Truck collision avoidance method, device, electronic device and container crane

Abstract

The application relates to a trolley anti-collision method, an electronic device, a trolley anti-collision system and a container crane. The trolley anti-collision method comprises the following steps: acquiring first point cloud data of a first scanner within a preset anti-collision distance and second point cloud data of a second scanner within the preset anti-collision distance; detecting whether there is a first abnormal point cloud in the first point cloud data and whether there is a second abnormal point cloud in the second point cloud data; if there is the first abnormal point cloud in the first point cloud data and / or there is the second abnormal point cloud in the second point cloud data, it is determined that an obstacle exists within the preset anti-collision distance. In this way, the first point cloud data of the first scanner within the preset anti-collision distance and the second point cloud data of the second scanner within the preset anti-collision distance under different scanning angles are utilized to detect the obstacle within the preset distance, so that more comprehensive space protection can be realized, and the accuracy of anti-collision detection can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of engineering machinery technology, specifically to a method for preventing collisions with large vehicles, electronic equipment, a system for preventing collisions with large vehicles, and a container crane. Background Technology

[0002] The trolley collision avoidance system is an essential safety subsystem for automated yard cranes and automated rail-mounted gantry cranes. It primarily prevents trolleys from colliding with other equipment or pedestrians during autonomous movement. Therefore, improving the accuracy of collision avoidance detection has always been a major concern. Summary of the Invention

[0003] In view of this, this application aims to provide a method, electronic device, system and container crane for avoiding collisions with large vehicles, which can effectively improve the accuracy of collision detection.

[0004] The first aspect of this application provides a method for preventing collisions with large vehicles, applied to a large vehicle collision prevention system. The large vehicle collision prevention system includes at least a first scanner and a second scanner installed on the large vehicle. The projection of the center line of the track of the large vehicle onto the ground plane passes through the projection of the first scanner onto the ground plane, and the laser of the first scanner performs a horizontal scan. The projection of the second scanner onto the ground plane is located on either side of the projection of the center line of the track onto the ground plane, and the laser of the second scanner performs a vertical scan. The first intersection point of the laser of the second scanner and the center line of the track, and the horizontal distance between the first scanner and the second scanner are both preset collision prevention distances.

[0005] The method includes:

[0006] Acquire first point cloud data of the first scanner within the preset anti-collision distance, and second point cloud data of the second scanner within the preset anti-collision distance;

[0007] Detect whether there is a first abnormal point cloud in the first point cloud data, and whether there is a second abnormal point cloud in the second point cloud data;

[0008] If a first abnormal point cloud exists in the first point cloud data, and / or a second abnormal point cloud exists in the second point cloud data, then it is determined that an obstacle exists within the preset anti-collision distance.

[0009] Optionally, detecting whether a first anomalous point cloud exists in the first point cloud data includes:

[0010] All points in the first point cloud data are clustered to obtain a first point cluster set; the first point cluster set includes at least one first point cluster subset.

[0011] Detect whether there exists a first subset of point clusters in the set of point clusters that meets the first preset condition; the first preset condition includes: all point clouds are non-reference point clouds, and the number of point clouds reaches a first threshold.

[0012] If there exists a first subset of point clusters that meets the first preset condition, then it is determined that there is a first abnormal point cloud in the first point cloud data;

[0013] The detection of whether a second abnormal point cloud exists in the second point cloud data includes:

[0014] All points in the second point cloud data are clustered to obtain a second point cluster set; the second point cluster set includes at least one subset of the second point clusters.

[0015] Detect whether there exists a subset of second point clusters in the second point cluster set that meets the second preset conditions; the second preset conditions include: all point clouds are non-reference point clouds, and the number of point clouds reaches the second threshold.

[0016] If a second subset of point clusters exists that meets the second preset conditions, then it is determined that a second abnormal point cloud exists in the second point cloud data.

[0017] Optionally, the large vehicle collision avoidance system further includes a third scanner installed on the large vehicle; the projection of the third scanner on the ground plane and the projection of the second scanner on the ground plane are respectively located on both sides of the projection of the track centerline on the ground plane; the laser of the third scanner performs vertical scanning, the second intersection point of the laser of the third scanner and the track centerline coincides with the first intersection point, and the horizontal distance between the third scanner and the first intersection point is a preset collision avoidance distance;

[0018] The method further includes:

[0019] The third scanner acquires third point cloud data within the preset anti-collision distance and detects whether there is a third abnormal point cloud in the third point cloud data;

[0020] If a third abnormal point cloud exists in the third point cloud data, then it is determined that an obstacle has appeared within the preset anti-collision distance.

[0021] Optionally, detecting whether a third abnormal point cloud exists in the third point cloud data includes:

[0022] All points in the third point cloud data are clustered to obtain a third point cluster set; the third point cluster set includes at least one third point cluster subset.

