Rail geometric position detection method and device, computer device and storage medium

Abstract

The present application provides a kind of track geometry detection method, device, computer equipment and storage medium, the method includes obtaining the dynamic coordinate of train in the process of driving on the line to be measured, constructs time coordinate model according to the dynamic coordinate;Obtain the coordinate mileage model of pre-setting;According to the time coordinate model and the coordinate mileage model, construct time mileage model;Through INS on axle box, the attitude angle data of train is collected;The attitude angle data is resampled, and the corresponding relationship of the attitude angle data and the track mileage is obtained;The geometry of track is calculated according to the attitude angle data.The dynamic coordinate of train on the line to be measured is obtained in real time, in the line section such as tunnel, satellite signal cannot be covered, the accurate positioning of train running mileage is realized, so that the geometry of the line to be measured calculated is more accurate.Based on axle box attitude angle, the geometry of track is detected, and the geometry of the line to be measured can be improved.

Description

Technical Field

[0001] This invention relates to the field of railway track inspection technology, and in particular to a method, apparatus, computer equipment, and storage medium for detecting track geometry and position. Background Technology

[0002] The geometry of railway tracks plays a decisive role in the operational safety, speed, comfort, and lifespan of railway track and vehicle components of the wheel-rail system. Track geometry inspection is an important part of railway maintenance.

[0003] Currently, there are three main methods for inspecting the geometric position of railway tracks. First, manual inspection, which is the most common method in engineering practice. It requires simple testing instruments, has low equipment costs, and is easy to implement, but its efficiency is low and the stability of the data is poor. Second, track inspection trolleys, which improve efficiency compared to manual inspection, save labor, and solve the problems of inaccurate, arbitrary, and unreliable inspection records from maintenance work areas. However, this method, like manual inspection, is a static inspection method and cannot reflect the true condition of the track. Third, track inspection vehicles, which are large-scale dynamic inspection devices for checking track defects. These vehicles have strong comprehensive inspection capabilities, high accuracy, good anti-interference capabilities, and fast inspection speed. They are also an important means of guiding line maintenance, ensuring traffic safety, and achieving scientific track management. However, they are expensive, few in number, have long inspection cycles, and occupy maintenance windows during inspections, making it impossible to frequently inspect all railway lines.

[0004] Therefore, how to accurately, efficiently, and cost-effectively detect the geometric shape and position of railway tracks is an urgent problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for detecting track geometry and position, in order to address the aforementioned technical problems.

[0006] A method for detecting the geometric position of an orbit includes:

[0007] The dynamic coordinates of the train during its journey on the track under test are obtained, and a time coordinate model is constructed based on the dynamic coordinates. The time coordinate model records the correspondence between the train's travel time and the train's dynamic coordinates.

[0008] Obtain a pre-set coordinate mileage model, wherein the coordinate mileage model records the correspondence between each static coordinate of the line to be tested and the track mileage;

[0009] Based on the time coordinate model and the coordinate mileage model, a time mileage model is constructed, wherein the time mileage model records the correspondence between the train's travel time and the track mileage;

[0010] The train's attitude angle data is collected by INS installed on the axle boxes of the train, and the train's travel time is obtained.

[0011] Based on the attitude angle data, the train's travel time, and the time-mileage model, the attitude angle data is resampled to obtain the correspondence between the attitude angle data and the track mileage.

[0012] The geometric shape and position of the track are calculated based on the attitude angle data;

[0013] Based on the geometry of the track and the correspondence between the attitude angle data and the track mileage, the geometry of the track under test is obtained.

[0014] In one embodiment, the step of obtaining the dynamic coordinates of the train during its journey on the track under test, and constructing a time coordinate model based on the dynamic coordinates, includes:

[0015] The coordinates of the train's location point on the track under test are obtained using GNSS, and the train's acceleration is obtained using INS.

[0016] Based on the coordinates of the positioning point and the acceleration, the speed of the train at the next positioning point is calculated;

[0017] Calculate the dynamic coordinates of the train in the northeast-sky coordinate system based on the speed of the train at the next positioning point.

[0018] A time coordinate model is constructed based on the dynamic coordinates of the train in the northeast celestial coordinate system.

[0019] In one embodiment, the step of constructing the time-mileage model based on the time coordinate model and the coordinate-mileage model includes:

[0020] Using the dynamic coordinates of the train in the northeast-central coordinate system in the time coordinate model as the positioning point and the static coordinates of the coordinate mileage model as the marker point, an index between the positioning point and the marker point is established by creating a grid. The optimal marker point is calculated by a weighted matching function and used as the real-time coordinates of the train.

[0021] The time-mileage model is constructed based on the real-time coordinates of the train, according to the time coordinate model and the coordinate mileage model.

[0022] In one embodiment, before the step of resampling the attitude angle data based on the attitude angle data, the train's travel time, and a time-mileage model to obtain the correspondence between the attitude angle data and the track mileage, the method further includes:

[0023] Obtain the preset distance threshold;

[0024] The first N position data points are selected on the line to be tested, and a fitted straight line is obtained based on the first N position data points;

[0025] Calculate the distance from each preset detection point on the line under test to the fitted line. If the distance from the preset detection point to the fitted line is less than the preset distance threshold, then add a fitting point to the fitted line in sequence.

[0026] When the distance from the preset detection point to the fitted straight line is greater than the preset distance threshold, a segmentation point is marked on the line under test;

[0027] Based on the segmentation points, the line to be tested is divided into multiple line segments;

[0028] The step of calculating the geometric position of the orbit based on the attitude angle data includes:

[0029] Based on the divided line segments, the geometric shape and position of each line segment are calculated according to the attitude angle data.

[0030] In one embodiment, the step of resampling the attitude angle data based on the attitude angle data, the train's travel time, and a time-mileage model to obtain the correspondence between the attitude angle data and the track mileage includes:

[0031] Based on the attitude angle data, the train's travel time, and the time-mileage model, the attitude angle data is resampled, and the attitude angle data obtained based on the travel time is converted into attitude angle data based on track mileage, thereby obtaining the correspondence between the attitude angle data and the track mileage.

