Track line spacing measurement method, device, equipment, medium and computer program

Abstract

The invention relates to the technical field of data processing, in particular to a track line spacing measurement method, device and equipment, a medium and a computer program.The method comprises the steps that point cloud data of tracks are determined, and target positions of all side tracks of a target frame are determined based on the point cloud data; wherein the target position is used for indicating a calibration position of the track; based on the target position and the point cloud center of the previous frame of the target frame, determining the point cloud center of each edge track in the target frame; wherein the point cloud center is used for indicating the center position of each side track; determining an orbit center of the orbit in the target frame based on the point cloud center of the target frame; and under the condition that the target frame is determined to be the last frame of the track, determining the inter-track line spacing of the track based on the track center corresponding to each frame of the track.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, device, medium and computer program for measuring track spacing. Background Technology

[0002] Currently, the spacing between the up and down tracks is an important indicator for track condition maintenance. However, existing methods for measuring and evaluating the spacing are mostly done manually, resulting in overly discrete measurement points and insufficient efficiency and accuracy to meet the needs of track maintenance.

[0003] In related technologies, the measurement and evaluation of track spacing is often obtained through manual measurement. However, manual measurement is slow, the measurement points are too sparse, the detection accuracy is low, the analysis and feedback cycle is long, and it cannot reflect the track status information in a timely manner. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, medium, and computer program for measuring track spacing to overcome the problem of low detection accuracy.

[0005] In a first aspect, this disclosure provides a method for measuring the spacing between track lines, including: The point cloud data of the track is determined, and the target position of each side track of the target frame is determined based on the point cloud data; wherein the target position is used to indicate the calibration position of the track; Based on the target location and the point cloud center of the previous frame of the target frame, the point cloud center of each side track in the target frame is determined; wherein, the point cloud center is used to indicate the center position of each side track. Based on the point cloud center of the target frame, the orbit is determined to be at the orbit center of the target frame; If the target frame is determined to be the last frame of the track, the track spacing is determined based on the track center corresponding to each frame of the track.

[0006] In some embodiments, after determining the orbit center of the target frame based on the point cloud center of the target frame, the method further includes: If it is determined that the target frame is not the last frame of the track, the track spacing between the target frames is determined based on the track center corresponding to the target frame. The next frame of the target frame is determined as the target frame, and the process returns to the step of determining the target position of each side track of the target frame based on the point cloud data.

[0007] In some embodiments, determining the center of each side track in the point cloud of the target frame based on the target location and the point cloud center of the previous frame of the target frame includes: Determine the first point cloud data of each side track within a preset range; Filter out interfering point cloud data from the first point cloud data to obtain the second point cloud data; The second point cloud data is processed to obtain the point cloud center of each side track in the target frame.

[0008] In some embodiments, processing the second point cloud data to obtain the center of the point cloud of each side track in the target frame includes: Based on the second point cloud data, determine the straight line vector data of each side track; Based on the endpoints of the straight line vector data, the center of the point cloud of each side track in the target frame is determined.

[0009] In some embodiments, determining the orbit's center in the target frame based on the point cloud center of the target frame includes: When the tracks corresponding to the target frame are symmetrical, the average value of the point cloud center of each side track in the target frame is determined as the first track center; The center of the first track is taken as the center of the track in the target frame.

[0010] In some embodiments, determining the orbit's center in the target frame based on the point cloud center of the target frame includes: In the case that the track corresponding to the target frame is asymmetrical, track offset data is determined; wherein, the track offset data is used to indicate the degree of offset of the track in each direction; The first orbital center is corrected based on the orbital offset data to obtain the second orbital center; The center of the second track is taken as the center of the track in the target frame.

[0011] Secondly, this disclosure provides a measuring device for track line spacing, comprising: The first determining module is used to determine the point cloud data of the track and determine the target position of each side track of the target frame based on the point cloud data; wherein the target position is used to indicate the calibration position of the track; The second determining module is used to determine the point cloud center of each side track in the target frame based on the target position and the point cloud center of the previous frame of the target frame; wherein, the point cloud center is used to indicate the center position of each side track. The third determining module is used to determine the orbit center of the target frame based on the point cloud center of the target frame; The fourth determining module is used to determine the inter-track spacing of the track based on the track center corresponding to each frame of the track when the target frame is determined to be the last frame of the track.

