High-speed rail fastener pre-configuration method based on virtual rail simulation

Abstract

The invention discloses a high-speed rail fastener pre-configuration method based on virtual rail simulation. The high-speed rail fastener pre-configuration method comprises the steps that S1, total element point cloud on a rail plate of a line to be finely adjusted is collected; s2, performing noise removal and data cleaning on the total element point cloud by using a filtering algorithm; s3, segmenting a target area; s4, characterizing the point cloud pose of the rail bearing table: subdividing the point cloud structure of the rail bearing table; carrying out point cloud attitude extraction on the rail bearing table; extracting the center position of the point cloud of the rail bearing table; s5, establishing a virtual track environment; s6, track simulation fine adjustment: calculating a TQI value of a target unit section; simulating a fine adjustment principle; judging whether the TQI value conforms to an acceptance index or not and outputting a simulation fine adjustment scheme; establishing an LP model; non-linearly optimizing and simulating the change quantity Z of the total adjustment quantity in the fine adjustment scheme; and S7, converting the simulation fine adjustment scheme into a buckle fitting pre-configuration scheme. According to the method, the construction period of the high-speed railway is shortened, the laying cost is reduced, and the construction quality and safety are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-speed railway ballastless track construction, and in particular relates to a high-speed railway fastener pre-configuration method based on virtual track simulation. Background Art

[0002] In order to make the high-speed railway track meet the conditions for the smooth and safe operation of high-speed trains, it is necessary to measure the smoothness of the high-speed railway track during the construction period and operation period, and fine-tune the long rails by adjusting the combination of fasteners and structural parts, so as to adjust the horizontal and vertical offsets of the long rails and ensure that the track smoothness is qualified. The traditional fastener configuration scheme is to install standard fasteners on each sleeper along the entire line before laying the rails, and perform static inspection of the track and reconfigure the fastener scheme after the long rails are stressed and locked. Among them, the actual situation of the rail support platform is not considered during the first measurement of the track, and a standard fastener system is uniformly used for installation. After the track is laid, a "fixed tooling" is used to obtain the track measurement data, and then the specifications of the adjustment parts are calculated, the fastener system is updated and the track data is remeasured, and the adjustment parts specifications are repeatedly calculated and the fastener system is updated until the track smoothness meets the requirements. Adjustment parts refer to structural parts that need to be adjusted, and structural parts are divided into different models.

[0003] The above solution has the following problems:

[0004] (1) The time efficiency of the working mode is low, which greatly slows down the construction progress. At the same time, changes in environmental conditions (temperature, light, external force, accidental interference) over a long period of time will also affect the accuracy and precision of the measurement results, thereby affecting the design effectiveness of the fine-tuning plan. Repeated operations will result in low fine-tuning efficiency.

[0005] (2) In addition to the reduction in manpower and material costs due to improved measurement efficiency, one of the economic costs that accounts for a relatively high proportion in the current working conditions is the cost of replacing standard fasteners with fine-tuning fasteners during the fine-tuning of long rails. In a certain project, the average replacement rate of insulating blocks during the fine-tuning of long rails was 95%, the average replacement rate of gauge blocks was 12%, and the average replacement rate of rail pads was 205%. The replacement cost was about RMB 93,000 per kilometer of track laying. Therefore, it is particularly important to carry out laying environment measurements before laying long rails, especially precise measurements of track plates and rail supports, and to carry out simulation fine-tuning and scheme simulation.

[0006] (3) At present, the measurement mode of "CPIII + total station + track support frame" is still used from the stage of track plate fine adjustment to track laying. Under this working mode, on the one hand, the measurement accuracy is severely limited by multiple factors such as the accuracy of CPIII point verification, the tolerance of total station setting and the overlap accuracy between multiple stations, the placement deviation of the track support frame and manual measurement errors; on the other hand, the large-scale three-dimensional spatial information is replaced by local sparse point information, which directly affects the coverage completeness, information distribution uniformity and feature description ability of the measurement results.

[0007] (4) The current measurement and fine-tuning perspective is still relatively small, focusing mainly on the local smoothness of the line and the static smoothness of the entire line, and it is impossible to intervene in the dynamic joint debugging and testing process in advance. Summary of the invention

[0008] In order to solve the problems existing in the prior art, the present invention provides a high-speed rail fastener pre-configuration method based on virtual track with high construction efficiency, low fastener replacement rate and low laying cost. The method uses virtual track simulation technology to achieve track scene reconstruction and precise configuration of fasteners during the laying stage of long steel rails on ballastless track of high-speed lines, thereby greatly reducing construction costs.

