Method and device for calculating response data delay of track irregularities

Abstract

The application discloses a track irregularity response data delay calculation method and device, which can be used in the field of high-speed railway engineering and comprises the following steps: cutting a plurality of data sections according to the vehicle body operation data of a specified wavelength; calculating the correlation coefficient of track high-low irregularity data and vehicle body vertical acceleration data in each data section, and screening track sections for response data delay calculation; obtaining a plurality of intrinsic mode functions with different frequencies of the track high-low irregularity data and the vehicle body vertical acceleration data through complete set empirical mode decomposition based on adaptive noise; determining the peak point sequence of the track high-low irregularity data and the vehicle body vertical acceleration data; determining a plurality of peak value pairs through matching; calculating the mileage difference of each peak value pair, and determining the response data delay of the track irregularity. The application can reasonably determine the irregular track section suitable for track monitoring and accurately calculate the response data delay of the track irregularity.

Description

Technical Field

[0001] This invention relates to the field of high-speed railway engineering, and in particular to a method and apparatus for calculating response data delays for track irregularities. Background Technology

[0002] This section is intended to provide background or context for embodiments of the present invention. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] As a crucial component of high-speed railways, the track system provides support and guidance for running trains, and its condition directly impacts train operation safety and passenger comfort. Therefore, monitoring the track system's condition is of paramount importance. Track geometric irregularities directly affect train performance; excessively large irregularities can cause abnormal vibrations in the train, accelerating component damage and severely impacting the train's service life and the track system's durability.

[0004] Compared to locations where track geometry irregularities exceed limits, locations where the vertical and lateral acceleration amplitudes of the car body exceed limits are more difficult to address. This is because the wheelsets, frame, and car body of the EMU are connected by primary and secondary suspension springs. Therefore, vibrations generated in the wheel-rail plane exhibit a lag in the frame and car body vibrations due to the presence of the spring-damping system. This means that the track geometry irregularities corresponding to locations where the car body acceleration amplitude exceeds limits are not necessarily the locations causing abnormal vibrations in the EMU. To improve maintenance accuracy, it is necessary to accurately locate the locations exceeding limits and determine the response data delays. Summary of the Invention

[0005] This invention provides a method for calculating the response data delay of track irregularities, used to reasonably determine the irregular track sections suitable for track monitoring and accurately calculate the response data delay of track irregularities. The method includes:

[0006] The vehicle operation data is filtered to select vehicle operation data of a specified wavelength. The vehicle operation data includes train speed, track unevenness data and vehicle vertical acceleration data.

[0007] Normalize the track elevation irregularity data and vehicle vertical acceleration data at specified wavelengths, and extract multiple data segments based on the normalization results.

[0008] Calculate the correlation coefficients of track elevation irregularity data and vehicle vertical acceleration data in each data segment, and select data segments with correlation coefficients greater than the correlation coefficient threshold as track segments for response data delay calculation;

[0009] For the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, complete set empirical mode decomposition based on adaptive noise is performed to obtain multiple intrinsic mode functions with different frequencies for the track elevation irregularity data and multiple intrinsic mode functions with different frequencies for the vehicle vertical acceleration data.

[0010] Based on the intrinsic mode functions of multiple frequencies of track elevation irregularity data, the peak point mileage sequence of track elevation irregularity data is determined. Based on the intrinsic mode functions of multiple frequencies of vehicle vertical acceleration data, the peak point mileage sequence of vehicle vertical acceleration data is determined.

[0011] The peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle body vertical acceleration data are matched to obtain the peak pairs of the track elevation irregularity data and the vehicle body vertical acceleration data.

[0012] Calculate the mileage difference for each peak, and based on the mileage difference for each peak and the train speed, calculate the response data delay for track irregularities.

[0013] This invention also provides a device for calculating the response data delay of track irregularities, used to reasonably determine irregular track sections suitable for track monitoring and accurately calculate the response data delay of track irregularities. The device includes:

[0014] The filtering module is used to filter the vehicle operation data and select the vehicle operation data of a specified wavelength. The vehicle operation data includes the train passing speed, track unevenness data and vehicle vertical acceleration data.

[0015] The normalization module is used to normalize the track elevation irregularity data and vehicle vertical acceleration data of a specified wavelength, and extract multiple data segments based on the normalization results.

[0016] The segment filtering module is used to calculate the correlation coefficients of track elevation irregularity data and vehicle vertical acceleration data within each data segment, and to filter data segments with correlation coefficients greater than the correlation coefficient threshold as track segments for response data delay calculation.

