Method and device for automatically calculating TACH parameters of high-speed comprehensive detection vehicle

Abstract

The invention discloses an automatic calculation method and device for TACH parameters of a high-speed comprehensive detection vehicle. The method comprises the steps that a detection data sequence containing at least two curve segments is obtained; extracting two curve segments from the detection data sequence, and performing straight line fitting on the two easement curves of each curve segment to obtain two fitting straight lines of each curve segment; determining the mileage at the intersection point of the two fitting straight lines of each curve segment as the feature point mileage of the corresponding curve segment; determining the absolute value of the mileage difference of the feature points of the two curve segments extracted from the detection data sequence as the actually measured mileage difference; obtaining the reference mileage of the geometric center point of each curve segment extracted from the detection data sequence in the standing book, and determining the absolute value of the difference between the reference mileages of the geometric center points of the two curve segments as the reference mileage difference; and taking the ratio of the actually measured mileage difference to the reference mileage difference as a correction factor, and updating the TACH parameter by using the correction factor. According to the invention, the TACH parameter can be accurately, efficiently and automatically corrected.

Description

Technical Field

[0001] This invention relates to the field of TACH parameter calculation technology, and in particular to an automatic calculation method and device for TACH parameters of a high-speed integrated inspection vehicle. Background Technology

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

[0003] The high-speed integrated inspection train uses GNSS (Global Navigation Satellite System) data sequences for mileage correction. When no GNSS data sequence is available, it relies on wheel rolling information for linear accumulation or reduction of mileage. For each wheel revolution, the encoder sends N pulses. Given the wheel diameter D, the wheel circumference L can be calculated. Therefore, the number of pulses N / L per unit distance traveled by the wheel can be calculated, which is the TACH parameter of the integrated system. The accuracy of the TACH parameter determines the accuracy of the high-speed integrated inspection train's mileage. In practical applications, the wheel diameter of the high-speed integrated inspection train changes due to natural wear and wheel turning. Therefore, the TACH parameter needs to be adjusted accordingly.

[0004] Traditional TACH parameter correction methods involve manually annotating the dynamic monitoring data of high-speed integrated inspection trains. The TACH parameters are then corrected based on the mileage corresponding to waveform transition points in the logbook data, as well as the mileage corresponding to the annotated waveform transition points. However, manual point selection is prone to errors. Furthermore, manually scrolling through the inspection data sequence and relying on experience to confirm mileage calibration points in the curves from a massive logbook database is inefficient. Summary of the Invention

[0005] This invention provides an automatic calculation method for TACH parameters of a high-speed integrated inspection train, which is used to accurately and efficiently correct TACH parameters, thereby improving the accuracy of the integrated mileage of the integrated inspection train and providing more accurate data support for the maintenance of high-speed railway infrastructure. The method includes:

[0006] Obtain a detection data sequence containing at least two curve segments; wherein each curve segment contains two transition curves; the detection data includes ultra-high values;

[0007] Two curve segments are extracted from the detection data sequence. Straight lines are fitted to the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment. The mileage at the intersection of the two fitted straight lines of each curve segment is determined as the feature point mileage of the corresponding curve segment. The absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence is determined as the measured mileage difference.

[0008] Obtain the reference mileage of the geometric center point of each curve segment from the two curve segments extracted from the detection data sequence in the ledger, and determine the absolute value of the difference between the reference mileages of the geometric center points of the two curve segments as the reference mileage difference.

[0009] The ratio of the measured mileage difference to the baseline mileage difference is used as a correction factor, and the TACH parameters are updated using this correction factor.

[0010] This invention also provides an automatic calculation device for TACH parameters of a high-speed integrated inspection train, used to accurately and efficiently correct TACH parameters automatically, thereby improving the accuracy of the integrated mileage of the integrated inspection train and providing more accurate data support for the maintenance of high-speed railway infrastructure. The device includes:

[0011] The detection data filtering module is used to: obtain a detection data sequence containing at least two curve segments; wherein each curve segment contains two transition curves; the detection data includes ultra-high values;

[0012] The detection data analysis module is used to: extract two curve segments from the detection data sequence; perform linear fitting on the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment; determine the mileage at the intersection of the two fitted straight lines of each curve segment as the feature point mileage of the corresponding curve segment; and determine the absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence as the measured mileage difference.

