Method and device for positioning steel rail telescopic adjuster based on dynamic and static data fusion

Abstract

The invention discloses a method and a device for positioning a steel rail telescopic regulator based on dynamic and static data fusion. The method comprises the following steps of: acquiring track geometric data, a railway track curve and machine account information of which the distance from the steel rail telescopic regulator is within a preset distance range; according to a railway track curve and ledger information, carrying out initial correction on the track geometric data; performing modal decomposition on the track geometric data after the initial correction by using an adaptive chirp signal modal decomposition algorithm; performing Wigner time-frequency analysis on the track geometric data after modal decomposition to generate a mileage frequency diagram; converting the mileage frequency diagram into a mileage energy diagram by utilizing a preset moving window edge spectrum analysis algorithm; according to the mileage energy diagram and the ledger information, the position of the steel rail telescopic adjuster is determined, the efficiency and accuracy of positioning the steel rail telescopic adjuster can be improved, the positioning time and labor cost are reduced, and then the stability and safety of high-speed railway driving performance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed railway engineering, and in particular to a method and device for positioning a rail expansion adjuster by integrating dynamic and static data. Background Art

[0002] This section is intended to provide a background or context for embodiments of the present invention. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] Rail expansion adjusters are devices used to adjust or expand the gap between rails in railway tracks, and are usually installed at both ends of bridges. As a key track component, it ensures that the rails can adapt to expansion and contraction when the temperature changes, thereby preventing the rails from being over-compressed or stretched due to temperature changes, and avoiding rail breakage or deformation. However, since the expansion adjuster disconnects the rails, its location is also a weak link in the track structure, which can easily cause uneven tracks in the beam end area, thus affecting the stability and safety of high-speed railway driving performance.

[0004] In the past decade or so, scholars have done a lot of research on the structural characteristics, standards, and monitoring methods of rail expansion adjusters. In the field of precise positioning of rail expansion adjusters, existing methods mostly rely on static data, that is, the previously counted records of the positions of rail expansion adjusters on each line. Although static data has high accuracy, the cost is that it needs to be updated in real time and cannot be linked to data such as dynamic response and track geometry, resulting in reduced detection efficiency and increased time and labor costs. In terms of dynamic data, in the actual operation of high-speed railways, due to the complex track conditions, there may be interference such as noise and uneven gauge in the gauge signal, which will seriously reduce the accuracy of using dynamic data to locate the rail expansion adjuster.

[0005] In summary, although a lot of research has been done in the application and monitoring of rail expansion adjusters in the prior art, there is still room for development in the mileage positioning of rail expansion adjusters. Summary of the invention

[0006] The embodiment of the present invention provides a method for positioning a rail expansion regulator by integrating dynamic and static data, which can improve the efficiency and accuracy of positioning the rail expansion regulator, reduce the time and labor costs of positioning, and thus improve the stability and safety of high-speed railway driving performance. The method includes:

[0007] Acquire track geometry data, railway track curve and ledger information within a preset distance range from a rail expansion and contraction adjuster; the rail expansion and contraction adjuster is arranged at both ends of a rail located on a bridge; the railway track includes a rail arranged on a bridge;

[0008] Performing preliminary correction on the track geometry data according to the railway track curve and record information;

[0009] The ACMD (Adaptive Chirp Mode Decomposition) algorithm is used to perform modal decomposition on the initially corrected orbit geometry data.

[0010] Perform WVD (Wigner-Ville distribution) time-frequency analysis on the track geometry data after modal decomposition to generate a mileage frequency diagram;

[0011] The mileage frequency graph is converted into a mileage energy graph using a preset moving window edge spectrum analysis algorithm;

[0012] Determine the position of the rail expansion adjuster based on the mileage energy diagram and ledger information.

[0013] The embodiment of the present invention provides a rail expansion regulator positioning device with dynamic and static data fusion, which can improve the efficiency and accuracy of positioning the rail expansion regulator, reduce the time and labor cost of positioning, and thus improve the stability and safety of high-speed railway driving performance. The device includes:

[0014] An acquisition module is used to acquire track geometry data, railway track curve and account information within a preset distance range from a rail expansion regulator; the rail expansion regulator is arranged at both ends of the rail located on the bridge; the railway track includes the rail arranged on the bridge;

[0015] An initial correction module is used to perform initial correction on the track geometry data according to the railway track curve and ledger information;

[0016] The modal decomposition module is used to perform modal decomposition on the initially corrected track geometry data using the ACMD algorithm;

[0017] The time-frequency analysis module is used to perform WVD time-frequency analysis on the track geometry data after modal decomposition and generate a mileage frequency diagram;

[0018] A conversion module, used to convert the mileage frequency graph into a mileage energy graph using a preset moving window edge spectrum analysis algorithm;

[0019] The positioning module is used to determine the position of the rail expansion adjuster based on the mileage energy diagram and ledger information.

[0020] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned rail expansion adjuster positioning method with dynamic and static data fusion when executing the computer program.

[0021] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the rail expansion adjuster positioning method with the above-mentioned dynamic and static data fusion is implemented.

[0022] 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 rail expansion adjuster positioning method with dynamic and static data fusion is implemented.

