CN116026249A - Real-time detection method and device for deformation of empty rail track beam - Google Patents
Real-time detection method and device for deformation of empty rail track beam Download PDFInfo
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Abstract
The embodiment of the application provides a real-time detection method and device for deformation of an empty rail track beam, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring laser point cloud data generated by scanning a track beam at a target position by a laser radar, and taking the laser point cloud data as original laser point cloud data; calculating and extracting internal structural parameters of the track beam at the target position based on the original laser point cloud data; and detecting deformation of the track beam at the target position through a pre-constructed sample parameter based on the internal structure parameter, and recording a detection log. According to the technical scheme, the deformation of the air rail track beam can be accurately detected in real time, and meanwhile remote operation and maintenance of the air rail track beam are realized.
Description
Technical Field
The application relates to the technical field of track detection, in particular to a method and a device for detecting deformation of an empty track beam in real time, a storage medium and electronic equipment.
Background
With the running of an aerial track in China, such as the running of an demonstration line of a collection and delivery system in a Qingdao port, the daily operation and maintenance inspection work of the interior of a high overhead track is carried out, because the settlement, the deformation, the groove clearance deformation and the like of the aerial track rail beam can be caused under the long-term load running of a vehicle, if the deformation of the aerial track rail beam is not timely processed, the potential safety hazard of the vehicle running can be generated, but because of the high pressure in the interior of the aerial track, the wide groove and the dark interior environment bring great safety risks and work load intensity to daily maintenance personnel of the aerial track. Based on the above, how to realize intelligent and accurate detection of the deformation of the empty rail track beam and realize the remote operation and maintenance of the empty rail track beam are technical problems to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a real-time detection method, device, storage medium and electronic equipment for deformation of an empty rail track beam, and further can realize intelligent accurate detection of deformation of the empty rail track beam, and simultaneously realize remote operation and maintenance of the empty rail track beam.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned in part by the practice of the application.
According to a first aspect of embodiments of the present application, there is provided a method for detecting deformation of an empty rail track beam in real time, the method including: acquiring laser point cloud data generated by scanning a track beam at a target position by a laser radar, and taking the laser point cloud data as original laser point cloud data; calculating and extracting internal structural parameters of the track beam at the target position based on the original laser point cloud data; and detecting the deformation of the track beam at the target position through a pre-constructed sample parameter based on the internal structure parameter.
In some embodiments of the present application, based on the foregoing solution, the original laser point cloud data is composed of laser data of a plurality of laser points, and the calculating and extracting internal structural parameters of the track beam at the target position based on the original laser point cloud data includes: replacing noise laser data in the original laser point cloud data to obtain reference laser point cloud data; smoothing the reference laser point cloud data to obtain target laser point cloud data; and calculating and extracting internal structural parameters of the track beam at the target position according to the target laser point cloud data.
In some embodiments of the present application, based on the foregoing solution, the laser data includes a distance between the laser point and the laser radar, and the replacing noise laser data in the original laser point cloud data includes: for each target laser point, acquiring a distance corresponding to the target laser point, wherein the target laser point is any one laser point of the plurality of laser points, and taking the distance as a target distance; and if the target distance is greater than a first preset distance or less than a second preset distance, defining laser data corresponding to the target laser point as the noise laser data, and replacing the noise laser data by the laser data corresponding to the laser point adjacent to the target laser point, wherein the first preset distance is greater than the second preset distance.
In some embodiments of the present application, based on the foregoing solution, the smoothing the reference laser point cloud data includes: performing sliding segmentation on the reference laser point cloud data according to a preset window to obtain a plurality of groups of sub-reference laser point cloud data; calculating smooth filtering laser data based on each laser data in each group of target sub-reference laser point cloud data, wherein the smooth filtering laser data is used for inhibiting fluctuation of each laser data in the target sub-reference laser point cloud data, and the target sub-reference laser point cloud data is any one group in the multiple groups of sub-reference laser point cloud data; for each set of target sub-reference laser point cloud data, replacing individual ones of the target sub-reference laser point cloud data by the smoothed filtered laser data.
In some embodiments of the present application, based on the foregoing solution, the computing and extracting the internal structural parameters of the track beam at the target location according to the target laser point cloud data includes: determining a laser breakpoint in the plurality of laser points based on the target laser point cloud data; dividing the plurality of laser points into a plurality of laser point sets through the laser breaking points, and respectively fitting and generating laser characteristic lines based on the laser points in each laser point set to obtain a laser characteristic line set, wherein the laser characteristic line set is used for representing the cross section profile of the track beam in the radial direction of the track beam at the target position; an internal structural parameter of the rail beam at the target location is determined based on the set of laser feature lines.
