A lithium battery grinding wheel rail intelligent grinding trolley, control system and method

Abstract

The present application belongs to the technical field of rail grinding, and provides a lithium battery grinding wheel rail intelligent grinding trolley, a control system and a method. The trolley comprises a controller, a chassis, and a plurality of wheels arranged at the bottom of the chassis for walking on the rail. The chassis is further provided with a grinding assembly, a swing assembly, a lifting assembly, a horizontal moving assembly, and a dust collecting assembly controlled by the controller. The grinding assembly comprises a grinding wheel driven by a grinding motor. The swing assembly is used to drive the grinding motor to swing. The lifting assembly comprises a lifting motor used to drive the lifting of a swing frame. The horizontal moving assembly comprises a horizontal moving motor used to drive the movement of a horizontal moving frame. The dust collecting assembly comprises a dust collecting box, a filter element, a dust collector, and a dust collecting pipe. The filter element and the dust collector are arranged in the dust collecting box, the filter element is installed at the air inlet of the dust collector, and one end of the dust collecting pipe is in communication with the dust collecting box and the other end thereof faces the grinding wheel or the rail. The present application can solve the problems of low grinding efficiency and long cycle in the maintenance and grinding of the existing technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rail grinding, and in particular to a lithium battery grinding wheel rail intelligent grinding trolley, a control system and a method. BACKGROUND

[0002] When full profile grinding is performed on the turnout area (including the part profile of the frog and the cross heart) of a railway, a subway, a tramcar and the like, corrugations, fish scales, cracks and the like defects on the rail can be removed. However, in the prior art, the length of the rail of a railway, a subway and the like is too large to achieve full track maintenance grinding, and generally, key grinding is performed on the defective part.

[0003] In the prior art, manual grinding or simple semi-automatic equipment grinding is generally used for rail grinding. The semi-automatic equipment grinds the rail in multiple aspects, but still needs manual assistance for remote control, and the efficiency is low, and it is difficult for the semi-automatic equipment to control the relative position of the grinding system and the rail.

[0004] In addition, in the daily maintenance of the rail, the rail defects are generally detected first. The traditional detection methods mainly include ultrasonic detection, magnetic powder detection, penetration detection and visual detection. However, these methods have many limitations in practical application, for example, the ultrasonic detection has poor detection effect on complex structures, the magnetic powder detection needs to magnetize the workpiece, the penetration detection has high requirements for surface treatment, and the visual detection depends on the experience of the operator and is easily affected by human factors. In addition, the above methods have low recognition efficiency, and all of them are seriously dependent on manual operation of the workers, have long processing cycle and low efficiency. SUMMARY

[0005] In view of the above technical problems, the present application provides a lithium battery grinding wheel rail intelligent grinding trolley, a control system and a method to solve the problems of low efficiency and long cycle in the maintenance and grinding of the rail in the prior art.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0007] According to one aspect of the present application, a lithium battery grinding wheel rail intelligent grinding trolley is disclosed, characterized in that the grinding trolley comprises a controller, a chassis and a plurality of wheels arranged at the bottom of the chassis for walking on the rail, and a grinding assembly, a swing assembly, a lifting assembly, a transverse moving assembly, a dust collecting assembly and a laser probe controlled by the controller are further arranged on the chassis.

[0008] The grinding assembly comprises a grinding wheel driven by a grinding motor.

[0009] The swing assembly comprises a swing frame, a swing motor, a worm gear reducer driven by the swing motor, the polishing motor is rotatably installed in the swing frame, the worm gear reducer is connected to the polishing motor for driving the polishing motor to swing;

[0010] The lifting assembly comprises a lifting motor for driving the swing frame to lift;

[0011] The transverse movement assembly comprises a transverse movement frame, a transverse movement motor for driving the transverse movement frame to move, the output end of the transverse movement motor is connected with a screw rod, the bottom of the transverse movement frame is provided with a nut seat matched with the screw rod, and the lifting assembly is installed on the transverse movement frame;

[0012] The dust collection assembly comprises a dust collection box, a filter core, a dust collector, and a dust collection pipe, the filter core and the dust collector are arranged in the dust collection box, the filter core is installed at the air inlet of the dust collector, one end of the dust collection pipe is communicated with the dust collection box, and the other end of the dust collection pipe faces the grinding wheel or the steel rail;

[0013] The laser probe is used for measuring the profile value of the steel rail to be polished, the controller is used for driving the polishing assembly, the swing assembly, the lifting assembly, the transverse movement assembly and the dust collection assembly to work after analyzing the difference between the profile value and a standard value according to the profile value, so that the profile value detected by the laser probe in real time tends to the standard value.

[0014] Further, the grinding wheel is also connected with a pressure sensor, the pressure sensor is used for sensing the polishing force of the grinding wheel, an outer cover is further arranged on the chassis, the polishing assembly, the swing assembly, the lifting assembly, the transverse movement assembly and the dust collection assembly are covered in the outer cover, and a lithium battery compartment is further arranged on the chassis, and the lithium battery compartment is located at one end of the chassis far away from the polishing assembly.

[0015] Further, the chassis is detachably arranged.

[0016] Based on the second aspect of the present application, a lithium battery grinding wheel steel rail intelligent polishing control system is provided, the control system is executed in the polishing trolley, and the control system comprises:

[0017] A motion control module is configured to control the lifting motor, the transverse motor and the swing motor to realize position control and polishing pressure control of the polishing motor, and to drive the walking motor to realize position control of the polishing trolley along the rail, and to control the speed of the polishing motor. When the polishing pressure control is performed, the position control corresponding polishing angle and deflection angle are used to correct the read pressure data so that the error between the pressure applied by the grinding wheel to the rail and the pressure set by the motion control module is within a threshold range. The pressure data is provided by a pressure sensor arranged between the grinding wheel and the polishing motor.

