Lithium battery grinding wheel steel rail intelligent grinding trolley, control system and method

Abstract

The invention belongs to the technical field of steel rail grinding, and provides a lithium battery grinding wheel steel rail intelligent grinding trolley and a control system and method. The trolley comprises a controller, a bottom frame and a plurality of wheels arranged at the bottom of the bottom frame and used for walking on a steel rail, the bottom frame is further provided with a grinding assembly, a swinging assembly, a lifting assembly, a transverse moving assembly and a dust collecting assembly which are controlled by the controller, and the grinding assembly comprises a grinding wheel driven by a grinding motor; the swing assembly is used for driving the polishing motor to swing. The lifting assembly comprises a lifting motor used for driving the swing frame to ascend and descend. The transverse moving assembly is used for driving a transverse moving motor of a transverse moving frame to move. 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 installed at an air inlet of the dust collector, one end of the dust collection pipe communicates with the dust collection box, and the other end faces the grinding wheel or the steel rail; the problems of low polishing efficiency and long period in steel rail maintenance polishing in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of rail grinding, and particularly to a lithium battery grinding wheel intelligent rail grinding trolley, a control system and a method thereof. Background Art

[0002] When performing full-profile grinding on turnout areas (including the partial profiles of switch points and crossing points) of railways, subways, trams, etc., defects such as corrugations, fish-scale patterns, and cracks on the rails can be removed. However, in the prior art, the lengths of rails for railways, subways, etc. are too large to achieve full-track maintenance grinding, and generally, key grinding is performed on the defective parts.

[0003] In the prior art, manual grinding or simple semi-automatic equipment grinding is mostly used for rail grinding. The semi-automatic equipment performs multi-faceted grinding on the rails, but still requires manual auxiliary remote control, with low efficiency, and it is difficult for these semi-automatic equipment to control the relative position between the grinding system and the rails.

[0004] Moreover, in the daily maintenance of rails, rail defects are usually detected first. Traditional detection methods mainly include ultrasonic detection, magnetic particle detection, penetrant detection, and visual inspection, etc. However, these methods have many limitations in practical applications. For example, ultrasonic detection has poor detection effects on complex structures, magnetic particle detection requires magnetization of workpieces, penetrant detection has high requirements for surface treatment, and visual inspection depends on the experience of operators and is easily affected by human factors. In addition, the recognition efficiency of the above methods is low, and they all rely heavily on manual operation by staff, with a long processing cycle and low efficiency. Summary of the Invention

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

[0006] Other features and advantages of the present disclosure will become apparent through the following detailed description, or be learned in part through the practice of the present disclosure.

[0007] According to one aspect of the present invention, a lithium battery grinding wheel intelligent rail grinding trolley is disclosed, characterized in that the grinding trolley includes a controller, a chassis, and a plurality of wheels disposed at the bottom of the chassis for traveling on the rail. A grinding assembly, a swing assembly, a lifting assembly, a transverse movement assembly, a dust collection assembly, and a laser probe controlled by the controller are further disposed on the chassis, wherein: The grinding assembly includes a grinding wheel driven by a grinding motor; The swing assembly includes a swing frame, a swing motor, and a worm and worm gear reducer driven by the swing motor. The grinding motor is rotatably installed in the swing frame, and the worm and worm gear reducer is connected to the grinding motor to drive the grinding motor to swing; The lifting assembly includes a lifting motor for driving the swing frame to lift; The transverse movement assembly includes 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 to a screw rod, and a nut seat matched with the screw rod is arranged at the bottom of the transverse movement frame. The lifting assembly is installed on the transverse movement frame; The dust collection assembly includes a dust collection box, a filter element, a vacuum cleaner, and a dust collection pipe. The filter element and the vacuum cleaner are arranged in the dust collection box. The filter element is installed at the air inlet of the vacuum cleaner. One end of the dust collection pipe is communicated with the dust collection box, and the other end thereof faces the grinding wheel or the steel rail; The laser probe is used to measure the profile value of the steel rail to be ground. After the controller analyzes the difference between the steel rail and the standard value according to the profile value, it drives the grinding 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.

[0008] Further, the grinding wheel is also connected to a pressure sensor, and the pressure sensor is used to sense the grinding force of the grinding wheel; an outer cover is also arranged on the chassis, and the outer cover covers the grinding assembly, the swing assembly, the lifting assembly, the transverse movement assembly, and the dust collection assembly therein; a lithium battery compartment is also arranged on the chassis, and the lithium battery compartment is located at one end of the chassis far from the grinding assembly.

[0009] Further, the chassis is detachably arranged.

[0010] Based on the second aspect of the present invention, a lithium battery grinding wheel steel rail intelligent grinding control system is provided. The control system is executed in the above-mentioned grinding trolley, and the control system includes: A motion control module, which is used to regulate the lifting motor, the transverse movement motor, and the swinging motor to achieve position control and grinding pressure control of the grinding motor, and to drive the traveling motor to achieve position control of the grinding trolley along the rail, and to control the speed of the grinding motor. When performing the grinding pressure control, the motion control module corrects the read pressure data according to the grinding angle and yaw angle corresponding to the position control, 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 the threshold range. The pressure data is provided by a pressure sensor disposed between the grinding wheel and the grinding motor; A vehicle-mounted control module, which is used to achieve wired or wireless control of the motion control module through touch or / and buttons; A remote control module, which is used to make the motion control module execute the position control, the grinding pressure control, and the speed control according to the operation mode after calling the operation mode stored in the database.