[0023] Compared with the rail expansion adjuster positioning solution in the prior art, the embodiment of the present invention obtains track geometry data, railway track curve and ledger information within a preset distance range from the rail expansion adjuster; the rail expansion adjuster is arranged at both ends of the rail located on the bridge; the railway track includes the rails arranged on the bridge; the track geometry data is initially corrected according to the railway track curve and the ledger information; the ACMD algorithm is used to perform modal decomposition on the initially corrected track geometry data; WVD time-frequency analysis is performed on the track geometry data after modal decomposition to generate a mileage frequency graph; the mileage frequency graph is converted into a mileage energy graph using a preset moving window edge spectrum analysis algorithm; the position of the rail expansion adjuster is determined according to the mileage energy graph and the ledger information, and the dynamic and static data fusion is used, combined with the gauge signal characteristics at the rail expansion adjuster, to effectively filter out the interference of other gauge irregularities and noise, and to accurately locate the rail expansion adjuster and extract the time-frequency characteristics, which can improve the efficiency and accuracy of locating the rail expansion adjuster, reduce the time and labor costs of positioning, and thus improve the stability and safety of the high-speed railway driving performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0025] Figure 1 It is a flow chart of a method for positioning a rail expansion adjuster with dynamic and static data fusion in an embodiment of the present invention;

[0026] Figure 2 It is a flowchart of a specific example of a method for positioning a rail expansion adjuster by integrating dynamic and static data in an embodiment of the present invention;

[0027] Figure 3 Schematic diagram of pulse signals of rail expansion regulators at both ends of a bridge in an embodiment of the present invention;

[0028] Figure 4 This is a waveform diagram of the gauge signal of the rail expansion regulators at the front and rear ends of the bridge before correction in the embodiment of the present invention;

[0029] Figure 5 This is a waveform diagram of the gauge signal of the rail expansion regulators at both ends of a bridge after correction in an embodiment of the present invention;

[0030] Figure 6 This is a waveform diagram of a track gauge signal after pulse screening in an embodiment of the present invention;

[0031] Figure 7 A waveform diagram of a local gauge signal including a rail expansion and contraction adjuster in an embodiment of the present invention;

[0032] Figure 8 is a waveform diagram of a first modal signal after ACMD decomposition in an embodiment of the present invention;

[0033] Fig. 9 is a mileage frequency diagram of the first modal signal in an embodiment of the present invention;

[0034] Fig.10 is a mileage energy diagram of the first modal signal in an embodiment of the present invention;

[0035] Fig.11 Schematic diagram of energy peak points in an embodiment of the present invention;

[0036] Fig.12 It is a schematic diagram of a rail expansion and contraction adjuster positioning device with dynamic and static data fusion in an embodiment of the present invention;

[0037] Fig.13 Schematic diagram of the computer device structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0039] Although a lot of research has been done in the application and monitoring of rail expansion adjusters, there is still room for development in the mileage positioning of rail expansion adjusters. There are no related literature reports or patent descriptions on how to use dynamic and static data fusion, combined with the gauge signal characteristics at the expansion adjuster, to effectively filter out the interference of other gauge irregularities and noise, and to accurately locate the adjuster and extract time-frequency features.

[0040] The embodiment of the present invention proposes a method for positioning a rail expansion adjuster by fusing dynamic and static data, aiming at the fact that the gauge signal of the rail expansion adjuster has characteristics similar to burr anomalies. Specifically, the method may include: using static ledger information to perform initial correction on track geometry data near the rail expansion adjuster; extracting the time-frequency characteristics of the expansion adjuster based on the pulse characteristics of the gauge signal; and using a moving window edge spectrum analysis method to accurately position the expansion adjuster.

[0041] The technical problem to be solved by the embodiments of the present invention is how to locate the expansion and contraction adjuster of the rails on a high-speed railway, specifically including: how to use dynamic and static data fusion to make an initial correction to the mileage of the track geometry data; how to extract the gauge signal characteristics of the expansion and contraction adjuster; how to use the time-frequency information of the gauge signal to accurately locate the expansion and contraction adjuster. The embodiments of the present invention provide a method for locating the expansion and contraction adjuster of a rail by fusion of dynamic and static data, so as to improve the efficiency and accuracy of locating the expansion and contraction adjuster of a rail, reduce the time and labor cost of locating, and thereby improve the stability and safety of the driving performance of the high-speed railway. Figure 1 FIG. 1 is a flow chart of a method for positioning a rail expansion regulator with dynamic and static data fusion in an embodiment of the present invention. Figure 1 As shown, the method may include:

[0042] Step 101, obtaining track geometry data, railway track curve and ledger information within a preset distance range from a rail expansion regulator; the rail expansion regulator is arranged at both ends of a rail located on a bridge; the railway track includes a rail arranged on a bridge;

[0043] Step 102, performing preliminary correction on the track geometry data according to the railway track curve and record information;

[0044] Step 103, using an adaptive chirp signal modal decomposition (ACMD) algorithm to perform modal decomposition on the initially corrected orbit geometry data;

[0045] Step 104, performing Wigner WVD time-frequency analysis on the track geometry data after modal decomposition to generate a mileage frequency diagram;

[0046] Step 105, using a preset moving window edge spectrum analysis algorithm, converting the mileage frequency graph into a mileage energy graph;

[0047] Step 106, determining the position of the rail expansion adjuster according to the mileage energy diagram and the ledger information.