[0018] A vehicle-mounted control module is configured to realize wired or wireless control of the motion control module through touch or / and button.

[0019] A remote control module is configured to call a working mode stored in a database, and then make the motion control module perform the position control, the polishing pressure control and the speed control according to the working mode.

[0020] Further, the motion control module is also configured to limit the lifting stroke of the lifting motor, the transverse stroke of the transverse motor and the swing stroke of the swing motor, and to control the zero position of the lifting stroke, the transverse stroke and the swing stroke. The zero position control includes limiting the starting position of the lifting stroke, the transverse stroke and the swing stroke. The limit control includes limiting the end position of the lifting stroke, the transverse stroke and the swing stroke. When the zero position control and the limit control are performed, two-stage sensors are used to trigger deceleration. When the sensor in the first stage is triggered, the corresponding lifting motor, transverse motor and swing motor are immediately decelerated. When the sensor in the second stage is triggered, the corresponding lifting motor, transverse motor and swing motor are immediately stopped.

[0021] Based on the third aspect of the present application, a lithium battery grinding wheel rail intelligent polishing method is provided. The method is performed in the polishing trolley as described above. The method comprises:

[0022] The center of the top of any one side of the rail is selected as the coordinate origin. The width of the rails on the left and right sides and the distance between them are measured and recorded. The initial profile of the rail surface is scanned based on the laser probe. The initial profile is obtained from the profile image formed by the laser line vertically irradiating the rail surface and the camera shooting the reflected light strip. The point cloud data of the initial profile is adjusted to match the standard rail model to obtain registration data, which includes a transformation rotation matrix and a transformation translation vector.

[0023] Collecting profile data scanned by the grinding trolley when moving on the steel rail, the grinding trolley performs fixed speed and triggers the laser probe to collect the profile data of the steel rail surface at fixed movement unit intervals, recording the position data and attitude data of the grinding trolley when collecting, and performing coordinate unification on the profile data, the position data and the attitude data based on the coordinate origin;

[0024] Taking the profile data of the n-1 frame and the continuous registration data of the standard rail model as the prediction initial value of the profile data of the n frame, and based on the prediction initial value, the continuous registration data of the profile data of the n frame and the standard rail model is calculated;

[0025] Point-by-point calculation of the deviation value between the standard rail model and the profile data registered based on the continuous registration data, combining a given threshold to obtain a defect point, merging the defect points continuously existing in multiple frames of the profile data into a defect point set, and dividing the defect point set into several defect regions through a K-means clustering algorithm, extracting the geometric features of each defect region, including position, area, depth, gradient, aspect ratio and minimum circumscribed rectangle, and constructing a decision tree classifier according to the feature differences of different types of defects in depth deviation, gradient, position and aspect ratio, and inputting the geometric features into the decision tree classifier to identify the defect type corresponding to the geometric features;

[0026] Driving the grinding trolley to reset the starting point, planning the grinding path according to the coordinate information of the defect type, combining the position data and the attitude data of the grinding trolley, and moving to each defect region according to the set speed and path to perform grinding operation.

[0027] Further, when the continuous registration data of the profile data of the n frame and the standard rail model is calculated, the method further comprises:

[0028] Dividing the profile data of the n frame into a rail head region and a rail bottom region, the rail head region being a region of the upper half of the profile data, and the rail bottom region being a region of the lower half of the profile data;

[0029] Taking the prediction initial value as the starting point, registering the rail head region and the rail bottom region respectively to obtain two groups of new registration data, and calculating the first average registration error of the two groups of new registration data and the standard rail model, comparing the first average registration error of the two groups of new registration data, and selecting the registration data with smaller first average registration error as the local optimal registration data of the profile data of the n frame;

[0030] Taking the predicted initial value as a starting point, calculate the overall registration data and the second average registration error of the entire profile data and the standard rail model, obtain the global value weight and the local optimal value weight based on the proportion between the first average registration error and the second average registration error;

[0031] Based on the fusion principle of Kalman filtering, the global value weight, the local optimal value weight, the local optimal registration data and the overall registration data are weighted and fused to obtain the continuous registration data of the profile data of the n-th frame.

[0032] Further, when calculating the deviation value point by point, it includes:

[0033] For each point in the registered profile data, find the closest point in the standard rail model under the same reference coordinate as the corresponding point to establish a point set of a group of corresponding points;

[0034] Calculate the distance of each two corresponding points under the same coordinate to obtain the deviation value between each two corresponding points.

[0035] Further, when the defect point set is divided into several defect regions by the K-means clustering algorithm, it further includes:

[0036] Each of the defect regions contains one or more subsets of the defect point set;

[0037] Calculate the number of defect points, the region center, the region radius and the distance to other defect regions of each defect region.

[0038] Further, after polishing each defect region, repeat the scanning of the profile data of the rail and the defect identification and classification to confirm whether to start the re-polishing.

[0039] The technical scheme of the present disclosure has the following beneficial effects:

[0040] The polishing trolley can be used for polishing in the turnout area of railway, subway, tram, etc. to remove defects such as unevenness, fat edge on the rail. During polishing, based on the polishing assembly, swing assembly, lifting assembly and transverse moving assembly, full profile polishing of the rail is realized, which has good effect and high efficiency, and automatic polishing is realized without manual intervention.

