Container alignment control method, device, equipment and gantry crane

Abstract

The embodiment of the application provides a container alignment control method, device and equipment and a yard crane, and relates to the technical field of port equipment. The method comprises the following steps: receiving radar data acquired by all laser radar sensors, transforming the radar data to a coordinate system in which a spreader center is located, and performing splicing processing on the radar data after the transformation processing to obtain three-dimensional point cloud data; based on the point cloud data, determining a height difference between corresponding end points of the spreader and a target container; and based on the height difference, generating and outputting a spreader control instruction. The method of the application effectively solves the problem of insufficient accuracy of alignment control based on a laser sensor.

Description

Technical Field

[0001] This application relates to the field of port equipment technology, and in particular to a container positioning control method, device, equipment and yard crane. Background Technology

[0002] In container terminals or yards, gantry cranes are a primary piece of equipment for handling and stacking containers. Gantry cranes move horizontally across the yard via a gantry crane, using spreaders to grab and unload containers. A trolley then drives the spreaders to move laterally and vertically in a direction perpendicular to the gantry crane's trajectory. During container grabbing and unloading, the control system in the trolley needs to control the spreader's movement to ensure the speed and distance of contact between the spreader and the container, and between the clamped container and the target container waiting to be loaded, thus avoiding potential collisions between the spreader and the container, and between containers themselves. This process is also known as container alignment.

[0003] In related technologies, container alignment control can be achieved by monitoring with laser sensors installed on the spreader. However, since there is usually some degree of shaking during the container alignment process, the accuracy of the monitoring results is insufficient, and the goal of soft landing of the container cannot be achieved. Summary of the Invention

[0004] This application provides a container alignment control method, apparatus, equipment, and yard crane to solve the problem of insufficient accuracy in alignment control based on laser sensors in related technologies.

[0005] In a first aspect, this application provides a container positioning control method, comprising:

[0006] Receive radar data acquired by all lidar sensors, wherein the lidar sensors are set at the bottom of the trolley corresponding to the lifting device, and there are at least two lidar sensors located at different positions on the bottom of the trolley;

[0007] The radar data is transformed to the coordinate system of the center of the spreader, and the transformed radar data is stitched together to obtain three-dimensional point cloud data.

[0008] Based on point cloud data, determine the height difference between the corresponding endpoints of the spreader and the target container;

[0009] Based on the height difference, spreader control commands are generated and output. These commands are used to control the spreader to complete the alignment process with the target container.

[0010] Secondly, this application provides a container positioning control device, comprising:

[0011] Different positions of the part;

[0012] The processing module is used to transform radar data to the coordinate system of the center of the spreader, and to stitch the transformed radar data to obtain three-dimensional point cloud data.

[0013] The analysis module is used to determine the height difference between the corresponding endpoints of the spreader and the target container based on point cloud data;

[0014] The output module is used to generate and output spreader control commands based on the height difference. These spreader control commands are used to control the spreader to complete the alignment process with the target container.

[0015] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0016] The memory stores computer-executed instructions;

[0017] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0018] Fourthly, embodiments of this application provide a yard crane, the yard crane including a container positioning control device as described in the second aspect and / or various possible implementations of the second aspect.

[0019] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0020] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0021] The container positioning control method, apparatus, equipment, and yard crane provided in this disclosure receive radar data acquired by all lidar sensors, transform the radar data to the coordinate system of the spreader center, and stitch the transformed radar data to obtain three-dimensional point cloud data. Based on the point cloud data, the height difference between the corresponding endpoints of the spreader and the target container is determined. Based on the height difference, spreader control commands are generated and output. Thus, by setting up and processing data from multiple lidar sensors, the accuracy of spreader positioning control is significantly improved. By transforming the data from multiple lidar sensors to a unified spreader center coordinate system and stitching it together to generate high-precision three-dimensional point cloud data, the system can accurately identify the height difference between the spreader and the target container. The spreader control commands generated based on this height difference ensure precise adjustment of the spreader during the positioning process, reduce monitoring errors caused by swaying, and achieve the goal of soft landing of the container. This solution not only improves the accuracy and reliability of positioning control but also enhances the system's adaptability in dynamic environments and optimizes the operational efficiency of port logistics. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] Figure 1 This is an application scenario diagram of the container positioning control method provided in the embodiments of this disclosure;