[0011] Furthermore, the motion control module is also used to perform limit control and zero position control on the lifting stroke of the lifting motor, the transverse movement stroke of the transverse movement motor, and the swinging stroke of the swinging motor. The zero position control includes restricting the starting positions of the lifting stroke, the transverse movement stroke, and the swinging stroke, and the limit control includes limiting the end positions of the lifting stroke, the transverse movement stroke, and the swinging stroke. When performing the zero position control and the limit control, the motion control module uses two-stage sensors to trigger deceleration. When the sensor in the first stage is triggered, it controls the corresponding lifting motor, transverse movement motor, and swinging motor to decelerate immediately. When the sensor in the second stage is triggered, it controls the corresponding lifting motor, transverse movement motor, and swinging motor to stop rotating immediately.

[0012] Based on the third aspect of the present invention, a method for intelligent grinding of a lithium battery grinding wheel on a rail is provided. The method is executed in the grinding trolley as described above. The method includes: Select the center of the top of the rail on either side as the coordinate origin, measure and record the widths of the rails on the left and right sides and the distance between them. Based on the laser probe scanning the initial contour of the rail surface, the initial contour is obtained from the contour image formed by the laser line vertically irradiating the rail surface and the camera capturing the reflected light band. Adjust the point cloud data of the initial contour to match it with the standard rail model to obtain registration data, which includes a transformation rotation matrix and a transformation translation vector; Collect the contour data obtained by scanning the rail when the grinding trolley moves on the rail. When the grinding trolley moves, it executes a fixed speed and triggers the laser probe at fixed moving unit intervals to collect the contour data of the rail surface, records the position data and attitude data of the grinding trolley during collection, and unifies the coordinates of the contour data, the position data, and the attitude data based on the coordinate origin; Use the contour data of the (n - 1)-th frame and the continuous registration data of the standard rail model as the prediction initial value of the contour data of the n-th frame. Based on the prediction initial value, calculate the continuous registration data of the contour data of the n-th frame and the standard rail model; Calculate the deviation value between the standard rail model and the contour data registered based on the continuous registration data point by point, combine the given threshold to obtain defect points, merge the continuously existing defect points in multiple frames of the contour data into a defect point set, and divide the defect point set into several defect regions through the K-means clustering algorithm. Extract the geometric features of each defect region, where the geometric features include position, area, depth, gradient, aspect ratio, and minimum bounding rectangle. According to the characteristic differences of different types of defects in depth deviation, gradient, position, and aspect ratio, construct a decision tree classifier, and input the geometric features into the decision tree classifier to identify the defect type corresponding to the geometric features; Drive the grinding trolley to reset to the starting point. According to the coordinate information of the defect type, combine the position data and the attitude data of the grinding trolley to perform grinding path planning, and move to each defect region one by one at the set speed and path to perform grinding operations.

[0013] Further, when calculating the continuous registration data of the contour data of the n-th frame and the standard rail model, the method further includes: Divide the contour data of the n-th frame into a rail head region and a rail bottom region. The rail head region is the upper half part of the contour data, and the rail bottom region is the lower half part of the contour data; Starting from the prediction initial value, register the rail head region and the rail bottom region respectively to obtain two sets of new registration data, and calculate the first average registration error between the two sets of new registration data and the standard rail model. Compare the first average registration errors of the two sets of new registration data, and select the registration data with the smaller first average registration error as the local optimal registration data of the contour data of the n-th frame; Starting from the prediction initial value, calculate the overall registration data and the second average registration error between the entire contour data and the standard rail model. Based on the ratio between the first average registration error and the second average registration error, obtain the overall value weight and the local optimal value weight; 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 contour data of the nth frame.

[0014] Further, when calculating the deviation value point by point, it includes: For each point in the registered contour data, find the closest point in the standard rail model under the same reference coordinate as the corresponding point, and establish a set of corresponding point sets. Calculate the distance between every two corresponding points under the same coordinate to obtain the deviation value between every two corresponding points.

[0015] Further, when dividing the set of defect points into several defect regions by the K-means clustering algorithm, it further includes: Each of the defect regions contains one or more subsets of the set of defect points; Calculate the number of defect points, the region center, the region radius, and the distance from other defect regions for each of the defect regions.

[0016] Further, after performing the grinding operation on each defect region, repeatedly scan the contour data of the rail and perform defect identification and classification to confirm whether to initiate re-grinding.

[0017] The technical solution of the present disclosure has the following beneficial effects: The grinding trolley can be used for grinding in turnout areas of railways, subways, trams, etc., to remove defects such as unevenness and flash on the rail. During grinding, based on the grinding component, the swing component, the lifting component, and the transverse movement component, full-profile grinding of the rail is achieved, with good effect and high efficiency, and automatic grinding can be realized without manual intervention.

