourier transform results of the track irregularity measured data and discrete Fourier transform results of the vehicle body vertical acceleration measured data; The energy spectrum density of the track irregularity measured data is obtained by dividing the square of the modulus of the discrete Fourier transform result of the track irregularity measured data by the square of the sampling frequency. The energy spectrum density of the measured data of the vehicle body vertical acceleration is obtained by dividing the square of the modulus of the discrete Fourier transform result of the measured data of the vehicle body vertical acceleration by the square of the sampling frequency; The power spectrum density of the track irregularity measured data is obtained by dividing the energy spectrum density of the track irregularity measured data by the total time; The power spectrum density of the measured data of the vertical acceleration of the vehicle body is obtained by dividing the energy spectrum density of the measured data of the vertical acceleration of the vehicle body by the total time; The power spectrum density of the track irregularity measured data and the power spectrum density of the vehicle body vertical acceleration measured data are windowed to obtain the auto-power spectrum of the track height irregularity measured data and the auto-power spectrum of the vehicle body vertical acceleration measured data; According to the auto-power spectrum of the track height irregularity measured data and the auto-power spectrum of the vehicle body vertical acceleration measured data, the cross-power spectrum of the track height irregularity measured data and the vehicle body vertical acceleration measured data is obtained; The auto-power spectrum of the measured data of track irregularity, the auto-power spectrum of the measured data of vehicle vertical acceleration, and the cross-power spectrum of the measured data of track irregularity and the measured data of vehicle vertical acceleration are taken as the power spectrum analysis results.

4. The high-speed railway vehicle body acceleration normalization method according to claim 3, characterized in that: According to the power spectrum analysis results, coherence analysis is performed to obtain the coherence analysis results, including: The coherence analysis results are calculated based on the auto-power spectrum of the measured data of track irregularity, the auto-power spectrum of the measured data of vehicle vertical acceleration, and the cross-power spectrum of the measured data of track irregularity and the measured data of vehicle vertical acceleration.

5. The high-speed railway vehicle body acceleration normalization method according to claim 2, characterized in that: The dynamic response measured data include: the measured data of the vehicle vertical acceleration; According to the coherence analysis results, the track irregularity strong excitation band is obtained, including: According to the coherence analysis results, it is determined that the spatial frequency of the measured data of the vehicle body vertical acceleration is within the preset spatial frequency range, and the multiples of the spatial frequency have a peak spectrum, and the band with a coherence coefficient greater than the preset coherence coefficient is a strong excitation band for track irregularity.

6. The high-speed railway vehicle body acceleration normalization method according to claim 1, characterized in that: According to the strong excitation band of track irregularity, the time domain characteristic analysis of the dynamic response measured data is carried out to obtain the envelope data of the dynamic response measured data, including: According to the strong excitation band of track irregularity, the upper and lower limits of the band-pass filter are determined; Perform Fourier transform on the dynamic response measured data of the high-speed rail to obtain the transformation result of the dynamic response measured data; According to the filtering upper limit, the filtering lower limit, and the corresponding frequency of the band-pass filter, the transformation result of the dynamic response measured data is filtered to obtain filtered data; Performing inverse Fourier transform on the filtered data to obtain filtered dynamic response measured data, and performing Hilbert envelope extraction on the filtered dynamic response measured data to obtain envelope data of the dynamic response measured data.

7. The high-speed railway vehicle body acceleration normalization method according to claim 6, characterized in that: According to the envelope data of the dynamic response measured data, a first section satisfying the maximum envelope correlation principle is determined from the dynamic response measured data section, including: According to the envelope data of the dynamic response measured data, the preset detection value and the preset continuous wave number, a segment having a peak detection value greater than the preset continuous wave number and greater than the preset detection value is determined from the dynamic response measured data segment as the first segment.

8. The high-speed railway vehicle body acceleration normalization method according to claim 7, characterized in that: The envelope data includes: track height irregularity envelope data and vehicle body vertical acceleration envelope data; the dynamic response measured data includes: track height irregularity measured data and vehicle body vertical acceleration measured data; According to the measured dynamic response data corresponding to the first section, the relationship between track height irregularity and vehicle acceleration transmission is fitted to obtain the transfer coefficient calculation model, including: Determine the correlation coefficient of the first section according to the track height irregularity envelope data corresponding to the first section and the vehicle body vertical acceleration envelope data; According to the correlation coefficient of the first section, the track height irregularity measured data and the vehicle body vertical acceleration measured data corresponding to the first section are displaced to adjust the correlation coefficient of the first section to be greater than the preset correlation coefficient; The least squares method is used to perform linear fitting on the measured data of track unevenness after adjustment in the first section and the measured data of vehicle vertical acceleration to find the minimum error between the estimated value and the observed value, and to obtain the transfer coefficient calculation model.

9. The high-speed railway vehicle body acceleration normalization method according to claim 1, characterized in that: The first transfer coefficients corresponding to different types of inspection vehicles are normalized to obtain the second transfer coefficients with unified dimensions, including: The first transfer coefficients corresponding to different types of inspection vehicles are ratioed to obtain the second transfer coefficient.

10. A high-speed railway vehicle body acceleration normalization device, characterized in that: include: The first processing module is used to perform frequency domain analysis on the measured data of dynamic response of high-speed railways to obtain the strong excitation band of track irregularity; The second processing module is used to perform time domain characteristic analysis on the dynamic response measured data according to the track irregularity strong excitation band to obtain envelope data of the dynamic response measured data; A third processing module is used to determine, from the dynamic response measured data segments, a first segment that satisfies a maximum envelope correlation principle according to the envelope data of the dynamic response measured data; The fourth processing module is used to fit the relationship between track height irregularity and vehicle acceleration transmission according to the dynamic response measured data corresponding to the first section, and obtain a transfer coefficient calculation model; A fifth processing module is used to calculate the first transfer coefficients corresponding to the different types of inspection vehicles respectively using the transfer coefficient calculation model according to the measured data of the different types of inspection vehicles on the same inspection route; A sixth processing module, used for normalizing the first transfer coefficients corresponding to different types of detection vehicles to obtain a second transfer coefficient with a unified dimension; The seventh processing module is used to normalize the vehicle body acceleration data in the dynamic response measured data of the high-speed rail according to the second transfer coefficient.