[0024] Figure 2 A flowchart of a container positioning control method provided in one embodiment of this disclosure;

[0025] Figure 3 A flowchart of a container positioning control method provided in yet another embodiment of this disclosure;

[0026] Figure 4 A schematic diagram of the structure of a container positioning control device provided in yet another embodiment of this disclosure;

[0027] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present disclosure;

[0028] Figure 6 This is a schematic diagram of the structure of a field bridge provided in yet another embodiment of this disclosure.

[0029] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0032] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0033] It should be noted that the container positioning control method, device, equipment, and yard crane provided in this application can be used in the field of port equipment technology, port logistics field, and any other field. The application field of the container positioning control method, device, equipment, and yard crane in this application is not limited.

[0034] Figure 1 This is a schematic diagram illustrating an application scenario of the spreader attitude control method provided in this application, such as... Figure 1 As shown, during the spreader attitude control process, when the gantry crane 100 is grabbing and releasing a container, it will collect data on the spreader 120 and the target container 130 through the lidar sensor 110 to control the movement of the spreader 120 and complete the alignment control between the spreader 120 and the target container 130.

[0035] It should be noted that, Figure 1The scenario shown includes a gantry crane, image sensor, lidar sensor, spreader, and target container. Only one or a specific number of these are used as examples for illustration, but this disclosure is not limited to this. That is to say, the number of gantry cranes, image sensors, lidar sensors, spreaders, and target containers can be arbitrary.

[0036] In port logistics, gantry cranes are key equipment for container handling and stacking, and their efficient operation directly impacts the overall operational efficiency of the terminal. Gantry cranes move horizontally across the container yard via yard cranes and use spreaders to grab and unload containers. To ensure the safe and accurate placement of containers, the movement of the spreader must be precisely controlled, especially during container alignment. During alignment, the spreader needs to maintain an appropriate contact speed and movement distance with the target container to avoid collisions and damage. However, traditional laser sensor-based alignment control methods are insufficient in accuracy due to potential swaying of the spreader during movement. Laser sensors are typically mounted on the spreader to monitor the distance between the spreader and the container. However, spreader swaying can cause instability in the laser sensor measurements, thus affecting the accuracy of alignment control.

[0037] In practical applications, laser sensors perform well under static conditions, but their measurement results are easily interfered with when the lifting device sways. Therefore, positioning problems based on lidar sensors are difficult to detect. Furthermore, solving this technical problem requires developing a control system that can operate stably in complex environments while maintaining both real-time performance and accuracy. Traditional laser sensor methods struggle to handle measurement errors caused by lifting device sway, necessitating the introduction of new technologies to improve the accuracy of positioning control. This requires efficient data processing capabilities and precise algorithm support. Therefore, current technologies lack a solution that can meet the demands of complex environments while maintaining the accuracy of calculation results.

[0038] The container positioning control method provided in this application transforms data from multiple lidar sensors to the spreader's central coordinate system and performs stitching processing to generate high-precision 3D point cloud data. Based on this data, the system can accurately calculate the height difference between the spreader and the target container. Subsequently, corresponding control commands are generated to ensure precise adjustment of the spreader during positioning. This method effectively overcomes the measurement errors caused by the sway of traditional lidar sensors, achieving more stable positioning control.

[0039] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0040] Figure 2 Flowchart of the container alignment control method provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0041] S201, Receive radar data acquired by all lidar sensors.

[0042] The lidar sensor is installed at the bottom of the trolley corresponding to the lifting device, and there are at least two lidar sensors located at different positions on the bottom of the trolley.

[0043] Specifically, this embodiment is used to provide an overall description of the main steps of the container alignment control method.

[0044] In this embodiment, the executing entity is a controller in the gantry crane that controls the position and movement of the trolley, crane, and lifting device of the yard bridge and can acquire data from the lidar sensor installed on the crane. Specifically, it can be a control system installed in the crane or a cloud server that communicates with the control system in the gantry crane. For ease of explanation, it is referred to here as the control system or the system.