In some embodiments of the present application, based on the foregoing solution, the determining a laser breakpoint in the number of laser points based on the target laser point cloud data includes: selecting any two adjacent laser points from the plurality of laser points; acquiring the distance corresponding to any two adjacent laser points based on the target laser point cloud data; and if the difference value of the distances corresponding to the adjacent arbitrary two laser points is larger than a preset parameterized self-adaptive threshold value, determining the adjacent arbitrary two laser points as the laser breakpoint.
In some embodiments of the present application, based on the foregoing solution, the generating, based on the laser points in each laser point set, the laser feature line by fitting includes: selecting a plurality of laser points from each target laser point set as seed laser points, wherein the target laser point set is any one of the plurality of laser point sets; and performing straight line fitting on seed laser points in the target laser point set to generate laser characteristic lines corresponding to the target laser point set.
According to a second aspect of embodiments of the present application, there is provided an empty rail track beam deformation detection device, the device comprising: the acquisition unit is used for acquiring laser point cloud data generated by scanning the track beam at the target position by the laser radar, and the laser point cloud data is used as original laser point cloud data; a data processing unit for computing and extracting internal structural parameters of the track beam at the target position based on the original laser point cloud data; and the detection recording unit is used for detecting the deformation of the track beam at the target position through a pre-constructed sample parameter based on the internal structure parameter and recording a detection log.
According to a third aspect of embodiments of the present application, there is provided a computer readable storage medium, wherein at least one program code is stored in the computer readable storage medium, the at least one program code being loaded and executed by a processor to implement operations performed by a method as described in any of the first aspects above.
According to a fourth aspect of embodiments of the present application, there is provided an electronic device comprising one or more processors and one or more memories, the one or more memories having stored therein at least one piece of program code loaded and executed by the one or more processors to implement the operations performed by the method of any of the first aspects described above.
According to the technical scheme, laser point cloud data generated by scanning a track beam at a target position by a laser radar are obtained and serve as original laser point cloud data; then, the internal structural parameters of the track beam at the target position are calculated and extracted through the original laser point cloud data; finally, through the internal structure parameter, detect through the sample parameter that builds in advance the deformation of track roof beam in target position department, in this application pass through laser radar scanning empty rail track roof beam in different positions and obtain the deformation result of empty rail track roof beam under the position and the time stamp, can form the detection log record of empty rail track roof beam, make relevant user can know through long-range fortune dimension whether empty rail track roof beam takes place deformation and the degree etc. information that takes place deformation in different positions department, thereby can in time pointedly get rid of the potential safety hazard that takes place deformation and produce by empty rail track roof beam, promote the security and the reliability of empty rail, realize the long-range fortune dimension of empty rail track roof beam.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 illustrates a flow chart of a method for real-time detection of deformation of an empty rail track beam according to one embodiment of the present application;
FIG. 2 illustrates a schematic view of a scenario in which a lidar scans the rail beam at a target location according to an embodiment of the present application;
FIG. 3 illustrates computing extraction of internal structural parameters of the track beam at the target location based on the raw laser point cloud data according to one embodiment of the present application;
FIG. 4 illustrates a detailed flow chart of extracting internal structural parameters of the track beam at the target location from the target laser point cloud data calculation according to one embodiment of the present application;
FIG. 5 illustrates a schematic diagram of extracting feature lines according to one embodiment of the present application;
FIG. 6 shows a block diagram of an empty rail track deformation real-time detection device according to one embodiment of the present application;
fig. 7 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many 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 thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may 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 embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It should be noted that: references herein to "a plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the objects so used may be interchanged where appropriate such that the embodiments of the present application described herein may be implemented in sequences other than those illustrated or described.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, a flowchart of a method for detecting deformation of an empty rail track beam in real time according to an embodiment of the present application is shown, where the method specifically includes steps 110 to 130.
In some embodiments, the laser radar may be a single-line laser radar, and the laser radar may be mounted on a patrol platform such as a patrol car, and the movement of the patrol platform is utilized to scan the track beam at different positions inside the empty track. The laser point cloud data consists of laser data of a plurality of laser points, and it can be understood that the laser radar comprises a plurality of laser beams, each laser beam has a serial number corresponding to the laser beam, each laser beam scans the track beam to generate a corresponding laser point, and the laser radar scans the empty track beam at a certain moment and generates laser point cloud data corresponding to the certain moment and the certain position.