[0018] The trolley can achieve automatic walking on the rail, integrating defect screening and intelligent grinding, reducing manual intervention, improving automation, greatly improving work efficiency, and reducing work costs. Description of the Drawings

[0019] Figure 1 It is a schematic structural diagram of the intelligent lithium battery grinding trolley for rail in the embodiment of this specification; Figure 2 It is a schematic structural diagram of the intelligent lithium battery grinding trolley for rail removing the outer cover in the embodiment of this specification; Figure 3 It is a partial schematic structural diagram of the intelligent lithium battery grinding trolley for rail in the embodiment of this specification; Figure 4Schematic structural diagrams of the grinding assembly, swinging assembly, and transverse movement assembly in the embodiments of this specification; Figure 5 Schematic structural diagrams of the grinding assembly and swinging assembly in the embodiments of this specification; Figure 6 Schematic structural diagrams of the lifting assembly and transverse movement assembly in the embodiments of this specification; Figure 7 Schematic structural diagram of the dust collection assembly in the embodiments of this specification; Figure 8 Block diagram of the structure of the intelligent grinding control system for lithium - ion battery - powered grinding wheels on rails in the embodiments of this specification; Figure 9 Flowchart of the intelligent grinding method for lithium - ion battery - powered grinding wheels on rails in the embodiments of this specification; Figure 10 Block diagram of the structure of the intelligent grinding device for lithium - ion battery - powered grinding wheels on rails in the embodiments of this specification; Figure 11 Computer - readable storage medium for the intelligent grinding method for lithium - ion battery - powered grinding wheels on rails in the embodiments of this specification.

[0020] Among them, reference numerals: 1, chassis; 11, connecting piece; 2, rail; 3, wheel; 4, grinding assembly; 41, grinding motor; 42, grinding wheel; 5, swinging assembly; 51, swinging frame; 52, swinging motor; 53, worm and worm gear reducer; 6, lifting assembly; 61, lifting motor; 7, transverse movement assembly; 71, transverse movement frame; 72, transverse movement motor; 73, screw; 74, nut seat; 8, dust collection assembly; 81, dust collection box; 82, filter element; 83, vacuum cleaner; 84, dust collection pipe; 9, lithium - battery compartment; 10, outer cover. Detailed implementation manners

[0021] Now, example embodiments will be described more comprehensively with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that one or more of the specific details can be omitted in practicing the technical solutions of the present disclosure, or other methods, components, devices, steps, etc. can be adopted. In other cases, well - known technical solutions are not shown or described in detail to avoid obscuring the various aspects of the present disclosure.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present disclosure. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0023] As Figures 1-7 shown, an exemplary embodiment of the present disclosure provides a rapid maintenance and grinding trolley for rails. The grinding trolley may vary significantly due to different configurations or performances, and may include: a controller, a chassis 1, and a plurality of wheels 3 disposed at the bottom of the chassis 1 for traveling on a rail 2. A grinding assembly 4, a swing assembly 5, a lifting assembly 6, a transverse movement assembly 7, and a dust collection assembly 8 controlled by the controller are further disposed on the chassis 1, wherein: the grinding assembly 4 includes a grinding wheel 42 driven by a grinding motor 41; the swing assembly 5 includes a swing frame 51, a swing motor 52, and a worm and worm 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 worm 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 a nut seat 74 cooperating with the screw rod 73 is disposed at the bottom of the transverse movement frame 71. 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 vacuum cleaner 83, and a dust collection pipe 84. The filter element 82 and the vacuum cleaner 83 are disposed in the dust collection box 81. The filter element 82 is mounted at the air inlet of the vacuum cleaner 83. One end of the dust collection pipe 84 communicates with the dust collection box 81, and the other end thereof faces the grinding wheel 42 or the rail 2.

[0024] The grinding wheel 42 is further connected to a pressure sensor for sensing the grinding force of the grinding wheel 42; an outer cover 10 is further disposed on the chassis 1, and the grinding assembly 4, the swing assembly 5, the lifting assembly 6, the transverse movement assembly 7, and the dust collection assembly 8 are enclosed therein; a lithium battery compartment 9 is further disposed on the chassis 1, and the lithium battery compartment 9 is located at one end of the chassis 1 away from the grinding assembly 4.

[0025] The grinding trolley further includes an eddy current probe and a camera. The eddy current probe is disposed at the bottom of the vehicle body. It is a coil driven by an AC power supply. When it approaches the rail 2, an eddy current signal is induced. The camera is disposed on the vehicle body and continuously photographs the rail 2. The controller is used to analyze whether there are defects in the rail 2 based on the eddy current signal, and then reads the photos of the corresponding positions of the rails 2 determined to be defective for secondary defect analysis. After analyzing and confirming the defect type, a grinding plan corresponding to the defect type is selected to grind the rail 2. During 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 plan.

[0026] The chassis 1 is detachably arranged. As Figure 1 shown, 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 realized through the connecting piece 11, so that the grinding part and the battery part can be separated, which is convenient to take the grinding trolley to a designated position for operation.