[0045] During container alignment, the system receives radar data from multiple LiDAR sensors mounted on the bottom of the corresponding spreader trolley. A LiDAR sensor is a device capable of providing high-precision distance measurements, typically acquiring three-dimensional information about the surrounding environment by emitting a laser beam and detecting the reflected signal.

[0046] In this design, at least two lidar sensors are installed at different locations on the bottom of the trolley to ensure comprehensive coverage of the environment around the lifting device. For example, four lidar sensors are installed symmetrically at the four corners of the bottom of the trolley, or two lidar sensors are installed in groups of two, with the two groups symmetrically installed at the edges or diagonals of the bottom of the trolley.

[0047] The process of receiving radar data needs to ensure data integrity and real-time performance in order to enable rapid response in dynamic environments.

[0048] Receiving and processing this radar data lays the foundation for subsequent coordinate transformation and point cloud generation.

[0049] S202. Transform the radar data to the coordinate system of the center of the lifting device, and stitch the transformed radar data to obtain three-dimensional point cloud data.

[0050] Specifically, the received radar data needs to be converted to the coordinate system of the spreader center in order to achieve unified data processing.

[0051] This process, known as coordinate transformation, involves converting measurement data from different sensors into a common reference frame. This can be achieved using rotation and translation matrices. The rotation matrix adjusts the orientation of the radar data, while the translation matrix adjusts its position. Through these matrix operations, the data from each sensor can be precisely aligned to the center coordinate system of the spreader. After the transformation, the system stitches the processed radar data together to generate complete 3D point cloud data.

[0052] In some embodiments, in order to improve the accuracy and integrity of point cloud data, filtering techniques can be used to remove noise points, or interpolation methods can be used to fill gaps in the data.

[0053] S203. Based on point cloud data, determine the height difference between the corresponding endpoints of the spreader and the target container.

[0054] Specifically, the system uses the generated 3D point cloud data to calculate the height difference between the spreader and the target container. The height difference refers to the vertical distance between the bottom of the spreader and the top of the target container, and this information is crucial for achieving precise alignment control.

[0055] In practice, the system needs to extract key feature points of the spreader and container from the point cloud data, which typically involves using point cloud segmentation and feature extraction algorithms. The segmentation algorithm divides the point cloud data into different regions to identify the outlines of the spreader and container, while the feature extraction algorithm identifies the endpoints and edges of these objects.

[0056] After extracting key feature points, the system calculates the height difference between the corresponding endpoints of the spreader and the container.

[0057] In some embodiments, in order to improve the accuracy of the calculation, a variety of algorithms can be used for verification and optimization, such as least squares fitting or RANSAC algorithm, to eliminate the influence of outliers.

[0058] By accurately determining the height difference between the spreader and the target container, the necessary input is provided for generating control commands.

[0059] S204. Based on the height difference, generate and output spreader control commands. The spreader control commands are used to control the spreader to complete the alignment process with the target container.

[0060] Specifically, the system generates spreader control commands based on the calculated height difference, enabling precise control of the container alignment process.

[0061] Control commands are instructions used to adjust the spreader's motion parameters, including position, speed, and direction, to ensure that the spreader can accurately align with and clamp the target container (or accurately place the clamped container onto the target container).

[0062] By combining multiple lidar sensors, redundant design for data acquisition is provided, improving the robustness of the control system. This effectively addresses environmental changes and uncertainties, ensuring that the system can achieve stable and reliable spreader positioning control in complex port operation environments.

[0063] The container positioning control method provided in this application receives radar data from all lidar sensors, transforms the radar data to the coordinate system of the spreader center, and stitches the transformed radar data to obtain three-dimensional point cloud data. Based on the point cloud data, the height difference between the corresponding endpoints of the spreader and the target container is determined. Based on the height difference, spreader control commands are generated and output. Thus, by using multiple lidar sensors and processing their data, the accuracy of spreader positioning control is significantly improved. By transforming the data from multiple lidar sensors to a unified spreader center coordinate system and stitching it together to generate high-precision three-dimensional point cloud data, the system can accurately identify the height difference between the spreader and the target container. The spreader control commands generated based on this height difference ensure precise adjustment of the spreader during positioning, reduce monitoring errors caused by swaying, and achieve the goal of soft landing of the container. This solution not only improves the accuracy and reliability of positioning control but also enhances the system's adaptability in dynamic environments and optimizes the operational efficiency of port logistics.

