Rapid measurement method and system for geometric irregularity of track in turnout zone

Abstract

The invention discloses a rapid measurement method and system for geometric irregularity of a track in a turnout zone. The method comprises the steps that complete three-dimensional point cloud data of the surface of a railway turnout steel rail are obtained in real time through a multi-angle line structure laser scanner on a special detection vehicle body; accurate attitude and position data of the detected vehicle body in the measurement process are obtained in real time through an inertial navigation measurement unit and an odometer; obtaining a high-precision pose of a detected vehicle body through a robust dead reckoning method of an IMU (Inertial Measurement Unit) assisted by a speedometer dynamic lever arm value based on a variable wheel-rail contact point; constructing a turnout area three-dimensional point cloud model; extracting geometric irregularity key points of turnout steel rail point cloud from the turnout area three-dimensional point cloud model; calculating parameters of track gauge, horizontal and rail direction irregularity and unevenness; the special detection vehicle body can quickly and stably pass through a turnout area to complete continuous and accurate measurement of geometric irregularity of a track.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge pile foundation construction, and more specifically, relates to a method and system for quickly measuring track geometric irregularities in a turnout area. Background Art

[0002] A turnout is a key facility to ensure the safe line change of locomotives and vehicles, and is widely used in stations and marshalling yards. However, its failure rate is relatively high. The track geometric irregularities in the turnout area directly affect the wheel-rail contact state, reduce the riding comfort and safety, and increase the maintenance cost. Therefore, the track geometric irregularities in the turnout area need to be measured efficiently and accurately. The parameters causing track geometric irregularities in the turnout area are divided into internal parameters and external parameters. The former mainly includes: alignment, cross level, superelevation, gauge, twist, etc., and the latter includes absolute position, elevation, etc. Compared with external geometric parameters such as the absolute position and elevation of the track, the internal geometric parameters of the track are the main factors affecting vehicle driving and are also the main measurement objects.

[0003] Currently, the track geometry in the turnout area is still measured mainly by manual hand-held tools. For example: manually establish a chord line of a fixed length and measure the versine of the chord at a specified position to measure the values of alignment and cross level irregularities. Use tools such as a hand-held gauge to measure gauge and cross level irregularities. However, such methods have low measurement efficiency and poor result reliability.

[0004] The long laser chord method determines alignment and cross level irregularities by establishing a chord line through a laser transmitter and a receiver and measuring the versine value of a specified chord length at an intermediate position. Such methods have high measurement accuracy, but low efficiency, and cannot measure gauge and superelevation.

[0005] The dynamic integrated inspection vehicle technology combines technologies such as positioning and attitude determination technology, vision measurement, and laser measurement. The inspection platform simulates the actual running state of the train. Usually, accelerometers, gyroscopes, displacement sensors and other devices are installed on the car body, axle, bogie, and wheels to achieve high-speed dynamic synchronous measurement of track irregularities and train comfort indicators, etc. The high-speed dynamic inspection train system is restricted by the car body structure and cannot meet the requirements of track irregularity measurement accuracy, and the positioning accuracy under high dynamic conditions cannot meet the needs of precise track maintenance.

[0006] The light rail geometry measuring instrument relies on gyroscopes, accelerometers or inertial measurement units carried by the device itself to sense the changes in the attitude and position of the detection device, and then combines the stable geometric relationship between the device and the contact point of the rail to inversely calculate the geometric parameters of the rail. It is divided into absolute and relative types. The absolute device adopts a stop-and-go mode, and uses a total station to transmit the control point coordinates at a certain interval to correct the errors of the detection device. It can obtain the internal geometric parameters, absolute position, elevation, etc. of the track at the same time, but its efficiency is low, about 300 meters per hour. The relative measurement type rail inspection trolley only detects the internal geometric parameters of the track and can measure at a speed of several kilometers per hour. Whether it is an absolute or relative type rail inspection trolley, when measuring the geometric irregularities of the turnout, it has the disadvantages of limited passability, unstable wheel-rail contact relationship, and insufficient three-dimensional display of the measurement results.

[0007] To sum up, the manual ruler-holding and laser long chord methods are inefficient, and the dynamic comprehensive detection train method has insufficient accuracy. The light rail geometry measuring instrument faces the problems of passability and measurement stability. Summary of the Invention

[0008] In view of the above defects or improvement requirements of the prior art, the present invention provides a method and system for rapidly measuring the geometric irregularities of the turnout area track. By designing a special detection vehicle body with multiple passing wheels in a 4-unit × 2-wheel mode and multiple lateral limiting wheels, the problems of limited passability and vibration interference caused by non-smooth structures in the horizontal and vertical directions are reduced; through a robust dead reckoning method based on the dynamic lever arm value of the odometer with variable wheel-rail contact points to assist the IMU, high-precision pose rapid and continuous acquisition of the detection device without relying on external position information constraints is realized; combined with multi-source data such as IMU, odometer, and multi-line structured laser scanner, through data fusion algorithms, high-precision point cloud data of the turnout is obtained, further improving the accuracy and reliability of the detection system; by using a transverse rail standard section model matching and key area constraint method based on a complete high-precision three-dimensional point cloud model of the complex turnout structure to determine the key points in different areas of the turnout, the key points in the turnout area can be accurately identified, providing an accurate reference for the measurement of track geometric irregularities; through the special detection vehicle body, the turnout area can be quickly and smoothly passed to complete continuous and precise measurement of track geometric irregularities, not only improving the detection efficiency, but also ensuring the accuracy and reliability of the measurement results.

[0009] To achieve the above object, one aspect of the present invention provides a method for rapidly measuring the geometric irregularities of the turnout area track, including the following steps:

[0010] S1: Install a special detection vehicle body in the turnout area to be measured; use the multi-angle line-structured laser scanner on the special detection vehicle body to scan the turnout rail in real time to obtain the complete three-dimensional point cloud data of the turnout rail surface;

[0011] S2: Obtain the accurate attitude and position data of the special detection vehicle body in real time through the inertial navigation measurement unit and odometer on the special detection vehicle body during the measurement process;

[0012] S3: Carry out fusion solution on the accurate attitude and position data of the special detection vehicle body in the measurement process through a robust dead reckoning method of the IMU assisted by the dynamic lever arm value of the odometer based on the variable wheel-rail contact point to obtain the high-precision pose estimation result of the special detection vehicle body;

[0013] S4: Construct a 3D point cloud model of the turnout area according to the complete 3D point cloud data of the surface of the turnout rail and the high-precision pose estimation result during the measurement process of the special detection vehicle body;

[0014] S5: Extract the geometric irregularity key points of the turnout rail point cloud from the 3D point cloud model of the turnout area by using the method of matching the standard cross-section model of the lateral rail and the constraint of the key area;

[0015] S6: Calculate the gauge, level, alignment irregularity and vertical irregularity parameters according to the geometric irregularity key points of the turnout rail point cloud.