[0023] Detect whether there exists a subset of third point clusters in the third point cluster set that meets the third preset conditions; the third preset conditions include: all point clouds are non-reference point clouds, and the number of point clouds reaches the third threshold.

[0024] If a third point cluster subset that meets the third preset condition exists, then it is determined that a third abnormal point cloud exists in the third point cloud data.

[0025] Optionally, after determining that an obstacle appears within the preset anti-collision distance, the method further includes:

[0026] A first alarm message is sent to the user so that the user can respond based on the alarm message;

[0027] Alternatively, control the vehicle to stop moving.

[0028] Optionally, acquiring the first point cloud data of the first scanner within the preset collision avoidance distance and the second point cloud data of the second scanner within the preset collision avoidance distance includes:

[0029] Acquire the first raw point cloud data from the first scanner and the second raw point cloud data from the second scanner;

[0030] Based on the preset collision avoidance distance, first point cloud data within the preset collision avoidance distance is obtained from the first original point cloud data, and second point cloud data within the preset collision avoidance distance is obtained from the second original point cloud data.

[0031] Optionally, it also includes:

[0032] If no obstacle appears within the preset anti-collision distance, the target point cloud data is determined; the target point cloud data is either the first point cloud data or the second point cloud data.

[0033] The actual starting point x-coordinate, the actual ending point x-coordinate, and the actual number of points in the target point cloud data are determined in a planar coordinate system; the planar coordinate system uses the straight line where the track center line is located as the x-axis and any straight line perpendicular to the track center line on the scanning surface corresponding to the target point cloud data as the y-axis.

[0034] Based on the actual starting point x-coordinate, the actual ending point x-coordinate, and the actual point cloud quantity, as well as the preset standard starting point x-coordinate, the preset standard ending point x-coordinate, and the preset standard point cloud quantity, determine whether the scanner corresponding to the target point cloud data is tilted.

[0035] If the scanner corresponding to the target point cloud data is tilted, then the tilt of the scanner corresponding to the target point cloud data is corrected, or a second alarm message is issued to the user so that the user can perform tilt correction.

[0036] A second aspect of this application provides an electronic device, comprising:

[0037] A processor, and a memory connected to the processor;

[0038] The memory is used to store computer programs;

[0039] The processor is used to call and execute the computer program in the memory to perform the large vehicle collision avoidance method as described in the first aspect of this application.

[0040] A third aspect of this application provides a large vehicle collision avoidance system, including a first scanner, a second scanner, and electronic equipment as described in the second aspect of this application;

[0041] Both the first scanner and the second scanner are used to be installed on the vehicle; wherein,

[0042] The projection of the center line of the track of the large vehicle onto the ground plane passes through the projection of the first scanner onto the ground plane, and the laser of the first scanner scans horizontally; the projection of the second scanner onto the ground plane is located on either side of the projection of the center line of the track onto the ground plane, the laser of the second scanner scans vertically, and the first intersection point of the laser of the second scanner and the center line of the track, the horizontal distance between the first scanner and the second scanner, are all preset anti-collision distances.

[0043] A fourth aspect of this application provides a container crane, including a crane body and the trolley anti-collision system described in the third aspect of this application.

[0044] In this application, the solution first acquires first point cloud data from a first scanner within a preset collision avoidance distance, and second point cloud data from a second scanner within the same preset collision avoidance distance, providing a basis for obstacle detection. Then, it detects whether a first anomalous point cloud exists in the first point cloud data, and whether a second anomalous point cloud exists in the second point cloud data. If either the first or second anomalous point cloud is present, an obstacle is determined to exist within the preset collision avoidance distance. Thus, by utilizing the first point cloud data from the first scanner within the preset collision avoidance distance and the second point cloud data from the second scanner at different viewpoints within the preset collision avoidance distance, obstacle detection within the preset distance can be achieved, enabling more comprehensive spatial protection and effectively improving the accuracy of collision avoidance detection. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of the structure of a large vehicle collision avoidance system provided in one embodiment of this application.

[0047] Figure 2 This is a point cloud diagram of a second scanner provided in one embodiment of this application.

[0048] Figure 3 This is a point cloud diagram of a second scanner provided in another embodiment of this application.

[0049] Figure 4 This is a schematic flowchart of a large vehicle collision avoidance method provided in one embodiment of this application.

[0050] Figure 5 This is a structural schematic diagram of a large vehicle collision avoidance system provided in another embodiment of this application.

[0051] Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] Embodiments of this application provide a method for preventing collisions with large vehicles, which can be applied to large vehicle collision prevention systems, such as... Figure 1As shown, the large vehicle collision avoidance system may include at least a first scanner A and a second scanner B installed on the large vehicle D; the projection of the track centerline of the large vehicle D onto the ground plane passes through the projection of the first scanner A onto the ground plane, and the laser of the first scanner A scans horizontally, that is, the laser scanning surface of the first scanner A is parallel to the ground plane; the projection of the second scanner B onto the ground plane is located on either side of the projection of the track centerline onto the ground plane, and the shortest distance between its projection onto the ground plane and the projection of the track centerline onto the ground plane is l1, the laser of the second scanner B scans vertically, and the horizontal distance between the first intersection point S of the laser of the second scanner B and the track centerline and the first scanner A and the second scanner B is a preset collision avoidance distance h.