[0041] The trolley can realize automatic walking on the rail, integrates defect screening and intelligent polishing, reduces manual intervention, improves automation, greatly improves work efficiency and reduces work cost. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1A structure schematic diagram of a lithium battery grinding wheel steel rail intelligent polishing trolley in an embodiment of the present specification;

[0043] Figure 2 A structure schematic diagram of a lithium battery grinding wheel steel rail intelligent polishing trolley in an embodiment of the present specification;

[0044] Figure 3 A structure schematic diagram of a lithium battery grinding wheel steel rail intelligent polishing trolley in an embodiment of the present specification;

[0045] Figure 4 A structure schematic diagram of a polishing assembly, a swing assembly, and a horizontal movement assembly in an embodiment of the present specification;

[0046] Figure 5 A structure schematic diagram of a polishing assembly and a swing assembly in an embodiment of the present specification;

[0047] Figure 6 A structure schematic diagram of a lifting assembly and a horizontal movement assembly in an embodiment of the present specification;

[0048] Figure 7 A structure schematic diagram of a dust collection assembly in an embodiment of the present specification;

[0049] Figure 8 A structure block diagram of a lithium battery grinding wheel steel rail intelligent polishing control system in an embodiment of the present specification;

[0050] Figure 9 A flowchart of a lithium battery grinding wheel steel rail intelligent polishing method in an embodiment of the present specification;

[0051] Figure 10 A structure block diagram of a lithium battery grinding wheel steel rail intelligent polishing device in an embodiment of the present specification;

[0052] Figure 11 A computer readable storage medium of a lithium battery grinding wheel steel rail intelligent polishing method in an embodiment of the present specification.

[0053] Wherein, the reference signs are:

[0054] 1, a chassis; 11, a connecting piece; 2, a steel rail; 3, a wheel; 4, a polishing assembly; 41, a polishing motor; 42, a grinding wheel; 5, a swing assembly; 51, a swing frame; 52, a swing motor; 53, a worm and gear reducer; 6, a lifting assembly; 61, a lifting motor; 7, a horizontal movement assembly; 71, a horizontal movement frame; 72, a horizontal movement motor; 73, a screw rod; 74, a nut seat; 8, a dust collection assembly; 81, a dust collection box; 82, a filter core; 83, a dust collector; 84, a dust collection pipe; 9, a lithium battery compartment; 10, an outer cover. DETAILED DESCRIPTION

[0055] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any of various forms, and are not limited to the specific implementations described herein. Rather, specific implementations are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example implementations to those skilled in the art. The described features, structures, or characteristics can be combined in one or more implementations. In the following description, numerous specific details are provided so as to give a thorough understanding of example implementations. One skilled in the relevant art will recognize, however, that the techniques described can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures have not been described in detail so as to avoid

[0056] Furthermore, the accompanying drawings are only intended to illustrate the present disclosure. The same reference numbers in the drawings represent the same or similar elements and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0057] As shown in the drawings, Figures 1-7 An exemplary embodiment of the present disclosure provides a rail rapid maintenance grinding trolley, which can be different in configuration or performance and can include a controller, a chassis 1, and a plurality of wheels 3 arranged at the bottom of the chassis 1 for running on a rail 2, and a grinding assembly 4, a swing assembly 5, a lifting assembly 6, a transverse movement assembly 7, and a dust collection assembly 8 arranged on the chassis 1 and controlled by the controller. The grinding assembly 4 includes a grinding motor 41 and a grinding wheel 42 driven by the grinding motor 41. The swing assembly 5 includes a swing frame 51, a swing motor 52, and a worm and gear reducer 53 driven by the swing motor 52. The grinding motor 41 is rotatably mounted in the swing frame 51, and the worm and gear reducer 53 is connected to the grinding motor 41 for driving the grinding motor 41 to swing. The lifting assembly 6 includes a lifting motor 61 for driving the swing frame 51 to lift. The transverse movement assembly 7 includes a transverse movement frame 71 and a transverse movement motor 72 for driving the transverse movement frame 71 to move. The output end of the transverse movement motor 72 is connected to a screw rod 73, and the transverse movement frame 71 is provided with a nut seat 74 matched with the screw rod 73 at the bottom. The lifting assembly 6 is mounted on the transverse movement frame 71. The dust collection assembly 8 includes a dust collection box 81, a filter element 82, a dust collector 83, and a dust collection pipe 84. The filter element 82 and the dust collector 83 are arranged in the dust collection box 81, and the filter element 82 is mounted at the air inlet of the dust collector 83. One end of the dust collection pipe 84 is in communication with the dust collection box 81, and the other end faces the grinding wheel 42 or the rail 2.

[0058] The grinding wheel 42 is also connected with a pressure sensor for sensing the grinding force of the grinding wheel 42; the chassis 1 is also provided with an outer cover 10, which encloses the grinding assembly 4, the swing assembly 5, the lifting assembly 6, the transverse movement assembly 7, and the dust collection assembly 8; the chassis 1 is also provided with a lithium battery compartment 9, which is located at one end of the chassis 1 away from the grinding assembly 4.

[0059] The grinding trolley further comprises an eddy current probe and a camera. The eddy current probe is arranged at the bottom of the trolley body. It is a coil driven by an alternating current source. When it approaches the rail 2, it induces an eddy current signal. The camera is arranged on the trolley body and continuously photographs the rail 2. The controller reads the photos of the corresponding positions of the rail 2 determined as defective after analyzing whether the rail 2 has defects based on the eddy current signal, performs secondary defect analysis, confirms the defect type, and selects a grinding scheme corresponding to the defect type to grind the rail 2. When grinding, the controller drives the grinding assembly 4, the swing assembly 5, the lifting assembly 6, the transverse movement assembly 7, and the dust collection assembly 8 to work according to the grinding scheme.

[0060] The chassis 1 is detachably arranged. As shown in Figure 1 The left and right parts of the chassis 1 can be separated. Specifically, the connection between the left and right parts of the chassis 1 can be achieved by a connecting piece 11, so that the grinding part and the battery part can be separated, facilitating the removal of the grinding trolley to a designated position for operation.