[0027] Working principle: During operation, the wheels 3 of the trolley can specifically be driven by in-wheel motors or achieved through belts / chains, etc. After determining the defects based on the method of the above embodiments, the controller controls the torque, rotation speed, etc. of the grinding motor 41 to drive the grinding wheel 42 according to a preset plan, controls the rotation speed of the swing motor 52 to control the swing speed of the grinding motor 41, controls the rotation of the lifting motor 61 to control the grinding height, and controls the rotation of the transverse movement motor 72 to control the grinding position. The vacuum cleaner 83 works to converge the dust generated during grinding along the dust collection pipe 84 into the dust collection box 81. Among them, there can be multiple dust collection pipes 84, which respectively suck dust from 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, and this hose is connected to the dust collection box 81.

[0028] From the above embodiments, it can be known that the grinding trolley can be used for grinding in turnout areas such as railways, subways, and trams to remove defects such as burrs and unevenness on the rail 2. During grinding, 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 realized, with good effect and high efficiency, and automatic grinding can be achieved without manual intervention.

[0029] In one embodiment, as Figure 8As shown, an intelligent grinding control system for a lithium battery grinding wheel on a rail is exemplarily provided. The control system is executed in the grinding trolley in the above-mentioned embodiment. The control system includes: a motion control module 201, which is used to regulate the lifting motor, the transverse movement motor, and the swing motor to achieve position control and grinding pressure control of the grinding motor, and is used to drive the traveling motor to achieve position control of the grinding trolley along the rail, and is used to control the speed of the grinding motor. When the motion control module performs the grinding pressure control, it corrects the read pressure data according to the grinding angle and yaw angle corresponding to the position control, 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 the threshold range. The pressure data is provided by a pressure sensor arranged between the grinding wheel and the grinding motor; a vehicle-mounted control module 202, which is used to achieve wired or wireless control of the motion control module through touch or / and buttons; a remote control module 203, which is used to call the operation mode stored in the database and then make the motion control module execute the position control, the grinding pressure control, and the speed control according to the operation mode.

[0030] Specifically, the motion control module is further used to perform limit control and zero position control on the lifting stroke of the lifting motor, the transverse movement stroke of the transverse movement motor, and the swing stroke of the swing motor. The zero position control includes restricting the starting positions of the lifting stroke, the transverse movement stroke, and the swing stroke. The limit control includes limiting the end positions of the lifting stroke, the transverse movement 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 movement motor, and swing motor are controlled to immediately decelerate. When the sensor in the second stage is triggered, the corresponding lifting motor, transverse movement motor, and swing motor are controlled to immediately stop rotating.

[0031] Working principle: 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, and the vehicle-mounted panel communicates with the PLC through 485 or a network; the remote control module can be a tablet computer installed with monitoring control software. The control system controls each motor through the PLC. The PLC runs the lower computer control program to realize the drive and limit control of the motor, and realizes the cooperation and grinding of each component of the trolley. It is mainly used for grinding the tracks in the turnout area. When the train travels in the turnout area, due to the need to change tracks, the train runs unsteadily, which will cause certain damage to the tracks in the turnout area. For example, the track surface becomes uneven, there are flash and cracks on both sides of the track, etc. When the track leaves the factory, there is a standard value for the profile. First, input the standard value into the system. According to the defect detection, a targeted defect grinding plan is obtained, and the position and angle of the grinding wheel are adjusted to grind the profile of the track, so that the profile after grinding is not much different from the profile value at the time of leaving the factory, ensuring the stability of the train operation.

[0032] As Figure 9 shown, an embodiment of this specification provides a lithium battery grinding wheel intelligent rail grinding method. The execution subject of this method can be the vehicle-mounted control module, remote control module, etc. of the above embodiment. This method can specifically include the following steps S101 to S105: In step S101, select the center of the top of the rail on either side as the coordinate origin, measure and record the widths of the rails on the left and right sides and the distance between them. Based on the laser probe scanning the initial profile of the rail surface, the initial profile is obtained from the contour image formed by the laser line vertically irradiating the rail surface and the camera capturing the reflected light band, and adjust the point cloud data of the initial profile to match the standard rail model to obtain registration data, and the registration data includes a transformation rotation matrix and a transformation translation vector.

[0033] Among them, the laser probe can specifically be a laser scanner. The trolley in the above embodiment can include an accelerometer, a gyroscope, a laser scanner, a GPS, an odometer, etc. required to execute this method. The scanning range and resolution of the laser scanner can cover the entire rail surface; the odometer is installed on the wheel and calibrates the driving distance according to the known wheel diameter, that is, it outputs corresponding pulses for each rotation; the GPS is placed on the top of the trolley to record the position information, and the gyroscope is fixed in the center of the trolley to obtain the attitude data.

[0034] In the detection of rail surface defects, the first step is to obtain the contour point cloud data of the rail surface. When the laser irradiates vertically, a light band is formed on the rail surface. After the camera is tilted at a certain angle relative to the laser probe, the rail surface is photographed. At this time, the light band on the rail surface will reflect the contour of the object surface on the laser projection plane. When the relative position relationship between the camera and the laser probe is determined and unchanged, for the same object contour, 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 contour, can be calculated. When the object is stationary, the grinding trolley moves accordingly, dividing the surface of the object to be measured into multiple contours, so as to achieve the purpose of measuring the physical coordinates of all surface points.