[0012] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0013] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0014] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0015] This disclosure provides a method, apparatus, device, medium, and computer program for measuring track spacing. By determining track point cloud data, it accurately determines the target position of each track edge in a target frame. Compared to manual measurement, point cloud data comprehensively and densely covers the track area, avoiding the problem of discrete measurement points. Based on the target position and the center of the point cloud in the previous frame, the center of the target frame's point cloud is determined, and then the track center is further determined, until the track spacing is determined in the last frame. The entire process, through a series of precise calculations and processing, can obtain continuous and accurate track center position information.

[0016] This technical solution improves the efficiency of track spacing measurement through automated calculation and processing, enabling the processing of large amounts of track data. Based on point cloud data and precise calculations, the measurement points are denser and more comprehensive, significantly improving measurement accuracy and reflecting the actual state of track spacing more accurately. Since it no longer relies on manual operation and lengthy analysis processes, track spacing data can be obtained promptly, shortening the analysis and feedback cycle. This allows for timely reflection of rail status information, providing timely and accurate data support for track maintenance work and effectively ensuring the safety and stability of track operation. Attached Figure Description

[0017] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a method for measuring track spacing provided in an embodiment of the present disclosure.

[0018] Figure 2This is a schematic diagram of the overall process of a method for measuring track spacing provided in an embodiment of this disclosure.

[0019] Figure 3 This is a schematic diagram of a track spacing measuring device provided in an embodiment of the present disclosure.

[0020] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present disclosure.

[0021] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0025] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0026] 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.

[0027] Research has found that the spacing between the up and down tracks is an important indicator for track condition maintenance. However, the existing methods for measuring and evaluating the spacing are mostly done manually, which results in overly discrete measurement points and an efficiency and accuracy that are far from meeting the needs of track maintenance.

[0028] In related technologies, the measurement and evaluation of track spacing is often obtained through manual measurement. However, manual measurement is slow, the measurement points are too sparse, the detection accuracy is low, the analysis and feedback cycle is long, and it cannot reflect the track status information in a timely manner.

[0029] Based on the above research, a method for measuring track spacing is proposed. This method uses track point cloud data to accurately determine the target positions of each track edge in the target frame. Compared to manual measurement, point cloud data can comprehensively and densely cover the track area, avoiding the problem of discrete measurement points. The target frame's point cloud center is determined based on the target position and the center of the previous frame's point cloud, and then the track center is further determined, until the track spacing is determined in the last frame. This entire process, through a series of precise calculations and processing, can obtain continuous and accurate track center position information.

[0030] This technical solution improves the efficiency of track spacing measurement through automated calculation and processing, enabling the processing of large amounts of track data. Based on point cloud data and precise calculations, the measurement points are denser and more comprehensive, significantly improving measurement accuracy and reflecting the actual state of track spacing more accurately. Since it no longer relies on manual operation and lengthy analysis processes, track spacing data can be obtained promptly, shortening the analysis and feedback cycle. This allows for timely reflection of rail status information, providing timely and accurate data support for track maintenance work and effectively ensuring the safety and stability of track operation.

[0031] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0032] Example 1 Figure 1 This is a schematic flowchart illustrating a method for measuring track spacing according to an embodiment of this disclosure. Figure 1 As shown, a method for measuring track spacing includes: S101. Determine the point cloud data of the track, and determine the target position of each side track of the target frame based on the point cloud data; wherein the target position is used to indicate the calibration position of the track.

[0033] In the embodiments of this disclosure, point cloud data of the track can be obtained by measuring with a lidar. Here, the lidar acquires the geometric information of the environment by measuring the returned laser beam, generating point cloud data composed of a large number of three-dimensional coordinate points. The lidar can be mounted on the front of a train or on a mobile track scanning vehicle to scan the track and acquire track point cloud data during train operation.

[0034] Point cloud data is key data for describing the spatial morphology of the orbit.

[0035] After determining the point cloud data of the track, the target position of each side track in the target frame can be calibrated based on the point cloud data in the target frame.

[0036] The track includes the left track (abbreviated as left rail) and the right track (abbreviated as right rail), which are the side tracks.

[0037] Here, when the target frame is the first frame, the target positions of the left and right tracks in the target frame can be marked based on the track characteristics.