[0061] Working principle:

[0062] In operation, the wheels 3 of the trolley can be driven by a hub motor or through a belt / chain, etc. After determining the defects based on the above-mentioned embodiment method, the controller controls the torque, rotational speed, etc. of the grinding motor 41, the rotational speed of the swing motor 52 to control the swing speed of the grinding motor 41, the rotation of the lifting motor 61 to control the grinding height, and the rotation of the transverse movement motor 72 to control the grinding position according to the preset scheme. The dust collector 83 works to collect the dust generated during grinding into the dust collection box 81 along the dust collection pipe 84. The dust collection pipe 84 can have multiple pipes for dust collection on the surface of the grinding wheel 42 and the surface of the rail 2. The multiple dust collection pipes 84 are connected to the same hose, which connects the dust collection box 81.

[0063] As known from the above embodiment, the grinding trolley can be used for grinding in the turnout area of a railway, a subway, a tram, etc. to remove the defects such as fat edges and unevenness on the rail 2. Based on the grinding assembly 4, the swing assembly 5, the lifting assembly 6, and the transverse movement assembly 7, full profile grinding of the rail 2 is achieved, which is effective and efficient without manual intervention for automatic grinding.

[0064] In an embodiment, asFigure 8 As shown, an example is provided with a lithium battery grinding wheel steel rail intelligent polishing control system, which is executed in the polishing trolley of the above embodiment, the control system comprises: a motion control module 201, which is used to control the lifting motor, the transverse motor and the swing motor, to realize position control and polishing pressure control of the polishing motor, and to drive the walking motor to realize position control of the polishing trolley along the steel rail, and to control the speed of the polishing motor. When the motion control module performs the polishing pressure control, the position control corresponding polishing angle and deflection angle are used to correct the read pressure data, so that the error between the pressure applied by the grinding wheel to the steel rail and the pressure set by the motion control module is within the threshold range, and the pressure data is provided by the pressure sensor arranged between the grinding wheel and the polishing motor; the vehicle-mounted control module 202 is used to realize wired or wireless control of the motion control module through touch or / and button; the remote control module 203 is used to call the working mode stored in the database, so that the motion control module performs the position control, the polishing pressure control and the speed control according to the working mode.

[0065] Specifically, the motion control module is also used to limit the lifting stroke of the lifting motor, the transverse stroke of the transverse motor and the swing stroke of the swing motor, and to control the zero position of the lifting stroke, the transverse stroke and the swing stroke. The zero position control includes limiting the starting position of the lifting stroke, the transverse stroke and the swing stroke, and the limit control includes limiting the end position of the lifting stroke, the transverse stroke and the swing stroke. When the motion control module performs the zero position control and the limit control, two-stage sensors are used to trigger deceleration. When the sensor in the first stage is triggered, the corresponding lifting motor, transverse motor and swing motor are immediately decelerated. When the sensor in the second stage is triggered, the corresponding lifting motor, transverse motor and swing motor are immediately stopped.

[0066] Working principle:

[0067] The motion control module can be composed of a PLC, a motor driver, a motor and a limit switch; the vehicle-mounted control module can be a vehicle-mounted panel, which communicates with the PLC through 485 or a network; and the remote control module can be a tablet computer installed with monitoring and control software. The control system controls the motors through the PLC. The PLC runs the lower computer control program to drive the motors and control the limits, so as to realize the cooperation of the components of the trolley for polishing and mainly polish the tracks in the turnout area. When the train runs in the turnout area, the train is unstable due to the change of the track, which can damage the tracks in the turnout area, such as uneven track surface, fat edge and cracks on both sides of the track, etc. The profile of the track has a standard value when it is delivered. The standard value is input into the system first, and then a targeted defect polishing scheme is obtained according to the defect detection, so as to adjust the position angle of the grinding wheel to polish the profile of the track, so that the profile after polishing is not much different from the profile when it is delivered, thereby ensuring the stability of the train running.

[0068] As shown in Figure 9 The embodiment of the present specification provides a lithium battery grinding wheel steel rail intelligent polishing method. The execution subject of the method can be the vehicle-mounted control module, the remote control module and the like of the above-mentioned embodiment. The method can specifically include the following steps S101-S105:

[0069] In step S101, the top center of any one side of the steel rail is selected as the coordinate origin, the width of the steel rails on the left and right sides and the distance between them are measured and recorded, the initial profile of the steel rail surface is scanned based on the laser probe, the initial profile is obtained from the profile image formed by the laser line vertically irradiating the steel rail surface and the camera shooting the reflected light band, the point cloud data of the initial profile is adjusted to match the standard steel rail model, and the registration data is obtained, the registration data includes a transformation rotation matrix and a transformation translation vector.

[0070] The laser probe can be a laser scanner. The trolley of the above-mentioned embodiment can include an accelerometer, a gyroscope, a laser scanner, a GPS and an odometer required to execute the method, etc. The scanning range and resolution of the laser scanner can cover the entire steel rail surface. The odometer is installed on the wheel and calibrates the running distance according to the known wheel diameter, i.e. outputs the corresponding pulse number for each rotation. The GPS is placed on the top of the trolley to record the position information, and the gyroscope is fixed on the central part of the trolley to obtain the attitude data.

[0071] In the detection of defects on the surface of a steel rail, the first step is to obtain the profile point cloud data of the surface of the steel rail. When a laser is vertically irradiated, a light band is formed on the surface of the steel rail. Then, a camera is tilted at a certain angle relative to the laser probe, and the surface of the steel rail is photographed. At this time, the light band on the surface of the steel rail will reflect the profile of the object surface on the laser projection plane. In the case where the relative positional relationship between the camera and the laser probe is determined and unchanged, for the same object profile, the imaging position of the light band on the camera will change with the depth of the object outside. Therefore, by detecting the imaging position and shape of the light band on the camera, the physical coordinates of the light band, that is, the physical coordinates of the surface profile, can be calculated. In the case where the object is stationary, the polishing trolley moves accordingly, and the surface of the measured object is divided into multiple profiles, so as to achieve the purpose of measuring the physical coordinates of all surface points.