[0054] It is important to understand that the first scanner A's horizontal laser scan can detect obstacles within a preset collision avoidance distance at an angle on the horizontal plane, while the second scanner B's vertical laser scan, meaning its laser scanning surface is perpendicular to the horizontal plane, can scan obstacles within a preset collision avoidance distance at an angle perpendicular to the horizontal plane. By using the first scanner A and the second scanner B in combination to scan obstacles within the preset collision avoidance distance, more comprehensive spatial protection can be achieved, effectively improving the accuracy of detection.

[0055] The preset anti-collision distance can be set according to actual needs and is not limited here. During implementation, the scanning angle between the vertical scanning laser of the second scanner and the center line of the track can be determined based on the preset anti-collision distance and the installation position of the second scanner on the main vehicle, thus enabling the installation of the second scanner. Furthermore, the installation of the first scanner can also be based on the installation environment and operational requirements, and is not limited here.

[0056] For example, a first scanner can be installed at the crane's gantry leg, scanning laterally (parallel to the ground plane) at a height of 0.2 meters above the ground. The projection of the track centerline onto the ground passes through the projection of the first scanner onto the ground. A second scanner can also be installed at the gantry leg, scanning vertically (perpendicular to the ground plane) at a height of 1 meter above the ground. Its projection onto the ground lies on either side of the track centerline, and the shortest distance between the projection of the second scanner onto the ground and the projection of the track centerline onto the ground is 0.3 meters.

[0057] Figure 2 This is a point cloud diagram of a second scanner provided in one embodiment of this application. Figure 2As shown, the second scanner B is 1 meter above the ground (l2), the preset anti-collision distance is 12 meters, and the scanning angle is (θ2-θ1). The angle θ2 can be determined based on the preset anti-collision distance and the height l2 of the second scanner B above the ground, while θ1 can be set according to actual needs (θ1 can be considered as the blind spot at the large vehicle door leg). During operation, under ideal conditions without obstacles, the second scanner B vertically scans to obtain the corresponding point cloud data P. B Among them, point cloud data P B This includes the second point cloud data P within the preset collision avoidance distance h. B0 And the second point cloud data P B0 All data are ground point cloud data.

[0058] When applying, such as Figure 3 As shown, the real-time point cloud data P of the second scanner B can be analyzed. B Ground point cloud data P within the preset collision avoidance distance h. B0 ′, Non-ground point cloud data P within the preset collision avoidance distance h B00 This allows us to determine whether there are obstacles within the preset collision avoidance distance h.

[0059] Among them, the second point cloud data obtained by the second scanner B within the preset anti-collision distance h includes ground point cloud data P. B0 ′ and non-ground point cloud data P B00 ′.

[0060] Specifically, such as Figure 4 As shown, a large vehicle collision avoidance method may include at least the following steps:

[0061] S101. Acquire the first point cloud data of the first scanner within the preset anti-collision distance, and the second point cloud data of the second scanner within the preset anti-collision distance.

[0062] In practice, both the first and second scanners can be line laser scanners (2D laser scanners). Using a line laser scanner for line laser scanning can effectively identify obstacles, reduce the impact of environmental factors such as raindrops on detection, and improve detection accuracy.

[0063] In practice, the first raw point cloud data can be obtained first through the first scanner, and then the first point cloud data can be obtained from the first raw point cloud data based on the preset anti-collision distance.

[0064] Similarly, a second original point cloud data can be obtained through a second scanner, and then a second point cloud data can be obtained from the second original point cloud data based on a preset collision avoidance distance.

[0065] Of course, this application is not limited to this. In some other embodiments, for a scanner whose scanning angle has been adjusted, the first point cloud data can be obtained directly through the first scanner, and the second point cloud data can be obtained directly through the second scanner.

[0066] The first point cloud data obtained by the first scanner, and the second point cloud data obtained by the scanner, can provide a basis for judging whether there are obstacles within the preset collision avoidance distance during the movement of the large vehicle on the track.

[0067] Furthermore, the first and second scanners provide point cloud data from different perspectives. The point cloud data from different perspectives can be cross-checked, which can effectively improve the accuracy of collision avoidance detection and enhance adaptability to the environment.

[0068] S102. Detect whether there is a first abnormal point cloud in the first point cloud data, and whether there is a second abnormal point cloud in the second point cloud data.

[0069] It is important to understand that when an obstacle appears within the preset collision avoidance distance, the point cloud data presented in the first or second point cloud data will show anomalies due to the size of the obstacle.