[0016] Further, in step S1, the special detection vehicle body includes a plurality of lateral limiting wheels in a 4-unit × 2-wheel mode arranged on the side of the track;

[0017] Among all the lateral limiting wheels, at least two lateral limiting wheels on each side are in contact with the rail;

[0018] The distance between the front and rear two units is greater than the distance of the frog;

[0019] The connection line between every two lateral limiting wheels forms an angle inclined inward with the advancing direction.

[0020] Further, the special detection vehicle body also includes a plurality of passing wheels in a 4-unit × 2-wheel mode arranged in the horizontal direction of the track;

[0021] Among the plurality of passing wheels in the 4-unit × 2-wheel mode, the distance between the two passing wheels in each unit is greater than the distance of the frog;

[0022] Among the plurality of passing wheels in the 4-unit × 2-wheel mode, the two passing wheels of one unit are connected by a synchronous belt;

[0023] The odometer is arranged on any one of the two passing wheels connected by a synchronous belt among the plurality of passing wheels in the 4-unit × 2-wheel mode.

[0024] Further, the dedicated inspection vehicle body further includes a plurality of line-structured laser scanners with the same quantity respectively arranged on the left side and the right side of the dedicated inspection vehicle body, and an inertial navigation measurement unit installed in the middle of the dedicated inspection vehicle body; the plurality of line-structured laser scanners form a multi-angle scanning unit.

[0025] Further, in step S2, the accurate attitude and position data of the dedicated inspection vehicle body during the measurement process are obtained in real time through the inertial navigation measurement unit and the odometer on the dedicated inspection vehicle body; it includes:

[0026] Measure the angular velocity and acceleration of the dedicated inspection vehicle body through a gyroscope and an accelerometer;

[0027] Measure the moving distance and speed of the dedicated inspection vehicle body through an odometer;

[0028] Perform attitude solution and position calculation on the angular velocity, acceleration, moving distance and speed data of the dedicated inspection vehicle body through a data processing unit and a navigation computer to obtain the accurate attitude and position data of the dedicated inspection vehicle body during the measurement process.

[0029] Further, in step S3, the accurate attitude and position data of the dedicated inspection vehicle body during the measurement process are fused and solved through a robust dead reckoning method of an IMU assisted by an odometer dynamic lever arm value based on a variable wheel-rail contact point to obtain a high-precision pose calculation result of the dedicated inspection vehicle body; it includes:

[0030] S31: Perform combined solution on the accurate attitude and position data of the dedicated inspection vehicle body during the measurement process by using Kalman filtering to obtain the state vector and state transition matrix of the dedicated inspection vehicle body;

[0031] S32: Establish an observation equation according to the relationship between the inertial center position of pure inertial navigation and the three-dimensional position of the dedicated inspection vehicle body obtained by odometer dead reckoning to describe the relationship between the measurement data and the state variables;

[0032] S33: Determine the passing wheel contact points corresponding to the odometer measurement values during the movement process according to the actual situation, model the lever arm value of the odometer installation parameters, and eliminate the error caused by the change of the lever arm value in the Kalman filtering observation quantity to obtain a corrected observation equation;

[0033] S34: Obtain the high-precision pose calculation result of the dedicated inspection vehicle body 1 according to the corrected observation equation.

[0034] Further, the observation equation Z in step S32 is represented by formula (3):

[0035]

[0036] Wherein, P ins is the position obtained by pure inertial navigation solution; M pvD is the intermediate calculation value; is the attitude transformation matrix from the vehicle system to the new navigation; P D is the position obtained by the combined calculation of inertial navigation and odometer; l b is the lever arm value of the odometer installation parameter at the measurement instant; l b is expressed by Equation (4):

[0037]

[0038] Wherein, is the theoretical approximation of l b ; Δl b is the constant deviation of the lever arm value between the two wheel-rail contact points corresponding to the odometer in one unit; δl b is the zero-mean random error part of l b ;

[0039] Since the odometer measures two corresponding wheels, the lever arm value should be two random constants;

[0040] When corresponding to the No. 1 wheel, the value of Δl b is [0 0 0];

[0041] When corresponding to the No. 2 wheel, the value of Δl b is [Δx max Δy max 0]; Δx max and Δy max are the maximum values in the corresponding directions respectively;

[0042] When the track is deflected to the left relative to the passing wheel, Δl b is expressed by Equation (5):

[0043]

[0044] When the track is deflected to the right relative to the passing wheel, Δl b is expressed by Equation (6):

[0045]

[0046] Wherein, R is the track curvature radius of the turnout to be measured;

[0047] The Δl b obtained by calculating through Formula (5) and Formula (6) is substituted into the above Formula (4) to obtain the lever arm value l b of the odometer installation parameter at the measurement instant, and then substituted into Formula (3) to obtain the calculation result of the observation equation after the kamman filter correction;

[0048] The corrected observation equation Z1 described in step S33 is as follows:

[0049] Z1 = δP ins -δP D (7)

[0050] Wherein, δP ins is the three-dimensional position error of pure inertial navigation solution; δP D is the three-dimensional position error obtained by odometer dead reckoning.

[0051] Furthermore, in step S5, the geometric irregularity key points of the turnout rail point cloud are extracted from the three-dimensional point cloud model of the turnout area by using the lateral rail standard section model matching and key area constraint method, including:

[0052] S51: Match the rail section design model with the rail scan point cloud to determine the range at the rail top and 16 mm below the rail;

[0053] S52: Search for the highest point at the approximate position of the rail top and the outermost point at the approximate position of 16 mm below the rail in the matched section model; the approximate search range for the vertex in the rail section model is 3 cm in total for the left and right distances and 2 mm in total for the up and down distances, and the approximate search range for the position 16 mm below the rail in the rail section model is 4 mm in total for the up and down distances and 2 mm in total for the left and right distances;

[0054] S53: Search for the point with the highest elevation as the key point at the rail top and the point with the outermost lateral coordinate as the key point at 16 mm below the rail top within the specified search range.

[0055] Furthermore, it is characterized in that:

[0056] The calculation of the gauge in step S6 includes: determining the key point 16 mm downward from the rail top on the inner side of the rail; measuring the minimum distance between the key points on the inner sides of the two rails, which is the gauge;

[0057] The calculation of the rail alignment irregularity includes:

[0058] Determining the key points at the starting point and the ending point according to the specified chord length on the side of the rail:

[0059] Calculating the versine value of the intermediate point from the chord;

[0060] Calculating the difference between the versine value of the intermediate point and the designed versine value, which is the rail alignment irregularity;

[0061] The calculation of the vertical irregularity includes:

[0062] Determining the key points at the starting point and the ending point according to the specified chord length on the top surface of the rail;

[0063] Calculating the versine value of the intermediate point from the chord;

[0064] Calculate the difference between the versine value of the intermediate point and the designed versine value, which is the vertical irregularity.

