1. A method of measuring wheel set tread parameters, the method comprising:

collecting wheel set tread point cloud data;

preprocessing the point cloud data to obtain registration data;

constructing the inner side surface of a fitting wheel;

performing three-dimensional reconstruction on the inner side surface of the fitted wheel according to the registration data to obtain a three-dimensional model of the wheel;

and determining parameters of the wheel set tread by taking the inner side surface of the wheel of the three-dimensional model as a datum plane according to the geometric relationship between the parameters of the wheel of the three-dimensional model and the datum plane.

2. The wheel set tread parameter measuring method according to claim 1, wherein the point cloud data is preprocessed to obtain registration data, and specifically comprises:

performing point cloud splicing on the point cloud data to obtain spliced data;

performing coordinate conversion on the spliced data to obtain converted data; the converted data comprises point cloud data M of the tread of the inner side surface of the wheel and point cloud data N of the tread of the outer side surface of the wheel;

and carrying out point cloud registration on the converted data to obtain registration data.

3. The wheel set tread parameter measuring method according to claim 2, wherein the point cloud registration is performed on the converted data to obtain registration data, and specifically comprises:

carrying out coarse registration on the converted data to obtain registration data;

or carrying out fine registration on the converted data to obtain registration data;

or carrying out coarse registration and fine registration on the converted data in sequence to obtain registration data.

4. The wheel set tread parameter measurement method according to claim 3, wherein the coarse registration is performed on the converted data to obtain registration data, and specifically comprises:

calculating according to the converted point cloud data to obtain a centroid of the point cloud data M and a centroid of the point cloud data N;

calculating key points of point cloud data M and point cloud data N by using an internal shape signature ISS algorithm on the converted data;

extracting feature descriptors of each key point by using an FPFH algorithm, comparing the two feature descriptors to obtain corresponding points of the key points of the point cloud data M relative to the key points of the point cloud data N, and determining the one-to-one corresponding relation of the two key points;

solving a corresponding point covariance matrix according to the one-to-one correspondence, the mass center of the point cloud data M and the mass center of the point cloud data N, and performing singular value decomposition to obtain a rotation matrix R₁And translation matrix T₁；

According to a rotation matrix R₁And translation matrix T₁Registration data is obtained.

5. The wheel set tread parameter measuring method according to claim 3, wherein the converted data is subjected to fine registration to obtain registration data, and specifically comprises:

performing point cloud fine registration on the converted data by adopting a closest point iteration ICP algorithm, and specifically comprising the following steps:

determining corresponding point sets P and Q according to curvature features of the point cloud data M and the point cloud data N, wherein the number of corresponding points is N;

selecting two corresponding points P in three-dimensional space_iAnd q is_jCalculating two corresponding points P_iAnd q is_jOf between, Euclidean distance, P_iIs a point in P, q_jIs a point in Q;

translating and rotating the corresponding point sets P and Q according to the Euclidean distance to obtain a rotation matrix R₂And translation matrix T₂；

Based on least square method according to rotation matrix R₂And translation matrix T₂Determining an error;

judging whether the error meets a set threshold value; if the error satisfies the set threshold, then according to the rotation matrix R₂Translation matrix T₂And error to obtain registration data; if the error does not meet the set threshold, returning to the step of rotating the matrix R according to the least square method₂And translation matrix T₂Determine error ";

or judging whether the iteration times are larger than the set iteration times; if the iteration times are more than the set iteration times, the rotation matrix R is used₂Translation matrix T₂And error to obtain registration data; if the iteration times are less than or equal to the set iteration times, adding one to the iteration times, and returning to the step of rotating the matrix R according to the least square method₂And translation matrix T₂Determine the error ".

