Anti-lifting method, device, crane and automation system for container transport vehicle

Abstract

The present application provides a method, device, crane and automation system for preventing container transport vehicles from being lifted. The specific implementation scheme is as follows: the acquired three-dimensional point cloud data of the container transport vehicle is separated to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle; wherein the target area includes: the vehicle plate of the container transport vehicle; the three-dimensional point cloud data of the target area is projected to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground; after receiving the lifting signal of the container, the container transport vehicle is determined to be lifted according to the preset template data and the first point cloud data statistical graph. According to the technical scheme of the present application, it is possible to accurately detect whether the container transport vehicle is lifted.

Description

Technical Field

[0001] The present application relates to the field of port technology, and in particular to an anti-lifting method, device, crane and automation system for a container transport vehicle. Background Art

[0002] During the unloading operation of the crane from the container transporter, the container and the container transporter may be lifted by the lifting device because the locking pin of the container transporter is not completely unlocked, which may cause an accident of the container transporter. In order to improve the safety of the container transporter operation, it is necessary to accurately detect whether the container transporter is at risk of being lifted. Summary of the invention

[0003] In view of this, the present application provides a method, device, crane and automation system for preventing a container transport vehicle from being lifted, which solves the technical problem in the prior art that the container cannot accurately detect whether the container transport vehicle is lifted.

[0004] According to a first aspect of an embodiment of the present application, a method for preventing a container transport vehicle from lifting is provided, comprising:

[0005] Separating the acquired three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle; wherein the target area includes: the vehicle plate of the container transport vehicle;

[0006] Projecting the three-dimensional point cloud data of the target area to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground;

[0007] After receiving the lifting signal of the container, it is determined that the container transport vehicle is lifted according to the preset template data and the first point cloud data statistical graph.

[0008] According to a second aspect of an embodiment of the present application, there is provided an anti-lifting device for a container transport vehicle, comprising:

[0009] An acquisition module is used to separate the acquired three-dimensional point cloud data of the container transport vehicle and determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle; wherein the target area includes: a vehicle plate of the container transport vehicle;

[0010] A projection module, used for projecting the three-dimensional point cloud data to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground;

[0011] The processing module is used to determine that the container transport vehicle is lifted according to preset template data and the first point cloud data statistical chart after receiving the lifting signal of the container.

[0012] A third aspect of the present application provides a container crane, comprising:

[0013] Container crane body;

[0014] A lifting device is installed on the container crane body; wherein the lifting device is configured to implement the above-mentioned anti-lifting method for the container transport vehicle.

[0015] A fourth aspect of the present application provides a container crane automation system, comprising:

[0016] A three-dimensional laser sensor for acquiring three-dimensional point cloud data of a target area including the container transport vehicle;

[0017] And a container crane, wherein the lifting equipment of the container crane is communicatively connected with the three-dimensional laser sensor.

[0018] One embodiment of the above application has the following advantages or beneficial effects: the acquired three-dimensional point cloud data of the container transport vehicle is separated to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle, and the three-dimensional point cloud data of the target area of ​​the container transport vehicle is projected to a first direction perpendicular to the ground to obtain a first point cloud data statistical graph. Since the target area includes the deck of the container transport vehicle, the first point cloud data statistical graph can reflect the height of the deck. Therefore, after receiving the lifting signal of the container, the height change of the deck can be determined based on the preset template data and the first point cloud data statistical graph, thereby accurately determining whether the container transport vehicle is lifted, thereby ensuring the safety of the container transport vehicle operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0020] Figure 1 A schematic flow chart of a method for preventing lifting of a container transport vehicle provided in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of a container and a container transport vehicle provided in one embodiment of the present application;

[0022] Figure 3 A schematic flow chart of a method for preventing lifting of a container transport vehicle provided in another embodiment of the present application;

[0023] Figure 4A schematic flow chart of a method for preventing lifting of a container transport vehicle provided in another embodiment of the present application;

[0024] Figure 5 A schematic diagram of a specific process of a method for preventing a container transport vehicle from lifting provided in one embodiment of the present application;

[0025] Figure 6 A schematic structural diagram of an anti-lifting device for a container transport vehicle provided in one embodiment of the present application;

[0026] Figure 7 A schematic diagram of the structure of a container crane provided in an embodiment of the present application;