[0032] In one embodiment, after the step of calculating the geometric position of the orbit based on the attitude angle data, the method further includes:

[0033] An error compensation algorithm is used to compensate for the geometric position of the track, wherein the compensation algorithm is the recursive least squares method.

[0034] A track geometry and position detection device, comprising:

[0035] The time coordinate model construction module is used to obtain the dynamic coordinates of the train during its travel on the track under test, and to construct a time coordinate model based on the dynamic coordinates. The time coordinate model records the correspondence between the train's travel time and the train's dynamic coordinates.

[0036] The coordinate mileage acquisition module is used to acquire a pre-set coordinate mileage model, wherein the coordinate mileage model records the correspondence between each static coordinate of the line under test and the track mileage.

[0037] The time-mileage construction module is used to construct a time-mileage model based on the time coordinate model and the coordinate mileage model, wherein the time-mileage model records the correspondence between the train's travel time and the track mileage;

[0038] The attitude angle data acquisition module is used to collect the train's attitude angle data and obtain the train's travel time through an INS installed on the axle box of the train.

[0039] The data resampling module is used to resample the attitude angle data based on the attitude angle data, the train's travel time, and the time-mileage model to obtain the correspondence between the attitude angle data and the track mileage.

[0040] The geometric position calculation module is used to calculate the geometric position of the track based on the attitude angle data;

[0041] The track geometry and position acquisition module is used to obtain the geometry and position of the track under test based on the geometry and position of the track and the correspondence between the attitude angle data and the track mileage.

[0042] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the orbit set shape and position detection method described in any of the above embodiments.

[0043] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the orbit set shape and position detection method described in any of the above embodiments.

[0044] A computer program, when executed by a processor, implements the steps of the orbit set shape and position detection method described in any of the above embodiments.

[0045] The aforementioned track geometry and position detection method, device, computer equipment, and storage medium acquire the dynamic coordinates of the train on the track under test in real time. Even in sections of track where satellite signals cannot cover, such as tunnels, accurate positioning of the train's mileage can be achieved, resulting in higher accuracy of the calculated geometry and position of the track under test. Furthermore, by collecting train attitude angle data through an INS installed on the train's axle boxes, and performing track geometry and position detection based on the axle box attitude angles, the geometric and position accuracy of the track under test can be effectively improved. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a track geometry detection method in one embodiment;

[0047] Figure 2This is a structural block diagram of the track geometry and position detection device in one embodiment;

[0048] Figure 3 This is an internal structural diagram of a computer device in one embodiment;

[0049] Figure 4 This is a schematic diagram of the on-board equipment of the track geometry detection system in one embodiment;

[0050] Figure 5 This is a schematic diagram of the undercarriage equipment of the track geometry detection system in one embodiment;

[0051] Figure 6 A flowchart illustrating the time-mileage model construction process in one embodiment;

[0052] Figure 7 This is a schematic diagram of the route segmentation process in one embodiment;

[0053] Figure 8 This is a schematic diagram illustrating the detection principle of horizontally uneven track geometry in one embodiment;

[0054] Figure 9 This is a schematic diagram illustrating the detection principle of uneven track geometry in one embodiment;

[0055] Figure 10 This is a schematic diagram illustrating the detection principle of track geometry irregularities in one embodiment.

[0056] Figures 11A to 11C These are schematic diagrams illustrating the detection results of track geometry irregularities in one embodiment, including horizontal irregularities, vertical irregularities, and track alignment irregularities. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] Example 1

[0059] In this embodiment, as Figure 1 As shown, a method for detecting the geometric position of an orbit is provided, which includes:

[0060] Step 110: Obtain the dynamic coordinates of the train during its journey on the track to be tested, and construct a time coordinate model based on the dynamic coordinates. The time coordinate model records the correspondence between the train's travel time and the train's dynamic coordinates.

[0061] In this embodiment, the track to be tested is a train track with the geometric shape and position to be tested. Since dynamic coordinates are acquired at preset time intervals during train operation, the correspondence between dynamic coordinates and travel time in this time coordinate model is established.

[0062] It is worth mentioning that this step acquires the dynamic coordinates of the train in real time during the train's operation. This step and step 140 can be executed synchronously, that is, the dynamic coordinates and attitude angle data of the train are acquired synchronously during the train's operation, and the dynamic coordinates and attitude angle data are acquired based on the same time reference frame.

[0063] In one embodiment, the step of obtaining the dynamic coordinates of the train during its journey on the test track and constructing a time coordinate model based on the dynamic coordinates includes: obtaining the coordinates of the train's positioning points on the test track using GNSS (Global Navigation Satellite System); obtaining the train's acceleration using INS (Inertial Navigation System); calculating the train's velocity at the next positioning point based on the positioning point coordinates and the acceleration; calculating the train's dynamic coordinates in the northeast-central coordinate system based on the train's velocity at the next positioning point; and constructing a time coordinate model based on the train's dynamic coordinates in the northeast-central coordinate system.

[0064] In this embodiment, the train speed is first calculated: Let the coordinates of the adjacent GNSS positioning points be (X... x0 Y y0 ) and (X xn Y yn ), (X x0 Y y0 The initial velocity of point (V) is x0 V y0 ), where (X x0 Y y0 (X) represents the coordinates of the current location point. xn Y yn ( ) represents the coordinates of the next positioning point, and the acceleration a measured by INS. x and a y Then the following relationship holds:

[0065]

[0066]

[0067] In the formula, n is the number of INS positioning points between two GNSS positioning points, and t represents the interval between adjacent INS positioning points (0.005 seconds). Based on the initial velocity, the velocity V of each positioning point K in the train's coordinate system b can be calculated.K .

[0068] V K =V K-1 +a K-1 ·t

[0069] Position calculation in the northeast-central coordinate system: The INS output is the velocity and acceleration in the b-frame, i.e., the longitudinal velocity V of the train. y Horizontal, longitudinal, and vertical accelerations a x a y and a z The odometer location information is in the Northeast-North (ENU) coordinate system, so the velocity and acceleration need to be converted to the B-frame. The East and North coordinates in the ENU are represented by X... E and Y N The velocity of system b and the velocity of system ENU satisfy a trigonometric function relationship with respect to the heading angle α.