6. The wheel-to-tread parameter measurement method according to claim 1, wherein the constructing of the fitted wheel inner side surface specifically comprises:

constructing a plane equation containing error parameters, which specifically comprises the following steps:

x+v_x＝b(y+v_y)+c(z+v_z)+d

wherein x, y and z are three-dimensional plane coordinates, v_x、v_y、v_zErrors in the x direction, the y direction and the z direction are respectively, and b, c and d are unknown parameters;

rewriting the plane equation into an EIV model specifically comprises:

(A+E_A)X＝L+E_L

wherein E is_AAnd E_LRespectively representing errors of the matrix A and the vector L, and X represents an unknown parameter matrix consisting of b, c and d;

decomposing the EIV model by using a singular value decomposition method to obtain the optimal estimated values of unknown parameters b, c and d;

and b, c and d are substituted into the plane equation containing the error parameters to obtain the inner side surface of the fitting wheel.

7. The wheel set tread parameter measurement method according to claim 1, wherein the three-dimensional reconstruction of the fitted wheel inner side face according to the registration data to obtain a three-dimensional model of the wheel specifically comprises:

the fitting wheel inner side surface rotation translation is coincided with the coordinate Z-Y surface, and a rotation matrix R is determined₀And translation matrix T₀；

Wherein (X)₂ Y₂ Z₂) To fit the coordinates of a point in the inboard surface of the wheel after transformation, (X)₁ Y₁ Z₁) To fit the coordinates of a point in the inboard surface of the wheel before transformation, (α β γ) is the rotation matrix R between the two coordinates₀，(X₀ Y₀ Z₀) Is a translation matrix T between two coordinates₀；

Substituting the registration data into a rotation matrix R₀And translation matrix T₀Performing coordinate transformation to obtain tread point cloud data after coordinate transformation;

converting the tread point cloud data after coordinate transformation according to the corresponding relation between the rectangular coordinate system and the cylindrical coordinate system to obtain point cloud data distributed circumferentially;

and drawing the wheel in MATLAB according to the point cloud data distributed circumferentially to obtain a wheel three-dimensional contour map.

8. A wheel set tread parameter measuring device, the device comprising: the gantry vertical column comprises a gantry vertical column base, a driving device, a positioning support module, two measurement modules and a calculation module;

the gantry upright column base consists of a top rod and two upright columns, and two upright columns are respectively arranged on two sides of the top rod; the top rod is used for mounting the measuring module, and the upright post is used for mounting the driving device and the positioning and supporting module;

the positioning support module is used for supporting and positioning the wheel pair to be detected;

the driving device is arranged behind the positioning support module and is used for clamping and driving the wheel pair to be detected to move;

the two measuring modules are respectively arranged corresponding to two wheels of the wheel pair to be measured, and each measuring module is used for measuring wheel tread point cloud data;

the calculation module is connected with the measurement module and used for determining wheel set and tread parameters by adopting the wheel set and tread parameter measurement method of any one of claims 1 to 7 according to the received wheel tread point cloud data.

9. The wheel-set tread parameter measuring device according to claim 8, wherein a driven device matched with the driving device is arranged at the other end of the wheel-set axle; the driving device and the driven device are pushed by an electric cylinder to move forwards so as to clamp the end face of the axle of the wheel pair.

10. The wheel-pair tread parameter measuring device of claim 8, wherein the measuring module comprises:

the two line-structured light sensors are respectively used for measuring the point cloud data of the tread on the outer side surface and the inner side surface of the wheel;

and the ball screw is used for driving the two line-structured light sensors to reach a measuring position.