[0027] Figure 8 The present invention is a block diagram of a device for implementing the anti-lifting method for a container transport vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] Exemplary Methods

[0030] Figure 1 1 is a flow chart of a method for preventing a container transport vehicle from lifting according to an embodiment of the present disclosure. In an exemplary embodiment, a method for preventing a container transport vehicle from lifting is provided, comprising:

[0031] S110, separating the acquired three-dimensional point cloud data of the container transport vehicle 2, and determining the three-dimensional point cloud data of the target area of ​​the container transport vehicle 2; wherein the target area includes: the vehicle plate 21 of the container transport vehicle 2;

[0032] S120, projecting the three-dimensional point cloud data of the target area to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground;

[0033] S130. After receiving the lifting signal of the container 1, determine that the container transport vehicle 2 is lifted according to the preset template data and the first point cloud data statistical chart.

[0034] Step S110, illustratively, Figure 2As shown, the target area includes the plate 21 of the container transport vehicle 2 and the part below the plate 21 of the container transport vehicle 2 (for example, tires, chassis, etc.). Optionally, the target area of ​​the container transport vehicle 2 can be scanned by a three-dimensional laser sensor 3 to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle 2; or the three-dimensional laser sensor 3 can be used to scan the container transport vehicle 2 to obtain the three-dimensional point cloud data of the container transport vehicle 2, and then the three-dimensional point cloud data of the container transport vehicle 2 is screened to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle 2. Optionally, the three-dimensional laser sensor 3 can be a 3D laser radar, or other sensors that can collect point cloud data. The installation height requirement of the 3D laser radar is that it can scan the plate 21 of the container transport vehicle 2. It can be installed on the container crane leg or independently installed on the bracket, which is not limited here. For example, the installation height of the 3D laser radar can be set to be consistent with the height of the plate 21 of the container transport vehicle 2 when it is not lifted.

[0035] In this embodiment, a 3D laser radar is installed on the side of the field bridge lane so that the 3D laser radar can scan the area where the vehicle plate 21 of the container transport vehicle 2 is located (i.e., the target area). At this time, the 3D point cloud data of the container transport vehicle 2 collected by the 3D laser radar includes the 3D point cloud data corresponding to the container 1 and the 3D point cloud data corresponding to the vehicle plate 21 and the parts below it. The collected 3D point cloud data of the container transport vehicle 2 is distinguished according to the distribution of the point cloud data to obtain the 3D point cloud data corresponding to the vehicle plate 21 and the parts below it (i.e., the target area).

[0036] In step S120, illustratively, the first point cloud data statistical graph is used to represent the distribution of the three-dimensional point cloud data of the target area in the first direction. The first point cloud data statistical graph can be a histogram or a line graph. In this embodiment, the three-dimensional point cloud data of the target area is projected to the first direction perpendicular to the ground to obtain a corresponding histogram, so as to determine the height of each area in the target area in the histogram.

[0037] In step S130, illustratively, the lifting signal is used to indicate a signal for lifting the container 1 from the container transport vehicle 2. The preset template data is used to indicate the standard height of the vehicle plate 21. The preset template data may be the number of point cloud data of each region and the corresponding height when the height of the vehicle plate 21 is the standard height, which is pre-stored. It may also be a statistical graph of point cloud data of each region when the height of the vehicle plate 21 is the standard height. In this way, by comparing the preset template data with the first point cloud data statistical graph and determining the height change of the vehicle plate 21, it is possible to determine whether the container transport vehicle 2 is lifted.

[0038] In the technical solution of the present application, the acquired three-dimensional point cloud data of the container transport vehicle 2 is separated, the three-dimensional point cloud data of the target area of ​​the container transport vehicle 2 is determined, and the detected three-dimensional point cloud data of the target area of ​​the container transport vehicle 2 is projected to a first direction perpendicular to the ground to obtain a first point cloud data statistical graph. Since the target area includes the vehicle plate 21 of the container transport vehicle 2, the first point cloud data statistical graph can reflect the height of the vehicle plate 21. Therefore, after receiving the lifting signal of the container 1, the height change of the vehicle plate 21 can be determined according to the preset template data and the first point cloud data statistical graph, thereby accurately determining whether the container transport vehicle 2 is lifted, thereby ensuring the safety of the operation of the container transport vehicle 2.