[0038] Track features include, but are not limited to: track outline, rail edges, etc.

[0039] Here, if the target frame is not the first frame, the target positions of the left and right rails in the target frame can be determined based on the positions of the left and right rails marked in the first frame.

[0040] S102. Based on the target position and the point cloud center of the previous frame of the target frame, determine the point cloud center of each side track in the target frame; wherein, the point cloud center is used to indicate the center position of each side track.

[0041] In the embodiments of this disclosure, the first point cloud data corresponding to the target frame within a preset radius can be determined with the left track position in the target frame as the center; the second point cloud data corresponding to the target frame within a preset radius can be determined with the right track position in the target frame as the center.

[0042] Here, the first point cloud data and the second point cloud data can be processed separately to obtain the first point cloud center of the left track and the second point cloud center of the right track, and the first point cloud center and the second point cloud center are determined as the point cloud centers of each side track.

[0043] S103. Based on the point cloud center of the target frame, determine the orbit center of the target frame.

[0044] In embodiments of this disclosure, the geometric center of the track corresponding to the target frame can be determined based on the first point cloud center and the second point cloud center.

[0045] Then, the geometric center can be determined as the center of the track in the target frame.

[0046] S104. If the target frame is determined to be the last frame of the track, the track spacing is determined based on the track center corresponding to each frame of the track.

[0047] In embodiments of this disclosure, the centerline of the track can be determined based on the track center. The centerline is a one-dimensional curve representing the geometric orientation of the track along its mileage direction.

[0048] After determining the centerline, the data on the impact of track offset are determined, and the offset impact deviation is identified. The offset impact data includes at least one of the following: different wear levels between the left and right rails, track twisting, or laying deviation.

[0049] Then, the track center can be processed based on the offset effect deviation to obtain the track spacing between the tracks.

[0050] Here, with the left and right rails symmetrical, the offset effect deviation is 0.

[0051] In the embodiments of this disclosure, firstly, point cloud data of the track is determined, and target positions of each side track of the target frame are determined based on the point cloud data; wherein, the target positions are used to indicate the calibration positions of the track; secondly, based on the target positions and the point cloud center of the previous frame of the target frame, the point cloud center of each side track in the target frame is determined; wherein, the point cloud center is used to indicate the center position of each side track; secondly, based on the point cloud center of the target frame, the track center in the target frame is determined; finally, if the target frame is determined to be the last frame of the track, the track spacing is determined based on the track center corresponding to each frame of the track.

[0052] In the above implementation, the target position of each side of the track in the target frame is accurately determined by determining the track point cloud data. Compared with manual measurement, the point cloud data can comprehensively and densely cover the track area, avoiding the problem of discrete measurement points. Based on the target position and the center of the point cloud of the previous frame, the center of the target frame point cloud is determined, and then the track center is further determined, until the spacing between the tracks is determined in the last frame. The entire process, through a series of precise calculations and processing, can obtain continuous and accurate track center position information.

[0053] This technical solution improves the efficiency of track spacing measurement through automated calculation and processing, enabling the processing of large amounts of track data. Based on point cloud data and precise calculations, the measurement points are denser and more comprehensive, significantly improving measurement accuracy and reflecting the actual state of track spacing more accurately. Since it no longer relies on manual operation and lengthy analysis processes, track spacing data can be obtained promptly, shortening the analysis and feedback cycle. This allows for timely reflection of rail status information, providing timely and accurate data support for track maintenance work and effectively ensuring the safety and stability of track operation.

[0054] Example 2 Based on the above embodiments, after determining the orbit center of the target frame based on the point cloud center of the target frame, the following steps are further included: First, if it is determined that the target frame is not the last frame of the track, the track spacing of the target frame is determined based on the track center corresponding to the target frame. Then, the next frame of the target frame is determined as the target frame, and the process returns to the step of determining the target position of each side track of the target frame based on the point cloud data.

[0055] In the embodiments of this disclosure, determining that the target frame is not the last frame of the track can be understood as the target frame being the first frame or an intermediate frame.

[0056] Here, the spacing between tracks of the target frame can be determined solely based on the track center corresponding to the target frame. If the target frame is an intermediate frame, the spacing between tracks of the target frame can be determined based on the track center corresponding to the target frame and the track centers corresponding to the frames preceding the target frame.