[0070] V E =V y ·cos(α)

[0071] V N =V y sin(α)

[0072] The b-series acceleration requires a strapdown matrix transformation, the transformation formula of which is as follows.

[0073]

[0074] Calculate the ENU coordinates of the measuring point based on the known GNSS point coordinates and the INS output velocity and acceleration.

[0075]

[0076]

[0077] In the formula, X Ek Y Nk This refers to the coordinates of the detection point under the ENU (Northeast Celestial Coordinate System).

[0078] Step 120: Obtain a pre-set coordinate mileage model, wherein the coordinate mileage model records the correspondence between the static coordinates and track mileage of the line under test.

[0079] In this embodiment, the static coordinates in the coordinate mileage model are the coordinates of the track of the line under test, which do not change with the movement of the train. This pre-set coordinate mileage model can be constructed by measuring the static coordinates of the main points of a curve on-site, calculating the static coordinates of the line based on the track mileage, and then building the coordinate mileage model. This process of constructing the coordinate mileage model can be performed before the train travels.

[0080] Step 130: Construct a time-mileage model based on the time coordinate model and the coordinate mileage model, wherein the time-mileage model records the correspondence between the train's travel time and the track mileage.

[0081] In this embodiment, since the time coordinate model records the correspondence between the train's travel time and its dynamic coordinates, and the coordinate mileage model records the correspondence between the static coordinates of the track under test and the track mileage, by mapping the dynamic coordinates to the static coordinates, the correspondence between the train's travel time and the track mileage can be established, thus constructing the time mileage model. Through this time mileage model, the track mileage of the train at a certain point in time can be determined.

[0082] In one embodiment, the step of constructing the time-mileage model based on the time coordinate model and the coordinate mileage model includes: using the dynamic coordinates of the train in the northeast-northeast coordinate system in the time coordinate model as the positioning point, and the static coordinates of the coordinate mileage model as the marker point, establishing an index between the positioning point and the marker point by creating a grid, calculating the optimal marker point through a weighted matching function, and using the optimal marker point as the real-time coordinates of the train; and constructing the time-mileage model based on the real-time coordinates of the train according to the time coordinate model and the coordinate mileage model.

[0083] In this embodiment, the data points completed by GNSS / INS are recorded as positioning points; the original line location information is recorded as marker points; an index between positioning points and marker points is realized by creating a grid; and the distance and angle are calculated by using a weighted matching function to determine the optimal marker point, which is then used as the position of the train.

[0084] Specifically, a grid is first created with a grid step size of 150 meters. Therefore, each grid contains no more than three markers, and a 200-meter railway line can be approximated as a straight line. In actual positioning, the number of markers is generally less than or equal to the number of positioning points, ensuring that each marker is utilized. The latitude and longitude of the lower left corner of the railway section grid table are (X... A Y A The latitude and longitude coordinates of the upper right corner are (X... B Y B The number of grids in the latitude and longitude directions are M and N respectively. Calculated using the latitude and longitude distance formula, the grid distance length in the longitude direction is H. lon (Unit: m), the grid distance length in the latitudinal direction is H. lat (Unit: m), then the values ​​of M and N are respectively:

[0085] M = H lon ÷150

[0086] N = H lat ÷150

[0087] For the bottom left corner coordinate (X) of the grid table A Y A ) and the coordinates of the upper right corner (X B Y B There are two situations:

[0088] First, when the grid is pre-divided, meaning all lines are included within it, (X) A Y A ) and (X B Y B () refers to the coordinates of the bottom left and top right corners of the grid table;

[0089] Second, the grid will include all lines, then (X) A Y A ) and (X B Y B The coordinates refer to the coordinates of the lowest and highest points on the route, respectively.

[0090] In the railway section grid table, the grid with step size in meters (m) needs to be converted to a grid with step size in latitude and longitude. The latitude and longitude step sizes of the grid are as follows:

[0091] S lon =(X B -X A )÷M

[0092] S lat =(Y B -Y A )÷N

[0093] In the formula: S lon S represents the step size in the longitude direction; lat This represents the step size in the latitudinal direction.

[0094] Latitude and longitude coordinates are P(x lon y lat The corresponding grid number P(p) lon p lat It is determined by the following formula:

[0095]

[0096]

[0097] In the formula: p lon The number for the longitude direction; p lat The numbers represent latitude; [] indicates rounding down.

[0098] Matching function

[0099]

[0100] In the formula, d is the straight-line distance between the positioning point and the marker point to be matched; D is the distance between adjacent marker points; θ is the angle between the velocity direction of the positioning point and the velocity direction of the marker point to be matched; ω is the weighting coefficient of the distance factor, and θ is the weighting coefficient of the direction angle factor, and they are related as follows: d +ω θ =1, adjacent S marker points D=100m. Within the allowable error range, all positioning points can be matched to the marker points, which can meet the requirement of precise positioning of the train during operation.

[0101] Step 140: Collect the train's attitude angle data and obtain the train's travel time by using an INS installed on the train's axle box.

[0102] In this embodiment, during the train's journey on the test track, the train's attitude angle data is collected in real time through an INS installed on the train's axle box, and the train's travel time is obtained, establishing a correspondence between the travel time and the attitude angle data.

[0103] Step 150: Based on the attitude angle data, the train's travel time, and the time-mileage model, the attitude angle data is resampled to obtain the correspondence between the attitude angle data and the track mileage.

[0104] In this embodiment, the attitude angle data is resampled to convert the original time-based attitude angle data into mileage-based attitude angle data. Specifically, the original attitude angle data corresponding to the travel time is mapped to the track mileage, thereby obtaining the correspondence between the attitude angle data and the track mileage, and realizing the resampling of the attitude angle data.

[0105] In one embodiment, the step of resampling the attitude angle data based on the attitude angle data, the train's travel time, and a time-mileage model to obtain the correspondence between the attitude angle data and the track mileage includes: resampling the attitude angle data based on the attitude angle data, the train's travel time, and a time-mileage model; converting the attitude angle data obtained based on the travel time into attitude angle data based on track mileage; and obtaining the correspondence between the attitude angle data and the track mileage.