[0065] The second aspect of the present invention provides a rapid measurement system for track geometric irregularities in a turnout area, which is used to implement the rapid measurement method for track geometric irregularities in the turnout area, including: a first main module, a second main module, a third main module, a fourth main module, a fifth main module, and a sixth main module;

[0066] The first main module is used to scan the railway turnout rail in real time through a multi-angle line-structured laser scanner on a special detection vehicle body to obtain complete three-dimensional point cloud data of the surface of the railway turnout rail;

[0067] The second main module is used to obtain the accurate attitude and position data of the special detection vehicle body during the measurement process in real time through the inertial measurement unit and odometer on the special detection vehicle body;

[0068] The third main module is used to fuse and solve the accurate attitude and position data of the special detection vehicle body during the measurement process through a robust dead reckoning method of an odometer dynamic lever arm value auxiliary IMU based on a variable wheel-rail contact point to obtain a high-precision pose reckoning result of the special detection vehicle body;

[0069] The fourth main module is used to construct a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the surface of the railway turnout rail and the high-precision pose reckoning result of the special detection vehicle body during the measurement process;

[0070] The fifth main module is used to extract the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area by using the method of matching the standard cross-section model of the lateral rail and constraining the key area;

[0071] The sixth main module is used to calculate the gauge, level, alignment irregularity, and vertical irregularity parameters according to the geometric irregularity key points of the turnout rail point cloud.

[0072] Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0073] (1) For the rapid measurement method and system for track geometric irregularities in a turnout area of the present invention, by designing a special detection vehicle body with 8 lateral limiting wheels in a 4-unit × 2-wheel mode, and the lateral limiting wheels are inclined, the problems of limited passability and vibration interference caused by non-smooth structures in the horizontal direction are reduced; by designing a special detection vehicle body with 8 passing wheels in a 4-unit × 2-wheel mode, the problems of limited passability and vibration interference caused by non-smooth structures in the vertical direction are reduced; the smoothness of the detection process and the accuracy of the data are ensured; the detection vehicle body of the present invention can better adapt to the complex track structure in the turnout area, and the detection efficiency and reliability are improved.

[0074] (2) A rapid measurement method and system for track geometric irregularities in a turnout area of the present invention, through an odometer dynamic lever arm value-assisted robust dead reckoning method of an IMU based on variable wheel-rail contact points, combines and solves IMU and odometer data through Kalman filtering, realizes continuous and accurate acquisition of track attitude data without relying on external position information, combines multi-source data such as IMU, odometer and multi-line structured laser scanners, and obtains high-precision point cloud data through a data fusion algorithm, further improving the accuracy and reliability of the detection system.

[0075] (3) A rapid measurement method and system for track geometric irregularities in a turnout area of the present invention constructs a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the rail surface of the railway turnout and the high-precision pose reckoning result during the measurement of the special detection vehicle body. Through the ICP algorithm, geometric irregularity key points of the turnout rail point cloud are extracted from the three-dimensional point cloud model of the turnout area by means of transverse rail standard section model matching and key area constraint, providing an accurate reference for the measurement of track geometric irregularities; calculates gauge, level, alignment irregularity and elevation irregularity parameters quickly according to the geometric irregularity key points of the turnout rail point cloud; through the above method, the special detection vehicle body can quickly and smoothly pass through the turnout area to complete continuous and accurate measurement of track geometric irregularities, not only improving the detection efficiency, but also ensuring the accuracy and reliability of the measurement results. Description of the Drawings

[0076] Figure 1 It is a schematic flow chart of a rapid measurement method for track geometric irregularities in a turnout area in an embodiment of the present invention;

[0077] Figure 2 It is a schematic layout structure diagram of 8 lateral limit wheels on a special detection vehicle body in a 4-unit × 2-wheel mode;

[0078] Figure 3 It is a schematic layout structure diagram of 8 passing wheels on a special detection vehicle body in a 4-unit × 2-wheel mode;

[0079] Figure 4 It is a schematic design effect diagram of a special detection vehicle body in a turnout area;

[0080] Figure 5 It is a schematic diagram of the left deviation of the wheel-rail contact point in the passing wheel associated with the odometer in an embodiment of the present invention;

[0081] Figure 6 It is a schematic diagram of the right deviation of the wheel-rail contact point in the passing wheel associated with the odometer in an embodiment of the present invention;

[0082] Figure 7Schematic diagram of the rail cross-section model matching and key point search principle in the embodiments of the present invention;

[0083] Figure 8 Schematic diagram of the structure of the rapid measurement system for track geometric irregularities in the turnout area in the embodiments of the present invention;

[0084] Figure 9 Schematic diagram of the structure of an electronic device in the embodiments of the present invention.

[0085] In all the drawings, the same reference numerals denote the same technical features, specifically: 1 - dedicated detection vehicle body, 11 - lateral limit wheel, 12 - passing wheel, 13 - synchronous belt, 2 - line-structured laser scanner, 3 - inertial navigation measurement unit. Detailed implementation manners

[0086] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0087] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, when an element is referred to as "fixed to", "disposed on" or "provided on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element; the terms "installed", "connected", "connected", "provided" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0088] Track geometric irregularity refers to the degree to which the railway track deviates from the ideal state in terms of geometric shape. Such deviation will affect the running smoothness, safety and comfort of the train. Track geometric irregularity mainly includes the following aspects:

[0089] Gauge

[0090] The gauge refers to the distance between the inner sides of two rails. The standard gauge is 1435 millimeters (4 feet 8.5 inches). Gauge irregularity refers to the deviation between the actual value and the standard value of the gauge. An oversized or undersized gauge can lead to unstable train operation and may even cause derailment accidents.

[0091] Level

[0092] Level refers to the flatness of the track in the vertical direction. An ideal track level should have the two rails on the same horizontal plane. Level irregularity refers to the height difference between the two rails, usually expressed by the level deviation. Level irregularity will cause the train to sway left and right during operation, affecting comfort and safety.

[0093] Profile

[0094] Profile refers to the flatness of the track in the longitudinal direction. An ideal track profile should be smooth without undulations. Profile irregularity refers to the unevenness of the track in the longitudinal direction, usually expressed by the profile deviation. Profile irregularity will cause the train to jolt up and down during operation, affecting comfort and safety.

[0095] Alignment

[0096] Alignment refers to the straightness or curvature of the track in the lateral direction. An ideal alignment should have straight sections straight and curved sections smooth. Alignment irregularity refers to the deviation of the track in the lateral direction, usually expressed by the alignment deviation. Alignment irregularity will cause the train to sway laterally during operation, affecting comfort and safety.

[0097] Twist

[0098] Twist refers to a complex state where profile and level irregularities coexist and interact within a certain length range of the track. Twist will cause the train to experience severe lateral and longitudinal swaying during operation, seriously affecting the stability and safety of the train.