[0048] The method provided by the embodiment of the present invention can effectively remove noise and interference items, strengthen the time-frequency characteristics at the rail expansion adjuster, integrate dynamic and static data, and finally accurately locate the rail expansion adjuster, which can improve the efficiency and accuracy of locating the rail expansion adjuster, reduce the time and labor costs of positioning, and thus improve the stability and safety of high-speed railway driving performance.

[0049] The embodiment of the present invention proposes a complete method and process for accurate positioning of a telescopic adjuster by fusion of dynamic and static data. The detailed steps can be as follows:

[0050] (1) The original high-speed railway gauge data is represented by {x(m)|m=0, 1, 2..., s-1}, where s is the number of sampling points and the sampling frequency is Fs;

[0051] (2) Using the railway track curve and ledger information, taking the 500m before and after the rail expansion adjusters at both ends of the bridge in the rail expansion adjuster ledger information as the starting point and focus, intercept the gauge signal and perform the initial correction of the mileage data through the curve ledger. The gauge signal after the initial correction is recorded as

[0052] (3) Using the characteristics of the pulse signal, identify the suspected rail expansion adjuster segment and generate a new filtered signal

[0053] (4) Targeting signals Perform ACMD-based modal decomposition to generate submodal signals satisfy δ is the noise which can be ignored;

[0054] (5) Extracting the first modal signal Conduct WVD time-frequency analysis and generate mileage frequency graph;

[0055] (6) Using moving window edge spectrum analysis on the generated mileage frequency map, extracting the energy characteristics of the gauge signal, and generating a mileage energy map;

[0056] (7) Calculate the energy threshold γ and perform the initial screening on the mileage energy map, where the energy threshold γ = 2.5% E, where E is the total energy of the first mode of the gauge signal.

[0057] (8) Using the distance A between the rail expansion adjusters at both ends of the bridge in the static data (ledger information) and the distance B between the energy peaks after the initial screening j For comparison, if B j ∈{A±0.005km}, then it is considered that B is generated j The two peak points are located at the rail expansion adjusters at both ends of the bridge.

[0058] Next, the key technologies of the rail expansion adjuster positioning method based on dynamic and static data fusion will be introduced in detail, including an adaptive chirp signal modal decomposition method based on pulse signal screening and a moving window-edge spectrum analysis method based on WVD.

[0059] Figure 2FIG. 1 is a flowchart of a specific example of a method for positioning a rail expansion adjuster with dynamic and static data fusion in an embodiment of the present invention. The detailed technical solution is as follows: Figure 2 As shown. The method for locating the rail expansion regulator with dynamic and static data fusion in the embodiment of the present invention mainly includes four parts: the first part uses the railway track curve (such as the curve of the high-speed railway line, which is dynamic data) and the ledger information (static data) to perform initial correction on the track geometry data of 500 meters before and after the rail expansion regulator at both ends of the bridge, such as correcting the mileage data; the second part uses the adaptive chirp signal modal decomposition method based on pulse signal screening to extract the pulse signal, extract the waveform of the suspected rail expansion regulator section in the gauge signal and filter it through ACMD modal decomposition to eliminate the influence of noise; the third part uses the moving window edge spectrum analysis method based on WVD, uses WVD to perform time-frequency analysis, extracts the time-frequency information of the signal, further performs moving window edge spectrum analysis, and generates a mileage energy map; the fourth part integrates the static ledger information, compares whether the peak distance in the mileage energy map is equal to the bridge length in the ledger information, if not, the peak is not the location of the rail expansion regulator, if yes, the location of the rail expansion regulator is determined, thereby realizing the precise positioning of the rail expansion regulator.

[0060] The rail expansion regulator positioning method of the embodiment of the present invention is applied to the data analysis of the track gauge signal collected by the high-speed comprehensive inspection train on a high-speed railway line. The rail expansion regulators at the front and rear ends of a bridge are taken as an example.

[0061] Figure 3 FIG. 1 is a schematic diagram of pulse signals of rail expansion regulators at both ends of a bridge according to an embodiment of the present invention. Figure 3 As shown in the figure, there are sections in the box of the gauge signal waveform of the rail expansion adjusters at the front and rear ends of the bridge where multiple pulse signals exist. If they are not filtered, identified, or processed, the precise position of the rail expansion adjusters cannot be accurately found.

[0062] First, the first part is introduced: using dynamic data and static data to perform initial correction on the track geometry data within the preset distance range before and after the rail expansion adjusters at both ends of the bridge.

[0063] In one embodiment, the preset distance range may be 500 meters.

[0064] In order to accurately position the rail expansion adjuster, it is necessary to analyze the gauge signal characteristics at the rail expansion adjuster. In one embodiment, the track geometry data may include the gauge signal sampled at a preset sampling frequency, the mileage data corresponding to the gauge signal, and the gauge signal waveform determined by the gauge signal and the mileage data; the track geometry data is initially corrected according to the railway track curve and ledger information, which may include: correcting the mileage data according to the railway track curve and ledger information.