[0106] Specifically, the mounted dynamic track inspection system performs isochronous sampling, which requires converting the acquired time-domain signal into a spatial-domain sequence. That is, using t as the sampling time interval, high-precision inertial navigation attitude angle data is converted, as shown in the following formula:

[0107] θ(s) = θ(t) × V(t)

[0108] In the formula, θ(t) is the attitude angle at time t, V(t) is the vehicle speed at time t, and θ(s) is the attitude angle at mileage s.

[0109] Attitude angle data at corresponding mileages are affected by factors such as the sampling environment, necessitating outlier removal. The Rheinda criterion (3σ method) is used to remove outliers from the data measurement sequence x1, x2, ... x. n Find the arithmetic mean. and residual error Next, calculate the root mean square deviation.

[0110] The following determinations are made:

[0111] Then x t This is normal data and should be retained.

[0112] Then x t This is abnormal data and should be discarded, replaced by the median value of its left and right adjacent data.

[0113] Step 160: Calculate the geometry of the track based on the attitude angle data.

[0114] In this embodiment, the orbital geometry is calculated based on the axle box attitude angle:

[0115] 1. Calculation of uneven horizontal surfaces

[0116] The horizontal dimension is represented by the height difference h between the top surfaces of the left and right rails on the same cross section of the track. For example... Figure 8 As shown, while maintaining wheel-rail contact, the roll angle φ of the axle box can be considered approximately equal to the inclination angle φ′ formed by the height difference h between the top surfaces of the left and right rails. By combining the track gauge information and establishing a trigonometric relationship between the roll angle and the level / superelevation, the track level irregularity can be obtained.

[0117] h=DsinΦ

[0118] In the formula, D is the measured track gauge; φ is the roll angle of the axle box.

[0119] 2. Solving uneven surfaces

[0120] like Figure 9 As shown, the unevenness reflects the deformation of a single rail in the vertical plane. The unevenness can be obtained by spatial domain integration of the wheelset pitch angle.

[0121] If the bogie wheels land on points A and B respectively, and L is equal to the bogie axle spacing, then the angle of the line connecting points A and B is the wheelset pitch angle, establishing the relationship between the pitch angle and elevation irregularities.

[0122]

[0123] In the formula, y1 and y2 are the elevations of points A and B, respectively. If point A is the reference, y2-y1 is the elevation deviation value, denoted by v.

[0124] In actual track geometry and position detection, it is necessary to represent the geometric relationship between multiple detection points through continuous measurements. When there are many detection points, this can be calculated using the cumulative deviation of angles.

[0125] v n =∑L n ×sinθ n =∫θds

[0126] In the formula, v n L represents the high and low values ​​of detection point n. n θ is the cumulative step size for the detection points. n The cumulative pitch angle of the detection point is s, and the mileage is s.

[0127] Therefore, the solution to the unevenness of the track can be achieved by integrating the pitch angle of the axle box once in the spatial domain.

[0128] 3. Solution for track irregularities

[0129] like Figure 10 As shown, track irregularities reflect the deformation of a single rail on the horizontal plane. Track irregularities can be obtained by spatial domain integration of the wheelset heading angle.

[0130] ω n =∫γds

[0131] In the formula, ω n Let γ be the orbital direction value of detection point n, γ be the heading angle, and s be the mileage.

[0132] Track irregularity estimation

[0133] Elevation and yaw irregularities can be obtained by spatial domain integration of the wheelset pitch and yaw angles. Commonly used integration methods include rectangular integration, trapezoidal integration, and Runge-Kutta integration. Runge-Kutta integration is suitable for integrating nonlinear data and offers advantages such as high accuracy, data stability, and ease of programming. Its calculation formula is shown below:

[0134]

[0135] In the formula, K1, K2, K3, and K4 are all undetermined coefficients, and v n x represents the track irregularity value. n Let θ be the attitude angle and h be the integration step size.

[0136] Step 170: Based on the geometry of the track and the correspondence between the attitude angle data and the track mileage, the geometry of the track to be tested is obtained.

[0137] like Figure 11A As shown, this is the geometric position calculation result for a horizontally uneven track. Figure 11B As shown, this is the geometric position calculation result of an uneven track, such as... Figure 11C As shown, this is the geometric position calculation result of the track with uneven track orientation. It can be seen that the test results meet the requirements of daily track inspection.

[0138] In one embodiment, before the step of resampling the attitude angle data based on the attitude angle data, the train's travel time, and a time-mileage model to obtain the correspondence between the attitude angle data and the track mileage, the method further includes: obtaining a preset distance threshold; selecting the first N position data on the test line and fitting a fitted straight line based on the first N position data; calculating the distance from each preset detection point on the test line to the fitted straight line; if the distance from the preset detection point to the fitted straight line is less than the preset distance threshold, then sequentially adding a fitting point to the fitted straight line; if the distance from the preset detection point to the fitted straight line is greater than the preset distance threshold, then marking segment points on the test line; and dividing the test line into multiple line segments based on the segment points. The step of calculating the geometric shape and position of the track based on the attitude angle data includes: calculating the geometric shape and position of each line segment based on the divided line segments and the attitude angle data.

[0139] In this embodiment, trajectory alignment segmentation based on attitude angles is the first step in trajectory geometry calculation. By setting a preset distance threshold w, segmentation points are determined to divide straight segments, transition curve segments, and circular curve segments. The trajectory alignment segmentation process is as follows: Figure 7 As shown.

[0140] The wheelset roll angle time history data shows a fitted line slope of 0 on straight sections, a non-zero constant value on circular curve sections, and a linear increase from 0 to a constant value on transition curve sections. The wheelset heading angle time history data shows a fitted line slope of constant on straight sections, a linear change with mileage on circular curve sections, and a quadratic parabolic change with mileage on transition curve sections. The wheelset pitch angle time history data shows a fitted line slope of constant on straight sections and a linear change on curved sections.

[0141] The fitting line uses the overall least squares method, which takes into account both independent and dependent variables and finds the optimal parameter estimates. The overall least squares method essentially fits a straight line that minimizes the sum of the squared orthogonal distances S to each detection point. The equation of the straight line S is as follows:

[0142] y = kx + b

[0143]

[0144] To calculate the minimum value of S, we should take the partial derivatives with respect to variables k and b, and set all the partial derivatives to 0. This gives us the stationary point of the function. The calculation formula is as follows.