[0099] Track geometric irregularities in the turnout area directly affect the running smoothness and safety of trains, and need to be accurately measured in a timely manner. However, the complex turnout structure makes its measurement much more difficult than that of the main line area.

[0100] For the above reasons, as Figure 1 shown, one aspect of the present invention provides a method for quickly measuring track geometric irregularities in the turnout area, including the following steps:

[0101] S1: Install a special detection vehicle body 1 in the turnout area to be measured; use the multi-angle line-structured laser scanner 2 on the special detection vehicle body 1 to scan the railway turnout rails in real time to obtain the complete three-dimensional point cloud data of the surface of the railway turnout rails;

[0102] S2: Obtain the precise attitude and position data of the dedicated inspection vehicle body 1 during the measurement process in real time through the inertial navigation measurement unit 3 and the odometer on the dedicated inspection vehicle body 1;

[0103] S3: Perform fusion calculation on the precise attitude and position data of the dedicated inspection vehicle body 1 during the measurement process through a robust dead reckoning method of the IMU assisted by the dynamic lever arm value of the odometer based on the variable wheel-rail contact point to obtain a high-precision pose estimation result of the dedicated inspection vehicle body 1;

[0104] S4: Construct a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the surface of the railway turnout rail and the high-precision pose estimation result during the measurement process of the dedicated inspection vehicle body 1;

[0105] S5: Extract the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area by means of lateral rail standard cross-section model matching and key area constraint;

[0106] S6: Calculate the gauge, level, alignment irregularity and vertical irregularity parameters according to the geometric irregularity key points of the turnout rail point cloud.

[0107] Furthermore, in step S1, the most challenging area in terms of passability is the adverse space area of the fixed switch rail. The dedicated inspection vehicle body 1 should ensure that the inspection equipment on the vehicle body can pass through the turnout area smoothly. Whether it is the left or right side of the equipment, the forward or backward movement mode, or the straight or curved track area, the vehicle body and the inspection equipment will not be stuck, derailed, or generate excessive impact vibration in the horizontal and vertical directions; To ensure that the vehicle body will not derail or generate excessive impact vibration in the horizontal direction, the dedicated inspection vehicle body 1 includes a plurality (preferably 8) of lateral limiting wheels 11 in a 4-unit × 2-wheel mode arranged on the side of the track, as Figure 2 shown. At least two lateral limiting wheels 11 on each side of all the lateral limiting wheels 11 are in contact with the rail to prevent the dedicated inspection vehicle body 1 from rotating and derailing in the horizontal direction. The distance between the front and rear two units is greater than the distance of the adverse space, so that it will not be stuck laterally. The connection line between every two lateral limiting wheels 11 forms an inwardly inclined angle with the forward direction, which can ensure that when passing through the adverse space, the lateral limiting wheels 11 can pass through the tip area of the switch rail smoothly at a certain angle instead of directly hitting the tip of the switch rail with a huge impact. The inclination angle between the lateral limiting wheels 11 should not be too large, otherwise it cannot pass through the gaps between the wing rail, guard rail and the straight and curved tracks.

[0108] Furthermore, as Figure 3As shown, to ensure that there is no jamming in the vertical direction and no excessive impact vibration, the dedicated inspection vehicle body 1 further includes a plurality of (preferably 8) passing wheels 12 in a 4-unit × 2-wheel mode arranged in the horizontal direction of the track; the distance between the two horizontal passing wheels 12 in each unit is greater than the distance of the harmful space, so as to ensure that each unit will not get stuck in the harmful space. Among the plurality of passing wheels 12 in the 4-unit × 2-wheel mode, the two passing wheels 12 of one unit are connected by a synchronous belt 13, and the rotation of any one wheel can drive the other wheel to rotate, as shown in Figure 3 the elliptical part in; among the plurality of passing wheels 12 in the 4-unit × 2-wheel mode, an odometer is installed on any one of the two passing wheels 12 connected by the synchronous belt 13; so when the dedicated inspection vehicle body 1 passes through the harmful space, at least one wheel can measure the moving distance of the device, and there will be no slipping, idling and other situations.

[0109] Furthermore, as shown in Figure 4 , in the embodiment of the present invention, a line-structured laser scanner 2 is used to scan the rail. Since the turnout structure is complex and variable, and the line-structured laser scanner can only ensure the required accuracy within a certain scanning range, it is necessary to scan the rail from multiple angles. Therefore, the dedicated inspection vehicle body 1 further includes a plurality of line-structured laser scanners 2 with the same quantity respectively arranged on the left and right sides of the dedicated inspection vehicle body 1 and an inertial navigation measurement unit 3 installed in the middle of the dedicated inspection vehicle body 1; the plurality of line-structured laser scanners 2 form a multi-angle scanning unit; in the embodiment of the present invention, a plurality of (preferably 4) line-structured laser scanners 2 are respectively arranged on the left and right sides of the dedicated inspection vehicle body 1; to ensure that the complete three-dimensional point cloud data of the turnout can be comprehensively obtained, Figure 4 in, the dotted line represents the scanning range of the line-structured laser scanner 2.

[0110] Furthermore, in step S2, the accurate attitude and position data of the dedicated inspection vehicle body 1 during the measurement process are obtained in real time through the inertial navigation measurement unit 3 and the odometer on the dedicated inspection vehicle body 1; including:

[0111] Measuring the angular velocity and acceleration of the dedicated inspection vehicle body 1 through a gyroscope and an accelerometer;

[0112] Measuring the moving distance and speed of the dedicated inspection vehicle body 1 through the odometer;

[0113] Through the data processing unit and the navigation computer, the angular velocity, acceleration, moving distance and speed data of the dedicated inspection vehicle body 1 are subjected to attitude solution and position calculation to obtain the accurate attitude and position data of the dedicated inspection vehicle body 1 during the measurement process, and realize the precise positioning and attitude control of the dedicated inspection vehicle body 1 in a complex track environment.

[0114] Furthermore, the complete three-dimensional point cloud data of the surface of the railway turnout rail obtained by scanning with multiple line-structured laser scanners 2 needs to be fused with the position and attitude information of the special detection vehicle body 1 to generate a three-dimensional point cloud model of the turnout area. Therefore, the position and attitude of the special detection vehicle body 1 are the key to obtaining track geometric irregularity parameters. The core sensors for positioning and attitude determination in the present invention are the IMU and the odometer. The present invention proposes a robust dead reckoning method based on the dynamic lever arm value of the odometer with variable wheel-rail contact points to assist the IMU, so as to achieve rapid and continuous acquisition of high-precision position and attitude without relying on external position information constraints.

