Multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method

Abstract

The invention relates to a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method. The method comprises the steps of integrating long-span bridge track vertical deviation three-dimensional historical data added with an environment temperature label; adopting empirical wavelet transform to construct an MRA component four-dimensional data set containing a temperature label and a wavelength label; after mileage dimension resampling, projecting data to an amplitude-wavelength space, drawing an accumulated amplitude growth curve, judging the type of the amplitude-wavelength curve, and determining a boundary wavelength of bridge deformation and track irregularity; projecting the data to an amplitude-temperature space, and judging an amplitude curve and temperature sensitivity; and in combination with the boundary wavelength and the temperature sensitivity, extracting a temperature sensitive component of track irregularity and a construction deviation component in actual bridge deformation. According to the method, synchronous identification and extraction of the temperature sensitive component in the track vertical deviation and the bridge construction deviation component can be realized, and a quantitative basis is provided for accurate maintenance and repair of a long-span railway bridge.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of track data processing, in particular to a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method. BACKGROUND

[0002] Long-span high-speed railway bridges are prone to be affected by temperature changes, wind loads, pier settlement and other factors due to their complex structural system and long-term exposure to natural environment, resulting in significant amplitude and periodic characteristics of bridge deformation, and further causing the deterioration of track geometry on the bridge. For example, the annual variation amplitude of the mid-span elevation of a kilometer-level suspension bridge can exceed 1000mm, far exceeding the adjustment capacity of the track structure itself. At present, it is generally believed in the industry that the main wavelength of the track vertical deviation caused by bridge deformation is basically consistent with the main span of the bridge, and when the wavelength is much larger than the sensitive wavelength of train operation, it has limited impact on the comfort and safety of high-speed train operation. Therefore, to scientifically formulate the maintenance and repair strategy of long-span bridge lines, it is a key problem to be solved to accurately separate the bridge long-wave deformation component sensitive to temperature in the track vertical deviation.

[0003] At present, the time-frequency decomposition and analysis method for long-span bridge track irregularities still takes signal decomposition technology as the core, but the existing technology has obvious limitations: on the one hand, the decomposition method relying on basis function expansion such as short-time Fourier transform is limited by the fixed characteristics of the basis function, and it is difficult to meet the high-precision analysis demand; on the other hand, the data-driven decomposition method such as empirical mode decomposition and variational mode decomposition cannot guarantee the number consistency and frequency uniformity of the track irregularity decomposition modes under different temperature conditions. The above defects make it difficult for the existing technology to accurately characterize the time-frequency distribution characteristics of track irregularities, especially to complete the fine characterization of the temperature sensitivity of long-wave irregularities, which restricts the accurate formulation of track maintenance strategy. SUMMARY

[0004] In view of the problems of insufficient precision and modal inconsistency of existing methods, the present application provides a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method, which realizes the separation of bridge deformation and track irregularity and the identification of temperature-sensitive components, and significantly improves the accuracy of maintenance strategy.

[0005] A multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method, the method comprising the following steps: S1, integrating the static track vertical deviation three-dimensional historical detection data of the long-span bridge with an additional environmental temperature label; S2, performing empirical wavelet transform processing on the three-dimensional historical detection data with different cut-off wavelengths, and constructing a four-dimensional data set of MRA components with additional temperature labels and wavelength labels. S3. Resample the four-dimensional dataset in the mileage dimension to obtain the resampled dataset; S4. Project the resampled dataset onto the amplitude-wavelength two-dimensional space, continuously add the corresponding wavelength MRA component as the wavelength increases, plot the cumulative amplitude growth curve with wavelength, and determine the corresponding amplitude-wavelength curve type in the mileage dimension and wavelength dimension respectively to obtain the amplitude-wavelength curve type determination result. S5. Based on the discrimination result of the amplitude-wavelength curve type, piecewise fitting is used to obtain the boundary wavelength between bridge deformation and track irregularity. S6. Project the resampled dataset onto the amplitude-temperature two-dimensional space, and determine the corresponding amplitude curve and temperature sensitivity in the mileage dimension and wavelength dimension, respectively, to obtain the determination results of amplitude curve and temperature sensitivity; S7. Based on the discrimination results of the boundary wavelength and temperature sensitivity, extract the temperature-sensitive components in the actual track irregularities and the construction deviation components in the actual bridge deformation.