Background

The wheel set of the rail train is used as a bearing and moving part of the train and bears larger load and impact force, and the wheel set tread is a surface of the wheel in direct contact with the rail, so that the geometric parameters of the wheel set tread are particularly important for the train needing to run at high speed. Because the wheel set tread is irregular curved surface, the traditional measuring method has large error of measuring the wheel set tread and low efficiency, and can not meet the current production requirement. With the development of computer vision related fields, such as camera calibration technology and computer image processing technology, structured light measurement is increasingly applied to the measurement of geometric parameters of a measuring wheel pair. The line structure light sensor can obtain a two-dimensional profile by single measurement, although the obtained data volume is smaller than that of the surface structure light sensor, the two-dimensional profile is far larger than that of a point laser displacement sensor, the line structure light sensor is simple in composition structure, low in cost and high in measurement speed, is easy to integrate with other motion coordinates, and is an ideal choice for realizing complete measurement of complex curved surfaces. When the linear structure light measures the wheel tread geometric parameters, the laser plane does not pass through the central axis of the wheel or a certain included angle exists between the laser plane and the wheel tread generatrix-central axis plane, so that the measured wheel tread geometric parameters have larger error, how to reduce or eliminate the measuring error is realized, the three-dimensional reconstruction is realized, more tread geometric parameters are obtained, and the linear structure light measures an important development direction of the wheel tread geometric parameters.

Disclosure of Invention

The invention provides a wheel set tread parameter measuring method and device, which can realize three-dimensional reconstruction of the whole wheel set tread, determine wheel set tread parameters and improve the measuring accuracy and efficiency.

In order to achieve the purpose, the invention provides the following scheme:

a method of wheel set tread parameter measurement, the method comprising:

collecting wheel set tread point cloud data;

preprocessing the point cloud data to obtain registration data;

constructing the inner side surface of a fitting wheel;

performing three-dimensional reconstruction on the inner side surface of the fitted wheel according to the registration data to obtain a three-dimensional model of the wheel;

and determining parameters of the wheel set tread by taking the inner side surface of the wheel of the three-dimensional model as a datum plane according to the geometric relationship between the parameters of the wheel of the three-dimensional model and the datum plane.

Preferably, the preprocessing the point cloud data to obtain registration data specifically includes:

performing point cloud splicing on the point cloud data to obtain spliced data;

performing coordinate conversion on the spliced data to obtain converted data; the converted data comprises point cloud data M of the tread of the inner side surface of the wheel and point cloud data N of the tread of the outer side surface of the wheel;

and carrying out point cloud registration on the converted data to obtain registration data.

Preferably, the point cloud registration of the converted data to obtain registration data specifically includes:

carrying out coarse registration on the converted data to obtain registration data;

or carrying out fine registration on the converted data to obtain registration data;

or carrying out coarse registration and fine registration on the converted data in sequence to obtain registration data.

Preferably, the coarse registration of the converted data is performed to obtain registration data, which specifically includes:

calculating according to the converted point cloud data to obtain a centroid of the point cloud data M and a centroid of the point cloud data N;

calculating key points of the point cloud data M and the point cloud data N by using an internal shape signature ISS algorithm on the converted data;

extracting feature descriptors of each key point by using an FPFH algorithm, comparing the two feature descriptors to obtain corresponding points of the key points of the point cloud data M relative to the key points of the point cloud data N, and determining the one-to-one corresponding relation of the two key points;

solving a corresponding point covariance matrix according to the one-to-one correspondence, the mass center of the point cloud data M and the mass center of the point cloud data N, and performing singular value decomposition to obtain a rotation matrix R₁And translation matrix T₁；

According to a rotation matrix R₁And translation matrix T₁Registration data is obtained.

Preferably, the fine registration of the converted data is performed to obtain registration data, which specifically includes:

performing point cloud fine registration on the converted data by adopting a closest point iteration ICP algorithm, and specifically comprising the following steps:

determining corresponding point sets P and Q according to curvature features of the point cloud data M and the point cloud data N, wherein the number of corresponding points is N;

selecting two corresponding points P in three-dimensional space_iAnd q is_jCalculating two corresponding points P_iAnd q is_jBetween the Euclidean distance, P_iIs a point in P, q_jIs a point in Q;

translating and rotating the corresponding point sets P and Q according to the Euclidean distance to obtain a rotation matrix R₂And translation matrix T₂；