[0072] In the initial state of detection, in order to eliminate the geometric deviation between the actual measurement data and the standard model, so that the subsequent defect detection is more accurate. In the actual measurement process, due to various factors (such as measurement error, sensor error or polishing trolley tilt, etc.), the collected track surface point cloud data often has certain deviation. These deviations may cause differences in position, shape or scale of the point cloud data, which may affect the accuracy of defect detection and classification. By registering the initial point cloud data with the standard rail model, noise and errors can be eliminated, and deviations caused by equipment errors or environmental interference can be removed, so that the data is more stable and accurate. The registered data is aligned with the standard rail model, which can help the control system to more accurately identify abnormal areas (such as wear, scratches and other defects) on the track surface, and reduce missed detection or false detection caused by deviation. At the same time, the standard rail model provides an ideal reference for the ideal shape of the track surface, which can help the algorithm to effectively identify the deviation of the actual track surface, and further analyze and locate the defects. Here, by registering with the standard model, the initial point cloud data can be more matched with the geometric shape of the actual track surface, providing more accurate basic data for subsequent defect detection.

[0073] In step S102, profile data scanned by the polishing trolley when moving on the steel rail is collected. The polishing trolley performs fixed speed and triggers the laser probe to collect the profile data of the surface of the steel rail at fixed movement unit intervals when moving. The position data and attitude data of the polishing trolley when collecting are recorded, and the coordinate of the profile data, the position data and the attitude data is unified based on the coordinate origin.

[0074] The track surface profile data collected by the laser probe cannot be directly used for extraction of track surface defects and needs to be pre-processed. Firstly, the error caused by slight rotation of the trolley needs to be corrected, and then the coordinates of each sensor are unified to the same coordinate system, which can be taken as the reference coordinate system of the gyroscope. The moving speed of the trolley can be 1.5 m / s, and the moving unit interval can be 10 mm.

[0075] As shown in Figures 1-4 , the trolley is supported by wheels on the rail, and the wheels do not leave the rail during the movement of the trolley, so it can be assumed that the gyroscope only rotates in the horizontal direction. It is assumed that the profile data obtained at time t1 is A1; at time t2, the trolley moves forward and obtains profile data A2; the rotation angle between adjacent profiles A1 and A2 is θ; if the correct profile at time t2 should be A3, then A3 will be parallel to A1, so A3 is the projection of A2 on A1. Therefore, assuming that the original point coordinates of the left and right rail surface profiles are PL0 and PR0 respectively; the corrected point coordinates of the left and right profile data will be:

[0076] ;

[0077] ;

[0078] Then, the unified coordinate transformation is performed, the gyroscope coordinate system is selected as the reference coordinate system, and it is assumed that the coordinate of the coordinate center of the left laser probe in the reference coordinate system is ; similarly, the coordinate center of the right laser probe is ; then, the coordinates of the left and right rail surface profiles in the reference coordinate system can be represented as:

[0079] ;

[0080] .

[0081] In actual work, when the gyroscope moves along the track direction, the motion trajectory of the coordinate origin of the gyroscope can be calculated through the GPS position, the gyroscope attitude and the odometer mileage, so that the processed point cloud reflects the relative position of the actual track surface points.

[0082] In step S103, the profile data of the n-1 frame and the continuous registration data of the standard rail model are taken as the predicted initial value of the n frame profile data, and the continuous registration data of the n frame profile data and the standard rail model are calculated based on the predicted initial value.

[0083] Specifically, when calculating the continuous registration data between the contour data of the nth frame and the standard rail model, the process includes: dividing the contour data of the nth frame into a rail head region and a rail bottom region, where the rail head region is the upper half of the contour data and the rail bottom region is the lower half of the contour data; starting from the predicted initial value, registering the rail head region and the rail bottom region respectively to obtain two sets of new registration data, and calculating the first average registration error between the two sets of new registration data and the standard rail model; comparing the first average registration error between the two sets of new registration data, and selecting the first average registration error... The registration data with the smaller average registration error is used as the locally optimal registration data of the contour data in the nth frame. Starting from the predicted initial value, the overall registration data and the second average registration error of the entire contour data with the standard rail model are calculated. Based on the ratio between the first average registration error and the second average registration error, the overall value weight and the local optimal value weight are obtained. Based on the fusion principle of Kalman filtering, the overall value weight, the local optimal value weight, the locally optimal registration data, and the overall registration data are weighted and fused to obtain the continuous registration data of the contour data in the nth frame.

[0084] The top and sides of the rail are susceptible to wear and scratches, leading to rail surface defects. Unlike the standard model, the bottom of the rail, not in contact with the wheel, typically only experiences corrosion. However, corrosion does not significantly alter the shape of the rail bottom compared to the standard model, making it an important reference for subsequent data registration. Simultaneously, as the trolley moves along the track, contour data is acquired every 1 millimeter. Within such a small interval, there is insufficient time for abrupt changes in the trolley's attitude when acquiring adjacent contours. Therefore, the transformation between the current rail surface contour and the standard model is closely related to the transformation between adjacent contours and the standard rail model. Based on this, this embodiment utilizes a Kalman filter model to recursively predict the transformation of the current contour using adjacent continuous contours. Then, it calculates the average distance between each part of the point cloud and the standard point cloud under the given transformation parameters. Finally, it selects the optimal transformation parameters by evaluating these average distances.

[0085] Specifically, the above method can be summarized as follows:

[0086] 1. Assume the Nth contour point set is P. n And the (N-1)th contour point set P n-1 and the standard model Q M Transformation between (translation T) n-1 and rotation R n-1 As from P n To Q M The predictive transformation.