[0006] Furthermore, the three-dimensional historical detection data mentioned in step S1 is the amplitude-mileage curve data obtained by subtracting the original design rail surface elevation from the absolute rail surface elevation obtained by the measuring trolley; the ambient temperature label is obtained through an ambient temperature sensor.

[0007] Furthermore, S2 specifically includes: S21. Perform a Fourier transform on the three-dimensional historical detection data to obtain the spatial frequency range [0, fs / 2] of the single-sided spectrum containing actual physical information, where fs is the sampling frequency; S22. Divide the spatial frequency range into multiple consecutive small intervals, starting from the initial wavelength and ending at the upper limit wavelength, with each interval being 1m. With wavelength The conversion relationship is ,according to【 , , Set boundary points in sequence ,in The starting wavelength; S23. Convert the spatial frequency boundary to a normalized frequency boundary. The conversion formula is as follows:

[0008] In the formula, The boundary point of the normalized frequency; fs is the sampling frequency; S24. Input the three-dimensional historical detection data into the EWT algorithm sequentially, and combine it with the normalized frequency boundary sequence. , constructing the detail coefficient and the scale coefficient corresponding to any period of data; performing convolution operation on the detail coefficient and the wavelet function, and on the scale coefficient and the scaling function, to obtain a plurality of MRA component amplitude-mileage curves corresponding to the center wavelength label, and forming a four-dimensional data set in combination with the temperature label.

[0009] Further, the resampling in step S3 specifically includes: dividing the mileage dimension into sections at 10m intervals, selecting a data point with the largest absolute value of amplitude in each section for sampling, and taking the original value of the data point with the largest absolute value of amplitude as the representative value of amplitude of the corresponding section, and taking the midpoint mileage of each section as the representative mileage of the corresponding section.

[0010] Further, the amplitude-wavelength curve type in step S4 is classified into reverse bending type, transition type and arch type according to the variation law.

[0011] Further, the variation law of the reverse bending type amplitude-wavelength curve is that as the wavelength increases, the amplitude remains basically unchanged, and after reaching the mutation wavelength, the amplitude begins to rapidly decrease; The variation law of the transition type amplitude-wavelength curve is that as the wavelength increases, the amplitude remains basically unchanged, and then a slope mutation wavelength and an extreme value wavelength appear respectively; The variation law of the arch type amplitude-wavelength curve is that as the wavelength increases, the amplitude remains basically unchanged. After reaching the slope mutation wavelength, the amplitude begins to rapidly increase.

[0012] Further, the segmented fitting in step S5 specifically includes: The Levenberg-Marquardt algorithm is used to solve the segmentation point, wherein: The reverse bending type amplitude-wavelength curve uses linear-linear fitting to solve the slope mutation point; The transition type amplitude-wavelength curve uses linear-curve-curve fitting to solve the slope mutation point and the extreme value point; The arch type amplitude-wavelength curve uses linear-linear fitting to solve the slope mutation point.

[0013] Further, the segmented function formula of the linear-linear fitting is:

[0014] In the formula, ~ is the fitting coefficient; 、 are the filter wavelength and the corresponding filter amplitude respectively; is the slope mutation point corresponding to the linear-linear fitting; The segmented function formula of the linear-curve-curve fitting is:

[0015] In the formula, ~ These are the fitting coefficients; , These are the filter wavelength and the corresponding filter amplitude, respectively. , These represent the wavelengths corresponding to the slope abrupt change points and extreme points of the straight-curve-curve fitting, respectively.

[0016] Furthermore, S6 specifically includes: The resampled dataset is projected into an amplitude-temperature two-dimensional space to form amplitude-temperature curves corresponding to different mileage and wavelength labels. The Pearson correlation coefficient and significance coefficient between amplitude and temperature in the amplitude-temperature curve are calculated, and the discrimination value of the significance coefficient is 0.05.