[0070] For example, ideally, when no obstacles are present within the preset collision avoidance distance, the first and second scanners will only detect the ground within this distance. Therefore, neither the first nor the second point cloud data will contain non-ground point cloud data; that is, the point clouds in both data will be ground-based. However, when obstacles are present within the preset collision avoidance distance, both the first and second scanners will detect both the ground and the obstacles. In this case, the first and second point cloud data will contain point cloud data other than ground-based point cloud data (non-ground-based point cloud data). Based on this, it is possible to detect whether a first anomalous point cloud exists in the first point cloud data, and whether a second anomalous point cloud exists in the second point cloud data.

[0071] The first anomalous point cloud is the point cloud of the obstacle obtained by the first scanner, and the second anomalous point cloud is the point cloud of the obstacle obtained by the second scanner.

[0072] Due to the different installation positions of the first and second scanners, as well as the influence of the shape of the obstacle, the first anomalous point cloud and the second anomalous point cloud may be the same or different.

[0073] S103. If there is a first abnormal point cloud in the first point cloud data, and / or there is a second abnormal point cloud in the second point cloud data, then it is determined that an obstacle has appeared within the preset anti-collision distance.

[0074] If the first point cloud data contains a first abnormal point cloud, it can be determined that an obstacle has entered the preset collision avoidance distance. Similarly, if the second point cloud data contains a second abnormal point cloud, it can also be determined that an obstacle has entered the preset collision avoidance distance.

[0075] The first and second scanners have different scanning angles. By using the first and second scanners together to achieve collision avoidance detection of large vehicles during their movement, blind spots can be reduced and the comprehensiveness and accuracy of the detection can be improved.

[0076] In the embodiments of this application, firstly, first point cloud data of the first scanner within a preset collision avoidance distance and second point cloud data of the second scanner within a preset collision avoidance distance are acquired, providing a basis for obstacle detection; then, it is detected whether there is a first abnormal point cloud in the first point cloud data and whether there is a second abnormal point cloud in the second point cloud data; if there is a first abnormal point cloud in the first point cloud data or a second abnormal point cloud in the second point cloud data, it is determined that an obstacle exists within the preset collision avoidance distance. Thus, by using the first point cloud data of the first scanner within a preset collision avoidance distance and the second point cloud data of the second scanner within a preset collision avoidance distance from another perspective to detect obstacles within the preset distance, more comprehensive spatial protection can be achieved, effectively improving the accuracy of collision avoidance detection.

[0077] In some embodiments, when detecting whether a first abnormal point cloud exists in the first point cloud data, all points in the first point cloud data can first be clustered to obtain a first point cluster set; the first point cluster set includes at least one first point cluster subset; then, it is detected whether there is a first point cluster subset in the point cluster set that meets a first preset condition; the first preset condition includes: all point clouds are non-reference point clouds, and the number of point clouds reaches a first threshold; if there is a first point cluster subset that meets the first preset condition, it is determined that a first abnormal point cloud exists in the first point cloud data.

[0078] Similarly, when detecting whether there is a second abnormal point cloud in the second point cloud data, all points in the second point cloud data can first be clustered to obtain a second point cluster set; the second point cluster set includes at least one second point cluster subset; then, it is detected whether there is a second point cluster subset in the second point cluster set that meets the second preset conditions; the second preset conditions include: all point clouds are non-reference point clouds, and the number of point clouds reaches a second threshold; if there is a second point cluster subset that meets the second preset conditions, it is determined that there is a second abnormal point cloud in the second point cloud data.

[0079] In this context, the reference object refers to the substance scanned during the scanning process that constitutes the scanning background and does not interfere with the detection. In the embodiments of this application, the reference object is the ground, and the non-reference object is non-ground. Taking the second point cloud data as an example, after clustering all points in the second point cloud data to obtain the second point cluster set, the second point cluster set representing ground point cloud data can be excluded first. Then, from the second point cluster set belonging to non-ground point cloud data, the second point cluster subset with the number of point clouds reaching the second threshold is selected and identified as the second point cluster subset that meets the second preset condition.

[0080] It should be understood that the embodiments of this application are only illustrated with the ground as the reference object, but this application is not limited to this. In some other embodiments, the reference object may also include other substances. For example, a box is fixedly placed on the ground, and the scanning laser passes through the box but the box does not interfere with the movement of the vehicle. In this case, the reference object includes the ground and the box.

[0081] In the above clustering process, points with similar features in the point cloud data can be aggregated into the same point cluster subset, which lays the foundation for accurate identification of abnormal point clouds in the future.

[0082] To improve detection accuracy, clustering methods can include Euclidean distance-based point cloud clustering, which is relatively simple to implement, has high computational efficiency, and is advantageous in processing 2D laser point clouds.

[0083] It should be noted that the above clustering processing method is not limited to this. In some other embodiments, other methods can be used for clustering point clouds, such as density clustering, hierarchical clustering, grid clustering, model clustering, fuzzy clustering, etc.