[0057] Here, after determining the track spacing of the target frame, the target position, point cloud center, and track center of the subsequent frames can be used until the target frame is the last frame. Based on the track center of each frame, the track spacing between the tracks can be determined.

[0058] Example 3 Based on the above embodiments, determining the center of each side track in the point cloud of the target frame based on the target location and the point cloud center of the previous frame of the target frame specifically includes the following steps: First, determine the first point cloud data of each side track within a preset range; Secondly, the interfering point cloud data in the first point cloud data is filtered out to obtain the second point cloud data; Finally, the second point cloud data is processed to obtain the point cloud center of each side track in the target frame.

[0059] In the embodiments of this disclosure, the preset range is a preset radius range. Those skilled in the art can set the preset range based on actual needs, which will not be elaborated here.

[0060] Here, firstly, the point cloud data of the target frame can be preprocessed to remove noise points in the point cloud data and focus on the track region of the target frame.

[0061] The point cloud data of the target frame can be preprocessed by cleaning the point cloud data and extracting the ROI (Region of Interest) to obtain the first point cloud center.

[0062] Point cloud data can be cleaned through downsampling and filtering. Voxel grid filtering can be used to reduce point cloud density and computational cost. Then, statistical outlier removal can be used to remove noise points that deviate significantly from the track shape (such as weeds or gravel outside the track).

[0063] Among them, the lateral range of the track (e.g., ±0.8m on the Y-axis) and the longitudinal range (e.g., a single frame corresponds to a 10-meter track) can be defined based on mileage data and prior knowledge of track width (e.g., standard track gauge 1435mm), and point clouds outside the track (e.g., utility poles, slope points) can be filtered out.

[0064] Here, after preprocessing the point cloud data, the point cloud data of the left and right tracks in the target frame can be segmented to obtain the second point cloud data.

[0065] Here, the second point cloud data of each side can be determined based on the geometric characteristics of the first point cloud data of each side.

[0066] First, the linear equations of a single track can be fitted using least-squares fitting of the spatial linear equations to determine the linear equations of each side track. Then, the center point of each side track can be determined based on the linear equations of each side track.

[0067] Here, the center point of each side track can be determined as follows: based on the straight line equation of each side track, determine the start and end points of each side track interval in the target frame, and determine the start and end points of each side track interval in the target frame as the center of the second point cloud.

[0068] Here, the first point cloud data on each side can also be clustered to determine the center of the second point cloud.

[0069] The first point cloud data can be clustered in the following way: First, project the first point cloud data onto a plane perpendicular to the track direction (YZ plane), cluster according to the Y coordinate (the Y value of the left track is smaller and the Y value of the right track is larger), and set a threshold (such as Y=0 as the center, 0.7m to the left and right) to divide the left and right track point clouds.

[0070] Secondly, a distance threshold (e.g., 0.5m) is set to cluster points that are close together in space into one class. Based on the parallel characteristics of the two tracks, two sets of parallel point cloud clusters (left track and right track) are selected. Finally, the two sets of parallel point cloud clusters can be identified as the second point cloud data.

[0071] Here, the second point cloud data is processed to obtain the point cloud center of each side track in the target frame, specifically including the following steps: First, based on the second point cloud data, the straight line vector data of each side track is determined; Secondly, based on the endpoints of the straight line vector data, the center of the point cloud of each side track in the target frame is determined.

[0072] In the embodiments of this disclosure, when the second point cloud data is determined based on the geometric features of the first point cloud data of each side, the mean value of the second point cloud data can be determined, and the mean value of the second point cloud data can be determined as the point cloud center of each side track in the target frame.

[0073] First, the second point cloud data can be vectorized to obtain the straight line vector data of each side track.

[0074] Then, the start and end points of each vectorized track interval can be determined as the endpoints of the straight line vector data of each track interval.

[0075] Here, the sum of the start and end points of each vectorized track interval can be determined. Then, the sum of each track interval can be averaged (i.e., the sum of the start and end points of each vectorized track interval can be divided by 2) to obtain the point cloud center of each track in the target frame.

[0076] Here, when the center of the second point cloud is determined by clustering the first point cloud data of each side track, the center of the point cloud of each side track in the target frame can be determined in the following way.