[0013] The benchmark data analysis module is used to: obtain the benchmark mileage of the geometric center point of each curve segment in two curve segments extracted from the detection data sequence in the ledger, and determine the absolute value of the difference between the benchmark mileages of the geometric center points of the two curve segments as the benchmark mileage difference.

[0014] The parameter correction module is used to update the TACH parameters by using the ratio of the measured mileage difference to the reference mileage difference as a correction factor.

[0015] 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-described automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle.

[0016] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle.

[0017] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle.

[0018] Compared with the existing technology of correcting TACH parameters based on manually marked feature points, the embodiments of the present invention utilize the geometric features of the detection data sequence to fit the transition curve of the curve segment of the detection data sequence into a straight line; and use the intersection of the fitted straight line and the geometric center point of the curve segment in the ledger to replace the manually marked curve feature points in the existing technology, thereby reducing the error caused by manually marked feature points, improving the accuracy and efficiency of TACH parameter updates, providing accurate parameters for the comprehensive inspection train in a timely manner, thereby improving the mileage accuracy of the detection data and providing more favorable technical support for railway infrastructure maintenance. Attached Figure Description

[0019] 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:

[0020] Figure 1 This is a flowchart of the automatic calculation method for TACH parameters of the high-speed comprehensive inspection vehicle in this embodiment of the invention;

[0021] Figure 2 This is an example of manually selecting superelevation curve feature points in existing technologies;

[0022] Figure 3 This is a flowchart illustrating a method for calculating the midpoint of a measured superelevation curve in an embodiment of the present invention.

[0023] Figure 4 This is an example diagram of the measured superelevation curve midpoint and the ledger superelevation curve midpoint in an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of the automatic TACH parameter calculation device for the high-speed comprehensive inspection vehicle in an embodiment of the present invention. Detailed Implementation

[0025] 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.

[0026] To overcome the limitations of existing technologies that correct TACH parameters based on manually labeled feature points, this invention proposes an automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle. Figure 1 This is a flowchart illustrating the automatic calculation method for TACH parameters of a high-speed comprehensive inspection vehicle in an embodiment of the present invention. Figure 1 As shown, the steps of this method are as follows:

[0027] Step 101: Obtain a detection data sequence containing at least two curve segments; wherein each curve segment contains two transition curves; the detection data includes ultra-high values;

[0028] Step 102: Extract two curve segments from the detection data sequence, perform linear fitting on the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment; determine the mileage at the intersection of the two fitted straight lines of each curve segment as the feature point mileage of the corresponding curve segment; determine the absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence as the measured mileage difference.

[0029] Step 103: Obtain the reference mileage of the geometric center point of each curve segment in the two curve segments extracted from the detection data sequence in the ledger, and determine the absolute value of the difference between the reference mileages of the geometric center points of the two curve segments as the reference mileage difference.

[0030] Step 104: Use the ratio of the measured mileage difference to the baseline mileage difference as a correction factor to update the TACH parameters.

[0031] Compared with the existing technology of correcting TACH parameters based on manually marked feature points, the embodiments of the present invention utilize the geometric features of the detection data sequence to fit the transition curve of the curve segment of the detection data sequence into a straight line; and use the intersection of the fitted straight line and the geometric center point of the curve segment in the ledger to replace the manually marked curve feature points in the existing technology, thereby reducing the error caused by manually marked feature points, improving the accuracy and efficiency of TACH parameter updates, providing accurate parameters for the comprehensive inspection train in a timely manner, thereby improving the mileage accuracy of the detection data and providing more favorable technical support for railway infrastructure maintenance.

[0032] First, superelevation, also known as "curved outer rail superelevation," refers to the height difference between the outer and inner rails on a curved railway track. When locomotives and rolling stock travel on curves, they generate centrifugal force, which puts significant pressure on the outer rail, causing severe lateral wear and discomfort for passengers. In severe cases, it can even lead to derailment accidents. Therefore, the outer rail must be raised to a certain level, using the centripetal force generated by the weight of the rolling stock to balance the centrifugal force.