[0084] Specifically, taking the second scanner B as an example, such as Figure 3 As shown, when no obstacle appears within the preset collision avoidance distance h, the non-ground point cloud data P in the second point cloud data obtained by the second scanner B within the preset collision avoidance distance h... B00 The value should be 0. Considering environmental factors such as rain and snow, a second threshold can be set. This second threshold represents the number of abnormal point clouds caused by potential external interference. Once a second subset of point clouds exists with a number exceeding the second threshold, this subset can be considered a set of obstacle point cloud data. This allows for more accurate identification of obstacles within the preset collision avoidance distance.

[0085] In practice, the first and second thresholds can be set according to actual needs, and no specific restrictions are made here.

[0086] In some embodiments, such as Figure 5As shown, the large vehicle collision avoidance system may also include a third scanner C installed on the large vehicle D; the projection of the third scanner C on the ground plane and the projection of the second scanner B on the ground plane are respectively located on both sides of the projection of the track centerline on the ground plane; the laser of the third scanner C performs vertical scanning, the second intersection point Q of the laser of the third scanner C and the track centerline coincides with the first intersection point S, and the horizontal distance between the third scanner C and the first intersection point S is a preset collision avoidance distance h.

[0087] The shortest distance between the projection of the third scanner onto the ground plane and the projection of the orbit centerline onto the ground plane is l3.

[0088] When used, l1 and l3 can be the same or different.

[0089] By installing a third scanner C on one side of the track centerline where the second scanner B is not installed, the scanning and detection field of view can be further increased, the detection blind spot can be further reduced, and the stability of the large vehicle anti-collision system can be improved, while the accuracy and comprehensiveness of the identification of obstacles appearing on the track within the preset anti-collision distance h can be improved.

[0090] Correspondingly, the large vehicle collision avoidance method may also include: acquiring third point cloud data within a preset collision avoidance distance from a third scanner, and detecting whether there is a third abnormal point cloud in the third point cloud data; if there is a third abnormal point cloud in the third point cloud data, then it is determined that an obstacle has appeared within the preset collision avoidance distance.

[0091] In practical applications, after acquiring the first point cloud data, the second point cloud data, and the third point cloud data from the first scanner, the second scanner, and the third scanner respectively, the first point cloud data, the second point cloud data, and the third point cloud data can be detected respectively. If at least one of the three point cloud data has a corresponding abnormal point cloud, it can be determined that an obstacle has entered within the preset anti-collision distance.

[0092] In some embodiments, when detecting whether a third anomalous point cloud exists in the third point cloud data, all points in the third point cloud data can be clustered to obtain a third point cluster set; the third point cluster set includes at least one third point cluster subset; it is detected whether there is a third point cluster subset in the third point cluster set that meets a third preset condition; the third preset condition includes: all point clouds are non-reference point clouds, and the number of point clouds reaches a third threshold; if there is a third point cluster subset that meets the third preset condition, it is determined that a third anomalous point cloud exists in the third point cloud data.

[0093] The third threshold can be set according to actual needs, and no specific limit is set here.

[0094] Specifically, the detection of the third abnormal point cloud can refer to the detection of the second abnormal point cloud described in the above embodiments, and will not be repeated here.

[0095] In some embodiments, after determining that an obstacle has appeared within the preset collision avoidance distance, the large vehicle collision avoidance method may further include: issuing a first alarm message to the user so that the user can respond based on the alarm message; or, controlling the large vehicle to stop driving.

[0096] The first alarm message can be a voice alarm message, a text alarm message, or a light alarm message.

[0097] In some embodiments, the large vehicle collision avoidance method may further include: if no obstacle appears within a preset collision avoidance distance, then determining target point cloud data; the target point cloud data is either first point cloud data or second point cloud data; determining the actual starting point x-coordinate, the actual ending point x-coordinate, and the actual number of points in the target point cloud data in a planar coordinate system; the planar coordinate system uses the straight line where the track centerline is located as the x-axis and any straight line perpendicular to the track centerline on the scanning surface corresponding to the target point cloud data as the y-axis; based on the actual starting point x-coordinate, the actual ending point x-coordinate, and the actual number of points, as well as the preset standard starting point x-coordinate, the preset standard ending point x-coordinate, and the preset standard number of points, determining whether the scanner corresponding to the target point cloud data is tilted; if the scanner corresponding to the target point cloud data is tilted, then performing tilt correction on the scanner corresponding to the target point cloud data, or issuing a second alarm message to the user so that the user can perform tilt correction.

[0098] The second alarm message can be a voice alarm message, a text alarm message, or a light alarm message.

[0099] In some embodiments, the target point cloud data may also be third point cloud data.

[0100] When determining whether the scanner corresponding to the target point cloud data is tilted based on the actual starting point x-coordinate, the actual ending point x-coordinate, the actual number of point clouds, and the preset standard starting point x-coordinate, the preset standard ending point x-coordinate, and the preset standard number of point clouds, it can determine whether the difference between the actual number of point clouds and the preset standard number of point clouds exceeds the preset deviation value, the relationship between the actual starting point x-coordinate and the preset standard starting point x-coordinate, and the relationship between the actual ending point x-coordinate and the preset standard ending point x-coordinate.