[0039] In one embodiment, the step of separating the acquired three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle includes:

[0040] Determine the three-dimensional point cloud data of the container in the acquired three-dimensional point cloud data of the container transport vehicle;

[0041] The three-dimensional point cloud data of the container is used to screen the three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle.

[0042] For example, the three-dimensional point cloud data of the container transport vehicle 2 can be scanned by a 3D laser radar. Since it is necessary to determine the area where the plate 21 of the container transport vehicle 2 is located, that is, the three-dimensional point cloud data of the target area, in order to determine whether the container transport vehicle 2 is lifted, it is necessary to separate the three-dimensional point cloud data of the container 1 and determine the three-dimensional point cloud data of the target area. Since the container 1 is a whole closed entity, the distribution of the obtained point cloud data should be relatively uniform, while the distribution of the point cloud data corresponding to the area where the plate 21 of the container transport vehicle 2 and the parts below it are located should be uneven. Therefore, the three-dimensional point cloud data of the area where the plate 21 and the parts below it are located can be distinguished in the three-dimensional point cloud data of the container transport vehicle 2 according to the sparse distribution of the three-dimensional point cloud data.

[0043] Furthermore, since the uniform area is relatively easy to determine, the area where the 3D point cloud data is uniformly distributed is first determined as container 1. Then, the 3D point cloud data corresponding to container 1 is filtered out from the 3D point cloud data of the container transport vehicle 2, thereby obtaining the 3D point cloud data corresponding to the vehicle plate 21 and the part below it (i.e., the target area), further ensuring the accuracy of the 3D point cloud data of the separated target area.

[0044] In one embodiment, if Figure 3As shown, the three-dimensional point cloud data of the target area is projected to a first direction to obtain a first point cloud data statistical graph, including:

[0045] S310, determining feature three-dimensional point cloud data corresponding to at least one feature area in the target area from the three-dimensional point cloud data of the target area;

[0046] S320: Project the characteristic three-dimensional point cloud data to a first direction to obtain a first point cloud data statistical graph.

[0047] Exemplarily, there may be one or more characteristic regions in the target area. The characteristic region is an area where the three-dimensional point cloud data is densely distributed. The characteristic region is used to reflect the target position with obvious characteristics in the vehicle plate 21 and the part below it. Optionally, the characteristic region can be determined by inputting the three-dimensional point cloud data of the target area into a pre-trained neural network model, or by clustering the three-dimensional point cloud data to determine the characteristic region. For example, when the 3D laser radar scans the side of the container transport vehicle 2, multiple tires are scanned, and the three-dimensional point cloud data in the area where the tires are located is densely distributed, so there may be multiple characteristic regions.

[0048] After determining the three-dimensional point cloud data corresponding to multiple feature areas, a first point cloud data statistical graph is determined based on the three-dimensional point cloud data corresponding to each feature area, so that different feature areas can be compared with the corresponding template data to more accurately determine whether the container transport vehicle 2 is lifted.

[0049] In one embodiment, if Figure 4 As shown, the determining of feature three-dimensional point cloud data corresponding to at least one feature area in the target area from the three-dimensional point cloud data of the target area includes:

[0050] S410, projecting the three-dimensional point cloud data of the target area to a second direction to obtain a second point cloud data statistical graph; wherein the second direction is parallel to the lane of the container transport vehicle 2;

[0051] S420, determining at least one characteristic region in the target region according to the second point cloud data statistical graph;

[0052] S430: Determine the characteristic three-dimensional point cloud data in the three-dimensional point cloud data of the target area by using the at least one characteristic area.

[0053] Exemplarily, the second point cloud data statistical graph is used to represent the distribution of the three-dimensional point cloud data of the target area in the second direction. Optionally, the second point cloud data statistical graph can be a histogram, a line graph, etc. Specifically, by projecting the three-dimensional point cloud data of the target area to a direction parallel to the lane of the container transport vehicle 2, a second point cloud data statistical graph is obtained, and the characteristic area can be determined by the distribution of the point cloud data in the statistical graph. For example, an area where the number of point cloud data exceeds a preset number threshold can be considered to be a location with obvious features under the vehicle plate 21 (i.e., a characteristic area). Therefore, the characteristic three-dimensional point cloud data can be determined more accurately based on the determined characteristic area.