[0064] Figure 3 The flowchart of container positioning control provided in this application Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the specific process of the container alignment control method is described in detail, which includes:

[0065] S301. Perform time synchronization processing on all lidar sensors.

[0066] Specifically, this embodiment further explains the process of container positioning control using multiple lidar sensors.

[0067] Before performing specific container alignment control, each LiDAR needs to be synchronized in time and space. For time synchronization, all LiDAR sensors need to be synchronized in time to ensure that the data from all LiDAR sensors can be fused under the same time reference.

[0068] In some embodiments, GPS clock synchronization can be used to synchronize the clock sources of each lidar to the GPS clock, and when processing data, the timestamps of the four lidar data can be kept within 10ms.

[0069] S302. Perform external parameter calibration for each lidar sensor.

[0070] Among them, the extrinsic parameter calibration is used to obtain the extrinsic parameter matrix that transforms radar data to the center of the spreader.

[0071] Specifically, for spatial synchronization, each lidar sensor needs to transform its coordinate system to the coordinate system of the lifting device center, which requires external parameter calibration:

[0072] ;

[0073] Where x, y, and z are the coordinate systems of the lidar sensors themselves, the matrix on the right side of the equals sign is the rotation transformation matrix, R and T are the rotation and translation matrices of the lidar respectively, and X, Y, and Z are the transformed coordinate systems based on the center of the lifting device. By calibrating the transformation methods of each lidar to the center of the lifting device, the coordinate systems of each lidar can be synchronized in space.

[0074] S303: Receive radar data acquired by all lidar sensors.

[0075] The lidar sensor is installed at the bottom of the trolley corresponding to the lifting device, and there are at least two lidar sensors located at different positions on the bottom of the trolley.

[0076] Specifically, this step is related to Figure 2 The corresponding steps in the illustrated embodiments are the same and will not be repeated here.

[0077] S304. Based on the extrinsic parameter matrix, transform the radar data to the coordinate system of the center of the lifting device.

[0078] Specifically, the point cloud data can be rotated to the coordinate system of the spreader center using the extrinsic parameter matrix, referring to the formula in the previous steps.

[0079] S305. Add the processed radar data together to obtain three-dimensional point cloud data.

[0080] Specifically, after the transformation is completed, the individual point cloud data are added together to obtain the fused point cloud (i.e., 3D point cloud data).

[0081] S306. Based on the pre-determined type of target container, determine the target area where the spreader and target container are located.

[0082] The type of target container includes corresponding size information.

[0083] Specifically, based on the 3D point cloud data obtained through the aforementioned steps, the point clouds corresponding to the spreader and the target container can be determined. Before making this specific determination, it is necessary to identify the target area where the spreader and the target container are located.

[0084] The system will predetermine the specific container type in the corresponding operation task (such as the size type of the target container that the clamp needs to place the container it is currently clamping into, or the size type of the target container to be clamped). Then, based on the size type, the target area size will be determined. For example, if the target container corresponding to the operation task is a 20-foot container, the target area can be defined as a range of -3.8 meters to 3.8 meters along the direction of travel of the large crane and a range of -2 meters to 2 meters along the direction of travel of the small trolley.

[0085] S307. Determine the three-dimensional point cloud data within the target area as the target point cloud data.

[0086] Specifically, the system can extract the 3D point cloud data within the identified target area and identify it as the target point cloud data.

[0087] In some embodiments, this process also requires the system to filter the point cloud data within the target area, removing noisy points and irrelevant points. For example, filtering techniques, such as statistical filtering or conditional filtering, are used to improve the quality and accuracy of the point cloud data.

[0088] S308. Determine the height range of the target container and spreader.