[0009] A high-speed rail fastener preconfiguration method based on virtual track simulation, comprising:

[0010] S1, data collection: collect and obtain the full-element point cloud on the track plate of the line to be fine-tuned, including the track plate point cloud, the track support platform point cloud and the CPIII point cloud;

[0011] S2, noise filtering: using a filtering algorithm to remove noise and clean the data of the full-factor point cloud;

[0012] S3, target area segmentation: the point cloud obtained in S2 is initially segmented into multiple sub-point clouds to form a sub-point cloud set, wherein each sub-point cloud contains only one rail support; the coordinates of each point in all sub-point clouds in the sub-point cloud set are converted to the virtual track coordinate system with the help of the CPIII control network; the rail support in each sub-point cloud is numbered and recorded; the rail support point cloud is extracted from each sub-point cloud; a standard rail support point cloud {p k}, where k represents the number of the midpoint of the standard rail support point cloud;

[0013] S4, extract the posture and center position of the point cloud of the rail support platform;

[0014] S5, virtual track environment establishment: perform step S4 on the rail support point clouds of all other sub-point clouds in S3 in sequence, obtain the position and posture representation information of all rail support point clouds, and establish a virtual track in combination with the design parameters of the long rails in the line to be fine-tuned;

[0015] S6, track simulation fine tuning, specifically including the following steps:

[0016] S61, calculating the TQI value of the virtual track of the target unit segment;

[0017] S62, simulation fine-tuning principle: simulate fine-tuning the virtual track and output a simulation fine-tuning plan, the steps are as follows:

[0018] S621, determining the reference track of the curve segment, transition curve segment and straight line segment;

[0019] S622: Adopting the peak-cutting and valley-filling principle, simulate and adjust the virtual track in S5 to make the smoothness index of the virtual track meet the overall standard;

[0020] S623, when adjusting the elevation locally, on the basis that the elevation deviation of the reference rail determined in S61 meets the acceptance requirements, the 30m medium wave height irregularity value and the 300m long wave height irregularity value of the reference rail are adjusted in sequence. When the medium and long wave height irregularity values ​​of the reference rail meet the acceptance requirements, the non-reference rail is adjusted and controlled using the level and distortion;

[0021] S644, when adjusting the plane locally, on the basis that the plane deviation of the reference rail determined in S61 meets the acceptance requirements, the 30m medium wave track irregularity value and the 300m long wave track irregularity value of the reference rail are adjusted in sequence. When the medium and long wave track irregularities of the reference rail meet the acceptance requirements, the non-reference rail is adjusted and controlled using the track gauge and track gauge change rate;

[0022] S63, calculating the TQI value of the target unit section of the virtual track after the simulation fine-tuning in S62, and judging whether the TQI value meets the fine-tuning acceptance index: if it meets, outputting the simulation fine-tuning scheme and executing step S64; if it does not meet, repeating S62-S63;

[0023] S64, taking the adjustable amount of the fastener as a constraint factor, minimizing the change Z of the total adjustment amount in the simulation fine-tuning scheme as the optimization goal, and establishing an LP model simplified from the integer programming model ILP:

[0024]

[0025] Among them, σ is the fine adjustment amount corresponding to each rail support platform number to be optimized; α is the global nonlinear optimization factor constant; β is the nonlinear optimization factor constant under local constraints; B is the optimization cutoff setting threshold; Among them, the values ​​of m and n depend on the acceptable degree of optimization;

[0026] S65, based on the LP model established in S64 and the branch and bound method, nonlinearly optimize the change amount Z of the total adjustment amount in the simulation fine-tuning scheme to generate a new simulation fine-tuning scheme;

[0027] S7, fastener configuration: configure fastener parts according to the new simulation fine-tuning scheme generated by S65.

[0028] In step S3, the origin of the virtual track coordinate system is set at the geometric center of the acquisition device during the first measurement; the Y-axis direction is the extension direction from the small mileage at the origin to the large mileage and is parallel to the track plate; the X-axis direction is the direction from the reference rail at the origin to the non-reference rail and is parallel to the track plate; the Z-axis direction is the direction of the right-hand coordinate system established by the X-axis and the Y-axis.

[0029] In step S3, the method for extracting the track support platform point cloud from each sub-point cloud is: use the random sampling consistency algorithm to eliminate the track plate surface from each sub-point cloud, and then use the minimum bounding box algorithm for each sub-point cloud after eliminating the track plate surface to accurately extract the track support platform point cloud in each sub-point cloud.