In some embodiments, a positioning module may also be provided in the inspection vehicle to monitor the current position of the inspection vehicle in real time to determine the target position; the inspection vehicle can be connected with a remote monitoring center through a wireless communication module, so that the remote monitoring center can receive and process laser point cloud data obtained by laser radar scanning in real time, deformation conditions of the empty rail track beam at different positions can be monitored in real time, and remote operation and maintenance of the deformation of the empty rail track beam can be realized.
In some embodiments, before the inspection vehicle starts to detect the deformation of the air rail track beam, the self-checking module is used for checking the communication state among the laser radar, the positioning module and the wireless communication module, so as to ensure the effectiveness of the deformation detection work of the inspection vehicle.
In some embodiments, the information of the target location may be determined by determining the position of the inspection vehicle in a flener (Frenet) coordinate system in which the empty track is located or in a global coordinate system relative to some reference point. Specifically, the inspection vehicle can be positioned by a vehicle-mounted sensor, such as an odometer (Odom) or a radio frequency identification (Radio Frequency Identification, RFID) beacon, so as to obtain the position information of the inspection vehicle; positioning information of the patrol vehicle under the global coordinate system can be obtained through a global positioning system (Global Positioning System, GPS) or a Beidou positioning system (Beidou Navigation System, BDS), and after coordinate conversion, relative position information of the patrol vehicle coordinate system on an empty rail is obtained; the position information of the inspection vehicle under the global coordinate system of a certain reference point of the inspection vehicle coordinate system can also be directly obtained by the inspection vehicle through an inertial measurement unit (Inertial measurement unit, IMU), and the specific mode of determining the target position is not limited herein.
In some embodiments, the laser point cloud data includes, but is not limited to, time information of the laser radar scanning the track beam at the target location, intensity information of each laser point, angle information of the laser radar when the laser point cloud data is acquired, distance information of each laser point to the laser radar, and the like.
In this embodiment, by recording the target position information and the time information of the laser radar for scanning the track beam at the target position, the formation of a log record for detecting the deformation of the track beam of the empty track is facilitated, and a traceable detection record is facilitated, so that the detected abnormal data can be processed in time.
With continued reference to fig. 1, step 120 extracts internal structural parameters of the track beam at the target location based on the raw laser point cloud data calculations.
In order to enable those skilled in the art to better understand the present embodiment, the following description will be made with reference to fig. 2.
Referring to fig. 2, a schematic view of a scenario in which a lidar scans the rail beam at a target location according to an embodiment of the present application is shown. The scenario shown in fig. 2 is that a laser radar mounted on a patrol platform scans a track beam at a target position. As can be seen from fig. 2, the cross-sectional profile of the track beam scanned at the target location in the radial direction includes the track beam left side surface, the track beam right side surface, the track beam top surface, the track beam bottom surface, and the groove.
It should be noted that the internal structural parameters in this embodiment include, but are not limited to, width information of a groove located on a bottom surface of a track beam, length information of a top surface of the track beam, length information of a left side surface of the track beam, length information of a right side surface of the track beam, length information of a bottom surface of the track beam, parallelism of a left side surface of the track beam with a right side surface of the track beam, parallelism of a bottom surface of the track beam with a top surface of the track beam, perpendicularity of a right side surface of the track beam with a top surface of the track beam, perpendicularity of a left side surface of the track beam with a bottom surface of the track beam, perpendicularity of a right side surface of the track beam with a bottom surface of the track beam, and so on.
In some embodiments, the specific implementation of computing and extracting the internal structural parameters of the track beam at the target location based on the raw laser point cloud data may be performed in the steps shown in fig. 3.
Referring to fig. 3, internal structural parameters of the track beam at the target position are extracted based on the raw laser point cloud data calculation according to one embodiment of the present application, specifically including steps 121 to 123.
And step 121, replacing noise laser data in the original laser point cloud data to obtain reference laser point cloud data.
It will be appreciated that when the lidar scans the track beam at the target location, some noisy laser data may appear when some laser points are over-scaled or some laser beams in the lidar are damaged, which may affect the accuracy of the post-extracted internal structural feature parameters if not processed.
In some embodiments, replacing noise laser data to obtain reference laser point cloud data may be accomplished specifically as follows steps 1211 through 1212.
Step 1211, for each target laser point, obtaining a distance corresponding to the target laser point, where the target laser point is any one of the plurality of laser points, as a target distance.