[0101] If the difference between the actual number of point clouds and the preset standard number of point clouds exceeds a preset deviation value, and the actual starting point x-coordinate is greater than the standard starting point x-coordinate, then the corresponding scanner is determined to be tilted upwards. If the difference between the actual number of point clouds and the preset standard number of point clouds exceeds a preset deviation value, and the actual ending point x-coordinate is greater than the standard ending point x-coordinate, then the corresponding scanner is determined to be tilted downwards.

[0102] After determining that the corresponding scanner has tilted, the correction angle can be determined based on the target point cloud data and the corresponding scanner installation information, and then the scanner can be tilted corrected according to the correction angle.

[0103] Specifically, the formula for calculating the correction angle is:

[0104]

[0105] Where Δθ is the deviation angle; H is the installation height of the scanner; L′0 is the actual measured distance between the starting point and the point cloud in the target point cloud data; and L0 is the actual measured distance between the preset standard starting point x-coordinate and the preset standard ending point x-coordinate.

[0106] In practical applications, the scanner can automatically correct itself when the tilt angle is less than the fourth threshold, and issue a second alarm to the user when the tilt angle is greater than or equal to the fourth threshold, prompting the user to correct the tilt. This allows for timely detection and resolution of problems when the scanner tilts, further improving the accuracy of collision avoidance detection.

[0107] The fourth threshold can be set according to actual needs, and is not limited here.

[0108] Embodiments of this application also provide a large vehicle collision avoidance device, which includes at least: an acquisition module for acquiring first point cloud data from a first scanner within a preset collision avoidance distance, and second point cloud data from a second scanner within the preset collision avoidance distance; a detection module for detecting whether a first abnormal point cloud exists in the first point cloud data, and whether a second abnormal point cloud exists in the second point cloud data; and a determination module for determining that an obstacle exists within the preset collision avoidance distance if the first abnormal point cloud exists in the first point cloud data, and / or the second abnormal point cloud exists in the second point cloud data.

[0109] Optionally, when detecting whether a first anomalous point cloud exists in the first point cloud data, the detection module may specifically be used to: perform clustering processing on all points in the first point cloud data to obtain a first point cluster set; the first point cluster set includes at least one first point cluster subset; detect whether there is a first point cluster subset in the point cluster set that meets a first preset condition; the first preset condition includes: all point clouds are non-reference point clouds, and the number of point clouds reaches a first threshold; if there is a first point cluster subset that meets the first preset condition, then it is determined that a first anomalous point cloud exists in the first point cloud data; similarly, when detecting whether a second anomalous point cloud exists in the second point cloud data, the detection module may also be used to: perform clustering processing on all points in the second point cloud data to obtain a second point cluster set; the second point cluster set includes at least one second point cluster subset; detect whether there is a second point cluster subset in the second point cluster set that meets a second preset condition; the second preset condition includes: all point clouds are non-reference point clouds, and the number of point clouds reaches a second threshold; if there is a second point cluster subset that meets the second preset condition, then it is determined that a second anomalous point cloud exists in the second point cloud data.

[0110] Optionally, the acquisition module can also be used to: acquire third point cloud data of the third scanner within a preset anti-collision distance; correspondingly, the detection module can also be used to: detect whether there is a third abnormal point cloud in the third point cloud data; the determination module can also be used to: determine that an obstacle exists within the preset anti-collision distance if there is a third abnormal point cloud in the third point cloud data.

[0111] Optionally, when detecting whether a third anomalous point cloud exists in the third point cloud data, the detection module can be used to: perform clustering processing on all points in the third point cloud data to obtain a third point cluster set; the third point cluster set includes at least one third point cluster subset; detect whether there is a third point cluster subset in the third point cluster set that meets a third preset condition; the third preset condition includes: all point clouds are non-reference point clouds, and the number of point clouds reaches a third threshold; if there is a third point cluster subset that meets the third preset condition, then it is determined that a third anomalous point cloud exists in the third point cloud data.

[0112] Optionally, the large vehicle collision avoidance device may also include a first alarm module or a control module, wherein the first alarm module may be used to: issue a first alarm message to the user so that the user can take action based on the alarm message; the control module may be used to: control the large vehicle to stop moving.

[0113] Optionally, when acquiring the first point cloud data of the first scanner within a preset anti-collision distance and the second point cloud data of the second scanner within a preset anti-collision distance, the acquisition module may specifically be used to: acquire the first original point cloud data of the first scanner and the second original point cloud data of the second scanner; based on the preset anti-collision distance, acquire the first point cloud data within the preset anti-collision distance from the first original point cloud data and acquire the second point cloud data within the preset anti-collision distance from the second original point cloud data.