[0030] Step S4 includes the following sub-steps:

[0031] S41, subdividing the point cloud structure of the rail support platform: extracting surface point cloud data of the point cloud of the rail support platform described in S3 based on point and line features, wherein the surface point cloud data includes point cloud data of the rail support surface, point cloud data of the inner and outer jaw surfaces, and point cloud data of the center of the embedded casing;

[0032] S42, extracting the point cloud posture of the rail support platform, including the following steps:

[0033] S421, calculate the attitude vector of the point cloud of the rail support platform: perform implicit surface parameterization on the point cloud data of the rail support surface obtained in S41 to obtain an implicit surface; solve the normal vector of each point of the implicit surface; normalize the normal vector of each point based on the error averaging concept and Abbe principle, and fit to obtain the three-axis attitude vector

[0034] S422, calculation of the registration vector of the track support platform point cloud posture: using the ICP algorithm to compare the track support surface point cloud data obtained in S41 with the standard track support platform point cloud {p k}Perform iterative registration of the nearest neighbor point and output the posture transformation vector

[0035] S423, point cloud posture correction of the support platform: if the three-axis posture vector and the attitude transformation vector The following conditions are met:

[0036]

[0037] The attitude of the current support platform point cloud is represented by the three-axis attitude vector If the above conditions are not met, then steps S421 to S423 are repeated;

[0038] S43, extracting the center position of the track support platform point cloud, including the following steps:

[0039] S431, performing data cleaning on the inner and outer jaw surface point cloud data obtained in S41, removing outliers and abnormal values ​​in the inner and outer jaw surface point cloud data, and obtaining a point set {p l}, where l represents the number of the midpoint of the point cloud data of the inner and outer jaw surfaces;

[0040] S432, fitting the point set {p l}: the centroid of the point set {p l The total number of points in the point set {p l The coordinates of the tth point in the virtual orbit coordinate system are Among them, t=1,2,3,…,s, then the point set {p l The center of mass point p in the virtual orbital coordinate system described in S32 l (x l ,y l ,z l ) is fitted as:

[0041]

[0042] S433, Track support platform point cloud center position correction: the center of mass point p l (x l ,y l ,z l ) along the three-axis attitude vector when satisfying equation (1) in S423 Vector in Project it onto the implicit surface corresponding to equation (1) in S423 to obtain the projection intersection point p r (x r ,y r ,z r );

[0043] S434, determining the projection intersection point p r (x r ,y r ,z r ) and the centroid point p l (x l ,y l ,z l ) is less than the threshold: If so, the center position of the support platform point cloud is (x r ,y r ,z r ); if not, repeat steps S431 to S434.

[0044] The three-axis attitude vector of the S421 middle, is the comprehensive average vector in the point cloud along the long side of the rail surface; is the comprehensive average vector in the point cloud along the short side of the rail surface; is the comprehensive average vector on the implicit surface obtained, and and and A right-handed coordinate system is formed.

[0045] Preferably, the threshold value described in S434 is 0.25 mm.

[0046] Preferably, the point cloud acquisition device in S1 is a self-moving RC-SLAM high-precision three-dimensional scanning system.

[0047] Preferably, the filtering algorithm in S2 uses a multi-size regional statistical filtering algorithm.

[0048] Preferably, in S3, the rail supports in each sub-point cloud are numbered and recorded according to the principle of "small mileage first, large mileage later; left side of the track first, right side of the track later".

[0049] Preferably, in S64, m=5 and n=8.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention can rely on virtual tracks to carry out simulated fine-tuning before construction, reducing the time consumption of actual fine-tuning work, avoiding inefficient fine-tuning and repeated operation processes, further accelerating the construction progress and improving work efficiency;

[0052] 2. Based on virtual track simulation technology, the present invention realizes the pre-configuration of high-speed rail fasteners before laying long rails, which greatly reduces the replacement rate and cost of replacing standard fasteners with fine-tuning fasteners in the original fine-tuning work;

[0053] 3. The present invention replaces the sparse sampling point data of the traditional working mode with the full-scene reconstruction data of the virtual track, further improving the coverage completeness, information distribution uniformity and feature description capability of the data required for fine-tuning, thereby reducing construction safety and quality issues;

[0054] 4. Carry out full-line simulation work during the long rail laying stage. The method of the present invention can intervene in the dynamic joint debugging and testing stage and the dynamic unevenness acceptance stage in advance, providing a basis for construction quality and cost control. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of the present invention;

[0056] Figure 2 It is a schematic diagram of all-element point cloud acquisition of the present invention;

[0057] Figure 3 It is a schematic diagram of the preliminary segmentation sub-point cloud of the present invention;