And step 1212, if the target distance is greater than a first preset distance or less than a second preset distance, defining the laser data corresponding to the target laser point as the noise laser data, and replacing the noise laser data by the laser data corresponding to the laser point adjacent to the target laser point, wherein the first preset distance is greater than the second preset distance.
It can be understood that if the target distance is greater than the first preset distance, the laser data corresponding to the target laser point may be considered as the laser data with an overscaled range, and if the target distance is less than the second preset distance, the laser data corresponding to the target laser point may be considered as the invalid laser data that is damaged, where the laser data corresponding to the target laser point in both cases is the abnormal noise laser data. The first preset distance may be a maximum range of the laser radar, and the second preset distance may be a minimum detection range of the laser radar.
Since each laser spot has a laser beam and a laser beam serial number corresponding to the laser spot, the laser spot adjacent to the target laser spot is the laser spot corresponding to the adjacent laser beam serial number of the laser beam serial number corresponding to the target laser spot.
In this embodiment, the accuracy of extracting the internal structural parameters of the track beam can be increased by neighborhood-replacement of noise laser data.
With continued reference to fig. 3, in step 122, the reference laser point cloud data is smoothed to obtain target laser point cloud data.
In some embodiments, the smoothing the reference laser point cloud data to obtain the target laser point cloud data may be performed according to the following steps 1221 to 1223.
Step 1221, performing sliding segmentation on the reference laser point cloud data according to a preset window to obtain multiple groups of sub-reference laser point cloud data.
It should be noted that, the preset window may determine the size of the window according to the number of adjacent laser points, and for example, 5 adjacent laser points may be used as a preset window, and then 1 laser point may be used as a sliding step, and sliding division is performed on the reference laser point cloud data, so as to obtain multiple groups of sub-reference laser point cloud data. It will be appreciated that the number of sets of sub-reference laser point cloud data is consistent with the number of laser points.
Step 1222, calculating smooth filtering laser data based on each laser data in each set of target sub-reference laser point cloud data, where the smooth filtering laser data is used to suppress the influence of fluctuation of each laser data in the target sub-reference laser point cloud data, and the target sub-reference laser point cloud data is any one set of the multiple sets of sub-reference laser point cloud data.
It can be understood that, for the obtained original laser point cloud data, the laser data of each laser point slightly fluctuates due to the laser radar system, that is, a system error is generated, so that the reference laser point cloud data needs to be subjected to smoothing processing to obtain the laser data after smoothing filtering, thereby eliminating the system error and improving the accuracy of extracting internal structural parameters in the later period.
In one embodiment, the smoothed filtered laser data is calculated by a weighted median filtering algorithm, and by way of example, taking the preset window as the preset window for 5 adjacent laser points, then:
r[i]＝a1*r[i-2]+a2*r[i-1]+a3*r[i]+a4*r[i+1]+a5*r[i+2]
wherein, r [ i ] represents the distance of the laser point positioned at the middle position in the target sub-reference laser point cloud data; a1, a2, a3, a4, and a5 are weight coefficients, and a1+a2+a3+a4+a5=1, and a3 > a4 > a5, and a3 > a2 > a1.
In another embodiment, the smoothed filtered laser data may be calculated by an algebraic averaging algorithm, and by way of example, taking the preset window as the preset window for 5 adjacent laser points, then:
r[i]＝(r[i-2]+r[i-1]+r[i]+r[i+1]+r[i+2])/5
where r [ i ] represents the distance of the laser point located at the intermediate position in the target sub-reference laser point cloud data.
It can be understood that the smoothing filter laser data calculated by using the weighted median filtering algorithm in the first embodiment can obtain a better smoothing effect, and of course, in this embodiment, the corresponding manner of smoothing the reference laser point cloud data may be designed according to different application scenarios, which is not limited herein.
Step 1223, for each set of target sub-reference laser point cloud data, replacing individual ones of the target sub-reference laser point cloud data by the smoothed filtered laser data.
It should be noted that, one smooth filter laser data may be obtained in each set of target sub-reference laser point cloud data, and the smooth filter laser data may correspondingly replace the laser data of several laser points in the present application. For example, if a smooth filtered laser data corresponding to the laser beam with the third sequence number is obtained in the first preset window, the smooth filtered laser data may be replaced with the laser data corresponding to the laser beam with the original third sequence number.
With continued reference to fig. 3, step 123 extracts internal structural parameters of the track beam at the target location from the target laser point cloud data calculations.