[0054] In one embodiment, projecting the characteristic three-dimensional point cloud data to a first direction to obtain a first point cloud data statistical graph includes:

[0055] The feature three-dimensional point cloud data corresponding to each of the feature areas is projected onto the Z axis of a preset first coordinate system, and the coordinates of the feature three-dimensional point cloud data are counted to obtain a first point cloud histogram; wherein the Z axis of the preset first coordinate system is the first direction.

[0056] Exemplarily, the Z axis of the first coordinate system is preset to be perpendicular to the ground. Preferably, the Z axis of the first coordinate system is preset to be perpendicular to the ground, the X axis is perpendicular to the container transport vehicle 2, and the Y axis is parallel to the lane.

[0057] Specifically, the three-dimensional point cloud data corresponding to each feature area is projected onto the Z axis, and multiple data intervals can be divided on the Z axis, and the coordinates of the three-dimensional point cloud data in each interval are counted. A first point cloud histogram is generated based on multiple data intervals and the number of three-dimensional point cloud data corresponding to each data interval. It can be understood that when there are multiple feature areas, there are multiple first point cloud histograms. In this way, each first point cloud histogram can be compared with its corresponding template data, so that the situation of the container transport vehicle 2 being lifted can be more comprehensively determined. At the same time, when the container transport vehicle 2 is lifted, the part of the container transport vehicle 2 that is lifted can be determined.

[0058] In one embodiment, projecting the three-dimensional point cloud data of the target area to a second direction to obtain a second point cloud data statistical graph includes:

[0059] The three-dimensional point cloud data of the target area is projected onto the Y-axis of a preset second coordinate system, and the coordinates of the feature three-dimensional point cloud data are counted to obtain a second point cloud histogram; wherein the Y-axis of the preset second coordinate system is the second direction.

[0060] Exemplarily, the Y axis of the preset second coordinate system is parallel to the lane. Preferably, the preset second coordinate system is the same as the preset first coordinate system, with the Z axis perpendicular to the ground, the X axis perpendicular to the container transport vehicle 2, and the Y axis parallel to the lane.

[0061] Specifically, after acquiring the three-dimensional point cloud data of the target area, the coordinate system of the three-dimensional point cloud data of the target area is transformed so that the Z axis is perpendicular to the ground, the X axis is perpendicular to the container transport vehicle 2, and the Y axis is parallel to the lane. That is, the coordinate system of the three-dimensional point cloud data is consistent with the preset second coordinate system. The three-dimensional point cloud data of the target area is projected onto the Y axis, and multiple data intervals can be divided on the Y axis, and the coordinates of the three-dimensional point cloud data in each interval are counted. According to the multiple data intervals and the number of three-dimensional point cloud data corresponding to each data interval, a second point cloud histogram is generated, so that the feature area can be intuitively determined according to the data intervals with obvious concave and convex in the second point cloud histogram.

[0062] In one embodiment, when N first point cloud histograms are obtained, correspondingly, determining that the container transport vehicle 2 is lifted according to the preset template data and the first point cloud data statistical graph includes:

[0063] For the i-th first point cloud histogram among N, determine the corresponding j-th template data;

[0064] The moving distance of the container transport vehicle 2 is determined according to the i-th first point cloud histogram and the j-th template data; wherein N, i, and j are all positive integers, and i is not greater than N.

[0065] When the moving distance exceeds a preset threshold, the container transport vehicle 2 is lifted.

[0066] Exemplarily, each first point cloud histogram may correspond to a template data. Optionally, each first point cloud histogram may correspond to a reference histogram.

[0067] Optionally, when the template data is a reference histogram, determining the moving distance of the container transport vehicle 2 according to the i-th first point cloud histogram and the j-th template data includes:

[0068] Determine the reference data interval with the highest number of three-dimensional point cloud data in the jth reference histogram, match the reference data interval with the array in the first point cloud histogram, and determine the target data interval corresponding to the array with the highest similarity. Calculate the moving distance of the container transport vehicle 2 based on the reference data interval and the target data interval.

[0069] In this embodiment, two first point cloud histograms are used as an example for explanation, and two reference histograms are compared with the two first point cloud histograms to determine the moving distances of the two container transport vehicles 2. The moving distances of the two container transport vehicles 2 are compared with the preset thresholds. If the moving distance does not exceed the preset threshold, it means that the container transport vehicle 2 is not lifted; if the moving distance exceeds the preset threshold, it means that the container transport vehicle 2 is lifted, and an alarm message is issued in time so that the staff can handle it in time.