[0017] The mode decomposition module is used to perform full set empirical mode decomposition based on adaptive noise on the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, respectively, to obtain multiple intrinsic mode functions with different frequencies of the track elevation irregularity data and multiple intrinsic mode functions with different frequencies of the vehicle vertical acceleration data.

[0018] The peak determination module is used to determine the peak point mileage sequence of track elevation irregularity data based on multiple intrinsic mode functions of different frequencies, and to determine the peak point mileage sequence of vehicle vertical acceleration data based on multiple intrinsic mode functions of different frequencies.

[0019] The matching module is used to match the peak point mileage sequence of track elevation irregularity data and the peak point mileage sequence of vehicle body vertical acceleration data to obtain peak pairs of track elevation irregularity data and vehicle body vertical acceleration data.

[0020] The delay calculation module is used to calculate the mileage difference between each peak and the train passing speed, and to calculate the response data delay for track irregularities.

[0021] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for calculating response data delay due to track irregularities.

[0022] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating response data delays in response to track irregularities.

[0023] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-mentioned method for calculating response data delay due to track irregularities.

[0024] In this embodiment of the invention, the vehicle operation data is filtered to select vehicle operation data of a specified wavelength. The vehicle operation data includes train speed, track elevation irregularity data, and vehicle vertical acceleration data. The track elevation irregularity data and vehicle vertical acceleration data of the specified wavelength are normalized respectively, and multiple data segments are extracted based on the normalization results. The correlation coefficient between the track elevation irregularity data and vehicle vertical acceleration data in each data segment is calculated, and data segments with correlation coefficients greater than the correlation coefficient threshold are selected as track segments for response data delay calculation. The track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation are subjected to complete ensemble empirical mode based on adaptive noise. The process involves decomposition to obtain multiple intrinsic mode functions (IMFs) of different frequencies for the track elevation irregularity data and the vehicle vertical acceleration data. Based on these IMFs, the peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle vertical acceleration data are determined. These two sequences are then matched to obtain peak pairs. The mileage difference between each peak pair is calculated, and the response data delay for the track irregularity is calculated based on the mileage difference and the train's passing speed. In this way, by filtering and screening vehicle operation data of a specified wavelength, and calculating the correlation coefficient after normalization, track sections suitable for response data delay calculation are selected. After empirical mode decomposition of the complete set based on adaptive noise, the peak point mileage sequence is determined, peak pairs are matched, the mileage difference between peak pairs is calculated, and then combined with the train passing speed, the response data delay is calculated. This is used to detect the geometric shape and position of the track, as well as the vertical and lateral vibration acceleration of the vehicle body when the train passes through the current track section. This allows for the reasonable determination of irregular track sections suitable for track monitoring and accurate calculation of the response data delay for track irregularities. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0026] Figure 1 This is a flowchart of the response data delay calculation method for track irregularities provided in this embodiment of the invention;

[0027] Figure 2 This is a schematic diagram of the original waveforms of the left and right elevation irregularities of the track segment, which are suitable for calculating response data delay in an embodiment of the present invention.

[0028] Figure 3 This is a schematic diagram of the original waveform of the vehicle body vertical acceleration data suitable for response data delay calculation provided in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the eigenmode function of the root mean square value corresponding to the uneven left and right height data provided in the embodiment of the present invention.

[0030] Figure 5 This is a schematic diagram of the eigenmode function corresponding to the root mean square value of the vehicle body vertical acceleration data provided in this embodiment of the invention;

[0031] Figure 6 This is a schematic diagram of the peak values ​​of the left and right elevation irregularities and the vehicle body vertical acceleration data provided in this embodiment of the invention to indicate the mileage information.

[0032] Figure 7 This is a schematic diagram of the time delay calculated by different peak values ​​of the track segment for response data delay calculation provided in an embodiment of the present invention.

[0033] Figure 8 This is a schematic diagram of the response data delay calculation device for track irregularities provided in an embodiment of the present invention;

[0034] Figure 9 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0036] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0037] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0038] Currently, track condition is evaluated from two perspectives: the amplitude of track geometric irregularities and the passing performance of the train. Regarding the amplitude of track geometric irregularities, different types of irregularities are managed differently based on severity by setting thresholds at different levels. For the passing performance of the train, the vertical and lateral accelerations of the car body are used as the basis for evaluating track condition by judging whether the amplitude exceeds the limits. With the extension of service life, dynamic testing data shows that track irregularities of the same amplitude but different wavelengths cause differences in the dynamic response of the vehicle. This is mainly because the vehicle system, as a complex mechanical system, has multiple inherent modal frequencies. When the external input is close to its own inherent modal frequency, the train will resonate, and the vibration at the car body will intensify, manifested as a significant increase in the amplitude of the vertical and lateral acceleration of the car body. When the amplitude exceeds the limits, a maintenance plan will be formulated, maintenance will be carried out, and safety hazards will be eliminated.