[0115] Furthermore, in step S3, the accurate attitude and position data of the special detection vehicle body 1 during the measurement process are fused and solved through the robust dead reckoning method based on the dynamic lever arm value of the odometer with variable wheel-rail contact points to assist the IMU, and a high-precision position and attitude estimation result of the special detection vehicle body 1 is obtained, including:

[0116] S31: Using the Kalman filter to perform combined solution on the accurate attitude and position data of the special detection vehicle body 1 during the measurement process to obtain the state vector and state transition matrix of the special detection vehicle body 1;

[0117] S32: Establish an observation equation based on the relationship between the position of the inertial navigation center of the pure inertial navigation and the three-dimensional position of the special detection vehicle body 1 obtained by odometer dead reckoning to describe the relationship between the measurement data and the state variables;

[0118] S33: Determine the passing wheel contact points corresponding to the odometer measurement values during the movement according to the actual situation, model the lever arm value of the odometer installation parameters, and eliminate the error caused by the change of the lever arm value in the Kalman filter observation quantity to obtain a corrected observation equation;

[0119] S34: Obtain the high-precision position and attitude estimation result of the special detection vehicle body 1 according to the corrected observation equation;

[0120] Furthermore, the state vector in step S31 includes the three-dimensional errors of attitude, velocity, and position solved by the pure inertial navigation, the three-axis zero biases of the gyroscope and the accelerometer, and the three-dimensional position error obtained by odometer dead reckoning. The state vector X is represented by formula (1):

[0121]

[0122] where φ T , δv n , δP ins are respectively the three-dimensional errors of attitude, velocity, and position solved by the pure inertial navigation, is the three-axis zero bias of the gyroscope and ε b is the three-axis zero bias of the accelerometer, δP Dis the three-dimensional position error obtained by odometer dead reckoning; T represents the transpose operation of a matrix or vector;

[0123] The state transition matrix described in step S31 is used to describe the change of the state of the dedicated detection vehicle body 1 over time; it is used to predict the state at the next moment; the state transition matrix is represented by equation (2):

[0124]

[0125] In the formula, F is the state transition matrix of the IMU odometer combination, F1 is the error transition matrix of pure inertial navigation of 15×15, L D is the latitude of dead reckoning, h is the geodetic height, v DE , v DN are the eastward and northward speeds measured by the odometer respectively, R MD , R ND are the radius of curvature of the meridian and the radius of curvature of the prime vertical calculated by dead reckoning respectively; is the attitude conversion matrix from the vehicle coordinate system to the new navigation; are the observed value errors of the gyroscope and accelerometer respectively; G is the control input matrix, W is the process noise matrix; F2, M pvD , M ppD are different intermediate calculation values respectively.

[0126] Furthermore, the observation equation described in step S32 relates the measurement data to the state variables and corrects the error in the observation equation through dynamic lever arm value modeling; the observation equation Z is represented by equation (3):

[0127]

[0128] In the formula, P ins is the position solved by pure inertial navigation; P D is the position calculated by the combination of inertial navigation and odometer; l b is the lever arm value of the odometer installation parameter at the measurement instant; it is represented by equation (4):

[0129]

[0130] Among them, is the theoretical approximation of l b ; Δl b is the constant deviation of the lever arm value between the two wheel-rail contact points corresponding to the odometer in one unit; δl b is the 0-mean random error part of l b ;

[0131] In step S33, the present invention determines the passing wheel contact points corresponding to the odometer measurement values according to the actual situation, models the dynamic lever arm value of the odometer installation parameters, and eliminates the error caused by the change of the lever arm value in the Kalman filter observation quantity to obtain a corrected observation equation.

[0132] When the inertial navigation and the odometer are combined for navigation, the odometer speed needs to be reduced to the inertial navigation center and combined with the attitude measured by the inertial navigation for state estimation. Since the installation position of the odometer does not coincide with the inertial navigation center, an installation parameter lever arm value is generated. According to the stable instrument structure design parameters, the odometer measurement corresponds to two wheels. Therefore, the lever arm value should be two random constant values.

[0133] When corresponding to wheel No. 1, the value of Δl b is [0 0 0];

[0134] When corresponding to wheel No. 2, the value of Δl b is [Δx max Δy max 0]; Δx max and Δy max are the maximum values in the corresponding directions respectively.

[0135] The dynamic lever arm value modeling includes: calculating the dynamic lever arm values when the corresponding track deviates to the left and right respectively according to the change of the passing wheel contact points.

[0136] Figure 5 and Figure 6 show that different contact points between the passing wheel and the rail result in changes in the lever arm value corresponding to the odometer measurement. Figure 5 In, A and B are two wheel-rail contact points among the passing wheels associated with the odometer. However, in actual situations, the wheel-rail contact point is C, and due to the influence of the limiting wheel on the side close to the IMU; Figure 5 in, the wheel-rail contact point will tend to A, Figure 6 in, the wheel-rail contact point will tend to B.

[0137] Therefore, the modeling of Δl b should depend on the instrument structure parameters, the turnout curvature information, and the movement direction in the turnout; where when the track deviates to the left, Δl b is represented by equation (5):

[0138]

[0139] When the track deviates to the right, Δl b is represented by equation (6):

[0140]

[0141] In the formula, R is the track curvature radius of the measured turnout.

[0142] Δl obtained by calculating through formula (5) and formula (6) b , substitute it into the above formula (4) to obtain the lever arm value l of the odometer installation parameter at the measurement moment b , and then substitute it into formula (3) to obtain the calculation result of the observation equation after kamman filtering correction; the corrected observation equation is:

[0143] Z1 = δP ins -δP D (7)

[0144] The observation equation coefficients are expressed by formula (8):

[0145] H = [0 3×6 I 3×3 0 3×6 -I 3×3 (8)

[0146] Furthermore,[[]] Figure 7 It reflects the key point extraction principle of the basic rail area in the single-frame point cloud model at the specified position, where the black is the rail section design model and the black frame is the search range. In step S5, the geometric irregularity key points of the turnout rail point cloud are extracted from the three-dimensional point cloud model of the turnout area by using the method of matching the transverse rail standard section model and key area constraint, including:

[0147] S51: Match the rail section design model with the rail scan point cloud to determine the range at the top of the rail and 16 mm below the rail; among them, the matching method is the ICP algorithm; the ICP algorithm calculates the nearest point pairs between the point clouds and gradually adjusts the pose of the point clouds to minimize the matching error between the point clouds. This algorithm has the characteristics of high precision and good stability and is suitable for the registration of point cloud data; in practical applications, in order to improve the calculation efficiency, the requirement for matching precision can be appropriately reduced. By simplifying the matching process and reducing the calculation amount, the overall processing speed can be improved;

[0148] S52: Search for the highest point at the approximate position of the top of the rail after matching and the outermost point at the approximate position of 16 mm below the rail; the search range is determined according to the wheel-rail contact point and the general condition of the rail unevenness. The approximate search range for the vertex in the rail section model is 3 cm in total for the left and right distances and 2 mm in total for the up and down distances. The approximate search range for 16 mm below the rail in the rail section model is 4 mm in total for the up and down distances and 2 mm in total for the left and right distances;

[0149] S53: Search for the point with the highest elevation as the key point at the top of the rail and the point with the outermost lateral coordinate as the key point at 16 mm below the top of the rail within the specified search range.