[0070] In one embodiment, the method further comprises:

[0071] The lifted portion of the container transport vehicle 2 is determined according to the feature area corresponding to the i-th first point cloud histogram.

[0072] Exemplarily, according to the feature area corresponding to the i-th first point cloud histogram, the three-dimensional point cloud data in the feature area is determined, and then the specific part of the container transport vehicle is restored based on the three-dimensional point cloud data, so that the part where the container transport vehicle 2 is lifted can be accurately determined.

[0073] In one embodiment, the method further comprises:

[0074] When the landing signal of the container 1 is received, target template data is determined according to the data in the first point cloud data statistical graph and the preset template data.

[0075] Exemplarily, the box loading signal is used to indicate that the container 1 is loaded on the container transporter 2. When the container transporter 2 is loading, the box loading signal is received. At this time, the container transporter 2 is in an unlifted state, so the first point cloud data statistical graph obtained from the three-dimensional point cloud data can represent the point cloud data distribution of the container transporter 2 in the unlifted target area. If there is no template data in the device, the first point cloud data statistical graph obtained is directly used as the template data. If the template data already exists in the device, the data in the first point cloud data statistical graph and the template data are averaged to obtain the latest template data, thereby continuously optimizing the template data to obtain more accurate template data.

[0076] In order to more thoroughly understand the features and technical contents of the embodiments of the present application, a specific application example is provided below for illustration. It should be understood that the following application example is only for reference and does not limit the specific implementation process.

[0077] In an application example, Figure 5 As shown, the anti-lifting method of the container transport vehicle 2 may include:

[0078] 1. Obtain the three-dimensional point cloud data collected by the 3D laser radar, and perform coordinate rotation transformation on the three-dimensional point cloud data so that the Z axis is perpendicular to the ground, the X axis is perpendicular to the container truck, and the Y axis is parallel to the lane; wherein, the 3D laser radar is set on the wall of the lane of the field bridge, and the scanning range of the 3D laser radar includes the vehicle plate 21 and the parts below it.

[0079] 2. According to the distribution of the point cloud data of the container 1 and the vehicle plate 21, the three-dimensional point cloud data of the vehicle plate 21 and the parts below are obtained in the three-dimensional point cloud data.

[0080] 3. Project the three-dimensional point cloud data of the vehicle plate 21 and the parts below in the Y-axis direction to obtain a point cloud histogram in the Y-axis direction (ie, a second point cloud histogram).

[0081] 4. The point cloud histogram in the Y-axis direction is tested to determine two intervals with obvious features, which are divided into the left histogram and the right histogram. Among them, the interval with obvious features is the interval with obvious concavity and convexity.

[0082] 5. Obtain the 3D point cloud data corresponding to the left histogram and the right histogram respectively, and perform statistics in the Z-axis direction to obtain the left histogram in the Z-axis direction and the right histogram in the Z-axis direction.

[0083] 6. If a box landing signal is received, the left histogram in the Z-axis direction and the right histogram in the Z-axis direction are stored as reference histograms. If there is already a stored reference histogram, the histograms are accumulated and averaged to obtain a histogram with mean conditions, which is used as the reference histogram.

[0084] 7. If a lifting signal is received, the left histogram in the Z-axis direction is compared with the reference histogram on the left to determine the moving distance of the feature on the left. The right histogram in the Z-axis direction is compared with the reference histogram on the right to determine the moving distance of the feature on the right. Taking the left histogram in the Z-axis direction as an example, the reference interval with the largest amount of point cloud data is selected in the reference histogram on the left, and sliding matching is performed according to the left histogram of the reference interval in the Z-axis direction. The interval with the closest point cloud data volume to the reference interval in the left histogram in the Z-axis direction is determined as the target interval. Then the reference interval is subtracted from the target interval to determine the moving distance of the feature on the left (i.e., the moving distance of the container transport vehicle 2). Then determine whether the moving distance exceeds the preset threshold, wherein the preset threshold can be 5 cm, and the preset threshold can also be set according to the actual situation, which is not limited here. If it does not exceed, it means that the container transport vehicle 2 has not been lifted. If it exceeds, it means that the container transport vehicle 2 has been lifted, and an alarm is issued. Similarly, the right histogram in the Z-axis direction also uses the same method to determine the moving distance of the feature on the right. It can be understood that an alarm will be triggered if the moving distance corresponding to any one of the left histogram in the Z-axis direction and the right histogram in the Z-axis direction exceeds a preset threshold.