[0039] Existing technologies still need to address the following issues:

[0040] (1) Not all segments can be used for time delay calculation. How to determine the single-wavelength track segment with uneven elevation for time delay calculation?

[0041] (2) How to process measured data containing noise and with non-unique wavelength components to meet the calculation requirements?

[0042] (3) How to use the unevenness of the track and the vertical acceleration of the vehicle body to calculate the time delay?

[0043] Based on this, embodiments of the present invention provide a method for calculating response data delay due to track irregularities. Figure 1 This is a flowchart of the response data delay calculation method for track irregularities provided in this embodiment of the invention, as shown below. Figure 1 As shown, it includes:

[0044] Step 101: Filter the vehicle operation data to select the vehicle operation data of a specified wavelength. The vehicle operation data includes the train speed, track unevenness data and vehicle vertical acceleration data.

[0045] Step 102: Normalize the track elevation irregularity data and vehicle vertical acceleration data of the specified wavelength, and extract multiple data segments based on the normalization results;

[0046] Step 103: Calculate the correlation coefficients of track elevation irregularity data and vehicle vertical acceleration data in each data segment, and select data segments with correlation coefficients greater than the correlation coefficient threshold as track segments for response data delay calculation;

[0047] Step 104: Perform full set empirical mode decomposition based on adaptive noise on the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, to obtain multiple intrinsic mode functions with different frequencies for the track elevation irregularity data and multiple intrinsic mode functions with different frequencies for the vehicle vertical acceleration data.

[0048] Step 105: Based on the multiple intrinsic mode functions of different frequencies of the track elevation irregularity data, determine the peak point mileage sequence of the track elevation irregularity data; based on the multiple intrinsic mode functions of different frequencies of the vehicle body vertical acceleration data, determine the peak point mileage sequence of the vehicle body vertical acceleration data.

[0049] Step 106: Match the peak point mileage sequence of the track elevation irregularity data with the peak point mileage sequence of the vehicle vertical acceleration data to obtain peak pairs of the track elevation irregularity data and the vehicle vertical acceleration data;

[0050] Step 107: Calculate the mileage difference for each peak pair, and calculate the response data delay for track irregularities based on the mileage difference for each peak pair and the train passing speed.

[0051] The method for calculating the response data delay of track irregularities proposed in this invention uses correlation coefficients to determine the bridge sections where the wavelengths of track irregularities are relatively unique. Then, it employs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to extract a single principal component, obtaining a sequence of track irregularities and a sequence of vehicle vertical accelerations with consistent components. Based on waveform characteristics, it calculates the distance difference between the peak value of the track irregularity and the corresponding peak value of the vehicle vertical acceleration. Finally, it divides the distance difference by the current operating speed to obtain the time delay between the track irregularity and the vehicle vertical acceleration.

[0052] In one embodiment, before filtering the vehicle operation data, the method further includes:

[0053] Delete train operation data when the train passes through at a speed below the speed threshold, train operation data in acceleration sections, and train operation data in deceleration sections.

[0054] In one embodiment, the track elevation irregularity data and vehicle vertical acceleration data at a specified wavelength are normalized, including:

[0055] Max-min normalization was used to normalize the track elevation irregularity data and vehicle vertical acceleration data for a specified wavelength.

[0056] In one embodiment, multiple data segments are extracted based on the normalization result, including:

[0057] After normalizing the track elevation irregularity data and vehicle vertical acceleration data of a specified wavelength, the sliding window method is used to extract the data after setting the segment length and step size, resulting in multiple data segments.

[0058] In one embodiment, the correlation coefficients of track elevation irregularity data and vehicle vertical acceleration data within each data segment are calculated, including:

[0059] The correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment is calculated using the following formula:

[0060]

[0061] In the formula, ρ n x represents the correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment. ni For the i-th track elevation irregularity data in the n-th segment; y ni This refers to the vertical acceleration data of the i-th vehicle body in the n-th segment; The average value of the track elevation unevenness data in the nth section; This represents the average vertical acceleration data of the i-th vehicle body in the n-th segment.