[0145]

[0146] The final solution to the equation is shown below:

[0147]

[0148] Therefore, the attitude angle time history data can eliminate the data characteristics of the line shape by fitting the line equation.

[0149] In one embodiment, after the step of calculating the geometry of the track based on the attitude angle data, the method further includes: using a compensation algorithm to compensate for errors in the geometry of the track, wherein the compensation algorithm is a recursive least squares method.

[0150] It should be understood that attitude angles introduce constant and linear term errors during integration. Trend term elimination methods are used to improve the accuracy of orbital geometry calculations. Therefore, a recursive least squares method is employed for trend elimination, with the iterative formula shown below:

[0151]

[0152] In the formula, N is the number of observation data, h is the integration step size, k is the polynomial order, I is the identity matrix, and x n These are the observed values. Based on the x-values ​​of the observation sequence... n The undetermined coefficient t can be obtained from the first n observations. N+1 P n+1 K N+1 Φ N+1 This allows us to determine the orbital geometry after eliminating the trend term:

[0153] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0154] Example 2

[0155] Figure 4 The equipment box is as follows: 101 is the satellite navigation system host, 102 is the A / D signal processor, 103 is the industrial control computer, 104 is the USB port, 105 is the main power switch, and 106 is the RTK antenna. Figure 5 The device is a fixed fixture, 201 is an upper fixing clamp and 202 is a base fixing clamp. The under-vehicle equipment includes: a high-precision inertial navigation system 301, a vertical acceleration sensor 302, a lateral acceleration sensor 303 and a laser rangefinder sensor 304.

[0156] The onboard equipment mainly consists of an industrial control computer, a satellite positioning system host, and an A / D signal processor, all integrated and installed within the onboard equipment box. It is used for communication transmission, positioning information acquisition, and analog-to-digital conversion.

[0157] The undercarriage equipment consists of an inertial navigation system, axle box accelerometers, and laser rangefinders, all integrated and installed within an undercarriage equipment housing. It is used to measure key information such as geometric characteristics, vibration patterns, and lateral displacement changes of the rails at the axle boxes of the operating vehicle.

[0158] The roof-mounted device is a satellite antenna for the satellite navigation system, fixed to the roof handle. Mounting the satellite antenna on the roof results in better and more stable satellite signal reception, and the signal is connected to the satellite host unit inside the vehicle's equipment compartment.

[0159] The mounted equipment box is a movable, compact, lightweight enclosure with a user-friendly control panel. It is independently placed within the vehicle's equipment cabinet without interfering with the operation of other equipment. The control panel includes an industrial control computer touchscreen, USB ports, and a main power switch. The enclosure is constructed from 3mm thick steel plate, bent and welded, with an insulated base fixed to the bottom. The control panel is made of 5mm thick acrylic sheet and secured to the equipment box with 4mm bolts.

[0160] The mounted undercarriage equipment box is a detachable, miniaturized, lightweight, and securely installed enclosure. It is independently installed at the axle box of the operating vehicle without altering the vehicle body structure or affecting normal train operation. The undercarriage equipment box is securely installed at the axle box using a suspension device consisting of one spreader beam and two lifting lugs, two base fixing clamps, and fastening steel wire ropes. The left and right laser rangefinders are placed in two separate laser protection boxes and installed under the left and right axle boxes using clamps. The lower part of the box is secured to the two base clamps at the bottom of the axle box lugs using 12mm bolts. The laser protection boxes are fixed to the laser mounting bracket with 10mm bolts, allowing for easy adjustment of the angle. The laser mounting bracket is designed to be flexible and easily deformable under intrusion, preventing damage to the axle box structure upon impact. The bracket is fixed to the bottom of the axle box with 12mm bolts and a 8mm long bolt through one end for positioning. To prevent accidents during train operation, safety wire ropes and tensioners are used to secure the undercarriage equipment boxes to the axle boxes.

[0161] Data acquisition operation: Safety check of all equipment on and off the vehicle → The operating vehicle travels to the starting point of the testing route, presses the equipment switch to start the equipment, and keeps the operating vehicle stationary for at least 5 minutes. The equipment acquisition program on the industrial control computer screen will be ready. → The operating vehicle starts running and performs the test. Click the "Start" button on the industrial control computer touch screen. All four indicator lights on the left will be green, indicating that the equipment is working normally. → The operating vehicle travels to the end of the testing route to complete the test. Click the "Stop" button to end the data acquisition work.

[0162] Data storage method: Data can be uploaded to the website for analysis and calculation of track geometry. After completing the inspection, submit the data. The inspection data is stored on the D drive of the industrial control computer, and the folder is named in the format "RtiDaq_time". The folder contains four types of data files with extensions .gams, .ins, .gams, and .rti. If the data is not uploaded, the entire folder can be copied to a USB drive and provided to the analysis and calculation personnel for track geometry analysis. After the train is finished, check the equipment again to ensure there are no abnormalities, and turn off the main power switch.

[0163] 1. Mileage positioning calculation method

[0164] Complete the mileage location points: such as Figure 6 As shown, track geometry detection requires accurate track geometry parameters at the specified mileage location. This method first calculates the mileage and then solves for the track geometry parameters. Mileage estimation is based on GNSS / INS fusion data. On the one hand, the amount of satellite measurement data is insufficient for positioning requirements, necessitating the expansion of more positioning point data; on the other hand, when the train passes through tunnels or other areas with poor signal coverage, satellite measurement data is missing, requiring the completion of positioning point data.