In some embodiments, the specific implementation of computing and extracting the internal structural parameters of the track beam at the target location according to the target laser point cloud data may be implemented in the manner as described in fig. 4.
Referring to fig. 4, a detailed flow chart of extracting internal structural parameters of the track beam at the target location from the target laser point cloud data calculation according to one embodiment of the present application is shown. Specifically, steps 1231 to 1233 are included.
In some embodiments, the laser break point may be determined as follows steps 12311 through 12313.
Step 12311, selecting any two adjacent laser points from the plurality of laser points.
Step 12312, obtaining the distance corresponding to any two adjacent laser points based on the target laser point cloud data.
And 12313, if the difference value of the distances corresponding to the arbitrary two laser points is greater than the preset parameterized adaptive threshold value, determining the arbitrary two laser points as the laser breakpoint.
In some embodiments, a neighboring point adaptive thresholding method may be specifically employed to determine the laser break point. Specifically, the laser breakpoint can be obtained according to the laser radar measurement error, the angle resolution and the distance adaptation of the laser point. The preset parameterized self-adaptive threshold value can be set as' lambda+k x r [ i ] [ delta ], wherein lambda is the laser radar ranging system error; k is an adjustment factor; r < i > is the distance of the ith laser spot in any two adjacent laser spots; delta is the radar angular resolution of the lidar. It should be noted that the preset parameterized adaptive threshold is not a fixed parameter, but varies with the distance of the i-th laser spot.
In order to enable those skilled in the art to better understand the present embodiment, the laser break point will be described with reference to fig. 5.
Referring to fig. 5, a schematic diagram of extracting feature lines according to one embodiment of the present application is shown.
As can be seen from fig. 5, the laser radar may encounter obstacles such as wires, baffles, etc. in the empty track during scanning of the track beam at the target location, resulting in a larger distribution spacing between the laser spots. As with the laser break points shown in fig. 5, it is understood that there may or may not be multiple other laser points between two adjacent laser break points (i.e., relatively independent laser break points).
In this embodiment, by extracting the laser breakpoint, the extraction speed of the laser feature line can be increased, so that the speed of extracting the internal structural parameter of the track beam is increased.
With continued reference to fig. 4, in step 1232, the plurality of laser points are divided into a plurality of laser point sets by the laser breaking points, and based on the laser points in each laser point set, laser characteristic lines are respectively generated by fitting, so as to obtain a laser characteristic line set, where the laser characteristic line set is used for characterizing a cross-sectional profile of the track beam in the radial direction of the track beam at the target position.
It will be appreciated that if a plurality of other laser points are included between two adjacent laser break points, the two adjacent laser break points and the other laser points included therebetween may be considered as one set of laser points.
In some implementations, specific examples of fitting the generated laser feature lines may be performed as follows steps 12321 through 12322.
Step 12321, selecting a plurality of laser points from the target laser point sets as seed laser points for each target laser point set, wherein the target laser point set is any one of the plurality of laser point sets.
In some embodiments, a portion of the laser points in the target set of laser points may be selected as seed laser points, and the number of seed laser points selected and whether the selected seed laser points are consecutive are not limited herein.
And step 12322, performing straight line fitting on the seed laser points in the target laser point set, and generating a laser characteristic line corresponding to the target laser point set.
In some embodiments, a seed region expansion calculation method may be used to determine a laser feature line, that is, a portion of continuous laser points in a target laser point set may be selected as seed laser points, and then the selected seed laser points are fitted into an initial straight line by a least square method, so as to obtain a straight line coefficient of the initial straight line; and searching laser points meeting the initial straight line growth rule at two ends of the initial straight line segment to serve as adding laser points, adding the adding laser points to the initial straight line if the adding laser points are found, fitting the initial straight line and the adding laser points into an updated straight line again by using a least square method to obtain straight line data of the updated straight line, and growing the initial straight line in a mode of continuously searching the adding laser points to obtain the laser characteristic lines corresponding to the target laser point set.
The above-mentioned judgment principle of searching the laser point meeting the initial linear growth rule as the added laser point can be based on
To judge, wherein a, b and c are linear coefficients of an initial line fitted by a least square method, X i And Y i Representing coordinates of the laser spot to be added; and defining the searched laser point to be added as a threshold value of the laser point to be added for judging whether the searched laser point to be added can be judged.
It should be noted that the above selection is related to the performance parameter of the vehicle-mounted sensor on the inspection vehicle.