[0062] In practice, the research object is the track elevation irregularity and vehicle vertical acceleration data collected by the high-speed integrated inspection train. It mainly consists of two parts: the first part is the track section determination method applicable to time delay calculation; the second part is the specific implementation of the geometry-response transfer time difference calculation method.

[0063] The high-speed integrated inspection train is equipped with a track geometry detection system to detect the track's geometry and the vertical and lateral vibration acceleration of the train body as it passes through the current track section. Using track unevenness and vertical acceleration collected by the high-speed integrated inspection train as examples, the specific implementation method is explained.

[0064] Existing research indicates that true track geometric irregularities can be viewed as the result of the superposition of track irregularities of different wavelengths. Track elevation irregularities of a specific wavelength will cause the train to produce vertical vibrations of the same wavelength, manifested as the presence of that wavelength component in the train's vertical acceleration. Therefore, when the wavelength component of track elevation irregularity data is relatively uniform, the corresponding wavelength component of the train's vertical acceleration data is also relatively uniform, and the waveforms of the two are highly similar, making it easier to discover the correlation between their data characteristics. Therefore, the correlation coefficient is used to measure the similarity between track elevation irregularity data and train vertical acceleration data. The specific implementation method is as follows:

[0065] (1) Deletion speed is less than 0.8×v max Data and acceleration / deceleration section data; v max Maximum operating speed;

[0066] (2) The signal is filtered with a filtering frequency range of [0, 1 / 30×Fs] to achieve the purpose of analyzing the relationship between track unevenness and vehicle vertical vibration acceleration with a wavelength of 30m or more.

[0067] (3) The Min-Max Normalization is used to normalize the track elevation irregularity data and the vehicle vertical acceleration data respectively, so that the values ​​all fall within [0,1]. The specific implementation is shown in formula (1).

[0068]

[0069] In the formula, X is the original dataset; max(X) is the maximum value in the dataset; and min(X) is the minimum value in the dataset.

[0070] (4) For the processed data, take a segment length of 500 meters and a step size of 20 meters, and use the sliding window method to extract the data to form N data segments;

[0071] (5) Calculate the correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment:

[0072]

[0073] In the formula, ρ nx represents the correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment. ni For the i-th track elevation irregularity data in the n-th segment; y ni This refers to the vertical acceleration data of the i-th vehicle body in the n-th segment; The average value of the track elevation unevenness data in the nth section; This represents the average vertical acceleration data of the i-th vehicle body in the n-th segment.

[0074] (6) Filter the data segments with a correlation coefficient greater than 0.7 and record them as the track segments suitable for response data delay calculation.

[0075] In one embodiment, determining the peak point mileage sequence of track elevation irregularity data based on multiple intrinsic mode functions (IMFs) of different frequencies, and determining the peak point mileage sequence of vehicle vertical acceleration data based on multiple IMFs of different frequencies, includes:

[0076] Based on the intrinsic mode functions of different frequencies in the track elevation irregularity data, the root mean square values ​​of the intrinsic mode functions of different frequencies in the track elevation irregularity data are extracted, and the intrinsic mode function with the largest root mean square value in the track elevation irregularity data is determined.

[0077] Extract the mileage information corresponding to each peak point from the intrinsic mode function with the largest root mean square value of the track elevation irregularity data to form the peak point mileage sequence of the track elevation irregularity data;

[0078] Based on the eigenmode functions of different frequencies in the vertical acceleration data of the vehicle body, the root mean square values ​​of the eigenmode functions of different frequencies in the vertical acceleration data of the vehicle body are extracted, and the eigenmode function with the largest root mean square value in the vertical acceleration data of the vehicle body is determined.

[0079] The mileage information corresponding to each peak point in the intrinsic mode function with the largest root mean square value of the vehicle body vertical acceleration data is extracted to form the peak point mileage sequence of the vehicle body vertical acceleration data.

[0080] In one embodiment, the peak point mileage sequence of track elevation irregularity data and the peak point mileage sequence of vehicle vertical acceleration data are matched to obtain peak pairs of track elevation irregularity data and vehicle vertical acceleration data, including:

[0081] The data characteristics of the peak point mileage sequence of track elevation irregularity data and the data characteristics of the peak point mileage sequence of vehicle body vertical acceleration data were determined respectively.

[0082] Based on the data characteristics, the peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle body vertical acceleration data are matched to obtain the peak pairs of the track elevation irregularity data and the vehicle body vertical acceleration data.