[0035] In the initial state of detection, in order to eliminate the geometric deviation between the actual measurement data and the standard model and make the subsequent defect detection more accurate. During the actual measurement process, due to various factors (such as measurement error, sensor error, or tilting of the grinding trolley, etc.), the acquired point cloud data of the track surface often has certain deviations. These deviations may lead to differences in the position, shape, or scale of the point cloud data, thereby affecting 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 the deviations caused by equipment errors or environmental interference can be removed, making the data more stable and accurate. After registration, the data is aligned with the standard rail model, which can help the control system more accurately identify the abnormal areas (such as defects like wear and scratches) on the track surface, reducing missed detections or false detections caused by deviations. At the same time, as an ideal reference, the standard rail model provides the ideal shape of the track surface, which can help the algorithm effectively identify the deviations 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.

[0036] In step S102, the contour data obtained by scanning the rail when the grinding trolley moves on the rail is collected. When the grinding trolley moves, it executes a fixed speed and triggers the laser probe at fixed moving unit intervals to collect the contour data of the rail surface, records the position data and attitude data of the grinding trolley during collection, and unifies the coordinates of the contour data, the position data, and the attitude data based on the coordinate origin.

[0037] Among them, the track surface profile data collected by the laser probe cannot be directly used to extract track surface defects, and preprocessing is required. First, the error caused by the slight rotation of the trolley needs to be corrected, and then the coordinates of each sensor are unified to the same coordinate system. The gyroscope coordinate system can be used as the reference coordinate system for demonstration. The specific moving speed of the trolley can be 1.5m / s, and the moving unit interval can be 10mm.

[0038] like Figures 1-4 As shown, the trolley is supported on the rails by wheels. During the movement of the trolley, the wheels will not leave the rails, so it can be assumed that the gyroscope only rotates in the horizontal direction. Assume 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 correction point coordinates of the left and right profile data will be: ; ; Then, a unified coordinate transformation is performed, and the gyroscope coordinate system is selected as the reference coordinate system. Assume that the coordinates of the left laser probe coordinate center in the reference coordinate system are ; 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 expressed as: ; .

[0039] In actual work, when the gyroscope moves along the track direction, the motion trajectory of the gyroscope coordinate origin can be calculated through the GPS position, gyroscope attitude and odometer mileage. Therefore, the processed point cloud reflects the relative position of the actual track surface points.

[0040] In step S103, the contour data of the n-1th frame and the continuous registration data of the standard rail model are used as the prediction initial values ​​of the contour data of the nth frame, and the continuous registration data of the contour data of the nth frame and the standard rail model are calculated based on the prediction initial values.

[0041] Specifically, when calculating the continuous registration data of the nth-frame contour data and the standard rail model, it includes: dividing the nth-frame contour data into a rail head area and a rail bottom area, where the rail head area is the upper half area of the contour data, and the rail bottom area is the lower half area of the contour data; starting from the prediction initial value, registering the rail head area and the rail bottom area respectively, obtaining two sets of new registration data respectively, 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 errors of the two sets of new registration data, and selecting the registration data with the smaller first average registration error as the locally optimal registration data of the nth-frame contour data; starting from the prediction initial value, calculating the overall registration data and the second average registration error of the entire contour data and the standard rail model, and obtaining the overall value weight and the locally optimal value weight based on the ratio between the first average registration error and the second average registration error; based on the fusion principle of the Kalman filter, performing weighted fusion on the overall value weight, the locally optimal value weight, the locally optimal registration data, and the overall registration data to obtain the continuous registration data of the nth-frame contour data.

[0042] Among them, the top and side of the rail are prone to wear and scratches, which will cause rail surface defects. Different from the standard model, since the bottom of the rail does not come into contact with the wheels, it usually only corrodes, but the corrosion will not cause a large difference in the shape of the rail bottom from the standard model. Therefore, this is an important reference for subsequent data registration. At the same time, when the trolley moves along the track, contour data is acquired every 1 millimeter. Within such a small interval, when acquiring adjacent contours, there is not enough time for the posture change of the trolley to mutate. 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. On this basis, this embodiment uses the Kalman filter model to recursively predict the transformation of the current contour through adjacent continuous contours, then calculates the average distance between each part of the point cloud and the standard point cloud under this transformation parameter, and finally selects the corresponding optimal transformation parameter by evaluating these average distances.

[0043] Specifically, the above method can be summarized as: 1. Assume that the Nth contour point set is P n , and take the transformation (translation T n-1 and rotation R M ) between the (N - 1)th contour point set P n-1 and the standard model Q n-1 as the predicted transformation from P n to Q M .

[0044] 2. Divide the rail surface profile into two parts: the rail head part and the rail base part. The rail head is a part of the rail head of the entire rail, which is the upper part of the contour data point set and is used to distinguish from the contour data point set of the rail base part to obtain the rail head point set P n H and the rail base point set P n B , with (R n-1 , T n-1 ) as the initial value. Calculate the transformations from P n H and P n B to the standard model Q M respectively, and record them as (R n H , T n H ), (R n B , T n B ), and then output the average distance d n H and d n B .