[0077] First, straight line fitting can be performed based on the second point cloud data to determine the straight line vector data of each side track. Then, the endpoints of the straight line vector data of each side track (i.e., the start and end points of the straight line vector data) can be determined. Finally, based on the endpoints of the straight line vector data of each side track, the center of the point cloud of each side track in the target frame can be determined.

[0078] Example 4 Based on the above embodiments, determining the orbit's center in the target frame based on the point cloud center of the target frame specifically includes the following steps: First, when the tracks corresponding to the target frame are symmetrical, the average value of the point cloud center of each side track in the target frame is determined as the first track center; Then, the center of the first track is taken as the track center of the target frame.

[0079] In the embodiments of this disclosure, the mean value of the point cloud center of each side track can be determined as the track center of the target frame.

[0080] Here, the center of the first orbit, D1, can be determined as follows: D1 = (Z + Y) / 2; where Z is the center vector of the point cloud on the left track and Y is the center vector of the point cloud on the right track.

[0081] The center of the point cloud can be in coordinate form, for example, the point cloud coordinates of the left track are z=(x1, y2), where x1 is the x-coordinate of the center of the left track point cloud and y1 is the y-coordinate of the center of the left track point cloud.

[0082] The point cloud coordinates of the right track are y = (x2, y2), where x2 is the x-coordinate of the center of the right track point cloud and y1 is the y-coordinate of the center of the right track point cloud.

[0083] Then, the point cloud center x of the left track and the point cloud center y of the right track can be vectorized to obtain the point cloud center vector Z of the left track and the point cloud center vector Y of the right track.

[0084] Here, determining the orbit's center in the target frame based on the point cloud center of the target frame specifically includes the following steps: First, in the case that the track corresponding to the target frame is asymmetrical, track offset data is determined; wherein, the track offset data is used to indicate the degree of offset of the track in each direction; Secondly, the first orbital center is corrected based on the orbital offset data to obtain the second orbital center; Finally, the center of the second track is taken as the track center of the target frame.

[0085] In the embodiments of this disclosure, horizontal offset, vertical height difference, and cross-sectional shape differences of the track can all lead to track asymmetry.

[0086] Here, when the track is asymmetrical due to horizontal offset, the point cloud center can be processed by the standard track gauge value constraint to determine the track center of the target frame.

[0087] For example, firstly, the track gauge can be determined using the point cloud centers of the left and right tracks. Secondly, the track gauge deviation can be determined based on the actual track gauge. Thirdly, the track gauge deviation can be corrected to obtain the corrected track center. Finally, the track center of the target frame can be determined based on the corrected track center.

[0088] Here, when the orbit is asymmetrical due to the vertical height difference, the orbit center of the target frame can be determined by weighted averaging of the point cloud centers.

[0089] Here, in the case of track asymmetry due to differences in cross-sectional shape, the track center of the target frame can be determined in the following way.

[0090] First, kernel density is estimated based on the point cloud centers of the left and right tracks. Second, the point cloud distribution probability density of the target frame is determined based on the kernel density estimation. Finally, a weighted average is performed based on the point cloud distribution density to obtain the track center of the target frame.

[0091] Example 5 Based on the above embodiments, this embodiment provides an application example.

[0092] Reference Figure 2 The diagram shown is an overall flowchart illustrating a method for measuring track spacing according to an embodiment of this disclosure, wherein: S10. Based on the point cloud data of the first frame of the track, and determine the first frame as the target frame, determine the target position of each side track of the target frame.

[0093] S20. Based on the target location, determine the center of the point cloud of each side track in the target frame.

[0094] S30. Based on the point cloud center of the target frame, determine the orbit center of the target frame.

[0095] S40. Determine the spacing between the tracks in the target frame based on the track center.

[0096] S50: Read the point cloud data of the next frame and determine the next frame as the target frame.

[0097] S60. Determine if the target frame is the last frame.

[0098] S70. If the target frame is not the last frame, return to the step of determining the point cloud center of each side track in the target frame; otherwise, if the target frame is the last frame, determine the track spacing of the target frame and determine the track spacing based on the track center of all frames.

[0099] In the above implementation, the target position of each side of the track in the target frame is accurately determined by determining the track point cloud data. Compared with manual measurement, the point cloud data can comprehensively and densely cover the track area, avoiding the problem of discrete measurement points. Based on the target position and the center of the point cloud of the previous frame, the center of the target frame point cloud is determined, and then the track center is further determined, until the spacing between the tracks is determined in the last frame. The entire process, through a series of precise calculations and processing, can obtain continuous and accurate track center position information.