[0083] In practice, after calculating the correlation coefficient and determining the track segment used for calculating the response data delay based on the correlation coefficient, the response data delay is calculated in the following manner:

[0084] (1) Using the proposed track segment suitable for response data delay calculation, obtain the track segment suitable for time delay calculation;

[0085] (2) For the above track section elevation irregularity data and vehicle vertical acceleration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is performed respectively. The track elevation irregularity yields K intrinsic mode functions (IMFs) with different frequencies, and the vehicle vertical acceleration yields P IMFs.

[0086] (3) Calculate the root mean square value of each IMF obtained from the decomposition of track elevation irregularity data and extract the IMF with the largest root mean square value, denoted as R. m

[0087]

[0088] In the formula, R m R is the IMF with the largest root mean square value for the orbital elevation irregularities data. i The i-th IMF is obtained by performing CEEMDAN decomposition on the track elevation irregularity data; K is the total number of IMFs obtained by track elevation irregularity decomposition; M is the signal length; and j is the current data point number.

[0089] (4) Calculate the root mean square value of each IMF obtained from the decomposition of the vehicle body vertical acceleration and extract the IMF with the largest root mean square value, denoted as A. m

[0090]

[0091] In the formula, A m A is the IMF with the largest root mean square value of the vehicle body vertical acceleration data. i P represents the i-th IMF obtained by performing CEEMDAN decomposition on the vehicle's vertical acceleration data; P is the total number of IMFs obtained by decomposing the vehicle's vertical acceleration data.

[0092] (5) Extracting Rm The mileage information corresponding to each peak point is used to form a peak point mileage sequence;

[0093] (6) Extract A m The mileage information corresponding to each peak point is used to form a peak point mileage sequence;

[0094] (7) Based on the data characteristics of each peak point mileage sequence, match the peak points of track elevation irregularities with the peak points of the vertical acceleration of the vehicle body caused by them to form peak pairs of track elevation irregularity data and vehicle body vertical acceleration data;

[0095] (8) Calculate the peak-to-mileage difference and calculate the response data delay based on the train passing speed.

[0096] For example, when a high-speed train passes through a certain line, the response data delay for track irregularities proposed in this embodiment of the invention is used to calculate track section 1. Figure 2 This is a schematic diagram of the original waveforms of the left and right elevation irregularities in the track segment, suitable for calculating response data delay, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the original waveform of the vehicle body vertical acceleration data suitable for response data delay calculation provided in this embodiment of the invention. The original waveforms showing the left and right geometric irregularities of the track are as follows: Figure 2 As shown, the original waveform of the corresponding vehicle body vertical acceleration is as follows: Figure 3 As shown.

[0097] Figure 4 This is a schematic diagram of the intrinsic mode functions (EMFs) corresponding to the root mean square (RMS) values ​​of the left and right elevation irregularities provided in this embodiment of the invention. The RMS values ​​of the left and right elevation irregularities and the vehicle's vertical acceleration data are calculated by performing Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) on the data, respectively, to obtain the R1 value corresponding to the left elevation irregularity. m R2 corresponds to the unevenness on the right. m The result is as follows Figure 4 As shown, ① represents the first peak point of track irregularity or the starting point of the sequence of peak points of track irregularity. Figure 5 This is a schematic diagram of the eigenmode function corresponding to the root mean square value of the vehicle body vertical acceleration data provided in this embodiment of the invention. After calculating the interval distance between the peak points of the left and right track elevation irregularities and the corresponding peak points of the vehicle body vertical acceleration, the time delay is calculated based on the running speed, and the result is as follows. Figure 5 As shown, ① represents the first peak point of the vehicle's vertical acceleration or the starting point of the sequence of peak points of the vehicle's vertical acceleration.

[0098] Figure 6 This is a schematic diagram of the peak values ​​of the left and right elevation irregularities and the vehicle body vertical acceleration data provided in the embodiments of the present invention, wherein "vehicle body vertical" refers to the vehicle body vertical acceleration; Figure 7 This is a schematic diagram illustrating the time delay calculated for different peak values ​​of track segments used in response data delay calculation according to an embodiment of the present invention. Taking the position sequence corresponding to the peak value of the left track elevation irregularity as a reference, the calculated time delay range between the vehicle's vertical acceleration and the left track elevation irregularity is 0.4125–0.5953 s; taking the position sequence corresponding to the peak value of the right track elevation irregularity as a reference, the calculated time delay range between the vehicle's vertical acceleration and the right track elevation irregularity is 0.4218–0.600 s. Figure 6 , Figure 7 As shown in the figure. In summary, the time delay between the vertical acceleration of the train body and the unevenness of the track is 0.4 to 0.6 seconds.