It can be understood that the above-mentioned linear coefficients are continuously changed with the addition of the laser spot, and each time it is determined whether the searched laser spot can be used as the added laser spot, the updated linear coefficients should be used. Finally, obtaining laser characteristic lines of the target laser spot set through fitting, and obtaining linear coefficients corresponding to the laser characteristic lines. Specifically, for each laser point set, a laser characteristic line corresponding to the laser point set can be generated, so that the laser characteristic line set is obtained.
For example, as shown in fig. 5, fig. 5 is some laser characteristic lines obtained after processing laser point cloud data generated by a laser radar scanning a track beam at a certain position in an empty track, it can be seen from fig. 5 that one laser characteristic line exists on the top surface of the track beam, one laser characteristic line exists on the left side surface of the track beam, one laser characteristic line exists on the right side surface of the track beam, and two laser characteristic lines exist on the bottom surface of the track beam. These laser features may then be used as a set of laser features.
In this embodiment, by extracting the laser feature lines, the internal structure of the track beam can be represented more accurately according to the cross geometric features between the lines, so as to detect the deformation of the track beam more accurately.
With continued reference to fig. 4, at step 1233, internal structural parameters of the rail beam at the target location are determined based on the set of laser signature lines.
In some embodiments, the obtained laser characteristic line set may be further screened according to length, that is, only some laser characteristic lines meeting a certain length requirement are left to determine internal structural parameters of the track beam at the target position, so that some interference laser characteristic lines can be eliminated, and an extraction rate for extracting the internal structural parameters is improved.
The laser characteristic lines in the laser characteristic line set include, but are not limited to, length data of line segments, end point data of line segments, linear coefficient data of line segments, position data of line segments, and the like.
It will be appreciated that the internal structural parameters of the rail beam can be determined by determining the angle, distance, etc. between the different laser profiles.
For example, it can be achieved by
To determine the perpendicularity of the left side surface and the top surface of the track beam; wherein- >
Representing the perpendicularity of the left side surface and the top surface of the track beam; a, a 1 ，b 1 ，c 1 The linear coefficient of the laser characteristic line representing the left side surface of the track beam is consistent with a 1 *X+b 1 Y+c 1 ＝0；a 2 ，b 2 ，c 2 The linear coefficient of the laser characteristic line representing the top surface of the track beam is consistent with a 2 *X+b 2 Y+c 2 ＝0。
With continued reference to FIG. 1, step 130 detects deformation of the rail beam at the target location based on the internal structural parameters via pre-constructed sample parameters.
It should be noted that the pre-constructed sample parameters may be comprehensive empty track structure parameter database data expanded according to the mileage of the empty track, or may be simplified track structure parameters designed in a segmented manner.
Exemplary, if the perpendicularity of the constructed sample parameters between the left side of the rail beam and the top surface of the rail beam at the target position is 90 °, this can be based on
Is compared with 90 deg. to detect deformation of the blank rail beam.
The method for detecting the deformation of the empty rail track beam has low requirements on environment, can automatically execute detection tasks, has high robustness, can improve the extraction accuracy of laser characteristic line extraction by designing and replacing noise laser data and the preprocessing step of smooth filtering laser data, ensures that the deformation detection method is more accurate, and has low calculation complexity by designing and confirming laser breakpoints, thereby meeting the real-time requirements.
In the technical solutions provided in some embodiments of the present application, laser point cloud data generated by scanning a track beam at a target position by a laser radar is obtained as original laser point cloud data; then, the internal structural parameters of the track beam at the target position are calculated and extracted through the original laser point cloud data; finally, through the internal structure parameter, detect through the sample parameter that builds in advance the deformation of track roof beam in target position department, in this application pass through laser radar scanning empty rail track roof beam in different positions and obtain the deformation result of empty rail track roof beam under the position and the time stamp, can form the detection log record of empty rail track roof beam, make relevant user can know through long-range fortune dimension whether empty rail track roof beam takes place deformation and the degree etc. information that takes place deformation in different positions department, thereby can in time pointedly get rid of the potential safety hazard that takes place deformation and produce by empty rail track roof beam, promote the security and the reliability of empty rail, realize the long-range fortune dimension of empty rail track roof beam.
The following describes an embodiment of the apparatus of the present application, which may be used to execute the method for detecting deformation of the hollow rail beam in the foregoing embodiment of the present application in real time. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for detecting deformation of the hollow rail beam in real time.