11. The high-speed railway vehicle body acceleration normalization device according to claim 10, characterized in that: The first processing module is specifically used to perform power spectrum analysis on the dynamic response measured data of the high-speed rail to obtain the power spectrum analysis results; According to the power spectrum analysis results, coherence analysis is performed to obtain the coherence analysis results; According to the coherence analysis results, the strong excitation band of track irregularity is obtained.

12. The high-speed railway vehicle body acceleration normalization device according to claim 11, characterized in that: The dynamic response measured data of the high-speed rail includes: track irregularity measured data and vehicle body vertical acceleration measured data; the first processing module is specifically used to perform discrete Fourier transform on the track irregularity measured data and the vehicle body vertical acceleration measured data to obtain discrete Fourier transform results of the track irregularity measured data and the vehicle body vertical acceleration measured data; The energy spectrum density of the track irregularity measured data is obtained by dividing the square of the modulus of the discrete Fourier transform result of the track irregularity measured data by the square of the sampling frequency. The energy spectrum density of the measured data of the vehicle body vertical acceleration is obtained by dividing the square of the modulus of the discrete Fourier transform result of the measured data of the vehicle body vertical acceleration by the square of the sampling frequency; The power spectrum density of the track irregularity measured data is obtained by dividing the energy spectrum density of the track irregularity measured data by the total time; The power spectrum density of the measured data of the vertical acceleration of the vehicle body is obtained by dividing the energy spectrum density of the measured data of the vertical acceleration of the vehicle body by the total time; The power spectrum density of the track irregularity measured data and the power spectrum density of the vehicle body vertical acceleration measured data are windowed to obtain the auto-power spectrum of the track height irregularity measured data and the auto-power spectrum of the vehicle body vertical acceleration measured data; According to the auto-power spectrum of the track height irregularity measured data and the auto-power spectrum of the vehicle body vertical acceleration measured data, the cross-power spectrum of the track height irregularity measured data and the vehicle body vertical acceleration measured data is obtained; The auto-power spectrum of the measured data of track irregularity, the auto-power spectrum of the measured data of vehicle vertical acceleration, and the cross-power spectrum of the measured data of track irregularity and the measured data of vehicle vertical acceleration are taken as the power spectrum analysis results.

13. The high-speed railway vehicle body acceleration normalization device according to claim 12, characterized in that: The first processing module is specifically used to calculate the coherence analysis result according to the autopower spectrum of the track unevenness measured data, the autopower spectrum of the vehicle body vertical acceleration measured data, and the cross-power spectrum of the track unevenness measured data and the vehicle body vertical acceleration measured data.

14. The high-speed railway vehicle body acceleration normalization device according to claim 11, characterized in that: The dynamic response measured data include: the measured data of the vehicle vertical acceleration; The first processing module is specifically used to determine, based on the coherence analysis result, that the spatial frequency of the measured data of the vehicle body vertical acceleration is within a preset spatial frequency range, that the multiples of the spatial frequency have a peak spectrum, and that the band whose coherence coefficient is greater than the preset coherence coefficient is a track irregularity strong excitation band.

15. The high-speed railway vehicle body acceleration normalization device according to claim 10, characterized in that: The second processing module is specifically used to determine the filtering upper limit and the filtering lower limit of the band-pass filter according to the track irregularity strong excitation band; Perform Fourier transform on the dynamic response measured data of the high-speed rail to obtain the transformation result of the dynamic response measured data; According to the filtering upper limit, the filtering lower limit, and the corresponding frequency of the band-pass filter, the transformation result of the dynamic response measured data is filtered to obtain filtered data; Performing inverse Fourier transform on the filtered data to obtain filtered dynamic response measured data, and performing Hilbert envelope extraction on the filtered dynamic response measured data to obtain envelope data of the dynamic response measured data.

16. The high-speed railway vehicle body acceleration normalization device according to claim 15, characterized in that: The third processing module is specifically used to determine a segment with a peak detection value greater than a preset continuous wave number and greater than a preset detection value from the dynamic response measured data segment as the first segment based on the envelope data of the dynamic response measured data, the preset detection value, and the preset continuous wave number.

17. The high-speed railway vehicle body acceleration normalization device according to claim 16, characterized in that: The envelope data includes: track height irregularity envelope data and vehicle body vertical acceleration envelope data; the dynamic response measured data includes: track height irregularity measured data and vehicle body vertical acceleration measured data; The fourth processing module is specifically used to determine the correlation coefficient of the first section according to the track height irregularity envelope data corresponding to the first section and the vehicle body vertical acceleration envelope data; According to the correlation coefficient of the first section, the track height irregularity measured data and the vehicle body vertical acceleration measured data corresponding to the first section are displaced to adjust the correlation coefficient of the first section to be greater than the preset correlation coefficient; The least squares method is used to perform linear fitting on the measured data of track unevenness after adjustment in the first section and the measured data of vehicle vertical acceleration to find the minimum error between the estimated value and the observed value, and to obtain the transfer coefficient calculation model.

18. The high-speed railway vehicle body acceleration normalization device according to claim 10, characterized in that: The sixth processing module is specifically used to calculate the ratio of the first transfer coefficients corresponding to different types of detection vehicles to obtain the second transfer coefficient.

19. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

21. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

Images