[0099] This invention also provides a device for calculating response data delay due to track irregularities, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the method for calculating response data delay due to track irregularities, the implementation of this device can refer to the implementation of the method for calculating response data delay due to track irregularities; repeated details will not be elaborated further.

[0100] Figure 8 This is a schematic diagram of the response data delay calculation device for track irregularities provided in an embodiment of the present invention, as shown below. Figure 8 As shown, the device includes:

[0101] The filtering module 801 is used to filter the vehicle operation data and select the vehicle operation data of a specified wavelength. The vehicle operation data includes the train passing speed, track unevenness data and vehicle vertical acceleration data.

[0102] The normalization module 802 is used to normalize the track elevation irregularity data and vehicle vertical acceleration data of a specified wavelength, and extract multiple data segments based on the normalization results.

[0103] The segment filtering module 803 is used to calculate the correlation coefficient of track elevation irregularity data and vehicle vertical acceleration data in each data segment, and filter data segments with correlation coefficients greater than the correlation coefficient threshold as track segments for response data delay calculation;

[0104] The mode decomposition module 804 is used to perform full set empirical mode decomposition based on adaptive noise on the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, respectively, to obtain multiple intrinsic mode functions with different frequencies of the track elevation irregularity data and multiple intrinsic mode functions with different frequencies of the vehicle vertical acceleration data.

[0105] The peak determination module 805 is used to determine the peak point mileage sequence of the track elevation irregularity data based on multiple intrinsic mode functions of different frequencies, and to determine the peak point mileage sequence of the vehicle body vertical acceleration data based on multiple intrinsic mode functions of different frequencies.

[0106] The matching module 806 is used to match the peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle body vertical acceleration data to obtain the peak pair of the track elevation irregularity data and the vehicle body vertical acceleration data.

[0107] The delay calculation module 807 is used to calculate the mileage difference between each peak and the train passing speed, and to calculate the response data delay of track irregularities.

[0108] In one embodiment, a preprocessing module is further included, specifically for:

[0109] Delete train operation data when the train passes through at a speed below the speed threshold, train operation data in acceleration sections, and train operation data in deceleration sections.

[0110] In one embodiment, the normalization module 802 is specifically used for:

[0111] Max-min normalization was used to normalize the track elevation irregularity data and vehicle vertical acceleration data for a specified wavelength.

[0112] In one embodiment, the normalization module 802 is specifically used for:

[0113] After normalizing the track elevation irregularity data and vehicle vertical acceleration data of a specified wavelength, the sliding window method is used to extract the data after setting the segment length and step size, resulting in multiple data segments.

[0114] In one embodiment, the segment filtering module 803 is specifically used for:

[0115] The correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment is calculated using the following formula:

[0116]

[0117] In the formula, ρ nx represents the correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment. ni For the i-th track elevation irregularity data in the n-th segment; y ni This refers to the vertical acceleration data of the i-th vehicle body in the n-th segment; The average value of the track elevation unevenness data in the nth section; This represents the average vertical acceleration data of the i-th vehicle body in the n-th segment.

[0118] In one embodiment, the peak value determination module 805 is specifically used for:

[0119] Based on the intrinsic mode functions of different frequencies in the track elevation irregularity data, the root mean square values ​​of the intrinsic mode functions of different frequencies in the track elevation irregularity data are extracted, and the intrinsic mode function with the largest root mean square value in the track elevation irregularity data is determined.

[0120] Extract the mileage information corresponding to each peak point from the intrinsic mode function with the largest root mean square value of the track elevation irregularity data to form the peak point mileage sequence of the track elevation irregularity data;

[0121] Based on the eigenmode functions of different frequencies in the vertical acceleration data of the vehicle body, the root mean square values ​​of the eigenmode functions of different frequencies in the vertical acceleration data of the vehicle body are extracted, and the eigenmode function with the largest root mean square value in the vertical acceleration data of the vehicle body is determined.

[0122] The mileage information corresponding to each peak point in the intrinsic mode function with the largest root mean square value of the vehicle body vertical acceleration data is extracted to form the peak point mileage sequence of the vehicle body vertical acceleration data.

[0123] In one embodiment, the matching module 806 is specifically used for:

[0124] The data characteristics of the peak point mileage sequence of track elevation irregularity data and the data characteristics of the peak point mileage sequence of vehicle body vertical acceleration data were determined respectively.