[0033] Figure 2 An example image showing the manual selection of superelevation curve feature points. Figure 2 This demonstrates a waveform file of track geometry dynamic detection without odometer correction. The superelevation channel in this file corresponds one-to-one with the design superelevation parameters in the logbook. For example... Figure 2 As shown, the solid blue line represents the measured superelevation, the dashed red line represents the design superelevation in the logbook, and the diamond mark indicates the midpoint of the curve segment. A curve segment consists of two transition curves (a transition curve is a curve with continuously changing curvature placed between a straight line and a circular curve, or between two circular curves in a horizontal alignment; a transition curve is one of the elements of railway horizontal alignment, and it is a curve with continuously changing curvature placed between a straight line and a circular curve, or between two circular curves with significantly different radii and the same direction of rotation) and a straight circular section. In the superelevation waveform, the superelevation curves form a shape resembling a symmetrical trapezoid, with the two transition curves forming the legs of the symmetrical trapezoid, represented by two oblique straight lines (see...). Figure 2 The section indicated by the red elliptical dashed box in the middle), the circular curve is represented by the straight plateau section (see...). Figure 2 (The section indicated by the red dashed rectangle). Excluding the aforementioned transition curves and circular curves, the remaining sections are straight lines. The intersection of a straight line and a transition curve is the straight-to-transition point, as shown by the green dashed circle in the figure. The intersection of a transition curve and a circular curve is the transition-to-circle point, as shown by the black dashed circle in the figure.

[0034] Typically, the inspector manually selects the straight-to-curve point or the point of the curve segment in the measured superelevation waveform as a feature point, records the mileage of this feature point in the measured superelevation curve as K1', and the mileage of this feature point in the logbook as T1'. Then, in this track geometry waveform file without mileage correction, a feature point in another curve segment is selected, and the mileage of this feature point in the measured superelevation curve is recorded as K2', and the mileage of this feature point in the logbook as T2'. Then, the corresponding data of the manually selected feature point is substituted into formula (1) to update the TACH parameters. Formula (1) is shown below:

[0035]

[0036] In the above formula, New_TACH is the updated TACH parameter, and Old_TACH is the original TACH parameter.

[0037] However, manually selecting feature points relies on the experience of the inspectors, and the accuracy of the feature point locations still needs improvement. Furthermore, this process requires manual scrolling to confirm the mileage calibration points (feature points) between two consecutive curves, which is relatively inefficient. The automatic calculation method for TACH parameters of the high-speed integrated inspection vehicle proposed in this embodiment of the invention does not require locating the straight or curvature points of the superelevation curve, saving labor costs, and the TACH parameter correction results are more objective.

[0038] In this embodiment of the invention, a detection data sequence containing at least two curve segments is obtained; wherein each curve segment contains two transition curves; the detection data includes ultra-high values.

[0039] In one embodiment, a detection data sequence that meets the following conditions is obtained: it includes at least two curve segments, and the length of the detection data sequence is greater than a preset length; wherein, the first curve segment in the detection data sequence is the first curve segment, and the last curve segment in the detection data sequence is the second curve segment.

[0040] For example, firstly, the system automatically reads the track geometry dynamic detection waveform files and ledgers. Then, it filters out sections of the waveforms that have not undergone mileage calibration and marks these sections. Further filtering is then performed on these sections based on two criteria: ① the section contains more than two curve segments; ② the section's length is greater than 10 km. The filtered sections can also be sorted from longest to shortest length, and the top three sections that best represent the track conditions are selected for subsequent calculations.

[0041] In track geometry inspection, superelevation curves typically exhibit a symmetrical trapezoidal distribution (excluding the bottom platform section). Measured superelevation data consists of a series of discrete points, where the horizontal axis represents the mileage coordinates and the vertical axis represents the superelevation value. Based on the symmetrical characteristics of the superelevation curve, the horizontal coordinate of the intersection point of the extensions of the transition curves at both ends of the superelevation curve should theoretically be consistent with the mileage coordinate of the midpoint of the superelevation curve. Therefore, in this embodiment of the invention, the aforementioned intersection point is used instead of the straight transition point or the transition circle point.

[0042] In this embodiment of the invention, two curve segments are extracted from the detection data sequence, and straight lines are fitted to the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of each curve segment is determined as the feature point mileage of the corresponding curve segment; the absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence is determined as the measured mileage difference.

[0043] Figure 3This is a flowchart illustrating a method for calculating the midpoint of a measured superelevation curve according to an embodiment of the present invention. To minimize the error caused by changes in wheel diameter within a segment and improve the accuracy of correcting the TACH parameters, in one embodiment, the first curve segment is selected as the first curve segment, and the last curve segment is selected as the second curve segment, according to... Figure 3 The steps shown are used to obtain the mileage at the midpoint of the measured superelevation curve fitting:

[0044] Step 301: Extract the first curve segment and the second curve segment from the detection data sequence;

[0045] Step 302: Perform straight line fitting on the two transition curves of the first curve segment to obtain two fitted straight lines for each curve segment; determine the mileage at the intersection of the two fitted straight lines of the first curve segment as the feature point mileage of the first curve segment.