[0085] Exemplary Devices

[0086] Correspondingly, such as Figure 6 As shown, the embodiment of the present application also provides an anti-lifting device for a container transport vehicle, comprising:

[0087] The acquisition module 610 is used to separate the acquired three-dimensional point cloud data of the container transport vehicle and determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle; wherein the target area includes: the vehicle plate of the container transport vehicle;

[0088] The projection module 620 is used to project the three-dimensional point cloud data of the target area to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground;

[0089] The processing module 630 is used to determine whether the container transport vehicle is lifted according to the preset template data and the first point cloud data statistical chart after receiving the lifting signal of the container.

[0090] In one implementation, the projection module 620 is further configured to:

[0091] Determining, from the three-dimensional point cloud data of the target area, feature three-dimensional point cloud data corresponding to at least one feature area in the target area;

[0092] The characteristic three-dimensional point cloud data is projected to a first direction to obtain a first point cloud data statistical graph.

[0093] In one embodiment, determining feature three-dimensional point cloud data corresponding to at least one feature area in the target area from the three-dimensional point cloud data of the target area includes:

[0094] Projecting the three-dimensional point cloud data of the target area to a second direction to obtain a second point cloud data statistical graph; wherein the second direction is parallel to the lane of the container transport vehicle;

[0095] Determine at least one characteristic area in the target area according to the second point cloud data statistical graph;

[0096] The characteristic three-dimensional point cloud data in the three-dimensional point cloud data of the target area is determined by using the at least one characteristic area.

[0097] In one embodiment, projecting the characteristic three-dimensional point cloud data to a first direction to obtain a first point cloud data statistical graph includes:

[0098] The feature three-dimensional point cloud data corresponding to each of the feature areas is projected onto the Z axis of a preset first coordinate system, and the coordinates of the feature three-dimensional point cloud data are counted to obtain a first point cloud histogram; wherein the Z axis of the preset first coordinate system is the first direction.

[0099] In one embodiment, projecting the three-dimensional point cloud data of the target area to a second direction to obtain a second point cloud data statistical graph includes:

[0100] The three-dimensional point cloud data of the target area is projected onto the Y-axis of a preset second coordinate system, and the coordinates of the feature three-dimensional point cloud data are counted to obtain a second point cloud histogram; wherein the Y-axis of the preset second coordinate system is the second direction.

[0101] In one implementation, when N first point cloud histograms are obtained, correspondingly, the processing module 630 is further used to:

[0102] For the i-th first point cloud histogram among N, determine the corresponding j-th template data;

[0103] Determine the moving distance of the container 1 transport vehicle according to the i-th first point cloud histogram and the j-th template data;

[0104] When the moving distance exceeds a preset threshold, the container 1 transport vehicle is lifted.

[0105] In one embodiment, the device further comprises:

[0106] The lifted portion of the container transport vehicle is determined according to the feature area corresponding to the i-th first point cloud histogram.

[0107] In one embodiment, the device further comprises:

[0108] The determination module is used to determine the target template data according to the data in the first point cloud data statistical map and the preset template data when receiving the container landing signal.

[0109] In one implementation, the acquisition module 610 is further configured to:

[0110] Determine the three-dimensional point cloud data of the container in the acquired three-dimensional point cloud data of the container transport vehicle;

[0111] The three-dimensional point cloud data of the container is used to screen the three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle.

[0112] The device provided in this embodiment belongs to the same application concept as the method provided in the above embodiment of this application, can execute the method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of the execution method. For the technical details not fully described in this embodiment, please refer to the specific processing content of the method provided in the above embodiment of this application, which will not be repeated here.

[0113] Exemplary Systems

[0114] Figure 7 This is a structural block diagram of a container crane provided by an exemplary embodiment of the present application. Figure 7 As shown, the container crane provided by the present application may include:

[0115] Container crane body 4;

[0116] The lifting device 5 is installed on the container crane body 4; wherein the lifting device 5 is configured to execute the aforementioned anti-lifting method for the container transport vehicle.