[0045] 3. Calculate the minimum value of d n H and d n B , and take the transformation corresponding to the minimum value as the calculation transformation from P n to Q M .

[0046] 4. Under the transformation parameters (R n-1 , T n-1 ), calculate the average distance d n from P M to Q n ; 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: ; ; 6. According to the Kalman filtering principle, update the transformation between P n and Q M as: .

[0047] In step S104, the deviation value between the standard rail model and the profile data registered based on the continuous registration data is calculated point by point. By combining a given threshold value, defect points are obtained. The defect points continuously present in multiple frames of the profile data are merged into a defect point set. And through the K-means clustering algorithm, the defect point set is divided into several defect regions. The geometric features of each defect region are extracted. The geometric features include position, area, depth, gradient, aspect ratio, and minimum bounding rectangle. According to the characteristic differences of different types of defects in depth deviation, gradient, position, and aspect ratio, a decision tree classifier is constructed. The geometric features are input into the decision tree classifier to identify the defect type corresponding to the geometric features.

[0048] Among them, by comparing the deviation between the registered rail surface profile and the standard rail model and combining a given threshold value, the candidate defect regions can be accurately located. The continuous defect profiles are merged, and the candidate defect points are merged into candidate defect regions. The minimum bounding rectangle and the center of each defect region are calculated. Using K-means clustering, the defect regions with smaller areas are connected to the defect regions with larger areas, and the defect regions with similar features are clustered together. Finally, the features such as the position, shape, depth, length, width, slope, and minimum bounding rectangle of the defects are calculated.

[0049] In actual operation, different defects have different characteristics. The train wheels contact and rub against the rail head, resulting in wear, corrugation, scratches, bulges, and spalling. Therefore, the corrosion at the rail bottom can be distinguished by its position, but the corrosion at the rail head is still mixed with other defects. Also, friction causes 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 distinguishes the rail head corrosion from the other four types of defects. Among these 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 their crests and troughs. Scratches are always long and narrow, that is, the aspect ratio is greater than that of spalling. Based on these judgments, a decision tree is used to classify the defects.

[0050] In step S105, the grinding trolley is driven to reset to the starting point. According to the coordinate information of the defect type, combined with the position data and the attitude data of the grinding trolley, the grinding path is planned, and it is moved to each defect region one by one at a set speed and along the path to perform the grinding operation.

[0051] In one embodiment, when calculating the deviation value point by point, it includes: for each point in the registered contour data, finding the closest point in the standard rail model under the same reference coordinate as the corresponding point, and establishing a set of corresponding point sets; calculating the distance between every two corresponding points under the same coordinate to obtain the deviation value between every two corresponding points.

[0052] In one embodiment, when dividing the set of defect points into several defect regions by the K-means clustering algorithm, it further includes: each of the defect regions 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 from other defect regions for each defect region.

[0053] In one embodiment, after performing a grinding operation on each defect region, the contour data of the rail is scanned repeatedly and defect identification and classification are performed to confirm whether to initiate re-grinding.

[0054] Among them, by re-scanning the contour point cloud data of the rail and comparing it with the standard rail surface model after each grinding operation on the rail defect region, the re-identification and classification judgment of the defect are realized, forming a set of closed-loop grinding effect verification mechanisms. Specifically, the system uses laser scanning to obtain the point cloud on the rail surface, calculates the deviation from the standard rail model, extracts the defect points and performs clustering and classification to identify defect types such as wear, scratches, erosion, and burrs. When a grinding is completed, the trolley travels again to scan and register the point cloud of this region, repeating the aforementioned defect identification process, and judging whether there is still a geometric deviation exceeding the threshold in the ground region. If the deviation is still significant, continuous grinding is required until the deviation is reduced to within the preset 1 mm (rail head) or 0.5 mm (rail bottom). This method not only ensures that the defects are effectively repaired, but also avoids over-grinding, improving the accuracy and efficiency of rail maintenance. In addition, through the re-judgment of the residual defects after grinding by the classifier, it can also provide a basis for adjusting the strategy of the grinding equipment, realizing intelligent control and decision-making, thus providing reliable technical support and data closed-loop for the rail grinding operation.