[0065] In one embodiment, Figure 2 As shown, the detailed steps of the first part can be as follows:

[0066] (1) The original high-speed railway gauge data is represented by {x(m)|m=0, 1, 2..., s-1}, where s is the total number of sampling points and the sampling frequency is Fs;

[0067] (2) Using the railway track curve and rail expansion adjuster ledger information, the 500m before and after the rail expansion adjusters at both ends of the bridge in the rail expansion adjuster ledger information is taken as the starting point and focus, the gauge signal is intercepted and the mileage data is initially corrected through the curve ledger. The corrected gauge signal is recorded as

[0068] The method of the embodiment of the present invention is used to firstly perform mileage correction on the original gauge signal using the railway track curve and record information. Figure 4 This is a waveform diagram of the gauge signal of the rail expansion regulators at both ends of the bridge before correction in the embodiment of the present invention. Figure 5 The following is a waveform diagram of the gauge signal of the rail expansion regulator at both ends of a bridge after correction in the embodiment of the present invention. Figure 4-Figure 5 As shown. Figure 4 , Figure 5 We can see that there is a 10m mileage data deviation between the corrected data and the data before and after correction.

[0069] The second part is introduced below: the pulse signal is extracted by using the adaptive chirp signal modal decomposition method based on pulse signal screening, the waveform of the suspected rail expansion adjuster section in the gauge signal is extracted and filtered through ACMD modal decomposition to eliminate the influence of noise.

[0070] The adaptive chirp signal modal decomposition method based on pulse signal screening is mainly divided into two steps: the first step is to use the characteristic that the position of the rail expansion adjuster in the gauge signal will generate a pulse signal, and extract the gauge signal 5m before and after the position as the screened signal; the second step is to use the ACMD method to perform modal decomposition on the screened signal and extract the signal of the first mode.

[0071] In one embodiment, after the track geometry data is initially corrected according to the railway track curve and ledger information, it may also include: looping through each sampling point within the first preset range, and determining the sampling point whose absolute value of the first difference between the corresponding gauge signal and the gauge signal of the previous sampling point is greater than a preset threshold as the starting sampling point; the first preset range is determined by the total number of sampling points and the preset point number threshold; looping through each sampling point within the second preset range, and determining the sampling point whose absolute value of the second difference between the gauge signal and the gauge signal of the next sampling point is greater than the preset threshold, and the second difference has the same sign as the first difference as the ending sampling point; the second preset range is determined by the starting sampling point and the preset point number threshold; the gauge signal is screened according to the starting sampling point and the ending sampling point to obtain the screened gauge signal.

[0072] After analysis, it is found that the gauge signal will generate a peak at the position of the rail expansion and contraction adjuster, and further research on the gauge signal is needed. It is found that the peak of the gauge signal at the rail expansion and contraction adjuster is generated by a pulse signal, and its characteristics are similar to the mathematical characteristics of the burr outlier. As a basic signal form, pulse signals are widely used in modern electronic and communication systems. The main characteristics of this signal are its short duration and high temporal locality, which usually manifests as high energy output in a short period of time. Through research in the embodiments of the present invention, it is found that the pulse signal generated by the expansion and contraction adjuster is very similar to the mathematical characteristics of the burr outlier, which can be described as the signal suddenly increasing or decreasing first, and then after a short distance of generally less than 2m, it suddenly increases or decreases again. Utilizing the characteristic that the position of the expansion and contraction adjuster in the gauge signal will generate a pulse signal, the embodiment of the present invention uses an adaptive chirp signal modal decomposition method based on pulse signal screening to perform modal decomposition on the signal after mileage data correction. As Figure 2 As shown, the first step of the second part is pulse signal segment extraction, that is, to screen the pulse signal and extract the track gauge signal 5m before and after the pulse signal as the screened signal.

[0073] The detailed steps of the first step in the second part can be as follows:

[0074] (1) The track gauge data after the first part of the processing is recorded as Where n is the total number of sampling points, the preset point threshold p and the standard threshold (preset threshold) ε are set;

[0075] (2) By looping through the sampling points ii, where 1≤ii≤np-2 (the first preset range), the starting position of the pulse signal suspected to be generated by the rail expansion regulator is screened out. The specific method is to compare the standard threshold ε with like Determine that this point may be the starting position;

[0076] (3) For the suspected starting position ii, further screen through the p points behind it. Loop variable jj, where ii+1≤jj≤ii+p (second preset range), and check the difference and sign of jj. If the difference between jj and ii has the same sign, then ii-1 is confirmed as the starting point and jj+1 is the ending point;

[0077] (4) Record all the starting and ending points of the signal, denoted as ii0, ..., ii k and jj0, ..., jj k , and generate a new signal The sampling frequency of the track gauge signal is 4 points per meter.

[0078]

[0079] Figure 6 This is a waveform diagram of the track gauge signal after pulse screening in an embodiment of the present invention. Figure 6 The gauge signal after pulse signal filtering is shown. Figure 7 This is a waveform diagram of a local gauge signal including a rail expansion regulator in an embodiment of the present invention. Figure 7 The gauge signal of a rail expansion regulator is shown. It can be seen from the yellow box at the mileage of 645.75 km that due to the influence of the point rail tip, the gauge signal of the rail expansion regulator will suddenly increase or decrease, and then after a short period of relatively stable changes, it will suddenly decrease or increase again. The embodiment of the present invention can use the forward difference method to set the distance threshold and intelligently identify this mathematical feature of the rail expansion regulator. Figure 7 It is also shown in FIG. 1 that even though the relative amplitude difference of the two peaks in the red box is greater than the relative amplitude difference of the rail expansion adjuster in the yellow box, the algorithm of the embodiment of the present invention does not misidentify them. Figure 7 The pulse start position (start sampling point) and the pulse end position (end sampling point) are also shown.