[0114] Optionally, the large vehicle collision avoidance device may also include a tilt detection module, which can be used to: determine the target point cloud data if no obstacle appears within the preset collision avoidance distance; the target point cloud data is either first point cloud data or second point cloud data; determine the actual starting point x-coordinate, the actual ending point x-coordinate, and the actual number of points in the target point cloud data in the planar coordinate system; the planar coordinate system uses the straight line where the track centerline is located as the x-axis and any straight line perpendicular to the track centerline on the scanning surface corresponding to the target point cloud data as the y-axis; based on the actual starting point x-coordinate, the actual ending point x-coordinate, and the actual number of points, as well as the preset standard starting point x-coordinate, the preset standard ending point x-coordinate, and the preset standard number of points, determine whether the scanner corresponding to the target point cloud data is tilted; if the scanner corresponding to the target point cloud data is tilted, perform tilt correction on the scanner corresponding to the target point cloud data, or issue a second alarm message to the user so that the user can perform tilt correction.

[0115] The specific implementation of the vehicle anti-collision device provided in the embodiments of this application can refer to the implementation of the vehicle anti-collision method described in any of the above embodiments, and will not be repeated here.

[0116] Embodiments of this application also provide an electronic device, such as... Figure 6 As shown, the electronic device may include: a memory 601 and a processor 602; wherein, the memory 601 is connected to the processor 602 and is used to store a program; the processor 602 is used to implement the large vehicle collision avoidance method disclosed in any of the above embodiments by running the program stored in the memory 601.

[0117] Specifically, the aforementioned electronic device may also include: a bus, a communication interface 603, an input device 604, and an output device 605.

[0118] The processor 602, memory 601, communication interface 603, input device 604, and output device 605 are interconnected via a bus. Among them:

[0119] A bus can include a pathway for transmitting information between various components of a computer system.

[0120] The processor 602 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present application. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0121] Processor 602 may include a main processor, as well as a baseband chip, modem, etc.

[0122] The memory 601 stores a program for executing the technical solution of this application, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 601 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0123] Input device 604 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0124] Output device 605 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0125] The communication interface 603 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0126] The processor 602 executes the program stored in the memory 601 and calls other devices, which can be used to implement the various steps of the large vehicle collision avoidance method provided in the above embodiments of this application.

[0127] Embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, causes the computer to perform the vehicle collision avoidance method in any of the above embodiments.

[0128] Embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the large vehicle collision avoidance method described in any of the above embodiments.

[0129] Embodiments of this application also provide a large vehicle collision avoidance system, including a first scanner, a second scanner, and an electronic device as described in any of the above embodiments. Both the first and second scanners are mounted on the large vehicle; wherein the projection of the large vehicle's track centerline onto the ground plane passes through the projection of the first scanner onto the ground plane, and the first scanner's laser performs a horizontal scan; the projection of the second scanner onto the ground plane is located on either side of the projection of the track centerline onto the ground plane, and the second scanner's laser performs a vertical scan, and the first intersection point of the second scanner's laser and the track centerline, along with the horizontal distance between the first and second scanners, is a preset collision avoidance distance.

[0130] Optionally, the vehicle collision avoidance system may also include a third scanner for mounting on the vehicle, wherein the projection of the third scanner onto the ground plane and the projection of the second scanner onto the ground plane are located on opposite sides of the projection of the track centerline onto the ground plane; the third scanner performs vertical laser scanning, and the second intersection point of the laser of the third scanner and the track centerline coincides with the first intersection point, and the horizontal distance between the third scanner and the first intersection point is a preset collision avoidance distance.

[0131] Embodiments of this application also provide a container crane, including a crane body and the trolley anti-collision system described in any of the above embodiments.

[0132] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments described herein, and are not intended to limit the scope of the invention.

[0133] It is understood that in the various embodiments described in this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments described in this specification.

[0134] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0135] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0136] It is understood that the processor in the embodiments of this specification can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0137] It is understood that the memory in the embodiments of this specification may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0138] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0140] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0142] In addition, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0143] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of this specification, in essence, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for preventing collision of a large vehicle, characterized by, The application is applied to a large vehicle anti-collision system, the large vehicle anti-collision system at least includes a first scanner and a second scanner arranged on the large vehicle; a projection of a track center line of the large vehicle on a horizontal plane passes through a projection of the first scanner on the horizontal plane, and the first scanner performs horizontal scanning of laser; the second scanner is located on either side of the projection of the track center line on the horizontal plane, the second scanner performs vertical scanning of laser, and a first intersection point of laser of the second scanner and the track center line is a preset anti-collision distance from the first scanner and the second scanner; The method comprises: acquiring first point cloud data of the first scanner within the preset anti-collision distance and second point cloud data of the second scanner within the preset anti-collision distance; detecting whether there is first abnormal point cloud in the first point cloud data and whether there is second abnormal point cloud in the second point cloud data; if there is first abnormal point cloud in the first point cloud data and / or there is second abnormal point cloud in the second point cloud data, it is determined that there is an obstacle within the preset anti-collision distance; if there is no obstacle within the preset anti-collision distance, target point cloud data is determined; the target point cloud data is the first point cloud data or the second point cloud data; determining actual start horizontal coordinate, actual end horizontal coordinate of the target point cloud data in a plane coordinate system, and actual point cloud quantity in the target point cloud data; the plane coordinate system takes a straight line where the track center line is located as a horizontal axis and takes any straight line perpendicular to the track center line on a scanning surface corresponding to the target point cloud data as a vertical coordinate axis; judging whether the scanner corresponding to the target point cloud data exists tilt according to the actual start horizontal coordinate, the actual end horizontal coordinate and the actual point cloud quantity, and preset standard start horizontal coordinate, preset standard end horizontal coordinate and preset standard point cloud quantity; if the scanner corresponding to the target point cloud data exists tilt, tilt correction is performed on the scanner corresponding to the target point cloud data or a second warning information is sent to the user to make the user perform tilt correction.