[0165] First, calculate the train speed:

[0166] Let the coordinates of adjacent GNSS positioning points be (X... x0 Y y0 ) and (X xn Y yn ), (X x0 Y y0 The initial velocity of point (V) is x0 V y0 ), the acceleration a measured by INS x and a y Then the following relationship holds:

[0167]

[0168]

[0169] In the formula, n is the number of INS positioning points between two GNSS positioning points, and t represents the interval between adjacent INS positioning points (0.005 seconds). Based on the initial velocity, the velocity V of each positioning point K in the train's coordinate system b can be calculated. K :

[0170] V K =V K-1 +a K-1 ·t

[0171] Position calculation in the northeast-central coordinate system: The INS output is the velocity and acceleration in the b-frame, i.e., the longitudinal velocity V of the train. y Horizontal, longitudinal, and vertical accelerations a x a y and a z The odometer location information is in the Northeast-North (ENU) coordinate system, so the velocity and acceleration need to be converted to the B-frame. The East and North coordinates in the ENU are represented by X... E and Y N The velocity of the b-system and the velocity of the ENU-system satisfy a trigonometric relationship with respect to the heading angle α:

[0172] V E =V y ·cos(α)

[0173] V N =V y sin(α)

[0174] b-series acceleration requires a strapdown matrix transformation, the transformation formula of which is as follows:

[0175]

[0176] Calculate the ENU coordinates of the measuring point based on the known GNSS point coordinates and the INS output velocity and acceleration.

[0177]

[0178]

[0179] In the formula, X Ek Y Nk This refers to the coordinates of the detection point ENU.

[0180] Mileage matching algorithm:

[0181] Data points completed by GNSS / INS are recorded as location points; original line location information is recorded as marker points; an index between location points and marker points is created by creating a grid; and the optimal marker point is determined by calculating (distance and angle) using a weighted matching function, which is then used as the train's position.

[0182] The grid step size is set to 150 meters. Therefore, no more than three markers are allowed within each grid. A 200-meter railway line can be approximated as a straight line. In actual positioning, the number of markers is generally less than or equal to the number of positioning points, ensuring that each marker is utilized. The latitude and longitude of the lower left corner of the railway section grid table are (X... A Y A The latitude and longitude coordinates of the upper right corner are (X... B Y B The number of grids in the latitude and longitude directions are M and N respectively. Calculated using the latitude and longitude distance formula, the grid distance length in the longitude direction is H. lon (Unit: m), the grid distance length in the latitudinal direction is H. lat (Unit: m), then the values ​​of M and N are respectively:

[0183] M = H lon ÷150

[0184] N = H lat ÷150

[0185] For the bottom left corner coordinate (X) of the grid table A Y A ) and the coordinates of the upper right corner (X B Y B There are two situations.

[0186] First, when the grid is pre-divided, meaning all lines are included within it, (X) A Y A ) and (X B Y B () refers to the coordinates of the bottom left and top right corners of the grid table;

[0187] Second, the grid will include all lines, then (X) A Y A ) and (X B Y B The coordinates refer to the coordinates of the lowest and highest points on the route, respectively.

[0188] In the railway section grid table, the grid with step size in meters (m) needs to be converted to a grid with step size in latitude and longitude. The latitude and longitude step sizes of the grid are as follows:

[0189] S lon =(X B -X A )÷M

[0190] S lat =(Y B -Y A )÷N

[0191] In the formula: S lon S represents the step size in the longitude direction; lat This represents the step size in the latitudinal direction.

[0192] Latitude and longitude coordinates are P(x lon y lat The corresponding grid number P(p) lon p lat It is determined by the following formula:

[0193]

[0194]

[0195] In the formula: p lon The number for the longitude direction; p lat The numbers represent latitude; [] indicates rounding down.

[0196] Matching function

[0197]

[0198] In the formula, d is the straight-line distance between the positioning point and the marker point to be matched; D is the distance between adjacent marker points; θ is the angle between the velocity direction of the positioning point and the velocity direction of the marker point to be matched; ω is the weighting coefficient of the distance factor, and θ is the weighting coefficient of the direction angle factor, and they are related as follows: d +ω θ =1, adjacent S marker points D=100m. Within the allowable error range, all positioning points can be matched to the marker points, which can meet the requirement of precise positioning of the train during operation.

[0199] This embodiment provides a track geometry and position detection method based on wheelset attitude angles. It establishes an "attitude angle-mileage" model by using attitude angle data and mileage data obtained from various measuring points on the track by an onboard track inspection instrument. An INS (Instrument System) is installed on the train axle box to measure wheelset attitude angles, wheelset attitude angular rates, and train longitudinal acceleration. A GNSS (GNSS Observation System) is installed inside the vehicle with a satellite antenna mounted on the roof to measure latitude, longitude, and train speed. An AMS (Axle Box Vibration Measurement System) measures axle box vibration, and a GMS (Gross Rail Measurement System) measures the lateral displacement of the left and right rails. The data acquisition equipment consists of an A / D signal processor for analog-to-digital conversion and an industrial control computer for system control, data storage, and processing.

[0200] Step 1: Line route segmentation

[0201] Orbit alignment segmentation based on attitude angles is the first step in orbit geometry calculation. By setting a threshold 'w', segmentation points are determined to divide straight segments, transition curve segments, and circular curve segments. The orbit alignment segmentation process is as follows: Figure 7 As shown.

[0202] The wheelset roll angle time history data shows a fitted line slope of 0 on straight sections, a non-zero constant value on circular curve sections, and a linear increase from 0 to a constant value on transition curve sections. The wheelset heading angle time history data shows a fitted line slope of constant on straight sections, a linear change with mileage on circular curve sections, and a quadratic parabolic change with mileage on transition curve sections. The wheelset pitch angle time history data shows a fitted line slope of constant on straight sections and a linear change on curved sections.

[0203] The fitting line uses the overall least squares method, which takes into account both independent and dependent variables and finds the optimal parameter estimates. The overall least squares method essentially fits a straight line that minimizes the sum of the squared orthogonal distances S to each detection point. The equation of the straight line S is as follows:

[0204] y = kx + b

[0205]

[0206] To calculate the minimum value of S, we should take the partial derivatives with respect to variables k and b, and set all the partial derivatives to 0. This gives us the stationary point of the function. The calculation formula is as follows.

[0207]

[0208] The final solution to the equation is shown below.

[0209]

[0210] Therefore, the attitude angle time history data can eliminate the data characteristics of the line shape by fitting the line equation.