[0046] Step 303: Perform straight line fitting on the two transition curves of the second curve segment to obtain two fitted straight lines for each curve segment; determine the mileage at the intersection of the two fitted straight lines of the second curve segment as the feature point mileage of the second curve segment.

[0047] Step 304: Determine the absolute value of the difference between the feature point mileage of the first curve segment and the feature point mileage of the second curve segment as the measured mileage difference.

[0048] For example, perform the following operations on each of the three segments selected above: ① Mark the first and last curve segments in each segment; calculate the midpoints of the first and last curve segments in the segment (since the curve position shape in the superelevation curve is a symmetrical trapezoid, the midpoint of the curve is located on the symmetrical central axis of the trapezoid); combine the transition curves on both sides of a set of superelevation waveforms (including the first and last curves in a superelevation waveform) using the least squares method to obtain the linear equations of the fitted lines on both sides of the first curve and the last curve, and find the intersection point of the two fitted lines.

[0049] In the log, curve parameters typically include the curve's starting point, ending point, initial transition curve length, final transition curve length, and superelevation value of the outer rail of the circular curve. Therefore, based on the curve's starting point, ending point, initial transition curve length, and final transition curve length in the log, the initial positions of the two transition curves for each curve segment can be preliminarily located. Then, some data points before and after the preliminarily located starting positions are discarded to improve the accuracy of the straight line fitting for the transition curves.

[0050] In one embodiment, two curve segments are extracted from the detection data sequence to obtain a first transition curve and a second transition curve in each curve segment; according to a preset ratio, a first subsequence is extracted from the first transition curve of each curve segment, and a second subsequence is extracted from the second transition curve of each curve segment; wherein, the preset ratio is less than 1; for each curve segment, the first subsequence and the second subsequence are fitted with straight lines to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of each curve segment is determined as the feature point mileage of the corresponding curve segment.

[0051] Figure 4 This is an example diagram showing the measured superelevation curve midpoint and the ledger superelevation curve midpoint in an embodiment of the present invention.

[0052] For example, referring to the calculation method in Table 1, it can automatically locate the position of the curve segment transition curve based on the ledger, and automatically calculate the mileage corresponding to the midpoint position of the curve segment fitting.

[0053] Table 1

[0054]

[0055] In Table 1, the first transition curve of a segment of the superelevation waveform is (x1, y1), and the second transition curve is (x2, y2). The x-axis position (mileage) of the intersection point is the mileage of the midpoint of the curve segment. Figure 4 The location indicated by the red circle.

[0056] By using the curve parameters in the ledger, the mileage at the midpoint of the curve segment can be analyzed, reflecting the track conditions before the wheel diameter value was changed, and providing data for correcting the TACH parameters.

[0057] In this embodiment of the invention, the reference mileage of the geometric center point of each of the two curve segments extracted from the detection data sequence in the ledger is obtained, and the absolute value of the difference between the reference mileages of the geometric center points of the two curve segments is determined as the reference mileage difference.

[0058] In one embodiment, curve segment parameters of two curve segments extracted from the detection data sequence are obtained from the ledger; wherein, the curve segment parameters include: the reference mileage at the start point of the curve segment and the reference mileage at the end point of the curve segment; the average value of the reference mileage at the start point and the reference mileage at the end point of the curve segment are calculated respectively, and the average value of each curve segment is determined as the reference mileage of the geometric center point of the corresponding curve segment; the absolute value of the difference between the reference mileages of the geometric center points of the two curve segments is determined as the reference mileage difference. Figure 4 As shown, the position indicated by the blue diamond is the midpoint of the ledger curve segment, and the x-coordinate of the midpoint of the ledger curve segment is the same as the x-coordinate of the geometric center point of the curve segment.

[0059] In this embodiment of the invention, the ratio of the measured mileage difference to the reference mileage difference is used as a correction factor, and the TACH parameters are updated using the correction factor.

[0060] In one embodiment, according to formula (2), the ratio of the measured mileage difference to the reference mileage difference is used as a correction factor, and the TACH parameters are updated using the correction factor:

[0061]

[0062] In the above formula, New_TACH is the updated TACH parameter, Old_TACH is the original TACH parameter, |K2-K1| is the measured mileage difference, and |T2-T1| is the baseline mileage difference.