[0080] In the second part, after obtaining the screened signal, it is necessary to perform ACMD decomposition on the screened signal, that is, to perform the second step of the second part.

[0081] In one embodiment, the ACMD algorithm is used to perform modal decomposition on the initially corrected track geometry data, including: using the ACMD algorithm to perform modal decomposition on the filtered track gauge signal to generate multiple sub-modal signals; the sum of the multiple sub-modal signals is equal to the filtered track gauge signal.

[0082] ACMD uses recursive decomposition to decompose the original nonlinear signal into various modes one by one. Since it avoids the problem of global decomposition and re-screening of the entire signal and requires less prior information, it is widely used in fault diagnosis, frequency extraction and other fields. Its basic idea is to transform the modal decomposition of the signal into an optimization problem, and use the characteristics of the chirp signal to decompose the signal into several sub-modal functions.

[0083] The detailed steps of the second step in the second part can be as follows:

[0084] (1) For newly generated Perform ACMD decomposition so that δ is the noise which can be ignored. The solution is based on is the instantaneous amplitude, is the instantaneous phase. is the initial instantaneous frequency. Solve Make Minimum, then update Continue iterating until the new The energy is less than the preset threshold.

[0085] The first mode signal is used in the subsequent WVD time-frequency analysis process, so it is necessary to determine the first mode signal in the signal after ACMD decomposition.

[0086] In one embodiment, WVD time-frequency analysis is performed on the track geometry data after modal decomposition to generate a mileage frequency diagram, including: determining the sub-modal signal with the strongest energy among multiple sub-modal signals as the first modal signal; performing WVD time-frequency analysis on the first modal signal using a preset discrete Wigner distribution definition formula to generate a mileage frequency diagram of the first modal signal.

[0087] In this embodiment, the energy is sorted Extract the first modal signal

[0088] Figure 8 4 is a waveform diagram of the first modal signal after ACMD decomposition in an embodiment of the present invention. Figure 8 It is clearly shown that after ACMD decomposition based on pulse signal screening, most of the noise has been filtered out, and the time-frequency characteristics of the gauge signal near the rail expansion adjuster are retained.

[0089] However, after modal decomposition, it can be found that the presented track gauge waveform is still somewhat messy, and there are still some interference items with the same mathematical characteristics of burr outliers. In view of this situation, an embodiment of the present invention provides a moving window edge spectrum analysis method based on WVD.

[0090] The third part is introduced below: using the WVD-based moving window edge spectrum analysis method, using WVD to perform time-frequency analysis, extracting the time-frequency information of the signal, further performing moving window edge spectrum analysis, and generating a mileage energy map.

[0091] Therefore, the third part is similar to the second part and also consists of two steps: the first step is WVD time-frequency analysis, and the second step is energy slicing.

[0092] In one embodiment, WVD time-frequency analysis is performed on the track geometry data after modal decomposition to generate a mileage frequency graph, including: performing WVD time-frequency analysis on the first modal signal using a preset discrete Wigner distribution definition formula to generate a mileage frequency graph of the first modal signal.

[0093] The first step in the third part is to use WVD to perform time-frequency analysis on the decomposed signal and generate a mileage frequency diagram. It is worth noting that since the gauge signal comes from the track geometry data, it is equi-spatially sampled. The time-frequency diagram is converted to form a "mileage frequency diagram". Fig. 9 is a mileage frequency diagram of the first modal signal in an embodiment of the present invention. The mileage frequency diagram of the first modal signal in an embodiment of the present invention is as follows: Fig. 9 shown.

[0094] The detailed steps of the first step in the third part can be as follows:

[0095] (1) Performing WVD time-frequency analysis on the first modal signal using a preset discrete Wigner distribution definition formula; the preset discrete Wigner distribution definition formula may be as follows:

[0096]

[0097] in for The conjugate transpose of .

[0098] WVD is a high-resolution time-frequency analysis method used to describe the instantaneous frequency of a signal. It can reveal the change of frequency in a signal over time and is very suitable for analyzing non-stationary signals. WVD combines the time domain information and frequency domain information of a signal by calculating the autocorrelation function of the signal and performing Fourier transform to generate a time-frequency distribution diagram. However, in the application of WVD, multiple frequency components in the original signal will generate cross terms, resulting in unreal interference terms on the time-frequency diagram.

[0099] In response to the defects of WVD, an embodiment of the present invention proposes a moving window edge spectrum analysis method to reduce the impact of cross-interference terms on the positioning of the rail expansion adjuster, that is, the second step in the third part: for the generated mileage frequency diagram, the moving window edge spectrum method of the embodiment of the present invention is used to further extract its energy characteristics to form a "mileage energy diagram".