[0058] Figure 4 It is a schematic diagram of redundant data elimination and accurate segmentation of the rail support platform of the present invention;

[0059] Figure 5 It is a schematic diagram of the subdivision of the rail support platform structure based on point and line features of the present invention;

[0060] Figure 6 It is a schematic diagram of the orientation fitting of the rail support platform of the present invention;

[0061] Figure 7 It is a schematic diagram of the virtual track establishment result of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0063] A high-speed rail fastener pre-configuration method based on virtual track simulation, the process is as follows Figure 1 As shown, the following steps are included:

[0064] S1, data collection: collect and obtain the full-element point cloud on the track plate of the line to be fine-tuned. The collection process is as follows: Figure 2 As shown in the figure, the collected full-element point cloud includes the track plate point cloud, the track support platform point cloud and the CPIII point cloud. The point cloud collection equipment is a self-movable RC-SLAM high-precision three-dimensional scanning system.

[0065] S2, noise filtering: Use a filtering algorithm to remove noise and clean the data of the full-factor point cloud. Among them, noise filtering can avoid the impact of various types of severe environmental noise on the accuracy and precision of the full-factor point cloud during the construction of high-speed railways; the filtering algorithm should avoid using threshold segmentation filtering algorithms. Among them, the filtering algorithm uses a multi-scale sub-region statistical filtering algorithm, which can retain the integrity of the internal features of the full-factor point cloud with uneven density as much as possible.

[0066] S3, target area segmentation, specifically includes the following steps:

[0067] S31, point cloud segmentation: Based on the design scheme, prior mileage and track plate standard size parameters, the point cloud obtained in S2 is initially segmented into multiple sub-point clouds. Multiple sub-point clouds constitute a sub-point cloud set, and each sub-point cloud contains only one track support. A sub-point cloud in the sub-point cloud set is as follows: Figure 3 shown.

[0068] S32, coordinate system conversion: the coordinates of each point in all sub-point clouds in the sub-point cloud set described in S31 are converted to the virtual track coordinate system with the help of the CPIII control network. The origin of the virtual track coordinate system is set at the geometric center of the acquisition device during the first measurement; the Y-axis direction is the extension direction from the small mileage at the origin to the large mileage and is parallel to the track plate; the X-axis direction is the direction from the reference rail at the origin to the non-reference rail and is parallel to the track plate; the Z-axis direction is the direction of the right-hand coordinate system established by the X-axis and the Y-axis.

[0069] S33, numbering and recording the unique track support platforms in all sub-point clouds in the sub-point cloud set under the virtual track coordinate system described in S32: According to the spatial distribution of the sub-point cloud set, in accordance with the principle of "small mileage first, large mileage later; left side of the track first, right side of the track later", the track support platform of each sub-point cloud in the sub-point cloud set is numbered and recorded, wherein the number of each track support platform does not change during the fastener configuration process.

[0070] S34, using a random sampling consistency algorithm to remove the track plate surface from each sub-point cloud in the sub-point cloud set under the virtual track coordinate system described in S32, and then using a minimum bounding box algorithm for each sub-point cloud of the removed track plate surface to accurately extract the track support platform point cloud of each sub-point cloud in the sub-point cloud set, such as Figure 4 shown.

[0071] S35, establish a standard rail support point cloud: according to the virtual track coordinate system of S32, establish a standard rail support point cloud in an ideal posture {p k}, where k represents the number of the point in the point cloud of the standard rail support platform.

[0072] S4, the point cloud position and posture representation of the rail support platform, specifically includes the following steps:

[0073] S41, subdivision of the rail support point cloud structure: extracting the surface point cloud data of the rail support point cloud in a sub-point cloud in S34 based on point and line features. The extracted surface point cloud data includes the rail support surface point cloud data, the inner and outer jaw surface point cloud data and the embedded casing center point cloud data, such as Figure 5 shown.

[0074] S42, extracting the point cloud posture of the rail support platform, including the following steps:

[0075] S421, calculate the attitude vector of the track support platform point cloud: perform implicit surface parameterization on the track support surface point cloud data obtained in S41, and the implicit surface obtained includes an inner surface and an outer surface; since the track support surface should be a plane under ideal conditions, solve the normal vector of each point on the implicit surface; based on the error averaging concept (Xie Zhongshu. On the random variation of sampling average error in interval estimation [J]. Statistical Research, 1985, (03): 77-79+69.) and Abbe's principle (Shi Zhaoyao, Zhang Bin, Fei Yetai. Re-understanding of Abbe's principle [J]. Chinese Journal of Scientific Instrument, 2012, 33 (05): 1128-1133.), normalize the normal vectors of each point on the implicit surface and fit it as follows: Figure 6 The three-axis attitude vectors shown

[0076] Among them, the vector is the comprehensive average vector in the point cloud along the long side of the rail surface; vector is the comprehensive average vector in the point cloud along the short side of the rail surface; vector is the comprehensive average vector on the implicit surface obtained, and the vector With vector and vector Form a right-handed coordinate system, that is, the vector vector and vector Two by two vertical.