[0063] Let the mileages of the characteristic points of the curve segment be K1 and K2, and the mileages of the geometric center point of the ledger be T1 and T2. The above formula does not limit the relationship between K1 and K2, or between T1 and T2.

[0064] This invention also provides an automatic TACH parameter calculation device for a high-speed integrated inspection vehicle, as described in the following embodiments. Since the principle behind this device's problem-solving is similar to the automatic TACH parameter calculation method for a high-speed integrated inspection vehicle, its implementation can refer to the implementation of the automatic TACH parameter calculation method for a high-speed integrated inspection vehicle; repeated details will not be elaborated further.

[0065] Figure 5 This is a schematic diagram of the automatic TACH parameter calculation device for a high-speed comprehensive inspection vehicle in an embodiment of the present invention. Figure 5 As shown, the device includes:

[0066] The detection data filtering module 501 is used to: acquire a detection data sequence containing at least two curve segments; wherein each curve segment contains two transition curves; and the detection data includes ultra-high values;

[0067] The detection data analysis module 502 is used to: extract two curve segments from the detection data sequence; perform linear fitting on the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment; determine the mileage at the intersection of the two fitted straight lines of each curve segment as the feature point mileage of the corresponding curve segment; and determine the absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence as the measured mileage difference.

[0068] The benchmark data analysis module 503 is used to: obtain the benchmark mileage of the geometric center point of each curve segment in two curve segments extracted from the detection data sequence in the ledger, and determine the absolute value of the difference between the benchmark mileages of the geometric center points of the two curve segments as the benchmark mileage difference.

[0069] The parameter correction module 504 is used to update the TACH parameters by using the ratio of the measured mileage difference to the reference mileage difference as a correction factor.

[0070] In one embodiment, the detection data filtering module 501 is specifically used for:

[0071] Obtain a detection data sequence that meets the following conditions: includes at least two curve segments, and the length of the detection data sequence is greater than a preset length; wherein, the first curve segment in the detection data sequence is the first curve segment, and the last curve segment in the detection data sequence is the second curve segment.

[0072] In one embodiment, the detection data analysis module 501 is specifically used for:

[0073] Extract the first curve segment and the second curve segment from the detection data sequence;

[0074] Straight line fitting is performed on the two transition curves of the first curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of the first curve segment is determined as the feature point mileage of the first curve segment.

[0075] Straight line fitting is performed on the two transition curves of the second curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of the second curve segment is determined as the characteristic point mileage of the second curve segment.

[0076] The absolute value of the difference between the characteristic point mileage of the first curve segment and the characteristic point mileage of the second curve segment is determined as the measured mileage difference.

[0077] In one embodiment, the detection data analysis module 502 is specifically used for:

[0078] Two curve segments are extracted from the detection data sequence, and the first and second transition curves in each curve segment are obtained.

[0079] According to a preset ratio, a first subsequence is extracted from the first transition curve of each curve segment, and a second subsequence is extracted from the second transition curve of each curve segment; wherein, the preset ratio is less than 1;

[0080] For the first and second subsequences of each curve segment, straight line fitting is performed to obtain two fitted straight lines for each curve segment.

[0081] The mileage at the intersection of the two fitted straight lines of each curve segment is determined as the characteristic point mileage of the corresponding curve segment.

[0082] In one embodiment, the benchmark data analysis module 503 is used for:

[0083] Obtain the curve segment parameters of two curve segments extracted from the detection data sequence in the ledger; wherein, the curve segment parameters include: the reference mileage at the start point of the curve segment and the reference mileage at the end point of the curve segment;

[0084] Calculate the average of the reference mileage at the start point and the reference mileage at the end point of the two curve segments respectively, and determine the average value of each curve segment as the reference mileage of the geometric center point of the corresponding curve segment.

[0085] The absolute value of the difference between the reference mileages of the geometric center points of the two curve segments is determined as the reference mileage difference.

[0086] In one embodiment, the parameter correction module 504 is used for:

[0087] The TACH parameters are updated using the following formula, with the ratio of the measured mileage difference to the baseline mileage difference as a correction factor:

[0088]

[0089] In the above formula, New_TACH is the updated TACH parameter, Old_TACH is the original TACH parameter, |K2-K1| is the measured mileage difference, and |T2-T1| is the baseline mileage difference.