[0055] Based on the same idea, as Figure 10 shown, an exemplary embodiment of the present disclosure also provides a lithium battery grinding wheel intelligent rail grinding device, including: The correction module 301 is used to select the center of the top of the rail on either side as the coordinate origin, measure and record the widths of the rails on the left and right sides and the distance between them, and based on the laser probe scanning the initial contour of the rail surface, where the initial contour is obtained from the contour image formed by the laser line vertically irradiating the rail surface and the camera capturing the reflected light band, adjust the point cloud data of the initial contour to match the standard rail model to obtain registration data, where the registration data includes a transformation rotation matrix and a transformation translation vector; The acquisition module 302 is used to acquire the contour data obtained by scanning the rail when the grinding trolley moves on the rail. When the grinding trolley moves, it executes a fixed speed and triggers the laser probe at fixed moving unit intervals to collect the contour data of the rail surface, records the position data and attitude data of the grinding trolley during acquisition, and unifies the coordinates of the contour data, the position data, and the attitude data based on the coordinate origin; The registration module 303 is used to use the contour data of the (n - 1)th frame and the continuous registration data of the standard rail model as the prediction initial value of the contour data of the nth frame, and based on the prediction initial value, calculate the continuous registration data of the contour data of the nth frame and the standard rail model; The defect identification module 304 is used to calculate the deviation value between the standard rail model and the contour data registered based on the continuous registration data point by point, combine a given threshold to obtain defect points, merge the continuously existing defect points in multiple frames of the contour data into a defect point set, and through the K - means clustering algorithm, divide the defect point set into several defect regions, extract the geometric features of each defect region, where the geometric features include position, area, depth, gradient, aspect ratio, and minimum circumscribed rectangle. According to the characteristic differences of different types of defects in depth deviation, gradient, position, and aspect ratio, construct a decision tree classifier, and input the geometric features into the decision tree classifier to identify the defect type corresponding to the geometric features; The motion control module 305 is used to drive the grinding trolley to reset to the starting point, plan the grinding path according to the coordinate information of the defect type, combine the position data and the attitude data of the grinding trolley, and move to each defect region one by one at a set speed and path to perform grinding operations.

[0056] This device integrates defect screening, specific identification, and intelligent grinding, reduces manual intervention, improves automation, greatly improves work efficiency, and reduces work costs.

[0057] The specific details of each module in the above device have been described in detail in the implementation manner of the method part. The undisclosed detailed content can be referred to the implementation manner content of the method part, so it will not be elaborated here.

[0058] Based on the same idea, exemplary embodiments of the present disclosure also provide a computer-readable storage medium, on which a program product capable of implementing the above methods in this specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0059] Reference Figure 11 As shown, a program product 400 for implementing the above method according to an exemplary embodiment of the present disclosure is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0060] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, 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.

[0061] A computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, 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 a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0062] The program code 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.

[0063] Program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the receiving computing device, partially on the receiving device, executed as a stand-alone software package, partially on the receiving computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the receiving computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or, alternatively, can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0064] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the exemplary embodiments of the present disclosure.

[0065] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easily understood that the processes shown in the above drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easily understood that these processes can be executed, for example, synchronously or asynchronously in multiple modules.

[0066] It should be noted that although several modules or units of devices for performing actions are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the exemplary embodiments of the present disclosure, the features and functions of two or more of the above-mentioned modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0067] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are to be considered as illustrative only, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

1. An intelligent grinding trolley for a lithium battery grinding wheel on a railway track, characterized in that, The grinding trolley includes a controller, a chassis, and a plurality of wheels arranged at the bottom of the chassis for traveling on the rail. A grinding assembly, a swing assembly, a lifting assembly, a transverse movement assembly, a dust collection assembly, and a laser probe controlled by the controller are further arranged on the chassis, wherein: The grinding assembly includes a grinding wheel driven by a grinding motor; The swing assembly includes a swing frame, a swing motor, and a worm and worm gear reducer driven by the swing motor. The grinding motor is rotatably installed in the swing frame, and the worm and worm gear reducer is connected to the grinding motor for driving the grinding motor to swing; The lifting assembly includes a lifting motor for driving the swing frame to lift; The transverse movement assembly includes 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 to a screw rod, and a nut seat matching with the screw rod is arranged at the bottom of the transverse movement frame. The lifting assembly is installed on the transverse movement frame; The dust collection assembly includes 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 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 thereof faces the grinding wheel or the rail; The laser probe is used for measuring the profile value of the rail to be ground. After analyzing the difference between the rail and the standard value according to the profile value, the controller drives the grinding 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.

2. The intelligent grinding trolley for lithium battery-powered grinding wheels on rails according to claim 1, characterized in that, The grinding wheel is further connected to a pressure sensor for sensing the grinding force of the grinding wheel; an outer cover is further arranged on the chassis, and the outer cover covers the grinding assembly, the swing assembly, the lifting assembly, the transverse movement assembly, and the dust collection assembly therein; a lithium battery compartment is further arranged on the chassis, and the lithium battery compartment is located at one end of the chassis far from the grinding assembly.

3. The intelligent grinding trolley for lithium battery grinding wheel on rail according to claim 1, characterized in that, The chassis is detachably arranged.

4. An intelligent grinding control system for a lithium battery grinding wheel on a rail, characterized in that, The control system is executed in the grinding trolley according to any one of claims 1-3. The control system includes: A motion control module for regulating the lifting motor, the transverse movement motor, and the swing motor to achieve position control and grinding pressure control of the grinding motor, and for driving the traveling motor to achieve position control of the grinding trolley along the rail, and for controlling the speed of the grinding motor. When performing the grinding pressure control, the motion control module corrects the read pressure data according to the grinding angle and yaw angle corresponding to the position control, 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 the threshold range. The pressure data is provided by a pressure sensor arranged between the grinding wheel and the grinding motor; A vehicle-mounted control module for realizing wired or wireless control of the motion control module through touch or / and buttons; The remote control module is used to call the job mode stored in the database, and then enable the motion control module to execute the position control, the grinding pressure control, and the speed control according to the job mode.