[0100] In one embodiment, the mileage frequency map is converted into a mileage energy map using a preset moving window edge spectrum analysis algorithm, which may include: traversing each sampling point within a third preset range, and determining a mileage frequency map slice corresponding to each sampling point in the mileage frequency map according to a preset window length; the third preset range is determined by the total number of sampling points and the preset window length; calculating the energy of the mileage frequency map slice, and assigning the ratio of the energy of the mileage frequency map slice to the total energy of the mileage frequency map to the corresponding sampling point; and generating a mileage energy map according to the assigned sampling points.

[0101] The basic principle of the moving window edge spectrum analysis method is to use slices with extremely small window lengths to weaken the impact of interference items on the target sampling points. By traversing each sampling point, slicing the corresponding mileage frequency graph, calculating the energy in the slice, and reassigning it to the corresponding sampling point, the three-dimensional mileage frequency graph is converted into a two-dimensional mileage energy graph.

[0102] The detailed steps of the second step in Part III can be as follows:

[0103] (1) Setting a preset window length L, where L<<n-1;

[0104] (2) Traverse all sampling points (Third preset range). For sampling points calculate The energy of the slice at r , slice range

[0105] (3) Let the total energy of the mileage frequency graph be E, and draw the mileage energy graph.

[0106] Fig.10 is a mileage energy diagram of the first modal signal in an embodiment of the present invention. The mileage energy diagram of the first modal signal is as follows: Fig.10 shown.

[0107] Finally, the fourth part is introduced: integrating static ledger information to determine the location of the rail expansion adjuster.

[0108] In one embodiment, determining the position of the rail expansion and contraction adjuster according to the mileage energy diagram and the ledger information may include: intercepting a curve segment in the mileage energy diagram whose energy value is greater than a preset energy threshold, and determining multiple energy peak points in the curve segment; determining the position of the rail expansion and contraction adjuster according to the horizontal coordinate of the energy peak point in the mileage energy diagram and the ledger information.

[0109] In one embodiment, determining the position of the rail expansion and contract adjuster according to the horizontal coordinate of the energy peak point in the mileage energy diagram and the ledger information can include: determining the horizontal coordinate difference between every two energy peak points among multiple energy peak points in the mileage energy diagram according to the horizontal coordinate of the energy peak point in the mileage energy diagram; determining the length of the bridge where the rail expansion and contract adjuster is located according to the ledger information; and determining the mileage data corresponding to the two energy peak points corresponding to the horizontal coordinate difference equal to the bridge length as the position of the rail expansion and contract adjuster.

[0110] In the fourth part, energy threshold indicators can be set to conduct the initial screening of the generated mileage energy graph. Then, based on the comparison between the bridge length and energy peak in the static ledger data, the "mileage energy graph" is screened again to achieve the precise positioning of the telescopic adjuster.

[0111] The detailed steps of the fourth part can be as follows:

[0112] (1) Setting a preset energy threshold γ and filtering out waveforms with y>γ;

[0113] (2) The energy peak point in the filtered waveform is extracted and compared with the distance between the two ends of the bridge (bridge length) in the ledger information. If the distance between the energy peak points is equal to the bridge length, it is determined to be the position of the rail expansion adjuster.

[0114] Fig.11 This is a schematic diagram of the energy peak point in the embodiment of the present invention. Finally, the rail expansion regulator is accurately located after screening by a preset energy threshold and comparing the energy peak point with the ledger information. Fig.10 , Fig.11 As shown, the preset energy threshold γ may be 2.5% E (total energy).

[0115] It can be seen from the above steps that the gauge signal is processed in sequence that although the peak of the original gauge signal of the rail expansion adjuster is not obvious and there are many interference items and noise, the method of the embodiment of the present invention can effectively remove the noise and interference items, enhance the time-frequency characteristics at the expansion adjuster, integrate dynamic and static data, and finally accurately locate the expansion adjuster.

[0116] In summary, compared with the rail expansion adjuster positioning solution in the prior art, the embodiment of the present invention obtains track geometry data, railway track curve and ledger information within a preset distance range from the rail expansion adjuster; the rail expansion adjuster is arranged at both ends of the rail located on the bridge; the railway track includes the rails arranged on the bridge; the track geometry data is initially corrected according to the railway track curve and the ledger information; the ACMD algorithm is used to perform modal decomposition on the initially corrected track geometry data; WVD time-frequency analysis is performed on the track geometry data after modal decomposition to generate a mileage frequency graph; the mileage frequency graph is converted into a mileage energy graph using a preset moving window edge spectrum analysis algorithm; the position of the rail expansion adjuster is determined according to the mileage energy graph and the ledger information, and the dynamic and static data fusion is used, combined with the gauge signal characteristics at the rail expansion adjuster, to effectively filter out the interference of other gauge irregularities and noise, and to accurately locate the rail expansion adjuster and extract the time-frequency characteristics, which can improve the efficiency and accuracy of locating the rail expansion adjuster, reduce the time and labor costs of positioning, and thus improve the stability and safety of the high-speed railway driving performance.

[0117] The embodiment of the present invention further proposes a rail expansion adjuster positioning device with dynamic and static data fusion, the principle of which is similar to the rail expansion adjuster positioning method with dynamic and static data fusion, and will not be repeated here.