[0150] Through the above steps, the method for extracting key points of rail geometric irregularities based on the turnout three-dimensional point cloud model can efficiently and accurately extract the key points of the rail, providing reliable data support for the subsequent calculation of track geometric irregularity parameters. This method combines the high-precision registration ability of the ICP algorithm and the precise processing of point cloud data, and is applicable to turnout detection in complex track environments.

[0151] In step S5, the gauge refers to the minimum distance between the inner sides of two rails at a specified position, 16 mm downward from the top of the rail (this point is also a key point); the alignment irregularity refers to the difference between the versine value of the side key point from the chord and the designed versine value within a specified chord length (usually 10 m at the turnout); the vertical irregularity refers to the difference between the versine value of the highest point on the rail surface from the chord and the designed versine value within a specified chord length (usually 10 m at the turnout).

[0152] The calculation of the gauge includes: determining the key point 16 mm downward from the rail top on the inner side of the rail; measuring the minimum distance between the key points on the inner sides of the two rails, which is the gauge.

[0153] The calculation of the alignment irregularity includes:

[0154] On the side of the rail, determine the key points at the starting and ending points according to the specified chord length (usually 10 m):

[0155] Calculate the versine value of the intermediate point from the chord;

[0156] Calculate the difference between the versine value of the intermediate point and the designed versine value, which is the alignment irregularity.

[0157] The calculation of the vertical irregularity includes:

[0158] On the top surface of the rail, determine the key points at the starting and ending points according to the specified chord length (usually 10 m);

[0159] Calculate the versine value of the intermediate point from the chord;

[0160] Calculate the difference between the versine value of the intermediate point and the designed versine value, which is the vertical irregularity.

[0161] The rapid measurement method for track geometric irregularities in the turnout area provided by the present invention solves the passability problem in the turnout area through a clever and reasonable vehicle body structure design, relies on INS / odometer, etc. to achieve continuous attitude acquisition without relying on external position information constraints, obtains fine point cloud data of the complex structure of the turnout through multi-line structured laser scanner multi-angle scanning, and finally quickly extracts the rail geometric irregularity parameters in the turnout area.

[0162] Such as Figure 8As shown in the figure, the second aspect of the present invention provides a rapid measurement system for track geometric irregularities in the turnout area, which is used to implement the above design method and includes a first main module, a second main module, a third main module, a fourth main module, a fifth main module, and a sixth main module;

[0163] The first main module is used to scan the railway turnout rail in real time through the multi-angle line structure laser scanner on the dedicated detection vehicle body 1 to obtain the complete three-dimensional point cloud data of the surface of the railway turnout rail;

[0164] The second main module is used to obtain the accurate attitude and position data of the dedicated detection vehicle body 1 during the measurement process in real time through the inertial navigation measurement unit 3 and the odometer on the dedicated detection vehicle body 1;

[0165] The third main module is used to fuse and solve the accurate attitude and position data of the dedicated detection vehicle body 1 during the measurement process by using a robust dead reckoning method of the dynamic lever arm value of the odometer based on the variable wheel-rail contact point to assist the IMU, and obtain the high-precision pose reckoning result of the dedicated detection vehicle body 1;

[0166] The fourth main module is used to construct a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the surface of the railway turnout rail and the high-precision pose reckoning result during the measurement process of the dedicated detection vehicle body 1;

[0167] The fifth main module is used to extract the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area by using the method of matching the standard cross-section model of the lateral rail and the key area constraint;

[0168] The sixth main module is used to calculate the gauge, level, alignment irregularity, and vertical irregularity parameters according to the geometric irregularity key points of the turnout rail point cloud.

[0169] It should be noted that the rapid measurement system for track geometric irregularities in the turnout area provided in this embodiment can be a computer program (including program code) running in a computer device. For example, the rapid measurement system for track geometric irregularities in the turnout area is an application software; the rapid measurement system for track geometric irregularities in the turnout area can be used to execute the corresponding steps in the above method provided in the embodiments of the present application.

[0170] In some feasible embodiments, the rapid measurement system for track geometric irregularities in the turnout area provided in this embodiment can be implemented in a combination of software and hardware. As an example, the rapid measurement system for track geometric irregularities in the turnout area provided in the embodiments of the present application can be a processor in the form of a hardware decoding processor, which is programmed to execute the rapid measurement method for track geometric irregularities in the turnout area provided in the embodiments of the present application. For example, a processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0171] In some feasible embodiments, the rapid measurement system for track geometric irregularities in the turnout area provided in this embodiment can be implemented in a software manner. It can be software in the form of programs and plugins, and includes a series of modules to implement the rapid measurement method for track geometric irregularities in the turnout area provided in the embodiments of the present invention.

[0172] The rapid measurement system for track geometric irregularities in the turnout area provided in this embodiment reduces the problems of limited passability and vibration interference caused by non-smooth structures in the horizontal and vertical directions by designing a dedicated detection vehicle body with multiple passing wheels in a 4-unit × 2-wheel mode and multiple lateral limiting wheels; realizes the rapid continuous acquisition of high-precision poses of detection devices without relying on external position information constraints through a robust dead reckoning method based on the dynamic lever arm value of an odometer with variable wheel-rail contact points to assist the IMU; combines multi-source data such as IMU, odometer, and multi-line structured laser scanners, and obtains high-precision point cloud data of the turnout through a data fusion algorithm, further improving the accuracy and reliability of the detection system; determines key points in different areas of the turnout by matching the standard cross-section model of the lateral rail and constraining key areas based on the complete high-precision three-dimensional point cloud model of the complex turnout structure, and can accurately identify the key points in the turnout area, providing an accurate reference for the measurement of track geometric irregularities; the dedicated detection vehicle body can quickly and smoothly pass through the turnout area to complete continuous and accurate measurement of track geometric irregularities, not only improving the detection efficiency, but also ensuring the accuracy and reliability of the measurement results.

[0173] The third aspect of the present invention also provides an electronic device. Figure 9 is a schematic structural diagram of the electronic device in this embodiment, as Figure 4As shown in the figure, the electronic device 1000 in this embodiment may include: a processor 1001, a network interface 1004, and a memory 1005. In addition, the above-mentioned electronic device 1000 may further include: a user interface 1003 and at least one communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. Among them, the user interface 1003 may include a display screen (Display) and a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. Optionally, the memory 1005 may also be at least one storage device located far from the aforementioned processor 1001. As Figure 9 As shown, the memory 1005, as a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a device control application program.