[0087] 2. Divide the rail profile into two parts: the head part and the bottom part, the head is a part of the head of the whole rail, which is the upper part of the profile data point set, used to distinguish the profile data point set from the bottom part, to get the head point set P n H and the bottom point set P n B , take (R n-1 , T n-1 ) as the initial value. Calculate P n H and P n B to the standard model Q M , and record as (R n H , T n H ), (R n B , T n B ), then output the average distance d n H and d n B of each part.

[0088] 3. Calculate the minimum value of d n H and d n B , and take the transformation corresponding to the minimum value as the calculated transformation from P n to Q M .

[0089] 4. Calculate the average distance d n-1 from P n-1 to Q n under the transformation parameters (R M , T n );

[0090] 5. Evaluate min (d n H , d n B ) and d n ; the greater the distance, the smaller the weight, and the smaller the probability. Update the probability as:

[0091] ;

[0092] ;

[0093] 6. According to the principle of Kalman filter, update the transformation between P n and Q M as:

[0094] .

[0095] In step S104, deviation values between the standard rail model and the profile data registered based on the continuous registration data are calculated point by point, a given threshold is combined to obtain defect points, defect points successively existing in multiple frames of the profile data are combined into a defect point set, the defect point set is divided into a plurality of defect regions through a K-means clustering algorithm, geometric features of each defect region are extracted, the geometric features including position, area, depth, gradient, aspect ratio and minimum circumscribed rectangle, a decision tree classifier is constructed according to feature differences of different types of defects in depth deviation, gradient, position and aspect ratio, and the geometric features are input into the decision tree classifier to identify a defect type corresponding to the geometric features.

[0096] In which, by comparing the deviation of the registered rail surface profile and the standard rail model, and combining a given threshold, the candidate defect region can be accurately positioned. The continuous defect profile is combined, and the candidate defect points are combined into candidate defect regions. The minimum circumscribed rectangle and center of each defect region are calculated, the K-means clustering is used to connect the defect regions with smaller area to the defect regions with larger area, and the defect regions with similar features are clustered together. Finally, the position, shape, depth, length, width, slope, minimum circumscribed rectangle and other features of the defect are calculated.

[0097] In actual operation, different defects have different features. The train wheels contact and rub with the rail head, causing wear, corrugation, scratches, hypertrophy and spalling. Therefore, the corrosion at the bottom of the rail can be distinguished by position, but the corrosion of the rail head is still mixed with other defects. And, friction will cause the rail head (the part in contact with the train wheels) to be lower than the standard rail model, so the depth deviation of the rail model is positive. On the contrary, the corrosion of the rail head will return a negative depth deviation, which will distinguish the rail head corrosion from the other four defects. Among the four types of defects, wear and corrugation have smooth profiles, while scratches and spalling have sharp edges, so the difference is reflected in the gradient. Wear and corrugation can be distinguished by peaks and troughs. Scratches are always long and narrow, that is, the aspect ratio is greater than spalling. Based on these judgments, a decision tree is used to classify the defects.

[0098] In step S105, the grinding trolley is reset to the starting point, the grinding path is planned according to the coordinate information of the defect type in combination with the position data and the attitude data of the grinding trolley, and the grinding trolley moves to each defect region one by one according to the set speed and path to perform grinding operation.

[0099] In an embodiment, when the deviation values are calculated point by point, it comprises: for each point in the registered profile data, finding the closest point in the standard rail model at the same reference coordinate as the corresponding point to establish a point set of a set of corresponding points; calculating the distance between each two corresponding points at the same coordinate to obtain the deviation value between each two corresponding points.

[0100] In an embodiment, when the set of defect points is divided into several defect regions by the K-means clustering algorithm, it further comprises: each defect region contains one or more subsets of the set of defect points; calculating the number of defect points, the region center, the region radius, and the distance to other defect regions of each defect region.

[0101] In an embodiment, after the grinding operation on each defect region, the profile data of the rail is repeatedly scanned and the defect identification and classification are performed to determine whether to start re-grinding.

[0102] Wherein, by re-scanning the profile point cloud data of the rail after each grinding operation on the defect region of the rail, and comparing with the standard rail surface model, the re-identification and classification judgment of the defect is realized, forming a closed-loop grinding effect verification mechanism. Specifically, the system uses laser scanning to obtain the rail surface point cloud, calculates the deviation with the standard rail model, extracts the defect points and performs clustering and classification, and identifies the defect types such as wear, scratch, corrosion, and fat edge. After completing a grinding, the trolley drives again to scan and register the point cloud of the region, repeats the above defect identification process, and judges whether the region after grinding still exists geometric deviation exceeding the threshold value. If the deviation is still significant, the grinding needs to continue until the deviation is reduced to within the preset 1mm (rail head) or 0.5mm (rail bottom). This method not only ensures that the defect is effectively repaired, but also avoids over-grinding, improves the precision and efficiency of rail maintenance. In addition, through the re-judgment of the classifier on the residual defects after grinding, it can also provide a basis for adjusting the strategy of the grinding equipment, realize intelligent control and decision-making, and thus provide reliable technical support and data closed loop for rail grinding operation.

[0103] Based on the same idea, as shown in Figure 10 The exemplary embodiments of the present disclosure also provide a lithium battery grinding wheel rail intelligent grinding device, comprising:

[0104] The correction module 301 is configured to select the center of the top of the rail on either side as a coordinate origin, measure and record the width of the rails on the left and right sides and the distance between the rails, scan the initial profile of the rail surface based on the laser probe, obtain the initial profile from the profile image formed by the laser line vertically irradiating the rail surface and the camera capturing the reflected light strip, adjust the point cloud data of the initial profile to match the standard rail model, and obtain registration data including a transformation rotation matrix and a transformation translation vector.