[0125] Based on the data characteristics, the peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle body vertical acceleration data are matched to obtain the peak pairs of the track elevation irregularity data and the vehicle body vertical acceleration data.

[0126] Based on the aforementioned inventive concept, such as Figure 9 As shown, the present invention also proposes a computer device 900, including a memory 910, a processor 920, and a computer program 930 stored in the memory 910 and executable on the processor 920. When the processor 920 executes the computer program 930, it implements the aforementioned method for calculating response data delay for track irregularities.

[0127] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calculating response data delays in response to track irregularities.

[0128] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-mentioned method for calculating response data delay due to track irregularities.

[0129] In summary, in this embodiment of the invention, the vehicle operation data is filtered to select vehicle operation data of a specified wavelength. The vehicle operation data includes train speed, track elevation irregularity data, and vehicle vertical acceleration data. The track elevation irregularity data and vehicle vertical acceleration data of the specified wavelength are normalized, and multiple data segments are extracted based on the normalization results. The correlation coefficient between the track elevation irregularity data and vehicle vertical acceleration data within each data segment is calculated, and data segments with correlation coefficients greater than a threshold are selected as track segments for response data delay calculation. For the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, a complete set of adaptive noise is applied. Modal decomposition is performed to obtain multiple intrinsic mode functions (IMFs) of different frequencies for track elevation irregularity data and multiple IMFs of different frequencies for vehicle vertical acceleration data. Based on the multiple IMFs of different frequencies for track elevation irregularity data, the peak point mileage sequence for track elevation irregularity data and the peak point mileage sequence for vehicle vertical acceleration data are determined. The peak point mileage sequences for track elevation irregularity data and vehicle vertical acceleration data are matched to obtain peak pairs for track elevation irregularity data and vehicle vertical acceleration data. The mileage difference between each peak pair is calculated, and the response data delay for track irregularity is calculated based on the mileage difference between each peak pair and the train passing speed. In this way, by filtering and screening vehicle operation data of a specified wavelength, and calculating the correlation coefficient after normalization, track sections suitable for response data delay calculation are selected. After empirical mode decomposition of the complete set based on adaptive noise, the peak point mileage sequence is determined, peak pairs are matched, the mileage difference between peak pairs is calculated, and then combined with the train passing speed, the response data delay is calculated. This is used to detect the geometric shape and position of the track, as well as the vertical and lateral vibration acceleration of the vehicle body when the train passes through the current track section. This allows for the reasonable determination of irregular track sections suitable for track monitoring and accurate calculation of the response data delay for track irregularities.

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0131] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are 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 within the scope of protection of the present invention.

Claims

1. A method for calculating the response data delay due to track irregularities, characterized in that, include: The vehicle operation data is filtered to select vehicle operation data of a specified wavelength. The vehicle operation data includes train speed, track unevenness data and vehicle vertical acceleration data. Normalize the track elevation irregularity data and vehicle vertical acceleration data at specified wavelengths, and extract multiple data segments based on the normalization results. Calculate the correlation coefficients of track elevation irregularity data and vehicle vertical acceleration data in each data segment, and select data segments with correlation coefficients greater than the correlation coefficient threshold as track segments for response data delay calculation; For the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, complete set empirical mode decomposition based on adaptive noise is performed to obtain multiple intrinsic mode functions with different frequencies for the track elevation irregularity data and multiple intrinsic mode functions with different frequencies for the vehicle vertical acceleration data. Based on the intrinsic mode functions of multiple frequencies of track elevation irregularity data, the peak point mileage sequence of track elevation irregularity data is determined. Based on the intrinsic mode functions of multiple frequencies of vehicle vertical acceleration data, the peak point mileage sequence of vehicle vertical acceleration data is determined. The peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle body vertical acceleration data are matched to obtain the peak pairs of the track elevation irregularity data and the vehicle body vertical acceleration data. Calculate the mileage difference for each peak, and based on the mileage difference for each peak and the train speed, calculate the response data delay for track irregularities.

2. The method as described in claim 1, characterized in that, Before filtering the vehicle operation data, the following steps are also included: Delete train operation data when the train passes through at a speed below the speed threshold, train operation data in acceleration sections, and train operation data in deceleration sections.

3. The method as described in claim 1, characterized in that, Normalization processing was performed on the track elevation irregularity data and vehicle vertical acceleration data at specified wavelengths, including: Max-min normalization was used to normalize the track elevation irregularity data and vehicle vertical acceleration data for a specified wavelength.