Fig. 6 shows a block diagram of an empty rail track deformation real-time detection device according to one embodiment of the present application.
Referring to fig. 6, an empty rail track beam deformation detection apparatus 600 according to an embodiment of the present application includes: an acquisition unit 601, a data processing unit 602, and a detection recording unit 603.
The acquiring unit 601 is configured to acquire laser point cloud data generated by scanning the track beam at a target position by the laser radar, as original laser point cloud data; a data processing unit 602, configured to computationally extract internal structural parameters of the track beam at the target location based on the raw laser point cloud data; a detection recording unit 603 for detecting deformation of the track beam at the target position by a pre-constructed sample parameter based on the internal structure parameter, and recording a detection log.
In some embodiments of the present application, based on the foregoing scheme, the data processing unit 602 further includes: the original laser point cloud data is composed of laser data of a plurality of laser points, the internal structural parameters of the track beam at the target position are calculated and extracted based on the original laser point cloud data, and the internal structural parameters comprise: replacing noise laser data in the original laser point cloud data to obtain reference laser point cloud data; smoothing the reference laser point cloud data to obtain target laser point cloud data; and calculating and extracting internal structural parameters of the track beam at the target position according to the target laser point cloud data.
In some embodiments of the present application, based on the foregoing scheme, the data processing unit 602 further includes: the laser data includes a distance between the laser point and the laser radar, and the replacing noise laser data in the original laser point cloud data includes: for each target laser point, acquiring a distance corresponding to the target laser point, wherein the target laser point is any one laser point of the plurality of laser points, and taking the distance as a target distance; and if the target distance is greater than a first preset distance or less than a second preset distance, defining laser data corresponding to the target laser point as the noise laser data, and replacing the noise laser data by the laser data corresponding to the laser point adjacent to the target laser point, wherein the first preset distance is greater than the second preset distance.
In some embodiments of the present application, based on the foregoing scheme, the data processing unit 602 further includes: the smoothing the reference laser point cloud data comprises the following steps: performing sliding segmentation on the reference laser point cloud data according to a preset window to obtain a plurality of groups of sub-reference laser point cloud data; calculating smooth filtering laser data based on each laser data in each group of target sub-reference laser point cloud data, wherein the smooth filtering laser data is used for inhibiting fluctuation of each laser data in the target sub-reference laser point cloud data, and the target sub-reference laser point cloud data is any one group in the multiple groups of sub-reference laser point cloud data; for each set of target sub-reference laser point cloud data, replacing individual ones of the target sub-reference laser point cloud data by the smoothed filtered laser data.
In some embodiments of the present application, based on the foregoing scheme, the data processing unit 602 further includes: the calculating and extracting internal structural parameters of the track beam at the target position according to the target laser point cloud data comprises the following steps: determining a laser breakpoint in the plurality of laser points based on the target laser point cloud data; dividing the plurality of laser points into a plurality of laser point sets through the laser breaking points, and respectively fitting and generating laser characteristic lines based on the laser points in each laser point set to obtain a laser characteristic line set, wherein the laser characteristic line set is used for representing the cross section profile of the track beam in the radial direction of the track beam at the target position; an internal structural parameter of the rail beam at the target location is determined based on the set of laser feature lines.
In some embodiments of the present application, based on the foregoing scheme, the data processing unit 602 further includes: selecting any two adjacent laser points from the plurality of laser points; acquiring the distance corresponding to any two adjacent laser points based on the target laser point cloud data; and if the difference value of the distances corresponding to the adjacent arbitrary two laser points is larger than a preset parameterized self-adaptive threshold value, determining the adjacent arbitrary two laser points as the laser breakpoint.
In some embodiments of the present application, based on the foregoing scheme, the data processing unit 602 further includes: selecting a plurality of laser points from each target laser point set as seed laser points, wherein the target laser point set is any one of the plurality of laser point sets; and performing straight line fitting on seed laser points in the target laser point set to generate laser characteristic lines corresponding to the target laser point set.