[0077] S422, calculation of the registration vector of the track support platform point cloud posture: using the ICP algorithm to calculate the track support surface point cloud data obtained in S41 and the standard track support platform point cloud {p k}Perform iterative registration of the nearest neighbor point and output the posture transformation vector

[0078] S423, point cloud posture correction of the support platform: If the three-axis posture vector in S421 and the attitude transformation vector The following conditions are met:

[0079]

[0080] The attitude of the current support platform point cloud is represented by the three-axis attitude vector If the above conditions are not met, then steps S421 to S423 are repeated;

[0081] S43, extracting the center position of the track support platform point cloud, including the following steps:

[0082] S431, cleaning the point cloud data of the inner and outer jaw surfaces: cleaning the point cloud data of the inner and outer jaw surfaces obtained in S41, removing outliers and abnormal values ​​in the point cloud data of the inner and outer jaw surfaces, and obtaining a point set {p l}, where l represents the number of the midpoint of the inner and outer jaw surface point cloud data.

[0083] S432, fitting the point set {p l}: the centroid of the point set {p l The total number of points in the point set {p l The coordinates of the t-th point in the virtual track coordinate system described in S32 are Among them, t=1,2,3,…,s, then the point set {p l The center of mass point p in the virtual orbital coordinate system described in S32 l (x l ,y l ,z l ) is fitted as:

[0084]

[0085] S433, Track support platform point cloud center position correction: the center of mass point p l (x l ,y l ,z l ) along the three-axis attitude vector when satisfying equation (1) in S423 Vector in Project it onto the implicit surface corresponding to equation (1) in S423 to obtain the projection intersection point p r (x r ,y r ,z r );

[0086] S434, determine the projection intersection point p r (x r ,y r ,z r ) and the centroid point p l (x l ,y l ,z l ) is less than the threshold: If so, the center position of the support platform point cloud is (x r ,y r ,z r ); if not, repeat steps S431 to S434, wherein, preferably, the threshold value is 0.25 mm.

[0087] S5, virtual track environment establishment: perform step S4 on the track support platform point clouds of all other sub-point clouds in S34 in sequence, and obtain the position and posture representation information of all track support platform point clouds (three-axis posture vector of the track support platform point cloud The center position of the point cloud of the support platform is (x r ,y r ,z r )) and combined with the design parameters of the long rails in the line to be fine-tuned, a virtual track is established, such as Figure 7 shown.

[0088] S6, track simulation fine tuning, specifically including the following steps:

[0089] S61, calculate the TQI value of the virtual track of the target unit section: calculate the TQI value of the virtual track in S5 within the length of the unit section. The TQI value is the sum of the standard deviations of the seven geometric deviations within the length of the unit section, namely, height (left and right strands), track direction (left and right strands), track gauge, level (superelevation) and triangular pit. The calculation formula is as follows:

[0090]

[0091] In the formula, σ i is the standard deviation of any one of the seven geometric deviations within the unit segment length:

[0092]

[0093] Where N is the number of centroids of all rail supports within the unit section length, x ij represents the amplitude of the center of mass of any rail support platform, Represents the amplitude x of the center of mass of N rail supports ij The arithmetic mean of .

[0094] The length of the corresponding unit section at different speeds is different, and the standards are as follows: the length of the unit section at a speed of 350km / h is 200m; the length of the unit section in the speed range of 200km / h to 350km / h is 150m; the length of the unit section at a speed less than 200km / h is 100m.

[0095] S62, simulation fine-tuning principle: simulate fine-tuning the virtual track described in S5, and output a simulation fine-tuning plan, which specifically includes the following steps:

[0096] S621, determine the reference track:

[0097] Determine the reference rail for curved sections and transition curve sections: When optimizing the track direction, the high rail (curved sections have a designed superelevation) is usually selected as the reference rail for curved sections and transition curve sections; when optimizing the height, the low rail should be selected as the reference rail for curved sections;

[0098] Determine the reference rail for the straight segment: When optimizing the track direction and height of the straight segment, the reference rail selected should be the same rail as the reference rail of the curve segment closest to the shortest mileage direction;

[0099] S622: Overall, waveforms of the seven geometric deviations of the virtual track are generated based on the seven geometric deviations, and the waveforms are analyzed. A principle similar to "peak shaving and valley filling" is used to simulate and adjust the virtual track in S5, so that the track smoothness indicators of the virtual track are qualified as a whole: the actual direction of the line is as consistent as possible with the designed line shape, achieving a state of straight lines, smooth curves, no sudden changes, and no periodic small oscillations.