[0105] The acquisition module 302 is configured to acquire profile data obtained by scanning the rail when the grinding trolley moves on the rail, perform the profile data acquisition of the rail surface by triggering the laser probe at a fixed speed and in a fixed movement unit interval when the grinding trolley moves, record the position data and attitude data of the grinding trolley when the profile data is acquired, and perform coordinate unification on the profile data, the position data and the attitude data based on the coordinate origin.

[0106] The registration module 303 is configured to use the profile data of the n-1 frame and the continuous registration data of the standard rail model as the predicted initial value of the profile data of the n frame, and calculate the continuous registration data of the profile data of the n frame and the standard rail model based on the predicted initial value.

[0107] The defect identification module 304 is configured to calculate the deviation value between the standard rail model and the profile data registered based on the continuous registration data point by point, obtain defect points by combining a given threshold value, merge defect points that exist continuously in multiple frames of the profile data into a defect point set, divide the defect point set into a plurality of defect regions by a K-means clustering algorithm, extract geometric features of each defect region, the geometric features include position, area, depth, gradient, aspect ratio and minimum bounding rectangle, construct a decision tree classifier according to the feature differences of different types of defects in depth deviation, gradient, position and aspect ratio, input the geometric features into the decision tree classifier to identify the defect type corresponding to the geometric features.

[0108] The motion control module 305 is configured to reset the starting point of the grinding trolley, plan a grinding path according to the coordinate information of the defect type in combination with the position data and the attitude data of the grinding trolley, and move to each defect region according to the set speed and path to perform grinding operation.

[0109] The device integrates defect screening, specific identification and intelligent grinding, reduces manual intervention, improves automation, greatly improves work efficiency and reduces work cost.

[0110] The specific details of the modules of the above apparatus have been described in the method embodiments, and the details not disclosed can be referred to the method embodiments, and thus will not be described again.

[0111] Based on the same idea, the exemplary embodiments of the present disclosure also provide a computer readable storage medium, which stores a program product capable of implementing the above method of the present disclosure. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing the terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Methods” section of the present disclosure when the program product is run on the terminal device.

[0112] Reference Figure 11 As shown, the program product 400 for implementing the above method according to the exemplary embodiments of the present disclosure is described, which can adopt a portable compact disc read-only memory (CD-ROM) and includes program codes, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in the present document, the readable storage medium can be any tangible medium containing or storing a program, which can be used or combined with an instruction execution system, device or apparatus.

[0113] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0114] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which readable program codes are borne. Such a propagated data signal can adopt various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in connection with an instruction execution system, device or apparatus.

[0115] The program codes contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0116] The program code may, through the use of program components, be implemented in any of various ways, including procedure-based execution, object-oriented execution, and / or virtual machine-based execution. A program component or a portion of program code may, for example, be implemented as individually-stored components, code from multiple programs, and / or as updates downloaded over-the-air to existing program components. The program code may be stored on a non-transitory computer-readable storage medium, which may be any available non-transitory computer-readable storage medium, including volatile or non-volatile storage. The non-transitory computer-readable storage medium can be a computer-readable storage medium other than an interface to the storage (such as a removable storage medium or a hardware interface). The storage medium may, for example, comprise memory such as high-speed random access memory and / or nonvolatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, or tape storage devices, a flash memory, etc.

[0117] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software with the necessary hardware or in software in combination with the necessary hardware. Therefore, the technical solutions according to the example embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the methods according to the example embodiments of the present disclosure.

[0118] In addition, the above-described diagrams are only schematic illustrations of the processes included in the methods according to the example embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above-described diagrams do not indicate or limit the time sequence of the processes. In addition, it will be readily understood that the processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0119] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. Indeed, according to the example embodiments of the present disclosure, the features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functionalities of one module or unit described above can be further divided into embodied by multiple modules or units.

[0120] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A lithium battery steel rail intelligent grinding trolley, characterized in that, The polishing trolley comprises a controller, a chassis, and a plurality of wheels arranged at the bottom of the chassis for walking on the steel rail, and a polishing assembly, a swing assembly, a lifting assembly, a transverse movement assembly, a dust collection assembly, and a laser probe are further arranged on the chassis and controlled by the controller, wherein: The polishing assembly comprises a grinding wheel driven by a polishing motor; The swing assembly comprises a swing frame, a swing motor, and a worm and gear reducer driven by the swing motor, the polishing motor is rotatably mounted in the swing frame, and the worm and gear reducer is connected to the polishing motor for driving the polishing motor to swing; The lifting assembly comprises a lifting motor for driving the swing frame to lift; The transverse movement assembly comprises a transverse movement frame and a transverse movement motor for driving the transverse movement frame to move, the output end of the transverse movement motor is connected with a screw rod, the bottom of the transverse movement frame is provided with a nut seat matched with the screw rod, and the lifting assembly is mounted on the transverse movement frame; The dust collection assembly comprises a dust collection box, a filter element, a dust collector, and a dust collection pipe, the filter element and the dust collector are arranged in the dust collection box, the filter element is mounted at the air inlet of the dust collector, one end of the dust collection pipe is communicated with the dust collection box, and the other end of the dust collection pipe faces the grinding wheel or the steel rail; The laser probe is used to measure the profile value of the steel rail to be polished, the controller is used to analyze the difference between the profile value and the standard value, and then drive the polishing assembly, the swing assembly, the lifting assembly, the transverse movement assembly, and the dust collection assembly to work, so that the profile value detected by the laser probe in real time tends to the standard value; The grinding wheel is further connected with a pressure sensor for sensing the polishing force of the grinding wheel, an outer cover is further arranged on the chassis, the polishing assembly, the swing assembly, the lifting assembly, the transverse movement assembly, and the dust collection assembly are covered in the outer cover, and a lithium battery compartment is further arranged on the chassis and located at one end of the chassis away from the polishing assembly.