[0117] The container crane provided in the present application, the lifting equipment 5 can determine a first point cloud data statistical graph that can reflect the height of the vehicle plate 21, so that after receiving the lifting signal of the container 1, the height change of the vehicle plate 21 is determined according to the preset template data and the first point cloud data statistical graph, and it can be accurately determined whether the container transport vehicle 2 is lifted. Then, the container crane can stop the operation in time when the container transport vehicle 2 is lifted, thereby ensuring the safety of the operation of the container transport vehicle 2.

[0118] An exemplary embodiment of the present application provides a container crane automation system. Figure 7 As shown, the system may include:

[0119] A three-dimensional laser sensor 3 is used to obtain three-dimensional point cloud data of a target area including the container transport vehicle 2;

[0120] And a container crane, wherein the lifting equipment 5 of the container crane is communicatively connected with the three-dimensional laser sensor 3 .

[0121] It should be noted that the number of the three-dimensional laser sensors 3 can be one or more, and the specific number is set according to actual needs. The three-dimensional laser sensor 3 can be installed on the container crane body 4.

[0122] In the container crane automation system provided by the present application, the hoisting equipment 5 of the container crane obtains the three-dimensional point cloud data of the target area including the container transport vehicle detected by the three-dimensional laser sensor, and determines the first point cloud data statistical graph that can reflect the height of the vehicle plate 21. Therefore, after receiving the lifting signal of the container 1, the height change of the vehicle plate 21 can be determined according to the preset template data and the first point cloud data statistical graph, and it can be determined whether the container transport vehicle 2 is lifted. Then, the container crane can stop the operation in time when the container transport vehicle 2 is lifted, ensuring the safety of the operation of the container transport vehicle 2.

[0123] Another embodiment of the present application also provides a lifting device, see Figure 8 As shown, the device includes:

[0124] Memory 731 and processor 732;

[0125] The memory 731 is connected to the processor 732 and is used to store programs;

[0126] The processor 732 is used to implement the anti-lifting method for the container transport vehicle disclosed in any of the above embodiments by running the program stored in the memory 731.

[0127] Specifically, the above-mentioned lifting equipment may also include: a bus, a communication interface 733 , an input device 734 and an output device 735 .

[0128] The processor 732, the memory 731, the communication interface 733, the input device 734 and the output device 735 are connected to each other via a bus.

[0129] A bus may include a pathway that transfers information between components of a computer system.

[0130] The processor 732 may 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 for controlling the execution of the program of the scheme of the present invention. It may also be 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, discrete hardware components.

[0131] The processor 732 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0132] The memory 731 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include a program code, and the program code includes a computer operation instruction. More specifically, the memory 731 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0133] The input device 734 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0134] Output device 735 may include devices that allow information to be output to a user, such as a display screen, printer, speakers, etc.

[0135] The communication interface 733 may include any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0136] The processor 732 executes the program stored in the memory 731 and calls other devices, which can be used to implement each step of the anti-lifting method for any container transport vehicle provided in the above embodiments of the present application.

[0137] Exemplary computer program products and storage media

[0138] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the anti-lifting method for a container transport vehicle according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0139] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0140] In addition, an embodiment of the present application may also be a storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the control method of the smart door lock according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0141] The specific working contents of the above-mentioned electronic device, as well as the specific working contents of the above-mentioned computer program product and the computer program on the storage medium when being executed by the processor, can all be referred to the contents of the above-mentioned method embodiments, which will not be repeated here.

[0142] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0143] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0144] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0145] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0146] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0147] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0149] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0150] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0151] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0152] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for preventing a container transport vehicle from lifting, characterized in that: include: Separating the acquired three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle; wherein the target area includes: the vehicle plate of the container transport vehicle; Projecting the three-dimensional point cloud data of the target area to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground, and the first point cloud data statistical graph can reflect the height of the vehicle plate of the container transport vehicle; After receiving the lifting signal of the container, determining that the container transport vehicle is lifted according to the preset template data and the first point cloud data statistical graph; the lifting signal is used to indicate a signal for lifting the container from the container transport vehicle; the preset template data is used to indicate a standard height of the vehicle plate of the container transport vehicle; The step of separating the acquired three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle includes: Determine the three-dimensional point cloud data of the container in the acquired three-dimensional point cloud data of the container transport vehicle; The three-dimensional point cloud data of the container is used to screen the three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle.