Based on least square method according to rotation matrix R₂And translation matrix T₂Determining an error;

judging whether the error meets a set threshold value; if the error satisfies the set threshold, then according to the rotation matrix R₂Translation matrix T₂And error to obtain registration data; if the error does not meet the set threshold, returning to the step of rotating the matrix R according to the least square method₂And translation matrix T₂Determine error ";

or judging whether the iteration times are larger than the set iteration times; if the iteration number is larger than the set iteration number, the rotation matrix R is used₂Translation matrix T₂And error to obtain registration data; if the iteration times are less than or equal to the set iteration times, adding one to the iteration times, and returning to the step of rotating the matrix R according to the least square method₂And translation matrix T₂Determine the error ".

Preferably, the building of the fitting wheel inner side surface specifically includes:

constructing a plane equation containing error parameters, which specifically comprises the following steps:

x+v_x＝b(y+v_y)+c(z+v_z)+d

wherein x, y and z are three-dimensional plane coordinates, v_x、v_y、v_zErrors in the x direction, the y direction and the z direction are respectively, and b, c and d are unknown parameters;

rewriting the plane equation into an EIV model specifically comprises:

(A+E_A)X＝L+E_L

wherein E is_AAnd E_LRespectively representing the errors of the matrix A and the vector L; x represents an unknown parameter matrix composed of b, c and d

Decomposing the EIV model by using a singular value decomposition method to obtain the optimal estimated values of unknown parameters b, c and d;

and b, c and d are substituted into the plane equation containing the error parameters to obtain the inner side surface of the fitting wheel.

Preferably, the three-dimensional reconstruction of the inner side surface of the fitted wheel according to the registration data to obtain a three-dimensional model of the wheel specifically includes:

the fitting wheel inner side surface rotation translation is coincided with the coordinate Z-Y surface, and a rotation matrix R is determined₀And translation matrix T₀；

Wherein (X)₂ Y₂ Z₂) To fit the coordinates of a point in the inboard surface of the wheel after transformation, (X)₁ Y₁ Z₁) To fit the coordinates of a point in the inboard surface of the wheel before transformation, (α β γ) is the rotation matrix R between the two coordinates₀，(X₀ Y₀ Z₀) Is a translation matrix T between two coordinates₀；

Substituting the registration data into a rotation matrix R₀And translation matrix T₀Performing coordinate transformation to obtain tread point cloud data after coordinate transformation;

converting the tread point cloud data after coordinate transformation according to a corresponding relation system of a rectangular coordinate system and a cylindrical coordinate system to obtain point cloud data distributed circumferentially;

and drawing the wheel in MATLAB according to the point cloud data distributed circumferentially to obtain a wheel three-dimensional contour map.

A wheel set tread parameter measuring device, the device comprising: the gantry vertical column comprises a gantry vertical column base, a driving device, a positioning support module, two measurement modules and a calculation module;

the gantry upright column base consists of a top rod and two upright columns, and two upright columns are respectively installed on two sides of the top rod; the top rod is used for mounting the measuring module, and the upright post is used for mounting the driving device and the positioning and supporting module;

the positioning support module is used for supporting and positioning the wheel pair to be detected;

the driving device is arranged behind the positioning support module and is used for clamping and driving the wheel pair to be detected to move;

the two measuring modules are respectively arranged corresponding to two wheels of a wheel pair to be measured, and each measuring module is used for measuring wheel tread point cloud data;

and the computing module is connected with the measuring module and used for determining wheel set tread parameters by adopting the wheel set tread parameter measuring method according to the received wheel tread point cloud data.

Preferably, a driven device matched with the driving device is arranged at the other end of the wheel set axle; the driving device and the driven device are pushed by an electric cylinder to move forwards so as to clamp the end face of the axle of the wheel pair.

Preferably, the measurement module comprises:

the two line-structured light sensors are respectively used for measuring the point cloud data of the tread on the outer side surface and the inner side surface of the wheel;

and the ball screw is used for driving the two line-structured light sensors to reach a measuring position.