[0100] This technical solution improves the efficiency of track spacing measurement through automated calculation and processing, enabling the processing of large amounts of track data. Based on point cloud data and precise calculations, the measurement points are denser and more comprehensive, significantly improving measurement accuracy and reflecting the actual state of track spacing more accurately. Since it no longer relies on manual operation and lengthy analysis processes, track spacing data can be obtained promptly, shortening the analysis and feedback cycle. This allows for timely reflection of rail status information, providing timely and accurate data support for track maintenance work and effectively ensuring the safety and stability of track operation.

[0101] Example 6 Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0102] Based on the same inventive concept, this disclosure also provides a track line spacing measuring device corresponding to the track line spacing measuring method. Since the principle of the device in this disclosure for solving the problem is similar to the track line spacing measuring method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0103] Reference Figure 3 The diagram shown is a schematic of a track spacing measuring device provided in an embodiment of this disclosure. The device includes: a first determining module 31, a second determining module 32, a third determining module 33, and a fourth determining module 34; wherein: The first determining module is used to determine the point cloud data of the track and determine the target position of each side track of the target frame based on the point cloud data; wherein the target position is used to indicate the calibration position of the track; The second determining module is used to determine the point cloud center of each side track in the target frame based on the target position and the point cloud center of the previous frame of the target frame; wherein, the point cloud center is used to indicate the center position of each side track. The third determining module is used to determine the orbit center of the target frame based on the point cloud center of the target frame; The fourth determining module is used to determine the inter-track spacing of the track based on the track center corresponding to each frame of the track when the target frame is determined to be the last frame of the track.

[0104] This embodiment of the disclosure determines the target position of each side of the track in the target frame by determining the track point cloud data. Compared with manual measurement, the point cloud data can comprehensively and densely cover the track area, avoiding the problem of discrete measurement points. Based on the target position and the center of the point cloud of the previous frame, the center of the target frame point cloud is determined, and then the track center is further determined, until the spacing between the tracks is determined in the last frame. The entire process, through a series of precise calculations and processing, can obtain continuous and accurate track center position information.

[0105] This technical solution improves the efficiency of track spacing measurement through automated calculation and processing, enabling the processing of large amounts of track data. Based on point cloud data and precise calculations, the measurement points are denser and more comprehensive, significantly improving measurement accuracy and reflecting the actual state of track spacing more accurately. Since it no longer relies on manual operation and lengthy analysis processes, track spacing data can be obtained promptly, shortening the analysis and feedback cycle. This allows for timely reflection of rail status information, providing timely and accurate data support for track maintenance work and effectively ensuring the safety and stability of track operation.

[0106] In one possible implementation, the fourth determining module is further configured to: determine the inter-track spacing of the target frame based on the track center corresponding to the target frame when it is determined that the target frame is not the last frame of the track; The next frame of the target frame is determined as the target frame, and the process returns to the step of determining the target position of each side track of the target frame based on the point cloud data.

[0107] In one possible implementation, the second determining module is further configured to: determine the first point cloud data of each side track within a preset range; Filter out interfering point cloud data from the first point cloud data to obtain the second point cloud data; The second point cloud data is processed to obtain the point cloud center of each side track in the target frame.

[0108] In one possible implementation, the second determining module is specifically used to: determine the straight line vector data of each side track based on the second point cloud data; Based on the endpoints of the straight line vector data, the center of the point cloud of each side track in the target frame is determined.

[0109] In one possible implementation, the third determining module is further configured to: determine the average value of the point cloud center of each side track in the target frame as the first track center when the track corresponding to the target frame is symmetrical; The center of the first track is taken as the center of the track in the target frame.

[0110] In one possible implementation, the third determining module is specifically used to: determine track offset data when the track corresponding to the target frame is asymmetrical; wherein the track offset data is used to indicate the degree of offset of the track in each direction; The first orbital center is corrected based on the orbital offset data to obtain the second orbital center; The center of the second track is taken as the center of the track in the target frame.