5. The intelligent grinding control system for lithium battery grinding wheels on railway rails according to claim 4, characterized in that, The motion control module is further used to perform limit control and zero position control on the lifting stroke of the lifting motor, the traversing stroke of the traversing motor, and the swinging stroke of the swinging motor. The zero position control includes restricting the starting positions of the lifting stroke, the traversing stroke, and the swinging stroke, and the limit control includes limiting the end positions of the lifting stroke, the traversing stroke, and the swinging stroke. When performing the zero position control and the limit control, the motion control module uses two-stage sensors to trigger deceleration. When the sensor in the first stage is triggered, it controls the corresponding lifting motor, traversing motor, and swinging motor to immediately decelerate. When the sensor in the second stage is triggered, it controls the corresponding lifting motor, traversing motor, and swinging motor to immediately stop rotating.

6. An intelligent grinding method for lithium battery grinding wheels on railway rails, characterized in that, The method is executed in the grinding trolley according to any one of claims 1-3, and the method includes: Select the center of the top of the rail on either side as the coordinate origin, measure and record the widths of the left and right rails and the distance between them. Based on the laser probe scanning the initial contour of the rail surface, the initial contour is obtained from the contour image formed by the laser line vertically irradiating the rail surface and the camera capturing the reflected light band, adjust the point cloud data of the initial contour to match the standard rail model to obtain the registration data, and the registration data includes the transformation rotation matrix and the transformation translation vector. Collect the contour data obtained by scanning the rail when the grinding trolley moves on the rail. When the grinding trolley moves, it executes at a fixed speed and triggers the laser probe at a fixed moving unit interval to collect the contour data of the rail surface, record the position data and attitude data of the grinding trolley during collection, and unify the coordinates of the contour data, the position data, and the attitude data based on the coordinate origin. Use the contour data of the (n - 1)th frame and the continuous registration data of the standard rail model as the prediction initial value of the contour data of the nth frame, and based on the prediction initial value, calculate the continuous registration data of the contour data of the nth frame and the standard rail model. Calculate the deviation value between each point of the standard rail model and the contour data registered based on the continuous registration data, combine it with a given threshold to obtain the defect points, merge the continuously existing defect points in multiple frames of the contour data into a defect point set, and use the K-means clustering algorithm to divide the defect point set into several defect regions, extract the geometric features of each defect region, and the geometric features include position, area, depth, gradient, aspect ratio, and minimum circumscribed rectangle. According to the characteristic differences of different types of defects in depth deviation, gradient, position, and aspect ratio, construct a decision tree classifier, and input the geometric features into the decision tree classifier to identify the defect type corresponding to the geometric features. Drive the grinding trolley to reset to the starting point. According to the coordinate information of the defect type, combine the position data and the attitude data of the grinding trolley to plan the grinding path, and move to each defect area one by one at a set speed and along the path for grinding operations.

7. The intelligent grinding method of the lithium battery grinding wheel for rail according to claim 6, wherein When the continuous registration data of the nth frame of the contour data and the standard rail model is calculated, the method further includes: Divide the nth frame of the contour data into a rail head area and a rail bottom area. The rail head area is the area of the upper half of the contour data, and the rail bottom area is the area of the lower half of the contour data; Starting from the predicted initial value, register the rail head area and the rail bottom area respectively to obtain two sets of new registration data, calculate the first average registration error between the two sets of new registration data and the standard rail model, compare the first average registration errors of the two sets of new registration data, and select the registration data with the smaller first average registration error as the local optimal registration data of the nth frame of the contour data; Starting from the predicted initial value, calculate the overall registration data and the second average registration error between the entire contour data and the standard rail model, and obtain the overall value weight and the local optimal value weight based on the ratio between the first average registration error and the second average registration error; Based on the fusion principle of the Kalman filter, perform weighted fusion on the overall value weight, the local optimal value weight, the local optimal registration data, and the overall registration data to obtain the continuous registration data of the nth frame of the contour data.

8. The intelligent grinding method of the lithium battery grinding wheel for rail according to claim 6, characterized in that, When calculating the deviation value point by point, it includes: For each point in the registered contour data, find the closest point in the standard rail model under the same reference coordinate as the corresponding point, and establish a point set of a group of corresponding points; Calculate the distance between every two corresponding points under the same coordinate to obtain the deviation value between every two corresponding points.

9. The intelligent grinding method for lithium battery grinding wheels on rail according to claim 6, characterized in that When dividing the defect point set into several defect areas by the K-means clustering algorithm, it further includes: Each defect area contains one or more subsets of the defect point set; Calculate the number of defect points, the area center, the area radius, and the distance from other defect areas of each defect area.

10. The intelligent grinding method of the lithium battery grinding wheel for railway rails according to claim 6, wherein After performing grinding operations on each defect area, repeat scanning the contour data of the rail and perform defect identification and classification to confirm whether to start re-grinding.

Images