2. The method of claim 1, wherein, The detection of whether there is first abnormal point cloud in the first point cloud data comprises: performing clustering processing on all points in the first point cloud data to obtain a first point cluster set; the first point cluster set includes at least one first point cluster subset; detecting whether there is a first point cluster subset meeting a first preset condition in the point cluster set; the first preset condition includes that all point clouds are non-reference point clouds and the number of point clouds reaches a first threshold; if there is a first point cluster subset meeting the first preset condition, it is determined that there is first abnormal point cloud in the first point cloud data; The detection of whether there is second abnormal point cloud in the second point cloud data comprises: performing clustering processing on all points in the second point cloud data to obtain a second point cluster set; the second point cluster set includes at least one second point cluster subset; detecting whether a second point cluster subset meeting a second preset condition exists in the second point cluster set; the second preset condition comprises that all point clouds are non-reference object point clouds and the number of point clouds reaches a second threshold value; if the second point cluster subset meeting the second preset condition exists, it is determined that the second abnormal point cloud exists in the second point cloud data.

3. The method of claim 1, wherein, The large vehicle anti-collision system further comprises a third scanner arranged on the large vehicle; a projection of the third scanner on the ground plane and a projection of the second scanner on the ground plane are respectively located on two sides of a projection of the track center line on the ground plane; the third scanner vertically scans laser; a second intersection point of the laser of the third scanner and the track center line coincides with the first intersection point, and a horizontal distance between the third scanner and the first intersection point is a preset anti-collision distance; The method further comprises: acquiring third point cloud data of the third scanner within the preset anti-collision distance, and detecting whether a third abnormal point cloud exists in the third point cloud data; if the third abnormal point cloud exists in the third point cloud data, it is determined that an obstacle appears within the preset anti-collision distance.

4. The method of claim 3, wherein, The detection of whether the third abnormal point cloud exists in the third point cloud data comprises: performing clustering processing on all points in the third point cloud data to obtain a third point cluster set; the third point cluster set comprises at least one third point cluster subset; detecting whether a third point cluster subset meeting a third preset condition exists in the third point cluster set; the third preset condition comprises that all point clouds are non-reference object point clouds and the number of point clouds reaches a third threshold value; if the third point cluster subset meeting the third preset condition exists, it is determined that the third abnormal point cloud exists in the third point cloud data.

5. The method according to claim 1 or 3, characterized in that, After the determination that the obstacle appears within the preset anti-collision distance, the method further comprises: sending first warning information to a user, so that the user makes a response based on the first warning information; or, controlling the large vehicle to stop running.

6. The method of claim 1, wherein, The acquisition of the first point cloud data of the first scanner within the preset anti-collision distance and the second point cloud data of the second scanner within the preset anti-collision distance comprises: acquiring first original point cloud data of the first scanner and second original point cloud data of the second scanner; based on the preset anti-collision distance, acquiring the first point cloud data within the preset anti-collision distance from the first original point cloud data and the second point cloud data within the preset anti-collision distance from the second original point cloud data.

7. An electronic device, comprising: comprise: a processor and a memory connected to the processor; the memory is used to store a computer program; the processor is used to call and execute the computer program in the memory to execute the large vehicle anti-collision method according to any one of claims 1-6.

8. A cart anti-collision system, characterized by, comprise a first scanner, a second scanner and an electronic device according to claim 7; the first scanner and the second scanner are both arranged on a large vehicle; wherein, The track center line projection of the large vehicle on the ground plane passes through the first scanner projection on the ground plane, and the horizontal scanning of the laser of the first scanner; the second scanner projection on the ground plane is located on either side of the track center line projection on the ground plane, the vertical scanning of the laser of the second scanner, and the first intersection point of the laser of the second scanner and the track center line is the preset anti-collision distance from the first scanner and the second scanner.

9. A container crane, characterized in that The crane body and the large vehicle anti-collision system of claim 8 are included.

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