[0118] Fig.12 FIG. 1 is a schematic diagram of a rail expansion regulator positioning device with dynamic and static data fusion in an embodiment of the present invention. Fig.12 As shown, the rail expansion regulator positioning device with dynamic and static data fusion includes:

[0119] The acquisition module 1201 is used to acquire the track geometry data, railway track curve and account information within a preset distance range from the rail expansion regulator; the rail expansion regulator is arranged at both ends of the rail located on the bridge; the railway track includes the rail arranged on the bridge;

[0120] The initial calibration module 1202 is used to perform initial calibration on the track geometry data according to the railway track curve and the record information;

[0121] A modal decomposition module 1203 is used to perform modal decomposition on the initially corrected orbit geometry data using an adaptive chirp signal modal decomposition (ACMD) algorithm;

[0122] The time-frequency analysis module 1204 is used to perform Wigner WVD time-frequency analysis on the track geometry data after modal decomposition to generate a mileage frequency diagram;

[0123] The conversion module 1205 is used to convert the mileage frequency graph into a mileage energy graph by using a preset moving window edge spectrum analysis algorithm;

[0124] The positioning module 1206 is used to determine the position of the rail expansion adjuster according to the mileage energy diagram and the ledger information.

[0125] In one embodiment, the track geometry data includes a track gauge signal sampled at a preset sampling frequency, mileage data corresponding to the track gauge signal, and a track gauge signal waveform diagram determined by the track gauge signal and the mileage data;

[0126] The initial calibration module 1202 is specifically used for:

[0127] The mileage data is corrected according to the railway track curve and ledger information.

[0128] In one embodiment, the rail expansion regulator positioning device with dynamic and static data fusion further includes: a screening module for:

[0129] Loop through each sampling point within a first preset range, and determine a sampling point whose absolute value of a first difference between a corresponding gauge signal and a gauge signal of a previous sampling point is greater than a preset threshold as a starting sampling point; the first preset range is determined by the total number of sampling points and a preset point number threshold;

[0130] Loop through each sampling point within the second preset range, and determine the sampling point where the absolute value of the second difference between the gauge signal and the gauge signal of the next sampling point is greater than the preset threshold and the second difference has the same sign as the first difference as the end sampling point; the second preset range is determined by the starting sampling point and the preset point number threshold;

[0131] The track gauge signal is screened according to the starting sampling point and the ending sampling point to obtain a screened track gauge signal.

[0132] In one embodiment, the modal decomposition module 1203 is specifically configured to:

[0133] The filtered track gauge signal is modally decomposed by using an ACMD algorithm to generate a plurality of sub-modal signals; the sum of the plurality of sub-modal signals is equal to the filtered track gauge signal.

[0134] In one embodiment, the time-frequency analysis module 1204 is specifically configured to:

[0135] Determining a submodal signal with the strongest energy among the multiple submodal signals as a first modal signal;

[0136] The WVD time-frequency analysis is performed on the first modal signal using a preset discrete Wigner distribution definition to generate a mileage-frequency diagram of the first modal signal.

[0137] In one embodiment, the conversion module 1205 is specifically configured to:

[0138] Traversing each sampling point within a third preset range, and determining a mileage frequency map slice corresponding to each sampling point in the mileage frequency map according to a preset window length; the third preset range is determined by the total number of sampling points and the preset window length;

[0139] Calculate the energy of the mileage frequency map slice, and assign the ratio of the energy of the mileage frequency map slice to the total energy of the mileage frequency map to the corresponding sampling point;

[0140] Generate a mileage energy graph based on the assigned sampling points.

[0141] In one embodiment, the positioning module 1206 is specifically configured to:

[0142] Intercepting a curve segment whose energy value is greater than a preset energy threshold in the mileage energy graph, and determining a plurality of energy peak points in the curve segment;

[0143] The position of the rail expansion adjuster is determined based on the horizontal coordinate of the energy peak point in the mileage energy diagram and the ledger information.

[0144] In one embodiment, the positioning module 1206 is specifically configured to:

[0145] Determine, according to the abscissa of the energy peak point in the mileage energy diagram, a difference between the abscissas of every two energy peak points in the mileage energy diagram among the multiple energy peak points;

[0146] According to the ledger information, determine the length of the bridge where the rail expansion adjuster is located;

[0147] The mileage data corresponding to the two energy peak points corresponding to the horizontal coordinate difference equal to the bridge length are determined as the positions of the rail expansion adjusters.

[0148] The beneficial effect of the embodiments of the present invention is that it can detect in real time the abnormal situation of the lateral acceleration waveform data of the vehicle body when passing through the switch section, improve the evaluation accuracy of the track state by correcting the abnormal data, and enable scientific researchers to have an in-depth understanding of the characteristics of the lateral acceleration data of the vehicle body.

[0149] Based on the above invention concept, Fig.13 As shown, the present invention also proposes 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, wherein the processor 1320 implements the rail expansion adjuster positioning method with the aforementioned dynamic and static data fusion when executing the computer program 1330.

[0150] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the rail expansion adjuster positioning method with the aforementioned dynamic and static data fusion is implemented.

[0151] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, a method for positioning a rail expansion adjuster with dynamic and static data fusion is implemented.

[0152] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0153] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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.) containing computer-usable program code.