[0211] Step 2: Data Resampling

[0212] The mounted dynamic track inspection system performs isochronous sampling, which requires converting the acquired time-domain signal into a spatial-domain sequence. Specifically, using t as the sampling time interval, high-precision inertial navigation attitude angle data is converted, as shown in the following equation:

[0213] θ(s) = θ(t) × V(t)

[0214] In the formula, θ(t) is the attitude angle at time t, V(t) is the vehicle speed at time t, and θ(s) is the attitude angle at mileage s.

[0215] Attitude angle data at corresponding mileages are affected by factors such as the sampling environment, necessitating outlier removal. A uses the Rheinda criterion (3σ method) to process the data measurement sequence x1, x2, ... x... n Find the arithmetic mean. and residual error Next, calculate the root mean square deviation. The following determinations are made:

[0216] Then x t This is normal data and should be retained.

[0217] Then x t This is abnormal data and should be discarded, replaced by the median value of its left and right adjacent data.

[0218] Step 3: Calculate the track geometry and position based on the axle box attitude angle

[0219] 1. Calculation of uneven horizontal surfaces

[0220] The horizontal dimension is represented by the height difference h between the top surfaces of the left and right rails on the same cross section of the track. For example... Figure 8 As shown, while maintaining wheel-rail contact, the roll angle φ of the axle box can be considered approximately equal to the inclination angle φ′ formed by the height difference h between the top surfaces of the left and right rails. By combining the track gauge information and establishing a trigonometric relationship between the roll angle and the level / superelevation, the track level irregularity can be obtained.

[0221] h=DsinΦ

[0222] In the formula, D is the measured track gauge; φ is the roll angle of the axle box.

[0223] 2. Solving uneven surfaces

[0224] like Figure 9 As shown, the unevenness reflects the deformation of a single rail in the vertical plane. The unevenness can be obtained by spatial domain integration of the wheelset pitch angle.

[0225] If the bogie wheels land on points A and B respectively, and L is equal to the bogie axle spacing, then the angle of the line connecting points A and B is the wheelset pitch angle, establishing the relationship between the pitch angle and elevation irregularities.

[0226]

[0227] In the formula, y1 and y2 are the elevations of points A and B, respectively. If point A is the reference, y2-y1 is the elevation deviation value, denoted by v.

[0228] In actual track geometry and position detection, it is necessary to represent the geometric relationship between multiple detection points through continuous measurements. When there are many detection points, this can be calculated using the cumulative deviation of angles.

[0229] v n =∑L n ×sinθ n =∫θds

[0230] In the formula, v n L represents the high and low values ​​of detection point n. n θ is the cumulative step size for the detection points. n The cumulative pitch angle of the detection point is s, and the mileage is s.

[0231] Therefore, the solution to the unevenness of the track can be achieved by integrating the pitch angle of the axle box once in the spatial domain.

[0232] 3. Solution for track irregularities

[0233] like Figure 10 As shown, track irregularities reflect the deformation of a single rail on the horizontal plane. Track irregularities can be obtained by spatial domain integration of the wheelset heading angle.

[0234] ω n =∫γds

[0235] In the formula, ω n Let γ be the orbital direction value of detection point n, γ be the heading angle, and s be the mileage.

[0236] Step 4: Track irregularity estimation

[0237] Elevation and yaw irregularities can be obtained by spatial domain integration of the wheelset pitch and yaw angles. Commonly used integration methods include rectangular integration, trapezoidal integration, and Runge-Kutta integration. Runge-Kutta integration is suitable for integrating nonlinear data and offers advantages such as high accuracy, data stability, and ease of programming. Its calculation formula is shown below:

[0238]

[0239] In the formula, K1, K2, K3, and K4 are all undetermined coefficients, and v nx represents the track irregularity value. n Let θ be the attitude angle and h be the integration step size.

[0240] Step 5: Error Compensation Algorithm

[0241] The attitude angles introduce constant and linear errors during integration. A trend term elimination method is employed to improve the accuracy of the orbital geometry calculation. Therefore, a recursive least squares method is used for trend elimination, and the iterative formula is shown below.

[0242]

[0243] In the formula, N is the number of observation data, h is the integration step size, k is the polynomial order, I is the identity matrix, and x n These are the observed values. Based on the x-values ​​of the observation sequence... n The undetermined coefficient t can be obtained from the first n observations. N+1 P n+1 K N+1 Φ N+1 This allows us to determine the orbital geometry after eliminating the trend term:

[0244] Step 6: Display of solution results

[0245] Figures 11A to 11C The results are calculated for horizontal, vertical, and track-oriented irregularities, respectively, which meet the requirements for daily track inspection.

[0246] Example 3

[0247] In this embodiment, as Figure 2 As shown, a track geometry and position detection device is provided, comprising:

[0248] The time coordinate model construction module 210 is used to obtain the dynamic coordinates of the train during its travel on the track to be tested, and to construct a time coordinate model based on the dynamic coordinates. The time coordinate model records the correspondence between the train's travel time and the train's dynamic coordinates.

[0249] The coordinate mileage acquisition module 220 is used to acquire a pre-set coordinate mileage model, wherein the coordinate mileage model records the correspondence between each static coordinate of the line under test and the track mileage.

[0250] The time-mileage construction module 230 is used to construct a time-mileage model based on the time coordinate model and the coordinate mileage model, wherein the time-mileage model records the correspondence between the train's travel time and the track mileage;

[0251] The attitude angle data acquisition module 240 is used to collect the attitude angle data of the train through the INS set on the axle box of the train, and to obtain the travel time of the train.

[0252] The data resampling module 250 is used to resample the attitude angle data based on the attitude angle data, the train's travel time, and the time-mileage model to obtain the correspondence between the attitude angle data and the track mileage.

[0253] The geometric position calculation module 260 is used to calculate the geometric position of the track based on the attitude angle data;

[0254] The track geometry and position acquisition module is used to obtain the geometry and position of the track under test based on the geometry and position of the track and the correspondence between the attitude angle data and the track mileage.

[0255] Specific limitations regarding the track geometry detection device can be found in the limitations of the track geometry detection method described above, and will not be repeated here. Each unit in the aforementioned track geometry detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each unit.