[0100] S623, when adjusting the elevation locally, on the basis that the elevation deviation of the reference rail determined in S61 meets the acceptance requirements, the 30m medium wave height unevenness value and the 300m long wave height unevenness value of the reference rail are adjusted in turn. When the medium and long wave height unevenness values ​​of the reference rail have met the acceptance requirements, the non-reference rail is adjusted and controlled using the level (superelevation) and distortion.

[0101] S644, when adjusting the plane locally, on the basis that the plane deviation of the reference rail determined in S61 meets the acceptance requirements, the 30m medium-wave track irregularity value and the 300m long-wave track irregularity value of the reference rail are adjusted in turn. When the medium- and long-wave track irregularities of the reference rail have met the acceptance requirements, the non-reference rail is adjusted and controlled using the track gauge and track gauge change rate.

[0102] Among them, after the simulation fine-tuning of the virtual track, the simulation fine-tuning plan is output to facilitate the subsequent determination of the fastener configuration plan:

[0103] Elevation adjustment: When the actual rail surface is higher than the reference rail surface, the adjustment amount sign is "minus (-)", indicating that the rail needs to be adjusted downward; when the actual rail surface is lower than the reference rail surface, the adjustment amount sign is "positive (+)", indicating that the rail needs to be adjusted upward.

[0104] Plane adjustment: When the actual position of the rail is on the right side of the design position, the adjustment amount sign is "minus (-)", indicating that the rail needs to be adjusted to the left; when the actual position of the rail is on the left side of the design position, the adjustment amount sign is "plus (+)", indicating that the rail needs to be adjusted to the right. The left and right sides are defined as follows: the forward direction of the track is from small mileage to large mileage, and the left and right sides of the track are defined according to the forward direction of the track.

[0105] S63, according to the TQI calculation formula described in S61, calculate the TQI value of the target unit section of the virtual track after the simulation fine-tuning in S62, and determine whether the TQI value meets the fine-tuning acceptance index: if it does, output the simulation fine-tuning plan and execute step S64; if it does not meet the requirements, repeat S62-S63.

[0106] S64, taking the adjustable amount of the fastener as a constraint factor, taking the minimization of the change Z of the total adjustment amount in the simulation fine-tuning scheme as the optimization goal, and establishing an integer programming model (ILP) (Luo Yucai. A hybrid algorithm for integer linear programming [J]. Journal of Guizhou University (Natural Science Edition), 1987, (04): 193-199.) The simplified LP model is:

[0107]

[0108] Among them, σ is the fine-tuning amount corresponding to each rail support platform number to be optimized; α is the global nonlinear optimization factor constant; β is the nonlinear optimization factor constant under local constraints; B is the optimization cutoff setting threshold; Among them, the values ​​of m and n depend on the acceptable degree of optimization. In the embodiment of the present invention, m=5 and n=8.

[0109] Among them, the reason for using the integer programming model to optimize the simulation fine-tuning scheme is that the adjustment amount given in the simulation fine-tuning scheme output by the simulation fine-tuning principle does not take into account the specification constraints of the track pad / gauge block, and because the ballastless track fine-tuning is achieved by increasing or decreasing the number of track pads / gauge blocks, the final adjustment effect of the track is limited by the experience and level of the on-site fine-tuning personnel; at the same time, due to the limited adjustable amount of the matching fasteners of the ballastless track rails, if the track is fine-tuned in accordance with the horizontal and vertical deviations of the track obtained according to the design value of the track geometry parameters, the adjustment amount will exceed the adjustable range of the fasteners. Therefore, on the basis of ensuring the smoothness of the virtual track, the integer programming model is used to re-optimize the simulation fine-tuning scheme, and the minimum number of track pads / gauge blocks that need to be adjusted at each sleeper is given to avoid the influence of human intervention on the track adjustment effect during on-site fine-tuning, so that the simulation fine-tuning scheme is more in line with the on-site operation conditions.