[0017] Furthermore, S7 specifically includes: S71. Based on the boundary wavelength, data with wavelength labels smaller than the boundary wavelength are classified as actual track irregularities, and data with wavelength labels larger than the boundary wavelength are classified as actual bridge deformations. S72. Based on the significance coefficient, determine the wavelength and mileage information of the temperature-sensitive component in the actual track irregularity and the construction deviation component in the actual bridge deformation. The component with a significance coefficient less than 0.05 is determined to be a temperature-sensitive component, and the component with a significance coefficient greater than 0.05 is determined to be a construction deviation component of the bridge deformation. S73. Reconstruct the construction deviation components using the following formula:

[0018] In the formula, The center mileage is The construction deviation amplitude within a 10m interval; n is the corresponding temperature label; K is the total number of wavelength labels for insensitive components within the interval; Wavelength labeling for insensitive components; MRA for multi-resolution analysis; For the central mileage is Within a 10m interval, the corresponding characteristic wavelength is The k One MRA component; By splicing together the construction deviation amplitudes corresponding to each interval, the linear shape of the construction deviation across the entire bridge at any temperature can be obtained. .

[0019] The technical solution of this invention can achieve the following beneficial effects: (1) Introducing adaptive signal processing algorithm, realizing efficient and fine spectrum decomposition driven by data. By adopting empirical wavelet transform (EWT) method and constructing adaptive spectrum segmentation scheme with fixed wavelength interval, four-dimensional data set of multi-resolution analysis (MRA) components containing temperature and wavelength label is generated at one time. Compared with the traditional fixed filter bank method, the scheme avoids multiple filtering calculation, significantly improves the calculation efficiency, and can more accurately match the actual spectrum characteristics of track geometric irregularities, providing a higher quality data basis for subsequent component identification; (2) Accurate identification of the demarcation characteristics of bridge deformation and track irregularities. By analyzing the cumulative amplitude growth curve with wavelength and the type classification of amplitude-wavelength curve, the demarcation wavelength is solved by using targeted segmented fitting algorithm, realizing the quantitative separation of bridge deformation and track irregularities, and solving the technical problem that the traditional method cannot clearly distinguish the two; (3) Accurate positioning of temperature sensitive components and construction deviation analysis. By projecting the data set into the amplitude-temperature two-dimensional space for correlation analysis, not only the wavelength and mileage range of track irregularities sensitive to temperature can be located, but also the construction deviation component insensitive to temperature can be separated from the bridge deformation. The synchronous fine identification and extraction of temperature sensitive source of track irregularities and inherent construction deviation of bridge are realized; (4) The method has strong universality and convenient operation, and has practical application value. The present application does not depend on specific structural parameters of the bridge, but can complete the analysis through detection data and algorithm, and has strong operability; the analysis result can directly provide quantitative basis for formulating long-span railway bridge line maintenance and repair strategy, effectively avoid track geometric position degradation caused by blind adjustment, and improve maintenance efficiency and accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flow chart of a multi-dimensional space long-span bridge track vertical deviation temperature sensitive component extraction method provided by the present application; Figure 2 is a multi-period static track vertical deviation detection result graph of a certain long-span high-speed railway bridge obtained by a measuring trolley in an embodiment of the present application; Figure 3 is a spectrum cutting scheme schematic diagram in an embodiment of the present application; Figure 4 is a four-dimensional data set of MRA components with additional temperature and wavelength labels in an embodiment of the present application; Figure 5 is a demarcation wavelength calculation result graph of bridge deformation and track irregularities in an embodiment of the present application; Figure 6 is a significant coefficient calculation result heat map of static track vertical deviation and temperature correlation in an embodiment of the present application; Figure 7is a construction deviation linear in the embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the described embodiments are only a part of examples of the present application, and do not represent all possible embodiments of the present application. Based on the embodiments of the present application, any other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0022] Embodiment 1 Figure 1 A flowchart of a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method provided by the present application.