The method and the device for measuring the wheel tread of the wheel pair suitable for the track row have the advantages that two linear structure optical sensors are adopted, a set of tread point cloud data is collected when the wheel pair rotates for a certain angle, the geometric parameters of the whole wheel pair tread are calculated by utilizing the tread point cloud data, three-dimensional reconstruction is carried out, the measuring process is automatic, the measuring efficiency is high, the measurement is more accurate, and the like. In addition, the invention aims to solve the problem that when the linear structured light is used for measuring the wheel tread, the measurement is carried out by adopting the two pairs of linear structured light visual sensors, so that the problem that the laser plane does not pass through the central axis of the wheel or a certain included angle exists between the laser plane and the generatrix-central axis plane of the wheel tread, and the problem that the obtained laser line striation center can not accurately reflect tread profile data is solved, the measuring process is more efficient, and the geometric parameters of the wheel tread are measured more accurately.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a flow chart of a wheel set tread parameter measuring method of the embodiment 1

FIG. 2 is a schematic structural view of the wheel set tread parameter measuring device of this embodiment 7

FIG. 3 is a partial view of the tread surface measured by structured light under the wheel-set tread surface parameter measuring device of the embodiment 7

Description of the symbols: the device comprises a gantry upright column base 1, a driving module 2-1, a driven module 2-2, a positioning and supporting module 3, a measuring module 4, a line structure optical sensor 4-1, a line structure optical sensor 4-2 and a wheel pair 5.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The invention aims to provide a wheel set tread parameter measuring method and device, which can realize three-dimensional reconstruction of the whole wheel set tread, determine wheel set tread parameters and improve the accuracy and efficiency of measurement.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

Example 1

As shown in FIG. 1, the invention discloses a wheel set tread parameter measuring method, which comprises the following steps:

and S1, collecting wheel set tread point cloud data.

And S2, preprocessing the point cloud data to obtain registration data.

S3, constructing a fitting inner side surface of the wheel.

And S4, performing three-dimensional reconstruction on the inner side surface of the fitted wheel according to the registration data to obtain a three-dimensional model of the wheel.

And S5, determining wheel set tread parameters according to the geometric relationship between the wheel parameters of the three-dimensional model and the reference plane by taking the inner side surfaces of the wheels of the three-dimensional model as the reference plane. Specifically, the wheel set tread parameters specifically include: wheel rolling circle diameter, rim thickness, rim height, rim width and rim QR value.

S2, preprocessing the point cloud data to obtain registration data, which specifically comprises the following steps:

and S2.1, performing point cloud splicing on the point cloud data to obtain spliced data.

S2.2, carrying out coordinate conversion on the spliced data to obtain converted data; the converted data comprises point cloud data M of the tread of the inner side surface of the wheel and point cloud data N of the tread of the outer side surface of the wheel.

And S2.3, carrying out point cloud registration on the converted data to obtain registration data.

Specifically, the point cloud registration of the converted data to obtain registration data specifically includes:

the method comprises the following steps: and carrying out coarse registration on the converted data to obtain registration data.

The method 2 comprises the following steps: or carrying out fine registration on the converted data to obtain registration data.

The method 3 comprises the following steps: or carrying out coarse registration and fine registration on the converted data in sequence to obtain registration data.

The method comprises the following steps: calculating to obtain the centroid of the point cloud data M and the centroid of the point cloud data N according to the converted point cloud data; the centroid formula is:

C_mis the centroid, C, of the point cloud data M_nIs the centroid, m, of the point cloud data N_i、n_iAnd (i is 1, 2, …, K) is the coordinates of each point cloud data dot, and K is the number of the point cloud data dots.

And (4) solving key points of the point cloud data M and the point cloud data N by using an internal shape signature ISS algorithm on the converted data. The key points are point sets with stability and distinctiveness on the point cloud, and are the existing nouns.