Fig. 7 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
It should be noted that, the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 7, the computer system 700 includes a central processing unit (Central Processing Unit, CPU) 701 that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 702 or a program loaded from a storage section 708 into a random access Memory (Random Access Memory, RAM) 703. In the RAM703, various programs and data required for the system operation are also stored. The CPU701, ROM 702, and RAM703 are connected to each other through a bus 704. An Input/Output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output section 707 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 710 as needed, so that a computer program read out therefrom is installed into the storage section 708 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. When executed by a Central Processing Unit (CPU) 701, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), 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 foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method for detecting deformation of the hollow rail track beam in real time described in the above embodiment.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs that, when executed by one of the electronic devices, cause the electronic device to implement the method for detecting deformation of an air rail beam in real time described in the above embodiment.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, in accordance with embodiments of the present application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. The method for detecting the deformation of the empty rail track beam in real time is characterized by comprising the following steps of:
acquiring laser point cloud data generated by scanning a track beam at a target position by a laser radar, and taking the laser point cloud data as original laser point cloud data;
calculating and extracting internal structural parameters of the track beam at the target position based on the original laser point cloud data;
and detecting deformation of the track beam at the target position through a pre-constructed sample parameter based on the internal structure parameter, and recording a detection log.
2. The method of claim 1, wherein the raw laser point cloud data consists of laser data of a number of laser points, the computing extracting internal structural parameters of the track beam at the target location based on the raw laser point cloud data comprises:
replacing noise laser data in the original laser point cloud data to obtain reference laser point cloud data;
smoothing the reference laser point cloud data to obtain target laser point cloud data;
and calculating and extracting internal structural parameters of the track beam at the target position according to the target laser point cloud data.
3. The method of claim 2, wherein the laser data comprises a distance between the laser point and the lidar, and the replacing noise laser data in the original laser point cloud data comprises:
for each target laser point, acquiring a distance corresponding to the target laser point, wherein the target laser point is any one laser point of the plurality of laser points, and taking the distance as a target distance;
and if the target distance is greater than a first preset distance or less than a second preset distance, defining laser data corresponding to the target laser point as the noise laser data, and replacing the noise laser data by the laser data corresponding to the laser point adjacent to the target laser point, wherein the first preset distance is greater than the second preset distance.
4. The method of claim 2, wherein smoothing the reference laser point cloud data comprises:
performing sliding segmentation on the reference laser point cloud data according to a preset window to obtain a plurality of groups of sub-reference laser point cloud data;
calculating smooth filtering laser data based on each laser data in each group of target sub-reference laser point cloud data, wherein the smooth filtering laser data is used for inhibiting fluctuation of each laser data in the target sub-reference laser point cloud data, and the target sub-reference laser point cloud data is any one group in the multiple groups of sub-reference laser point cloud data;
For each set of target sub-reference laser point cloud data, replacing individual ones of the target sub-reference laser point cloud data by the smoothed filtered laser data.
5. The method of claim 2, wherein the computing the internal structural parameters of the track beam at the target location from the target laser point cloud data comprises:
determining a laser breakpoint in the plurality of laser points based on the target laser point cloud data;
dividing the plurality of laser points into a plurality of laser point sets through the laser breaking points, and respectively fitting and generating laser characteristic lines based on the laser points in each laser point set to obtain a laser characteristic line set, wherein the laser characteristic line set is used for representing the cross section profile of the track beam in the radial direction of the track beam at the target position;
an internal structural parameter of the rail beam at the target location is determined based on the set of laser feature lines.
6. The method of claim 5, wherein determining a laser breakpoint among the number of laser points based on the target laser point cloud data comprises:
selecting any two adjacent laser points from the plurality of laser points;
Acquiring the distance corresponding to any two adjacent laser points based on the target laser point cloud data;
and if the difference value of the distances corresponding to the adjacent arbitrary two laser points is larger than a preset parameterized self-adaptive threshold value, determining the adjacent arbitrary two laser points as the laser breakpoint.
7. The method of claim 5, wherein generating laser feature lines based on the laser points in each set of laser points by fitting respectively comprises:
selecting a plurality of laser points from each target laser point set as seed laser points, wherein the target laser point set is any one of the plurality of laser point sets;
and performing straight line fitting on seed laser points in the target laser point set to generate laser characteristic lines corresponding to the target laser point set.
8. An empty rail track beam deformation detection device, the device comprising:
the acquisition unit is used for acquiring laser point cloud data generated by scanning the track beam at the target position by the laser radar, and the laser point cloud data is used as original laser point cloud data;
a data processing unit for computing and extracting internal structural parameters of the track beam at the target position based on the original laser point cloud data;
And the detection recording unit is used for detecting the deformation of the track beam at the target position through a pre-constructed sample parameter based on the internal structure parameter.
9. A computer readable storage medium having stored therein at least one program code loaded and executed by a processor to implement operations performed by the method of any of claims 1 to 7.
10. An electronic device comprising a memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for performing the method of any of claims 1-7.
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