[0090] 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-described automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle.

[0091] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle.

[0092] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described automatic calculation method for TACH parameters of a high-speed integrated inspection vehicle.

[0093] Compared with the existing technology of correcting TACH parameters based on manually marked feature points, this invention selects sections in the track geometry dynamic detection waveform file that have not undergone mileage correction, and performs TACH parameter correction calculations for each section according to its specific working conditions. By utilizing the geometric features of the detection data sequence, the first and last curve segments in the current section are selected, and the transition curves of the first and last curve segments are fitted as straight lines. By discarding data points with a preset proportion before and after the transition curves, the accuracy of straight line fitting is further improved. The intersection of the fitted straight lines and the geometric center points of the curve segments in the ledger are used to replace the manually marked curve feature points in the existing technology, reducing the errors caused by manually marked feature points, improving the accuracy and efficiency of TACH parameter updates, and providing timely and accurate parameters for the comprehensive inspection train. This improves the mileage accuracy of the detection data and provides more favorable technical support for railway infrastructure maintenance.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 automatically calculating TACH parameters of a high-speed integrated inspection vehicle, characterized in that, include: Obtain a detection data sequence containing at least two curve segments; wherein each curve segment contains two transition curves; the detection data includes ultra-high values; Two curve segments are extracted from the detection data sequence. Straight lines are fitted to the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment. The mileage at the intersection of the two fitted straight lines of each curve segment is determined as the feature point mileage of the corresponding curve segment. The absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence is determined as the measured mileage difference. Obtain the reference mileage of the geometric center point of each curve segment from the two curve segments extracted from the detection data sequence in the ledger, and determine the absolute value of the difference between the reference mileages of the geometric center points of the two curve segments as the reference mileage difference. The ratio of the measured mileage difference to the baseline mileage difference is used as a correction factor, and the TACH parameters are updated using this correction factor.

2. The method as described in claim 1, characterized in that, Obtain a detection data sequence containing at least two curve segments, including: Obtain a detection data sequence that meets the following conditions: includes at least two curve segments, and the length of the detection data sequence is greater than a preset length; wherein, the first curve segment in the detection data sequence is the first curve segment, and the last curve segment in the detection data sequence is the second curve segment.

3. The method as described in claim 2, characterized in that, Two curve segments are extracted from the detection data sequence. Straight lines are fitted to the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment. The mileage at the intersection of the two fitted straight lines of each curve segment is determined as the feature point mileage of the corresponding curve segment. The absolute value of the difference in mileage between feature points of two curve segments extracted from the detection data sequence is determined as the measured mileage difference, including: Extract the first curve segment and the second curve segment from the detection data sequence; Straight line fitting is performed on the two transition curves of the first curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of the first curve segment is determined as the feature point mileage of the first curve segment. Straight line fitting is performed on the two transition curves of the second curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of the second curve segment is determined as the characteristic point mileage of the second curve segment. The absolute value of the difference between the characteristic point mileage of the first curve segment and the characteristic point mileage of the second curve segment is determined as the measured mileage difference.

4. The method as described in claim 1, characterized in that, Two curve segments are extracted from the detection data sequence. Straight line fitting is performed on the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment. The mileage at the intersection of the two fitted straight lines for each curve segment is determined as the feature point mileage of the corresponding curve segment, including: Two curve segments are extracted from the detection data sequence, and the first and second transition curves in each curve segment are obtained. According to a preset ratio, a first subsequence is extracted from the first transition curve of each curve segment, and a second subsequence is extracted from the second transition curve of each curve segment; wherein, the preset ratio is less than 1; For the first and second subsequences of each curve segment, straight line fitting is performed to obtain two fitted straight lines for each curve segment. The mileage at the intersection of the two fitted straight lines of each curve segment is determined as the characteristic point mileage of the corresponding curve segment.

5. The method as described in claim 1, characterized in that, Obtain the reference mileage of the geometric center point of each curve segment from the two curve segments extracted from the detection data sequence in the ledger. The absolute value of the difference between the reference mileages of the geometric center points of the two curve segments is determined as the reference mileage difference, including: Obtain the curve segment parameters of two curve segments extracted from the detection data sequence in the ledger; wherein, the curve segment parameters include: the reference mileage at the start point of the curve segment and the reference mileage at the end point of the curve segment; Calculate the average of the reference mileage at the start point and the reference mileage at the end point of the two curve segments respectively, and determine the average value of each curve segment as the reference mileage of the geometric center point of the corresponding curve segment. The absolute value of the difference between the reference mileages of the geometric center points of the two curve segments is determined as the reference mileage difference.