And extracting feature descriptors of each key point by using an FPFH algorithm, comparing the two feature descriptors to obtain corresponding points of the key points of the point cloud data M relative to the key points of the point cloud data N, and determining the one-to-one corresponding relation of the two key points.

Solving a corresponding point covariance matrix E according to the one-to-one correspondence, the mass center of the point cloud data M and the mass center of the point cloud data N_3×3The covariance matrix is:

performing singular value decomposition on the covariance matrix to obtain a rotation matrix R₁And translation matrix T₁The method comprises the following specific steps:

solution E ═ U Λ V^TThe matrix E is decomposed into a unitary matrix U, a diagonal matrix Lambda and another unitary matrix V^TThe product of (a).

Get X ═ UV^TAnd (3) obtaining:

R₁＝X

T₁＝C_n-RC_m

according to a rotation matrix R₁And translation matrix T₁Registration data is obtained.

The method 2 comprises the following steps: carrying out point cloud fine registration on the converted data by adopting a closest point iteration ICP algorithm, and specifically comprising the following steps:

and establishing corresponding point sets P and Q according to the curvature characteristics of the point cloud data M and the point cloud data N, wherein the number of the corresponding points is N.

Selecting two corresponding points P in three-dimensional space_iAnd q is_jCalculating two corresponding points P_iAnd q is_jThe specific formula of the Euclidean distance between the two elements is as follows:

wherein Xi, Yi and Zi are points P_iCoordinates Xj, Yj, Zj are points q_jCoordinate, P_iIs a point in P, q_jIs a point in Q.

Translating and rotating the corresponding point sets P and Q according to the Euclidean distance to obtain a rotation matrix R₂And translation matrix T₂。

Based on least square method according to rotation matrix R₂And translation matrix T₂Determining an error, wherein the error formula is as follows:

wherein e is the error, R₂For a rotation matrix, T₂Translation matrix, p_i q_jTwo point coordinates.

Judging whether the error meets a set threshold value; if the error satisfies the set threshold, then according to the rotation matrix R₂Translation matrix T₂And error to obtain registration data; if the error does not meet the set threshold, returning to the step of rotating the matrix R according to the least square method₂And translation matrix T₂Determine the error ".

Or judging whether the iteration times are larger than the set iteration times; if the iteration number is larger than the set iteration number, the rotation matrix R is used₂Translation matrix T₂And error to obtain registration data; if the iteration times are less than or equal to the set iteration times, adding one to the iteration times, and returning to the step of rotating the matrix R according to the least square method₂And translation matrix T₂Determine the error ".

The method 3 comprises the following steps: obtaining a rotation matrix R by coarse registration of point cloud data after conversion₁And translation matrix T₁Substituting into a formula:

solving the error, and judging whether the error meets a set threshold value; if the error satisfies the set threshold, then according to the rotation matrix R₁Translation matrix T₁And error to obtain registration data; if the error does not meet the set threshold, returning to the least square method based rotation matrix R₁And translation matrix T₁Determine the error ".

Or judging whether the iteration times are larger than the set iteration times; if the iteration number is larger than the set iteration number, the rotation matrix R is used₁Translation matrix T₁And error to obtain registration data; if the number of iterations is less than or equal to the set iterationAnd if so, adding one to the iteration number and returning to the step of rotating the matrix R according to the least square method₁And translation matrix T₁Determine the error ".

S3: constructing a fitting wheel inner side surface and constructing a plane equation containing error parameters, and specifically comprising the following steps:

x+v_x＝b(y+v_y)+c(z+v_z)+d

wherein x, y and z are three-dimensional plane coordinates, v_x、v_y、v_zErrors in the x, y and z directions are respectively, and b, c and d are unknown parameters.