[0110] S65, based on the LP model established in S64 and the branch and bound method, nonlinear optimization is performed on the change Z of the total adjustment amount in the simulation fine-tuning scheme: the calculation is started from the relaxation problem without considering the integer constraints of the decision variables. If the optimal solution obtained does not meet the integer constraints, the original problem is decomposed into several problems (branch problems), and the original feasible solution area is compressed by continuously adding new constraints (constraints determined by the integer requirements) on the basis of the original constraints, and the integer optimal solution is gradually approached until the TQI value of the target segment and other constraint indicators meet the acceptance requirements, and a new simulation fine-tuning scheme is generated.

[0111] S7, fastener configuration: configure fastener parts according to the new simulation fine-tuning scheme generated by S65: select the specifications and corresponding models of adjustable parts, and purchase, distribute and install fastener parts at the construction site according to the scheme.

[0112] Preferably, the method further comprises:

[0113] S8, process review during fine tuning: Use the track irregularity evaluation model based on the vector distance difference method to verify the irregularity of the fine tuning solution obtained above. If the irregularity value is higher than the target end acceptance standard, re-execute step S6 and change the fine tuning solution.

Claims

1. A high-speed rail fastener pre-configuration method based on virtual track simulation, characterized in that include: S1, data collection: collect and obtain the full-element point cloud on the track plate of the line to be fine-tuned, including the track plate point cloud, the track support platform point cloud and the CPIII point cloud; S2, noise filtering: using a filtering algorithm to remove noise and clean the data of the full-factor point cloud; S3, target area segmentation: the point cloud obtained in S2 is initially segmented into multiple sub-point clouds to form a sub-point cloud set, wherein each sub-point cloud contains only one rail support; the coordinates of each point in all sub-point clouds in the sub-point cloud set are converted to the virtual track coordinate system with the help of the CPIII control network; the rail support in each sub-point cloud is numbered and recorded; the rail support point cloud is extracted from each sub-point cloud; a standard rail support point cloud {p k }, where k represents the number of the midpoint of the standard rail support point cloud; S4, extract the posture and center position of the point cloud of the rail support platform; S5, virtual track environment establishment: perform step S4 on the rail support point clouds of all other sub-point clouds in S3 in sequence, obtain the position and posture representation information of all rail support point clouds, and establish a virtual track in combination with the design parameters of the long rails in the line to be fine-tuned; S6, track simulation fine tuning, specifically including the following steps: S61, calculating the TQI value of the virtual track of the target unit segment; S62, simulation fine-tuning principle: simulate fine-tuning the virtual track and output a simulation fine-tuning plan, the steps are as follows: S621, determining the reference track of the curve segment, transition curve segment and straight line segment; S622: Adopting the peak-cutting and valley-filling principle, simulate and adjust the virtual track in S5 to make the smoothness index of the virtual track meet the overall standard; S623, when adjusting the elevation locally, on the basis that the elevation deviation of the reference rail determined in S61 meets the acceptance requirements, the 30m medium wave height irregularity value and the 300m long wave height irregularity value of the reference rail are adjusted in sequence. When the medium and long wave height irregularity values ​​of the reference rail meet the acceptance requirements, the non-reference rail is adjusted and controlled using the level and distortion; S644, when adjusting the plane locally, on the basis that the plane deviation of the reference rail determined in S61 meets the acceptance requirements, the 30m medium wave track irregularity value and the 300m long wave track irregularity value of the reference rail are adjusted in sequence. When the medium and long wave track irregularities of the reference rail meet the acceptance requirements, the non-reference rail is adjusted and controlled using the track gauge and track gauge change rate; S63, calculating the TQI value of the target unit section of the virtual track after the simulation fine-tuning in S62, and judging whether the TQI value meets the fine-tuning acceptance index: if it meets, outputting the simulation fine-tuning scheme and executing step S64; if it does not meet, repeating S62-S63; S64, taking the adjustable amount of the fastener as a constraint factor, minimizing the change Z of the total adjustment amount in the simulation fine-tuning scheme as the optimization goal, and establishing an LP model simplified from the integer programming model ILP: Among them, σ is the fine adjustment amount corresponding to each rail support platform number to be optimized; α is the global nonlinear optimization factor constant; β is the nonlinear optimization factor constant under local constraints; B is the optimization cutoff setting threshold; Among them, the values ​​of m and n depend on the acceptable degree of optimization; S65, based on the LP model established in S64 and the branch and bound method, nonlinearly optimize the change amount Z of the total adjustment amount in the simulation fine-tuning scheme to generate a new simulation fine-tuning scheme; S7, fastener configuration: configure fastener parts according to the new simulation fine-tuning scheme generated by S65.