[0174] As Figure 9 In the electronic device 1000 shown in the figure, the network interface 1004 can provide network communication functions; while the user interface 1003 is mainly used to provide an input interface for the user; and the processor 1001 can be used to call the device control application program stored in the memory 1005 to achieve:

[0175] Real-time scanning of the railway turnout rail by a multi-angle line-structured laser scanner on the dedicated detection vehicle body 1 to obtain complete three-dimensional point cloud data of the surface of the railway turnout rail;

[0176] Real-time obtaining of the accurate attitude and position data of the dedicated detection vehicle body 1 during the measurement process through the inertial navigation measurement unit 3 and the odometer on the dedicated detection vehicle body 1;

[0177] Fusing and resolving the accurate attitude and position data of the dedicated detection vehicle body 1 during the measurement process through a robust dead reckoning method of the odometer dynamic lever arm value based on the variable wheel-rail contact point to assist the IMU, and obtaining a high-precision pose reckoning result of the dedicated detection vehicle body 1;

[0178] Constructing a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the surface of the railway turnout rail and the high-precision pose reckoning result of the dedicated detection vehicle body 1 during the measurement process;

[0179] Extracting the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area by using the method of matching the standard cross-section model of the lateral rail and the key area constraint;

[0180] Calculate the gauge, level, alignment irregularity, and vertical irregularity parameters based on the geometric irregularity key points of the turnout rail point cloud.

[0181] It should be understood that in some feasible embodiments, the above-mentioned processor 1001 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, DSPs, ASICs, FPGAs, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0182] In specific implementation, the above-mentioned electronic device 1000 may execute the implementation methods provided by the respective steps as described above Figure 1 through its built-in various functional modules. Specifically, reference may be made to the implementation methods provided by the respective steps as described above, which will not be elaborated here.

[0183] The electronic device provided in this embodiment reduces the problems of limited passing performance and vibration interference caused by non-smooth structures in the horizontal and vertical directions by designing a special detection vehicle body with multiple passing wheels and multiple lateral limiting wheels in a 4-unit × 2-wheel mode; realizes the rapid and continuous acquisition of high-precision pose of the detection device without relying on external position information constraints through a robust dead reckoning method based on the dynamic lever arm value of the odometer with variable wheel-rail contact points; combines multi-source data such as IMU, odometer, and multi-line structured laser scanner, and through a data fusion algorithm, obtains high-precision point cloud data of the turnout, further improving the accuracy and reliability of the detection system; determines the key points in different areas of the turnout by using a transverse rail standard section model matching and key area constraint method based on a complete high-precision three-dimensional point cloud model of the complex structure of the turnout, and can accurately identify the key points in the turnout area, providing an accurate reference for the measurement of track geometric irregularities; can quickly and smoothly pass through the turnout area through the special detection vehicle body to complete continuous and accurate measurement of track geometric irregularities, not only improving the detection efficiency, but also ensuring the accuracy and reliability of the measurement results.

[0184] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program that is executed by a processor to implement Figure 1 the methods provided by the respective steps as described above. Specifically, reference may be made to the implementation methods provided by the respective steps as described above, which will not be elaborated here.

[0185] The computer-readable storage medium provided in this embodiment reduces the problems of limited passability and vibration interference caused by non-smooth structures in the horizontal and vertical directions by designing a dedicated detection vehicle body with multiple passing wheels and multiple lateral limiting wheels in a 4-unit × 2-round mode; realizes the rapid and continuous acquisition of high-precision pose of the detection device without relying on external position information constraints through a robust dead reckoning method based on the dynamic lever arm value of the odometer with variable wheel-rail contact points; combines multi-source data such as IMU, odometer, and multi-line structured laser scanner, and through a data fusion algorithm, obtains high-precision point cloud data of the turnout, further improving the accuracy and reliability of the detection system; determines the key points in different areas of the turnout by matching the standard cross-section model of the lateral rail and constraining the key areas based on the complete and high-precision three-dimensional point cloud model of the complex structure of the turnout, and can accurately identify the key points in the turnout area, providing an accurate reference for the measurement of track geometric irregularities; the dedicated detection vehicle body can quickly and smoothly pass through the turnout area to complete the continuous and accurate measurement of track geometric irregularities, not only improving the detection efficiency, but also ensuring the accuracy and reliability of the measurement results.

[0186] Any reference to a memory, storage, database, or other medium used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0187] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A rapid measurement method for track geometric irregularities in a turnout area, characterized in that, It includes the following steps: S1: Install a special detection vehicle body (1) in the turnout area to be measured; use the multi-angle line-structured laser scanner (2) on the special detection vehicle body (1) to scan the railway turnout rails in real time, and obtain the complete three-dimensional point cloud data of the surface of the railway turnout rails; S2: Use the inertial navigation measurement unit (3) and the odometer on the special detection vehicle body (1) to obtain the accurate attitude and position data of the special detection vehicle body (1) during the measurement process in real time; S3: Use the robust dead reckoning method of the dynamic lever arm value of the odometer based on the variable wheel-rail contact point to assist the IMU to fuse and solve the accurate attitude and position data of the special detection vehicle body (1) during the measurement process, and obtain the high-precision pose estimation result of the special detection vehicle body (1); S4: Construct a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the surface of the railway turnout rails and the high-precision pose estimation result of the special detection vehicle body (1) during the measurement process; S5: Extract the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area by using the method of matching the standard cross-section model of the lateral rail and the constraint of the key area; S6: Calculate the gauge, level, alignment irregularity and vertical irregularity parameters according to the geometric irregularity key points of the turnout rail point cloud.

2. The rapid measurement method for track geometric irregularity in turnout area according to claim 1, characterized in that: In step S1, the special detection vehicle body (1) includes a plurality of lateral limiting wheels (11) in a 4-unit × 2-wheel mode arranged on the side of the track; Among all the lateral limiting wheels (11), at least two lateral limiting wheels (11) on each side are in contact with the rail; The distance between the front and rear two units is greater than the distance of the frog; The connection line between every two lateral limiting wheels (11) forms an angle inclined inward with the advancing direction.

3. A rapid measurement method for track geometric irregularities in a turnout area according to claim 2, characterized in that: The special detection vehicle body (1) further includes a plurality of passing wheels (12) in a 4-unit × 2-wheel mode arranged in the horizontal direction of the track; Among the plurality of passing wheels (12) in the 4-unit × 2-wheel mode, the distance between the two passing wheels (12) in each unit is greater than the distance of the frog; Among the plurality of passing wheels (12) in the 4-unit × 2-wheel mode, the two passing wheels (12) of one unit are connected by a synchronous belt (13); The odometer is arranged on any one of the two passing wheels (12) connected by the synchronous belt (13) among the plurality of passing wheels (12) in the 4-unit × 2-wheel mode.

4. A rapid measurement method for track geometric irregularities in a turnout area according to any one of claims 1-3, characterized in that: The special detection vehicle body (1) further includes a plurality of line-structured laser scanners (2) with the same quantity arranged on the left and right sides of the special detection vehicle body (1) respectively and an inertial navigation measurement unit (3) installed in the middle of the special detection vehicle body (1); the plurality of line-structured laser scanners (2) form a multi-angle scanning unit.