[0023] The method flow steps of the embodiments of the present application are as follows: S1, integrating static track vertical deviation three-dimensional historical detection data of a long-span bridge with an additional environmental temperature label; S2, performing empirical wavelet transform processing on the three-dimensional historical detection data with different cutoff wavelengths to construct a four-dimensional data set of MRA components with additional temperature labels and wavelength labels; S3, resampling the four-dimensional data set in the mileage dimension to obtain a resampled data set; S4, projecting the resampled data set to an amplitude-wavelength two-dimensional space, continuously adding MRA components of corresponding wavelengths as the wavelength increases, drawing an accumulated amplitude growth curve with wavelength, and respectively identifying the corresponding amplitude-wavelength curve types in the mileage dimension and the wavelength dimension to obtain the identification result of the amplitude-wavelength curve type; S5, according to the identification result of the amplitude-wavelength curve type, using piecewise fitting to obtain the demarcation wavelength of the bridge deformation and the track itself irregularity; S6, projecting the resampled data set to an amplitude-temperature two-dimensional space, respectively identifying the corresponding amplitude curve and temperature sensitivity in the mileage dimension and the wavelength dimension to obtain the identification result of the amplitude curve and the temperature sensitivity; S7, combining the demarcation wavelength and the identification result of the temperature sensitivity to extract the temperature-sensitive component in the actual track irregularity and the construction deviation component in the actual bridge deformation.

[0024] Specifically, taking multi-period measured static track vertical deviation of a certain long-span high-speed railway bridge as an example.

[0025] Step S1 specifically includes: Historical static monitoring data of track vertical deviation collected by the measuring trolley, along with the corresponding temperatures synchronously measured by the temperature sensors, are extracted to form three-dimensional historical detection data of static track vertical deviation with temperature labels, such as... Figure 2 As shown.

[0026] in, Figure 2 The horizontal axis represents the measured mileage, with a sampling interval of 1m; the vertical axis represents the static track vertical deviation at different temperatures at each mileage of the bridge. Within the ambient temperature range of 0.2~31.5℃, the amplitude of track unevenness caused by bridge deformation exceeds 1000mm, a value far exceeding the adjustment capacity of the existing track structure.

[0027] Step S2 specifically includes: S21. Perform a Fourier transform on the three-dimensional historical detection data to obtain the spatial frequency range [0, fs / 2] of the single-sided spectrum containing actual physical information, where fs is the sampling frequency.

[0028] S22. Divide the spatial frequency range into multiple consecutive small intervals, starting from the initial wavelength and ending at the upper limit wavelength, with each interval being 1m. With wavelength The conversion relationship is ,according to【 , , Set boundary points in sequence ,in The starting wavelength is shown in the diagram below. A schematic of the refined spectrum cutting scheme is also shown. Figure 3 As shown.

[0029] S23. Convert the spatial frequency boundary obtained above into a normalized frequency boundary. The conversion formula is as follows:

[0030] In the formula, is the boundary point of the normalized frequency; fs is the sampling frequency.

[0031] S24. Input the three-dimensional historical detection data into the EWT algorithm sequentially, and combine it with the normalized frequency boundary sequence constructed above. It can adaptively construct detail coefficients and scaling coefficients corresponding to any period of data. By convolving the detail coefficients with the wavelet function, scaling coefficients, and scaling function, multiple MRA component amplitude-otter curves corresponding to the center wavelength label can be obtained. Combining these with temperature labels forms a four-dimensional dataset. A schematic diagram of the resulting four-dimensional dataset structure including temperature and wavelength labels is shown below. Figure 4 As shown.

[0032] Step S3 specifically includes: All the amplitude-wavelength curves are divided into several sections at intervals of 10 m in the mileage dimension; in each section, the data point with the largest absolute value of amplitude is selected for resampling, and the original amplitude of the data point is taken as the representative value of the amplitude of the section, and the mileage of the midpoint of the section is taken as the representative mileage of the section.

[0033] Step S4 specifically includes: For each data point with a specific mileage label and a temperature label in the resampled data set, the MRA component amplitudes of corresponding wavelengths are sequentially accumulated in ascending order of wavelength, and a growth curve of accumulated amplitude changing with increasing wavelength is drawn.

[0034] According to the variation law of the above-mentioned accumulated amplitude-wavelength growth curve, the amplitude-wavelength curve types with different mileage labels and temperature labels are classified.

[0035] The amplitude-wavelength curve type is classified into reverse bending type, transition type and arch type according to the variation law.