4. The method as described in claim 1, characterized in that, Based on the normalization results, multiple data segments were extracted, including: After normalizing the track elevation irregularity data and vehicle vertical acceleration data of a specified wavelength, the sliding window method is used to extract the data after setting the segment length and step size, resulting in multiple data segments.

5. The method as described in claim 1, characterized in that, Calculate the correlation coefficients of track elevation irregularities and vehicle vertical acceleration data within each data segment, including: The correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment is calculated using the following formula: In the formula, ρ n x represents the correlation coefficient between track elevation irregularities and vehicle vertical acceleration data within each data segment. ni For the i-th track elevation irregularity data in the n-th segment; y ni This refers to the vertical acceleration data of the i-th vehicle body in the n-th segment; The average value of the track elevation unevenness data in the nth section; This represents the average vertical acceleration data of the i-th vehicle body in the n-th segment.

6. The method as described in claim 1, characterized in that, Based on multiple intrinsic mode functions (IMFs) of different frequencies in the track elevation irregularity data, the peak point mileage sequence of the track elevation irregularity data is determined. Similarly, based on multiple IMFs of different frequencies in the vehicle body vertical acceleration data, the peak point mileage sequence of the vehicle body vertical acceleration data is determined, including: Based on the intrinsic mode functions of different frequencies in the track elevation irregularity data, the root mean square values ​​of the intrinsic mode functions of different frequencies in the track elevation irregularity data are extracted, and the intrinsic mode function with the largest root mean square value in the track elevation irregularity data is determined. Extract the mileage information corresponding to each peak point from the intrinsic mode function with the largest root mean square value of the track elevation irregularity data to form the peak point mileage sequence of the track elevation irregularity data; Based on the eigenmode functions of different frequencies in the vertical acceleration data of the vehicle body, the root mean square values ​​of the eigenmode functions of different frequencies in the vertical acceleration data of the vehicle body are extracted, and the eigenmode function with the largest root mean square value in the vertical acceleration data of the vehicle body is determined. The mileage information corresponding to each peak point in the intrinsic mode function with the largest root mean square value of the vehicle body vertical acceleration data is extracted to form the peak point mileage sequence of the vehicle body vertical acceleration data.

7. The method as described in claim 1, characterized in that, Matching the peak point mileage sequences of track elevation irregularities data with the peak point mileage sequences of vehicle vertical acceleration data yields peak pairs of track elevation irregularities data and vehicle vertical acceleration data, including: The data characteristics of the peak point mileage sequence of track elevation irregularity data and the data characteristics of the peak point mileage sequence of vehicle body vertical acceleration data were determined respectively. Based on the data characteristics, the peak point mileage sequence of the track elevation irregularity data and the peak point mileage sequence of the vehicle body vertical acceleration data are matched to obtain the peak pairs of the track elevation irregularity data and the vehicle body vertical acceleration data.

8. A device for calculating response data delay due to track irregularities, characterized in that, include: The filtering module is used to filter the vehicle operation data and select the vehicle operation data of a specified wavelength. The vehicle operation data includes the train passing speed, track unevenness data and vehicle vertical acceleration data. The normalization module is used to normalize the track elevation irregularity data and vehicle vertical acceleration data of a specified wavelength, and extract multiple data segments based on the normalization results. The segment filtering module is used to calculate the correlation coefficients of track elevation irregularity data and vehicle vertical acceleration data within each data segment, and to filter data segments with correlation coefficients greater than the correlation coefficient threshold as track segments for response data delay calculation. The mode decomposition module is used to perform full set empirical mode decomposition based on adaptive noise on the track elevation irregularity data and vehicle vertical acceleration data of each track segment used for response data delay calculation, respectively, to obtain multiple intrinsic mode functions with different frequencies of the track elevation irregularity data and multiple intrinsic mode functions with different frequencies of the vehicle vertical acceleration data. The peak determination module is used to determine the peak point mileage sequence of track elevation irregularity data based on multiple intrinsic mode functions of different frequencies, and to determine the peak point mileage sequence of vehicle vertical acceleration data based on multiple intrinsic mode functions of different frequencies. The matching module is used to match the peak point mileage sequence of track elevation irregularity data and the peak point mileage sequence of vehicle body vertical acceleration data to obtain peak pairs of track elevation irregularity data and vehicle body vertical acceleration data. The delay calculation module is used to calculate the mileage difference between each peak and the train passing speed, and to calculate the response data delay for track irregularities.

9. 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, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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