2. The high-speed rail fastener pre-configuration method according to claim 1, characterized in that: The origin of the virtual track coordinate system described in S3 is set at the geometric center of the acquisition device during the first measurement; the Y-axis direction is the extension direction from the small mileage at the origin to the large mileage and is parallel to the track plate; the X-axis direction is the direction from the reference rail at the origin to the non-reference rail and is parallel to the track plate; the Z-axis direction is the direction of the right-hand coordinate system established by the X-axis and the Y-axis.

3. The high-speed rail fastener pre-configuration method according to claim 1, characterized in that: The method for extracting the track support platform point cloud from each sub-point cloud in S3 is: use the random sampling consistency algorithm to eliminate the track plate surface from each sub-point cloud, and then use the minimum bounding box algorithm for each sub-point cloud after eliminating the track plate surface to accurately extract the track support platform point cloud in each sub-point cloud.

4. The high-speed rail fastener pre-configuration method according to claim 3, characterized in that: S4 includes the following sub-steps: S41, subdividing the point cloud structure of the rail support platform: extracting surface point cloud data of the point cloud of the rail support platform described in S3 based on point and line features, wherein the surface point cloud data includes point cloud data of the rail support surface, point cloud data of the inner and outer jaw surfaces, and point cloud data of the center of the embedded casing; S42, extracting the point cloud posture of the rail support platform, including the following steps: S421, calculate the attitude vector of the point cloud of the rail support platform: perform implicit surface parameterization on the point cloud data of the rail support surface obtained in S41 to obtain an implicit surface; solve the normal vector of each point of the implicit surface; normalize the normal vector of each point based on the error averaging concept and Abbe principle, and fit to obtain the three-axis attitude vector S422, calculation of the registration vector of the track support platform point cloud posture: using the ICP algorithm to compare the track support surface point cloud data obtained in S41 with the standard track support platform point cloud {p k }Perform iterative registration of the nearest neighbor point and output the posture transformation vector S423, point cloud posture correction of the platform: if the three-axis posture vector and the attitude transformation vector The following conditions are met: The attitude of the current support platform point cloud is represented by the three-axis attitude vector If the above conditions are not met, then steps S421 to S423 are repeated; S43, extracting the center position of the track support platform point cloud, including the following steps: S431, performing data cleaning on the inner and outer jaw surface point cloud data obtained in S41, removing outliers and abnormal values ​​in the inner and outer jaw surface point cloud data, and obtaining a point set {p l }, where l represents the number of the midpoint of the point cloud data of the inner and outer jaw surfaces; S432, fitting the point set {p l }: the centroid of the point set {p l The total number of points in the point set {p l The coordinates of the tth point in the virtual orbit coordinate system are Among them, t=1,2,3,…,s, then the point set {p l The center of mass point p in the virtual orbital coordinate system described in S32 l (x l ,y l ,z l ) is fitted as: S433, Track support platform point cloud center position correction: the center of mass point p l (x l ,y l ,z l ) along the three-axis attitude vector when satisfying equation (1) in S423 Vector in Project it onto the implicit surface corresponding to equation (1) in S423 to obtain the projection intersection point p r (x r ,y r ,z r ); S434, determining the projection intersection point p r (x r ,y r ,z r ) and the centroid point p l (x l ,y l ,z l ) is less than the threshold: If so, the center position of the support platform point cloud is (x r ,y r ,z r ); if not, repeat steps S431 to S434.

5. The high-speed rail fastener pre-configuration method according to claim 4, characterized in that: S421's three-axis attitude vector middle, is the comprehensive average vector in the point cloud along the long side of the rail surface; is the comprehensive average vector in the point cloud along the short side of the rail surface; is the comprehensive average vector on the implicit surface obtained, and and and A right-handed coordinate system is formed.

6. The high-speed rail fastener pre-configuration method according to claim 4, characterized in that: The threshold value described in S434 is 0.25 mm.

7. The high-speed rail fastener pre-configuration method according to claim 1, characterized in that: The point cloud acquisition device in S1 is a self-mobile RC-SLAM high-precision three-dimensional scanning system.

8. The high-speed rail fastener pre-configuration method according to claim 1, characterized in that: The filtering algorithm described in S2 uses a multi-size regional statistical filtering algorithm.

9. The high-speed rail fastener pre-configuration method according to claim 1, characterized in that: In S3, the rail supports in each sub-point cloud are numbered and recorded according to the principle of "small mileage first, large mileage later; left side of the track first, right side of the track later".

10. The high-speed rail fastener pre-configuration method according to claim 1, characterized in that: In S64, m=5, n=8.

Images