6. The method as described in claim 1, characterized in that, The ratio of the measured mileage difference to the baseline mileage difference is used as a correction factor. This correction factor is then used to update the TACH parameters, including: The TACH parameters are updated using the following formula, with the ratio of the measured mileage difference to the baseline mileage difference as a correction factor: In the above formula, New_TACH is the updated TACH parameter, Old_TACH is the original TACH parameter, |K2-K1| is the measured mileage difference, and |T2-T1| is the baseline mileage difference.

7. An automatic calculation device for TACH parameters of a high-speed integrated inspection vehicle, characterized in that, include: The detection data filtering module is used to: obtain a detection data sequence containing at least two curve segments; wherein each curve segment contains two transition curves; the detection data includes ultra-high values; The detection data analysis module is used to: extract two curve segments from the detection data sequence; perform linear fitting on the two transition curves of each curve segment to obtain two fitted straight lines for each curve segment; determine the mileage at the intersection of the two fitted straight lines of each curve segment as the feature point mileage of the corresponding curve segment; and determine the absolute value of the difference between the feature point mileages of the two curve segments extracted from the detection data sequence as the measured mileage difference. The benchmark data analysis module is used to: obtain the benchmark mileage of the geometric center point of each curve segment in two curve segments extracted from the detection data sequence in the ledger, and determine the absolute value of the difference between the benchmark mileages of the geometric center points of the two curve segments as the benchmark mileage difference. The parameter correction module is used to update the TACH parameters by using the ratio of the measured mileage difference to the reference mileage difference as a correction factor.

8. The apparatus as claimed in claim 7, characterized in that, The detection data filtering module is specifically used for: Obtain a detection data sequence that meets the following conditions: includes at least two curve segments, and the length of the detection data sequence is greater than a preset length; wherein, the first curve segment in the detection data sequence is the first curve segment, and the last curve segment in the detection data sequence is the second curve segment.

9. The apparatus as claimed in claim 8, characterized in that, The detection data analysis module is specifically used for: Extract the first curve segment and the second curve segment from the detection data sequence; Straight line fitting is performed on the two transition curves of the first curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of the first curve segment is determined as the feature point mileage of the first curve segment. Straight line fitting is performed on the two transition curves of the second curve segment to obtain two fitted straight lines for each curve segment; the mileage at the intersection of the two fitted straight lines of the second curve segment is determined as the characteristic point mileage of the second curve segment. The absolute value of the difference between the characteristic point mileage of the first curve segment and the characteristic point mileage of the second curve segment is determined as the measured mileage difference.

10. The apparatus as claimed in claim 7, characterized in that, The detection data analysis module is specifically used for: Two curve segments are extracted from the detection data sequence, and the first and second transition curves in each curve segment are obtained. According to a preset ratio, a first subsequence is extracted from the first transition curve of each curve segment, and a second subsequence is extracted from the second transition curve of each curve segment; wherein, the preset ratio is less than 1; For the first and second subsequences of each curve segment, straight line fitting is performed to obtain two fitted straight lines for each curve segment. The mileage at the intersection of the two fitted straight lines of each curve segment is determined as the characteristic point mileage of the corresponding curve segment.

11. The apparatus as claimed in claim 7, characterized in that, The benchmark data analysis module is used for: Obtain the curve segment parameters of two curve segments extracted from the detection data sequence in the ledger; wherein, the curve segment parameters include: the reference mileage at the start point of the curve segment and the reference mileage at the end point of the curve segment; Calculate the average of the reference mileage at the start point and the reference mileage at the end point of the two curve segments respectively, and determine the average value of each curve segment as the reference mileage of the geometric center point of the corresponding curve segment. The absolute value of the difference between the reference mileages of the geometric center points of the two curve segments is determined as the reference mileage difference.

12. The apparatus as claimed in claim 7, characterized in that, The parameter correction module is used for: The TACH parameters are updated using the following formula, with the ratio of the measured mileage difference to the baseline mileage difference as a correction factor: In the above formula, New_TACH is the updated TACH parameter, Old_TACH is the original TACH parameter, |K2-K1| is the measured mileage difference, and |T2-T1| is the baseline mileage difference.

13. 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 6.

14. 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 6.

15. 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 6.

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