[0256] Example 4

[0257] In this embodiment, a computer device is provided. Its internal structure diagram can be shown as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs, and also contains a database for storing train movement data, including coordinate data, acceleration data, and attitude angle data. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with other computer devices that have deployed application software. When executed by the processor, the computer program implements a track geometry detection method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0258] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0259] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the orbit set shape and position detection method described in any of the above embodiments.

[0260] Example 5

[0261] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the orbit set shape and position detection method described in any of the above embodiments.

[0262] Example 6

[0263] In this embodiment, a computer program is provided, which, when executed by a processor, implements the steps of the orbit set shape and position detection method described in any of the above embodiments.

[0264] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0265] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0266] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for detecting the geometric shape and position of a track, characterized in that, include: The dynamic coordinates of the train during its journey on the track under test are obtained, and a time coordinate model is constructed based on the dynamic coordinates. The time coordinate model records the correspondence between the train's travel time and its dynamic coordinates. The step of constructing the time coordinate model based on the dynamic coordinates includes: The coordinates of the train's location point on the track under test are obtained using GNSS, and the train's acceleration is obtained using INS. Based on the coordinates of the positioning point and the acceleration, the speed of the train at the next positioning point is calculated; Calculate the dynamic coordinates of the train in the northeast-sky coordinate system based on the speed of the train at the next positioning point. Based on the dynamic coordinates of the train in the northeast celestial coordinate system, a time coordinate model is constructed; Obtain a pre-set coordinate mileage model, wherein the coordinate mileage model records the correspondence between each static coordinate of the line under test and the track mileage; the static coordinates are the coordinates of the track of the line under test. Based on the time coordinate model and the coordinate mileage model, a time mileage model is constructed, wherein the time mileage model records the correspondence between the train's travel time and the track mileage; based on the correspondence between dynamic coordinates and static coordinates, the correspondence between the train's travel time and the track mileage is established, thereby constructing the time mileage model. The train's attitude angle data is collected by INS installed on the axle boxes of the train, and the train's travel time is obtained. Based on the attitude angle data, the train's travel time, and the time-mileage model, the attitude angle data is resampled to obtain the correspondence between the attitude angle data and the track mileage. The geometric shape and position of the track are calculated based on the attitude angle data; Based on the geometry of the track and the correspondence between the attitude angle data and the track mileage, the geometry of the track under test is obtained.

2. The method according to claim 1, characterized in that, The step of constructing the time-mileage model based on the time coordinate model and the coordinate mileage model includes: Using the dynamic coordinates of the train in the northeast-central coordinate system in the time coordinate model as the positioning point and the static coordinates of the coordinate mileage model as the marker point, an index between the positioning point and the marker point is established by creating a grid. The optimal marker point is calculated by a weighted matching function and used as the real-time coordinates of the train. The time-mileage model is constructed based on the real-time coordinates of the train, according to the time coordinate model and the coordinate mileage model.

3. The method according to claim 1, characterized in that, Before the step of resampling the attitude angle data based on the attitude angle data, the train's travel time, and the time-mileage model to obtain the correspondence between the attitude angle data and the track mileage, the method further includes: Obtain the preset distance threshold; The first N position data points are selected on the line to be tested, and a fitted straight line is obtained based on the first N position data points; Calculate the distance from each preset detection point on the line under test to the fitted line. If the distance from the preset detection point to the fitted line is less than the preset distance threshold, then add a fitting point to the fitted line in sequence. When the distance from the preset detection point to the fitted straight line is greater than the preset distance threshold, a segmentation point is marked on the line under test; Based on the segmentation points, the line to be tested is divided into multiple line segments; The step of calculating the geometric position of the orbit based on the attitude angle data includes: Based on the divided line segments, the geometric shape and position of each line segment are calculated according to the attitude angle data.

4. The method according to claim 1, characterized in that, The step of resampling the attitude angle data based on the attitude angle data, the train's travel time, and the time-mileage model to obtain the correspondence between the attitude angle data and the track mileage includes: Based on the attitude angle data, the train's travel time, and the time-mileage model, the attitude angle data is resampled, and the attitude angle data obtained based on the travel time is converted into attitude angle data based on track mileage, thereby obtaining the correspondence between the attitude angle data and the track mileage.

5. The method according to any one of claims 1-4, characterized in that, The step of calculating the geometry of the orbit based on the attitude angle data further includes: An error compensation algorithm is used to compensate for the geometric position of the track, wherein the compensation algorithm is the recursive least squares method.

6. A track geometry and position detection device, characterized in that, include: A time coordinate model construction module is used to acquire the dynamic coordinates of a train traveling on the test track, and to construct a time coordinate model based on the dynamic coordinates. The time coordinate model records the correspondence between the train's travel time and its dynamic coordinates. The steps of constructing the time coordinate model based on the dynamic coordinates include: acquiring the coordinates of the train's positioning points on the test track using GNSS, and acquiring the train's acceleration using INS; calculating the train's speed at the next positioning point based on the positioning point coordinates and the acceleration; calculating the train's dynamic coordinates in the northeast-central coordinate system based on the speed at the next positioning point; and constructing the time coordinate model based on the train's dynamic coordinates in the northeast-central coordinate system. The coordinate mileage acquisition module is used to acquire a pre-set coordinate mileage model, wherein the coordinate mileage model records the correspondence between the static coordinates of the line under test and the track mileage; the static coordinates are the coordinates of the track of the line under test. The time-mileage construction module is used to construct a time-mileage model based on the time coordinate model and the coordinate mileage model, wherein the time-mileage model records the correspondence between the train's travel time and the track mileage; based on the correspondence between dynamic coordinates and static coordinates, the correspondence between the train's travel time and the track mileage is established, thereby constructing the time-mileage model. The attitude angle data acquisition module is used to collect the train's attitude angle data and obtain the train's travel time through an INS installed on the axle box of the train. The data resampling module is used to resample the attitude angle data based on the attitude angle data, the train's travel time, and the time-mileage model to obtain the correspondence between the attitude angle data and the track mileage. The geometric position calculation module is used to calculate the geometric position of the track based on the attitude angle data; The track geometry and position acquisition module is used to obtain the geometry and position of the track under test based on the geometry and position of the track and the correspondence between the attitude angle data and the track mileage.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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