Rewriting the plane equation into an EIV model specifically comprises:

(A+E_A)X＝L+E_L

wherein E is_AAnd E_LThe errors of matrix a and vector L are represented separately, i.e.:

and decomposing the EIV model by using a singular value decomposition method:

wherein, [ A L ]]To augment the matrix, [ U ]₁ U₂]Representing the left singular matrix of the augmented matrix,representing the right singular matrix of the augmented matrix,representing a diagonal matrix containing singular values.

Obtaining the best estimation value of unknown parameters b, c and d

And b, c and d are substituted into the plane equation containing the error parameters to obtain the inner side surface of the fitting wheel.

S4: performing three-dimensional reconstruction on the inner side surface of the fitted wheel according to the registration data to obtain a three-dimensional model of the wheel, which specifically comprises the following steps:

the fitting wheel inner side surface rotation translation is coincided with the coordinate Z-Y surface, and a rotation matrix R is determined₀And translation matrix T₀。

Wherein (X)₂ Y₂ Z₂) To fit the coordinates of a point in the inboard surface of the wheel after transformation, (X)₁ Y₁ Z₁) To fit the coordinates of a point in the inboard surface of the wheel before transformation, (α β γ) is the rotation matrix R between the two coordinates₀，(X₀ Y₀ Z₀) Is a translation matrix T between two coordinates₀。

The rectangular coordinate system xyz and the cylindrical coordinate system ρ φ z have the following correspondence:

in the formula, ρ represents a radial distance, φ represents an azimuth angle, and z represents an altitude.

Substituting the registration data into a rotation matrix R₀And translation matrix T₀And carrying out coordinate transformation to obtain the tread point cloud data after coordinate transformation.

And converting the tread point cloud data after coordinate transformation according to a corresponding relation system of the rectangular coordinate system and the cylindrical coordinate system to obtain point cloud data distributed circumferentially.

And drawing the wheel in MATLAB according to the point cloud data distributed circumferentially to obtain a wheel three-dimensional contour map.

Example 2

As shown in fig. 2 and 3, the present invention also discloses a wheel set tread parameter measuring device, which comprises:

the gantry upright column base 1 consists of a top rod and two upright columns, wherein the two sides of the top rod are respectively provided with one upright column; the top rod is used for installing the measuring module, and the upright post is used for installing the driving device and the positioning support module.

And the positioning support module 3 is used for supporting and positioning the wheel pair to be detected.

And the driving device 2-1 is arranged behind the positioning support module 3 and is used for clamping and driving the wheel pair to be detected to move.

And the two measuring modules 4 are respectively arranged corresponding to the two wheels of the wheel pair to be measured, and each measuring module 4 is used for measuring the point cloud data of the wheel tread.

And the calculation module is connected with the measurement module 4 and used for determining wheel set tread parameters according to the received wheel tread point cloud data by using the method in the embodiment 1.

Specifically, a driven device 2-2 matched with the driving device 2-1 is arranged at the other end of the axle of the wheel set; the driving device 2-1 and the driven device 2-2 are pushed by an electric cylinder to move forwards so as to clamp the end faces of the wheels to the axle.

Specifically, the measurement module 4 includes:

the device comprises a line structure light sensor 4-1 and a line structure light sensor 4-2, wherein the line structure light sensor 4-1 is used for measuring tread point cloud data of the inner side face of the wheel, and the line structure light sensor 4-2 is used for measuring tread point cloud data of the inner side face of the wheel.

And the ball screw is used for driving the two line-structured light sensors to reach a measuring position.

Specifically, each time the wheel rotates by 0.36 degrees, the linear structure light sensor 4-1 and the linear structure light sensor 4-2 respectively collect wheel tread surface point cloud data once.

The principle and the implementation of the present invention are explained herein by using specific examples, and the above description of the embodiments is only used to help understand the core idea of the present invention; meanwhile, for a person skilled in the art, the specific embodiments and the application range may be changed according to the idea of the present invention. In view of the above, the present disclosure should not be construed as limiting the invention.