2. The lithium battery steel rail intelligent grinding trolley according to claim 1, characterized in that, The chassis is detachably arranged.

3. A lithium battery steel rail intelligent grinding control system, characterized in that, The control system is used in the polishing trolley of any one of claims 1-2, and the control system comprises: a motion control module, which is used to control the lifting motor, the transverse movement motor, and the swing motor, to realize position control and polishing pressure control of the polishing motor, to drive the walking motor to realize position control of the polishing trolley along the steel rail, and to control the speed of the polishing motor, and when the motion control module performs the polishing pressure control, the motion control module corrects the read pressure data according to the corresponding polishing angle and deflection angle of the position control, so that the error between the pressure applied by the grinding wheel to the steel rail and the pressure set by the motion control module is within a threshold range, and the pressure data is provided by a pressure sensor arranged between the grinding wheel and the polishing motor; a vehicle-mounted control module, which is used to realize wired or wireless control of the motion control module through touch or / and buttons. A remote control module is configured to call a working mode stored in a database, and then make the motion control module execute the position control, the polishing pressure control and the speed control according to the working mode.

4. The lithium-battery steel rail grinding control system of claim 3, wherein, The motion control module is further configured to limit and control zero positions of the lifting motor for lifting stroke, the transverse motor for transverse stroke and the swing motor for swing stroke, the zero position control including limiting starting positions of the lifting stroke, the transverse stroke and the swing stroke, and the limit control including limiting end positions of the lifting stroke, the transverse stroke and the swing stroke, and the motion control module uses two-stage sensors to trigger deceleration when performing the zero position control and the limit control, the corresponding lifting motor, transverse motor and swing motor are immediately decelerated when a sensor in the first stage is triggered, and the corresponding lifting motor, transverse motor and swing motor are immediately stopped when a sensor in the second stage is triggered.

5. A lithium battery steel rail intelligent grinding method, characterized in that, The method is performed in the polishing trolley according to any one of claims 1-2, and the method comprises: A center of a top of a rail on any one side is selected as a coordinate origin, a width of the rail on the left side and a width of the rail on the right side and a distance between the rail on the left side and the rail on the right side are measured and recorded, an initial profile of a rail surface is scanned based on a laser probe, the initial profile is obtained from a profile image formed by a laser line vertically irradiating the rail surface and a camera capturing a reflected light band, point cloud data of the initial profile is adjusted to match a standard rail model to obtain registration data, the registration data includes a transformation rotation matrix and a transformation translation vector; Profile data of the rail surface scanned by the polishing trolley when moving on the rail is collected, the polishing trolley performs fixed speed and triggers the laser probe to collect the profile data of the rail surface at fixed moving unit intervals when moving, position data and attitude data of the polishing trolley when collecting are recorded, and the profile data, the position data and the attitude data are unified in coordinates based on the coordinate origin; The profile data of the n-1 frame and the continuous registration data of the standard rail model are used as a predicted initial value of the profile data of the n frame, and the profile data of the n frame and the continuous registration data of the standard rail model are calculated based on the predicted initial value; A deviation value between the standard rail model and the profile data registered based on the continuous registration data is calculated point by point, a given threshold is combined to obtain a defect point, defect points continuously existing in multiple frames of the profile data are merged into a defect point set, the defect point set is divided into a plurality of defect regions through a K-means clustering algorithm, geometric features of each defect region are extracted, the geometric features include position, area, depth, gradient, aspect ratio and minimum bounding rectangle, a decision tree classifier is constructed according to feature differences of different types of defects in depth deviation, gradient, position and aspect ratio, and the geometric features are input into the decision tree classifier to identify a defect type corresponding to the geometric features. The grinding trolley is driven to reset to a starting point, coordinates of the defect types are combined with the position data and the attitude data of the grinding trolley to plan a grinding path, and the grinding trolley is moved to each defect area according to a set speed and path to perform grinding operation; When the continuous registration data of the n-th frame of the profile data and the standard rail model is calculated, the method further comprises: The n-th frame of the profile data is divided into a rail head region and a rail bottom region, the rail head region is a region of an upper half of the profile data, and the rail bottom region is a region of a lower half of the profile data; Taking the predicted initial value as a starting point, the rail head region and the rail bottom region are registered respectively to obtain two groups of new registration data, and a first average registration error of the two groups of new registration data and the standard rail model is calculated, the first average registration error of the two groups of new registration data is compared, and the registration data with a smaller first average registration error is selected as the local optimal registration data of the n-th frame of the profile data; Taking the predicted initial value as a starting point, the overall registration data of the entire profile data and the standard rail model and a second average registration error are calculated, and based on a ratio between the first average registration error and the second average registration error, an overall value weight and a local optimal value weight are obtained; Based on the fusion principle of Kalman filtering, the overall value weight, the local optimal value weight, the local optimal registration data, and the overall registration data are weighted and fused to obtain the continuous registration data of the n-th frame of the profile data.

6. The method of claim 5, wherein the lithium battery steel rail grinding is performed by a robot. When the deviation value is calculated point by point, it includes: For each point in the registered profile data, find the closest point in the standard rail model under the same reference coordinate as the corresponding point to establish a point set of corresponding points; The distance between each two corresponding points under the same coordinate is calculated to obtain the deviation value between each two corresponding points.

7. The method of claim 5, wherein the lithium battery steel rail grinding is performed by a robot. When the defect point set is divided into a plurality of defect regions by the K-means clustering algorithm, it further includes: Each defect region contains one or more subsets of the defect point set; The number of defect points, the region center, the region radius, and the distance to other defect regions of each defect region are calculated.

8. The intelligent grinding method for steel rails using lithium-ion battery-powered grinding wheels according to claim 5, characterized in that, After the grinding operation on each defect region, the profile data of the rail is repeatedly scanned, and the defect identification and classification are performed to confirm whether to start the re-grinding.

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