5. A rapid measurement method for track geometric irregularities in a turnout area according to any one of claims 1-3, characterized in that: In step S2, use the inertial navigation measurement unit (3) and the odometer on the special detection vehicle body (1) to obtain the accurate attitude and position data of the special detection vehicle body (1) during the measurement process in real time; it includes: Measure the angular velocity and acceleration of the special detection vehicle body (1) through the gyroscope and accelerometer; Measure the moving distance and speed of the special detection vehicle body (1) through the odometer; The angular velocity, acceleration, moving distance and speed data of the dedicated detection vehicle body (1) are subjected to attitude solution and position calculation by a data processing unit and a navigation computer to obtain the accurate attitude and position data of the dedicated detection vehicle body (1) during the measurement process.

6. A rapid measurement method for track geometric irregularities in a turnout area according to any one of claims 1-3, characterized in that: In step S3, the accurate attitude and position data of the dedicated detection vehicle body (1) during the measurement process are fused and solved by a robust dead reckoning method of an odometer dynamic lever arm value based on a variable wheel-rail contact point to obtain a high-precision pose calculation result of the dedicated detection vehicle body (1); including: S31: Using Kalman filtering to perform combined solution on the accurate attitude and position data of the dedicated detection vehicle body (1) during the measurement process to obtain the state vector and state transition matrix of the dedicated detection vehicle body (1); S32: Establish an observation equation based on the relationship between the inertial navigation center position of pure inertial navigation and the three-dimensional position of the dedicated detection vehicle body (1) obtained by odometer dead reckoning to describe the relationship between the measurement data and the state variables; S33: Determine the passing wheel contact points corresponding to the odometer measurement values during the movement according to the actual situation, model the lever arm value of the odometer installation parameters, and eliminate the error caused by the change of the lever arm value in the Kalman filter observation quantity to obtain a corrected observation equation; S34: Obtain a high-precision pose calculation result of the dedicated detection vehicle body (1) according to the corrected observation equation.

7. A rapid measurement method for track geometric irregularities in a turnout area according to claim 6, characterized in that: The observation equation Z in step S32 is expressed by formula (3): Wherein, P ins is the position obtained by pure inertial navigation solution; M pvD is the intermediate calculation value; is the attitude transformation matrix from the vehicle body coordinate system to the new navigation coordinate system; P D is the position obtained by the combined calculation of inertial navigation and odometer; l b is the lever arm value of the odometer installation parameter at the measurement instant; l b is expressed by Equation (4): Among them, is the theoretical approximation of l; Δl b is the constant deviation of the lever arm value between two wheel-rail contact points corresponding to the odometer within a unit; δl b is the zero-mean random error part of l; b is the zero-mean random error part of l; b is the zero-mean random error part of l; When the odometer measures, it corresponds to two wheels. Therefore, the lever arm value should be two random constant values; When corresponding to the first wheel, Δl b has a value of [0 0 0]; When corresponding to the second wheel, Δl b The value of is [Δx max Δy max 0]; Δx max and Δy max are the maximum values in the corresponding directions respectively; When the track is deflected to the left relative to the wheel passing through it, Δl b It is expressed by formula (5): When the track is deflected to the right relative to the wheel, Δl b It is expressed by formula (6): In the formula, R is the track curvature radius of the turnout to be measured; Δl obtained by calculating through formula (5) and formula (6) b , substitute it into the above formula (4) to obtain the lever arm value l of the odometer installation parameter at the measurement moment b , and then substitute it into formula (3) to obtain the calculation result of the observation equation after kamman filter correction; The corrected observation equation Z1 in step S33 is: Z1 = δP ins -δP D (7) where δP ins is the three-dimensional position error of pure inertial navigation solution; δP D is the three-dimensional position error obtained by odometer dead reckoning.

8. A rapid measurement method for track geometric irregularities in a turnout area according to any one of claims 1-3, characterized in that: In step S5, a method of matching the transverse rail standard section model and key area constraint is used to extract the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area, including: S51: Matching the rail section design model with the rail scan point cloud to determine the range at 16 mm below the rail top; S52: Search for the highest point at the approximate position of the rail top in the matched section model and the outermost point at the approximate position of 16 mm below the rail; the approximate search range of the vertex in the rail section model is 3 cm in total left and right distance and 2 mm in total up and down distance, and the approximate search range at 16 mm below the rail in the rail section model is 4 mm in total up and down distance and 2 mm in total left and right distance; S53: Search for the point with the highest elevation within the specified search range as the key point of the rail top, and the point with the outermost lateral coordinate as the key point at 16 mm below the rail top.

9. A rapid measurement method for track geometric irregularities in a turnout area according to claim 8, characterized in that: The calculation of the gauge in step S6 includes: determining key points 16 mm below the rail top on the inner side of the rail; measuring the minimum distance between the key points on the inner sides of the two rails, which is the gauge; The calculation of rail alignment irregularity includes: Determining key points at the starting point and the ending point according to the specified chord length on the side of the rail: Calculating the versine value of the intermediate point from the chord; Calculating the difference between the versine value of the intermediate point and the designed versine value, which is the rail alignment irregularity; The calculation of vertical irregularity includes: On the top surface of the rail, determine the key points of the starting point and the ending point according to the specified chord length; Calculate the versine value of the intermediate point from the chord; Calculate the difference between the versine value of the intermediate point and the designed versine value, which is the vertical irregularity.

10. A rapid measurement system for track geometric irregularities in a turnout area, characterized in that, Used to implement the rapid measurement method for track geometric irregularities in the turnout area described in any one of claims 1-9, including a first main module, a second main module, a third main module, a fourth main module, a fifth main module, and a sixth main module; The first main module is used to obtain the complete three-dimensional point cloud data of the surface of the railway turnout rail by real-time scanning the railway turnout rail with a multi-angle line-structured laser scanner on the dedicated detection vehicle body (1); The second main module is used to obtain the accurate attitude and position data of the dedicated detection vehicle body (1) during the measurement process in real time through the inertial measurement unit (3) and the odometer on the dedicated detection vehicle body (1); The third main module is used to perform a fusion solution on the accurate attitude and position data of the dedicated detection vehicle body (1) during the measurement process by a robust dead reckoning method of the auxiliary IMU with the dynamic lever arm value of the odometer based on the variable wheel-rail contact point to obtain the high-precision pose estimation result of the dedicated detection vehicle body (1); The fourth main module is used to construct a three-dimensional point cloud model of the turnout area according to the complete three-dimensional point cloud data of the surface of the railway turnout rail and the high-precision pose estimation result of the dedicated detection vehicle body (1) during the measurement process; The fifth main module is used to extract the geometric irregularity key points of the turnout rail point cloud from the three-dimensional point cloud model of the turnout area by using the method of matching the transverse rail standard section model and the key area constraint; The sixth main module is used to calculate the gauge, level, alignment irregularity, and vertical irregularity parameters according to the geometric irregularity key points of the turnout rail point cloud.

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