[0154] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rail expansion regulator positioning method based on dynamic and static data fusion, characterized in that: include: Acquire track geometry data, railway track curve and ledger information within a preset distance range from a rail expansion and contraction adjuster; the rail expansion and contraction adjuster is arranged at both ends of a rail located on a bridge; the railway track includes a rail arranged on a bridge; Performing preliminary correction on the track geometry data according to the railway track curve and record information; Adaptive chirp signal modal decomposition (ACMD) algorithm is used to perform modal decomposition on the initially corrected orbit geometry data. Perform Wigner WVD time-frequency analysis on the track geometry data after modal decomposition to generate a mileage frequency diagram; The mileage frequency graph is converted into a mileage energy graph using a preset moving window edge spectrum analysis algorithm; Determine the position of the rail expansion adjuster based on the mileage energy diagram and ledger information.

2. The method according to claim 1, characterized in that The track geometry data includes a track gauge signal sampled at a preset sampling frequency, mileage data corresponding to the track gauge signal, and a track gauge signal waveform diagram determined by the track gauge signal and the mileage data; According to the railway track curve and record information, the track geometry data is initially corrected, including: The mileage data is corrected according to the railway track curve and ledger information.

3. The method according to claim 2, characterized in that After the track geometry data is initially corrected according to the railway track curve and the ledger information, it also includes: Loop through each sampling point within a first preset range, and determine a sampling point whose absolute value of a first difference between a corresponding gauge signal and a gauge signal of a previous sampling point is greater than a preset threshold as a starting sampling point; the first preset range is determined by the total number of sampling points and a preset point number threshold; Loop through each sampling point within a second preset range, and determine as a termination sampling point a sampling point where the absolute value of the second difference between the gauge signal and the gauge signal of the next sampling point is greater than a preset threshold and the second difference has the same sign as the first difference; the second preset range is determined by the starting sampling point and the preset point number threshold; The track gauge signal is screened according to the starting sampling point and the ending sampling point to obtain a screened track gauge signal.

4. The method according to claim 3, characterized in that The ACMD algorithm is used to perform modal decomposition on the initially corrected track geometry data, including: The filtered track gauge signal is modally decomposed by using an ACMD algorithm to generate a plurality of sub-modal signals; the sum of the plurality of sub-modal signals is equal to the filtered track gauge signal.

5. The method according to claim 4, characterized in that Perform WVD time-frequency analysis on the track geometry data after modal decomposition to generate mileage frequency diagrams, including: Determining a submodal signal with the strongest energy among the multiple submodal signals as a first modal signal; The WVD time-frequency analysis is performed on the first modal signal using a preset discrete Wigner distribution definition to generate a mileage-frequency diagram of the first modal signal.

6. The method according to claim 5, characterized in that The mileage frequency graph is converted into a mileage energy graph using the preset moving window edge spectrum analysis algorithm, including: Traversing each sampling point within a third preset range, and determining a mileage frequency map slice corresponding to each sampling point in the mileage frequency map according to a preset window length; the third preset range is determined by the total number of sampling points and the preset window length; Calculate the energy of the mileage frequency map slice, and assign the ratio of the energy of the mileage frequency map slice to the total energy of the mileage frequency map to the corresponding sampling point; Generate a mileage energy graph based on the assigned sampling points.

7. The method according to claim 6, characterized in that According to the mileage energy diagram and ledger information, determine the position of the rail expansion adjuster, including: Intercepting a curve segment whose energy value is greater than a preset energy threshold in the mileage energy graph, and determining a plurality of energy peak points in the curve segment; The position of the rail expansion adjuster is determined based on the horizontal coordinate of the energy peak point in the mileage energy diagram and the ledger information.

8. The method according to claim 7, characterized in that According to the abscissa of the energy peak point in the mileage energy diagram and the ledger information, the position of the rail expansion regulator is determined, including: Determine, according to the abscissa of the energy peak point in the mileage energy diagram, a difference between the abscissas of every two energy peak points in the mileage energy diagram among the multiple energy peak points; According to the ledger information, determine the length of the bridge where the rail expansion adjuster is located; The mileage data corresponding to the two energy peak points corresponding to the horizontal coordinate difference equal to the bridge length are determined as the positions of the rail expansion adjusters.

9. A rail expansion regulator positioning device with dynamic and static data fusion, characterized in that: include: An acquisition module is used to acquire track geometry data, railway track curve and account information within a preset distance range from a rail expansion regulator; the rail expansion regulator is arranged at both ends of the rail located on the bridge; the railway track includes the rail arranged on the bridge; An initial calibration module, used to perform initial calibration on the track geometry data according to the railway track curve and record information; A modal decomposition module is used to perform modal decomposition on the initially corrected orbit geometry data using an adaptive chirp signal modal decomposition (ACMD) algorithm; The time-frequency analysis module is used to perform Wigner WVD time-frequency analysis on the track geometry data after modal decomposition and generate a mileage frequency diagram; A conversion module, used to convert the mileage frequency graph into a mileage energy graph using a preset moving window edge spectrum analysis algorithm; The positioning module is used to determine the position of the rail expansion adjuster based on the mileage energy diagram and ledger information.

10. 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 8 is implemented.

11. 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 8 is implemented.

12. 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 8 is implemented.

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