[0036] The variation law of the reverse bending type amplitude-wavelength curve is that as the wavelength increases, the amplitude remains basically unchanged, and after reaching the mutation wavelength, the amplitude begins to rapidly decrease; The variation law of the transition type amplitude-wavelength curve is that as the wavelength increases, the amplitude remains basically unchanged, and then a slope mutation wavelength and an extreme value wavelength appear respectively; The variation law of the arch type amplitude-wavelength curve is that as the wavelength increases, the amplitude remains basically unchanged. After reaching the slope mutation wavelength, the amplitude begins to rapidly increase.

[0037] Step S5 specifically includes: Based on the classification and determination results of the amplitude-wavelength curve type, corresponding segmentation functions are selected for different types of curves, and the Levenberg-Marquardt algorithm is used to segmentally fit the amplitude-wavelength curve, wherein: The reverse bending type amplitude-wavelength curve uses linear-linear fitting to solve the slope mutation point; The transition type amplitude-wavelength curve uses linear-curve-curve fitting to solve the slope mutation point and the extreme value point; The arch type amplitude-wavelength curve uses linear-linear fitting to solve the slope mutation point.

[0038] The segmentation function formula of linear-linear fitting is:

[0039] In the formula, ~ is a fitting coefficient; 、 are the filtering wavelength and the corresponding filtering amplitude respectively; a slope mutation point corresponding to the linear-linear fitting; The piecewise function formula of the linear-curve-curve fitting is:

[0040] In the formula, ~ are fitting coefficients; 、 are the filtering wavelength and the corresponding filtering amplitude, respectively; 、 are a slope mutation point corresponding to the linear-linear fitting and a wavelength corresponding to an extreme point, respectively.

[0041] An example of the calculation results of the slope mutation points and the wavelengths corresponding to the extreme points corresponding to different mileage labels and temperature labels is shown in Figure 4 From Figure 5 , it can be observed that the distribution of the bridge deformation and the demarcation wavelength of the track irregularity itself in the mileage domain is basically not affected by the temperature change.

[0042] Step S6 specifically includes: Projecting the resampled data set to the amplitude-temperature space to form an amplitude-temperature curve corresponding to different mileage labels and wavelength labels; calculating the Pearson correlation coefficient and the significance coefficient between the amplitude and the temperature in the amplitude-temperature curve, and taking the significance coefficient less than 0.05 as a judgment index to judge whether the amplitude and the temperature in the curve are significantly correlated.

[0043] The temperature-sensitive wavelengths of each mileage track irregularity calculated by the above method are shown in Figure 5 As can be seen from Figure 5 , as the mileage approaches the midspan position, the wavelength sensitive to the temperature gradually increases and reaches 140 m at the midspan. In combination with Figure 4 , it can be known that the demarcation wavelength is located in the dark blue area, which indicates that the wavelength component corresponding to the bridge deformation has significant sensitivity to the temperature change.

[0044] Step S7 specifically includes: S71, first classify the actual track irregularity based on the demarcation wavelength obtained in step S5, and then determine the wavelength and the mileage information of the temperature-sensitive component in the actual track irregularity and the construction deviation component in the actual bridge deformation in combination with the distribution of the significance coefficient obtained in step S6.

[0045] According to the demarcation wavelength, data with a wavelength label less than the demarcation wavelength are classified as actual track irregularities, and data with a wavelength label greater than the demarcation wavelength are classified as actual bridge deformations.

[0046] S72. Based on the significance coefficient, determine the wavelength and mileage information of the temperature-sensitive component in the actual track irregularity and the construction deviation component in the actual bridge deformation. The component with a significance coefficient less than 0.05 is determined to be the temperature-sensitive component of the track irregularity, and the component with a significance coefficient greater than 0.05 is determined to be the construction deviation component of the bridge deformation.

[0047] S73. To obtain the construction deviation alignment across the entire bridge, the extracted construction deviation components are reconstructed. The reconstruction is performed on the four-dimensional dataset before resampling, according to the following formula:

[0048] In the formula, The center mileage is The construction deviation amplitude within a 10m interval; n is the corresponding temperature label; K is the total number of wavelength labels for insensitive components within the interval; Wavelength labeling for insensitive components; MRA for multi-resolution analysis; For the central mileage is Within a 10m interval, the corresponding characteristic wavelength is The k MRA components.