[0111] Example 7 Corresponding to Figure 1 In addition to the method for measuring the track spacing, this disclosure also provides an electronic device 400, such as... Figure 4 The diagram shown is a structural schematic of an electronic device 400 provided in an embodiment of this disclosure, including: The system includes a processor 41, a memory 42, a communication bus 43, a communication interface 44, and a human-machine interface 45. The memory 42 stores execution instructions and includes both main memory and external memory. The communication interface 44 is communicatively connected to a measurement unit 441, which includes a lidar 442 for measuring point cloud data of the track. The human-machine interface 45 communicates with the processor 41 via the communication bus 43, allowing the user to interact with the processor through the interface. When the electronic device is running, the processor 41 communicates with the memory 42 via the bus 43, causing the processor 41 to execute the following instructions: The point cloud data of the track is determined, and the target position of each side track of the target frame is determined based on the point cloud data; wherein the target position is used to indicate the calibration position of the track; Based on the target location and the point cloud center of the previous frame of the target frame, the point cloud center of each side track in the target frame is determined; wherein, the point cloud center is used to indicate the center position of each side track. Based on the point cloud center of the target frame, the orbit is determined to be at the orbit center of the target frame; If the target frame is determined to be the last frame of the track, the track spacing is determined based on the track center corresponding to each frame of the track.

[0112] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0113] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0114] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0115] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0116] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, Blu-ray discs, etc.).

[0117] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0118] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0119] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0120] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0121] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0122] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0123] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for measuring track line spacing, characterized in that, include: The point cloud data of the track is determined, and the target position of each side track of the target frame is determined based on the point cloud data; wherein the target position is used to indicate the calibration position of the track; Based on the target location and the point cloud center of the previous frame of the target frame, the point cloud center of each side track in the target frame is determined; wherein, the point cloud center is used to indicate the center position of each side track. Based on the point cloud center of the target frame, the orbit is determined to be at the orbit center of the target frame; If the target frame is determined to be the last frame of the track, the track spacing is determined based on the track center corresponding to each frame of the track.

2. The method according to claim 1, characterized in that, After determining the orbit's center in the target frame based on the point cloud center of the target frame, the method further includes: If it is determined that the target frame is not the last frame of the track, the track spacing between the target frames is determined based on the track center corresponding to the target frame. The next frame of the target frame is determined as the target frame, and the process returns to the step of determining the target position of each side track of the target frame based on the point cloud data.

3. The method according to claim 1, characterized in that, Determining the center of each side track in the point cloud of the target frame based on the target location and the point cloud center of the previous frame of the target frame includes: Determine the first point cloud data of each side track within a preset range; Filter out interfering point cloud data from the first point cloud data to obtain the second point cloud data; The second point cloud data is processed to obtain the point cloud center of each side track in the target frame.

4. The method according to claim 3, characterized in that, The process of processing the second point cloud data to obtain the center of each side track in the point cloud of the target frame includes: Based on the second point cloud data, determine the straight line vector data of each side track; Based on the endpoints of the straight line vector data, the center of the point cloud of each side track in the target frame is determined.

5. The method according to claim 1, characterized in that, Determining the orbit's center in the target frame based on the point cloud center of the target frame includes: When the tracks corresponding to the target frame are symmetrical, the average value of the point cloud center of each side track in the target frame is determined as the first track center; The center of the first track is taken as the center of the track in the target frame.

6. The method according to claim 5, characterized in that, Determining the orbit's center in the target frame based on the point cloud center of the target frame includes: In the case that the track corresponding to the target frame is asymmetrical, track offset data is determined; wherein, the track offset data is used to indicate the degree of offset of the track in each direction; The first orbital center is corrected based on the orbital offset data to obtain the second orbital center; The center of the second track is taken as the center of the track in the target frame.

7. A measuring device for track line spacing, characterized in that, include: The first determining module is used to determine the point cloud data of the track and determine the target position of each side track of the target frame based on the point cloud data; wherein the target position is used to indicate the calibration position of the track; The second determining module is used to determine the point cloud center of each side track in the target frame based on the target position and the point cloud center of the previous frame of the target frame; wherein, the point cloud center is used to indicate the center position of each side track. The third determining module is used to determine the orbit center of the target frame based on the point cloud center of the target frame; The fourth determining module is used to determine the inter-track spacing of the track based on the track center corresponding to each frame of the track when the target frame is determined to be the last frame of the track.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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