2. The method according to claim 1, characterized in that The step of projecting the three-dimensional point cloud data of the target area to a first direction to obtain a first point cloud data statistical graph includes: Determining, from the three-dimensional point cloud data of the target area, feature three-dimensional point cloud data corresponding to at least one feature area in the target area; The characteristic three-dimensional point cloud data is projected to a first direction to obtain a first point cloud data statistical graph.

3. The method according to claim 2, characterized in that The determining, from the three-dimensional point cloud data of the target area, feature three-dimensional point cloud data corresponding to at least one feature area in the target area comprises: Projecting the three-dimensional point cloud data of the target area to a second direction to obtain a second point cloud data statistical graph; wherein the second direction is parallel to the lane of the container transport vehicle; Determine at least one characteristic area in the target area according to the second point cloud data statistical graph; The characteristic three-dimensional point cloud data in the three-dimensional point cloud data of the target area is determined by using the at least one characteristic area.

4. The method according to claim 3, characterized in that The projecting the characteristic three-dimensional point cloud data to a first direction to obtain a first point cloud data statistical graph includes: The feature three-dimensional point cloud data corresponding to each of the feature areas is projected onto the Z axis of a preset first coordinate system, and the coordinates of the feature three-dimensional point cloud data are counted to obtain a first point cloud histogram; wherein the Z axis of the preset first coordinate system is the first direction.

5. The method according to claim 3, characterized in that: The step of projecting the three-dimensional point cloud data of the target area to a second direction to obtain a second point cloud data statistical graph includes: The three-dimensional point cloud data of the target area is projected onto the Y-axis of a preset second coordinate system, and the coordinates of the feature three-dimensional point cloud data are counted to obtain a second point cloud histogram; wherein the Y-axis of the preset second coordinate system is the second direction.

6. The method according to claim 4, characterized in that When N first point cloud histograms are obtained, correspondingly, determining that the container transport vehicle is lifted according to the preset template data and the first point cloud data statistical graph includes: For the i-th first point cloud histogram among N, determine the corresponding j-th template data; Determining a moving distance of the container transport vehicle according to the i-th first point cloud histogram and the j-th template data; When the moving distance exceeds a preset threshold, the container transport vehicle is lifted; wherein N, i, and j are all positive integers, and i is not greater than N.

7. The method according to claim 6, characterized in that The method further comprises: The lifted portion of the container transport vehicle is determined according to the feature area corresponding to the i-th first point cloud histogram.

8. The method according to claim 1, characterized in that The method further comprises: When the container landing signal is received, target template data is determined according to the data in the first point cloud data statistical graph and the preset template data.

9. An anti-lifting device for a container transport vehicle, characterized in that: include: An acquisition module is used to separate the acquired three-dimensional point cloud data of the container transport vehicle and determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle; wherein the target area includes: a vehicle plate of the container transport vehicle; A projection module, used to project the three-dimensional point cloud data of the target area to a first direction to obtain a first point cloud data statistical graph; wherein the first direction is perpendicular to the ground, and the first point cloud data statistical graph can reflect the height of the vehicle plate of the container transport vehicle; A processing module, configured to determine that the container transport vehicle is lifted after receiving a lifting signal of the container, according to preset template data and the first point cloud data statistical graph; the lifting signal is used to indicate a signal for lifting the container from the container transport vehicle; the preset template data is used to indicate a standard height of a vehicle plate of the container transport vehicle; The step of separating the acquired three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle includes: Determine the three-dimensional point cloud data of the container in the acquired three-dimensional point cloud data of the container transport vehicle; The three-dimensional point cloud data of the container is used to screen the three-dimensional point cloud data of the container transport vehicle to determine the three-dimensional point cloud data of the target area of ​​the container transport vehicle.

10. A container crane, characterized in that: include: Container crane body; A lifting device is installed on the container crane body; wherein the lifting device is configured to execute the anti-lifting method of the container transport vehicle according to any one of claims 1 to 8.

11. A container crane automation system, characterized in that: include: A three-dimensional laser sensor for acquiring three-dimensional point cloud data of a target area including the container transport vehicle; And the container crane according to claim 10, wherein the lifting equipment of the container crane is communicatively connected to the three-dimensional laser sensor.

Images