[0049] By splicing together the construction deviation amplitudes corresponding to each interval, the construction deviation linearity across the entire bridge at any temperature can be obtained. .

[0050] Figure 6 The results show that the 50-80m band in actual track irregularities exhibits a discontinuous correlation with temperature in the mileage domain.

[0051] The construction deviation line shape extracted based on the above-mentioned boundary wavelength and significance coefficient discrimination results is as follows: Figure 7 As shown.

[0052] Based on the calculation results of the embodiments, the multi-dimensional spatial long-span bridge track vertical deviation temperature-sensitive component extraction method provided by the present invention can identify and finely characterize the temperature-sensitive components of bridge deformation and track irregularities in static track vertical deviation. This method can effectively distinguish between bridge deformation and actual track irregularities from static track vertical deviation, and accurately identify the temperature-sensitive wavelengths of track irregularities at different bridge mileages, as well as deviation components reflecting inherent construction quality. This provides support for the formulation of long-span railway bridge line adjustment schemes and effectively avoids track geometry deterioration caused by blind adjustments.

[0053] Various embodiments described in this specification are described with reference to a particular sequence or order, but the order of events can differ from that described without materially affecting the end result or process described. Also, some steps that are described can be combined or divided into multiple steps. Similarly, some steps can be performed in parallel rather than sequentially. Various embodiments described in this specification are described with reference to a particular system, but other embodiments can be implemented with other systems without materially departing from the spirit and scope of the description. For example, some steps can be performed by a different system or in a different order than described. Similarly, some steps can be performed in parallel rather than sequentially. Also, some steps can be performed by a different system or in a different order than described without materially affecting the end result or process described.

[0054] The above description of certain embodiments of the disclosure has been presented for the purposes of illustration and description. Other embodiments are within the scope of the claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the process depicted in the accompanying figures does not necessarily require the particular order shown or sequential order to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

[0055] The above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of embodiments included in the following claims, along with the legal equivalents thereto, should not be limited to the advantages described above. Changes can be made without departing from the spirit and scope of the disclosure, which is defined by the following claims.

Claims

1. A method for extracting temperature-sensitive components of vertical deviation of track in a long-span bridge in multidimensional space, characterized in that, Includes the following steps: S1. Three-dimensional historical detection data of static track vertical deviation of long-span bridges with added ambient temperature labels; S2. Perform empirical wavelet transform processing on the three-dimensional historical detection data with different cutoff wavelengths to construct a four-dimensional dataset of MRA components with additional temperature and wavelength labels. S3. Resample the four-dimensional dataset in the mileage dimension to obtain the resampled dataset; S4. Project the resampled dataset onto the amplitude-wavelength two-dimensional space, continuously add the corresponding wavelength MRA component as the wavelength increases, plot the cumulative amplitude growth curve with wavelength, and determine the corresponding amplitude-wavelength curve type in the mileage dimension and wavelength dimension respectively to obtain the amplitude-wavelength curve type determination result. S5. Based on the discrimination result of the amplitude-wavelength curve type, piecewise fitting is used to obtain the boundary wavelength between bridge deformation and track irregularity. S6. Project the resampled dataset onto the amplitude-temperature two-dimensional space, and determine the corresponding amplitude curve and temperature sensitivity in the mileage dimension and wavelength dimension, respectively, to obtain the determination results of amplitude curve and temperature sensitivity; S7. Based on the discrimination results of the boundary wavelength and temperature sensitivity, extract the temperature-sensitive components in the actual track irregularities and the construction deviation components in the actual bridge deformation.

2. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 1, characterized in that, The three-dimensional historical detection data mentioned in step S1 is the amplitude-mileage curve data obtained by subtracting the original design rail surface elevation from the absolute rail surface elevation obtained by the measuring trolley; the ambient temperature label is obtained through an ambient temperature sensor.

3. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 1, characterized in that, S2 specifically includes: S21. Perform a Fourier transform on the three-dimensional historical detection data to obtain the spatial frequency range [0, fs / 2] of the single-sided spectrum containing actual physical information, where fs is the sampling frequency; S22. Divide the spatial frequency range into multiple consecutive small intervals, starting from the initial wavelength and ending at the upper limit wavelength, with each interval being 1m. With wavelength The conversion relationship is ,according to【 , , Set boundary points in the order of 】 ,in The starting wavelength; S23. Convert the spatial frequency boundary to a normalized frequency boundary. The conversion formula is as follows: In the formula, The boundary point of the normalized frequency; fs is the sampling frequency; S24. Input the three-dimensional historical detection data into the EWT algorithm sequentially, and combine it with the normalized frequency boundary sequence. Construct detail coefficients and scaling coefficients for any given period of data; perform convolution operations between detail coefficients and wavelet functions, and between scaling coefficients and scaling functions to obtain multiple MRA component amplitude-mileage curves corresponding to the center wavelength labels, and combine them with temperature labels to form a four-dimensional dataset.

4. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 1, characterized in that, The resampling in step S3 specifically includes: The mileage dimension is divided into segments at 10m intervals. Within each segment, the data point with the largest absolute amplitude value is selected for sampling. The original value of the data point with the largest absolute amplitude value is used as the representative amplitude value of the corresponding segment, and the midpoint mileage of each segment is used as the representative mileage of the corresponding segment.

5. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 1, characterized in that, The amplitude-wavelength curve types mentioned in step S4 are classified into inverted curve type, transition type and upward arch type according to the variation law.

6. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 5, characterized in that, The piecewise fitting in step S5 specifically includes: The Levenberg-Marquardt algorithm is used to solve for the piecewise points, where: The slope abrupt change point is solved by straight-line fitting of the inverted amplitude-wavelength curve. The transitional amplitude-wavelength curve is solved by using a straight-line-curve-curve fitting method to find the slope abrupt change points and extreme points. The slope abrupt change point is solved by straight-line fitting of the upward-arched amplitude-wavelength curve.

7. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 6, characterized in that, The piecewise function formula for the line-line fitting is: In the formula, ~ These are the fitting coefficients; , These are the filter wavelength and the corresponding filter amplitude, respectively. This represents the point of abrupt slope change corresponding to a line-to-line fit. The piecewise function formula for the line-curve-curve fitting is: In the formula, ~ These are the fitting coefficients; , These are the filter wavelength and the corresponding filter amplitude, respectively. , These represent the wavelengths corresponding to the slope abrupt change points and extreme points of the straight-curve-curve fitting, respectively.

8. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 1, characterized in that, S6 specifically includes: The resampled dataset is projected into an amplitude-temperature two-dimensional space to form amplitude-temperature curves corresponding to different mileage and wavelength labels. The Pearson correlation coefficient and significance coefficient between amplitude and temperature in the amplitude-temperature curve are calculated, and the discrimination value of the significance coefficient is 0.

05.

9. The method for extracting temperature-sensitive components of vertical deviation of track in a multi-dimensional long-span bridge according to claim 8, characterized in that, Specifically, S7 includes: S71. Based on the boundary wavelength, data with wavelength labels smaller than the boundary wavelength are classified as actual track irregularities, and data with wavelength labels larger than the boundary wavelength are classified as actual bridge deformations. S72. Based on the significance coefficient, determine the wavelength and mileage information of the temperature-sensitive component in the actual track irregularity and the construction deviation component in the actual bridge deformation. The component with a significance coefficient less than 0.05 is determined to be a temperature-sensitive component, and the component with a significance coefficient greater than 0.05 is determined to be a construction deviation component of the bridge deformation. S73. Reconstruct the construction deviation components using the following formula: In the formula, The center mileage is The construction deviation amplitude within a 10m interval; n is the corresponding temperature label; K is the total number of wavelength labels for insensitive components within the interval; Wavelength labeling for insensitive components; MRA for multi-resolution analysis; For the central mileage is Within a 10m interval, the corresponding characteristic wavelength is The k One MRA component; By splicing together the construction deviation amplitudes corresponding to each interval, the linear shape of the construction deviation across the entire bridge at any temperature can be obtained. .

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