[0089] Specifically, the system needs to determine the height range of the target container and the spreader in order to perform subsequent point cloud search and height difference calculation. In this process, the system uses a microcontroller chip to control the position of the spreader relative to the trolley, thus determining the approximate height range of the spreader. Similarly, the system pre-determines the location of the target container to be processed based on the task, and can then determine the height range of the target container based on this location.

[0090] The spreader height range and the target container height range obtained from the above steps are theoretical values ​​that are pre-entered or automatically calculated by the system. However, due to the actual swaying of the spreader and the different clamping states of the container (such as a certain gap between the spreader and the container), the distance between the spreader and the target container may differ from the theoretical value. If the movement of the spreader is controlled solely by this value, collisions between the spreader (or the container clamped by the spreader) and the target container are likely to occur. Therefore, further control based on point cloud data is required.

[0091] S309. Based on the height range of the target container, perform the first block search on the target point cloud data to obtain the point cloud corresponding to the target container.

[0092] Specifically, based on the height range of the target container, the system performs an initial block search on the target point cloud data to extract the point cloud data corresponding to the target container. Block search is an efficient point cloud data processing method that, by dividing the point cloud data into multiple small blocks, can quickly locate and extract point cloud information within the target area.

[0093] In some embodiments, the method for determining the point cloud of the target container specifically includes the following steps:

[0094] A1. Based on the set block size, establish the block index corresponding to the target point cloud data.

[0095] The block index includes the corresponding point cloud block.

[0096] Specifically, when performing block search, a block index is created for the target point cloud data. That is, in the height direction, a block index is created with a set block size as the unit (such as 10cm as the unit), and a unique index identifier is assigned to each point cloud block.

[0097] In some embodiments, the choice of block size can also be optimized based on the density of the point cloud data and the size of the target object to ensure that each block contains enough point cloud information without being too sparse.

[0098] A2. Map the target point cloud data to the point cloud blocks in the block index.

[0099] Specifically, the system needs to assign each point cloud data point to a corresponding point cloud block based on its spatial coordinates.

[0100] In the specific processing, the system traverses each 3D point in the target point cloud data and calculates its position in the block index. By using the block size and spatial coordinates, the point cloud block to which each point belongs can be quickly determined. Next, the system adds each point to its corresponding cloud block and updates the block index to reflect the data distribution of the point cloud.

[0101] A3. Traverse the point cloud blocks from bottom to top along the height direction, and determine the point cloud blocks that are located within the height range of the target container and have more than the first set value of three-dimensional points as the first target point cloud blocks.

[0102] Specifically, when performing block search, the system needs to traverse the point cloud blocks from bottom to top along the height direction to identify the point cloud block corresponding to the target container.

[0103] For example, the system can traverse each block from bottom to top. If the number of point clouds in a block is greater than a first set value (such as 3000), and the height of the block matches the height range of the target container, then it is the first target point cloud block to which the target container point cloud belongs.

[0104] A4. Extract the 3D points from all the first target point cloud blocks to obtain the point cloud corresponding to the target container.

[0105] Specifically, the system needs to extract 3D points from all blocks marked as the first target point cloud block and merge them into a complete point cloud dataset to generate the point cloud data corresponding to the target container.

[0106] S310. Based on the point cloud corresponding to the target container, determine the top point cloud of the target container and the height value of the top endpoint.

[0107] Specifically, the system needs to determine the top surface point cloud and the top surface endpoint height value based on the point cloud data corresponding to the target container.

[0108] In some embodiments, the method for determining the endpoint height value of the top surface of the target container specifically includes the following steps:

[0109] B1. A plane fitting algorithm based on random sample consistency identifies the plane in the point cloud corresponding to the target container that is parallel to the horizontal plane, has an allowable angle range, and a distance threshold that is less than the corresponding threshold as the top surface of the target container.

[0110] Specifically, the system can use the Random Sample Consistency Plane Fitting Algorithm (RANSAC) to identify the top surface of the point cloud corresponding to the target container. RANSAC is an iterative algorithm suitable for identifying model parameters from data containing a large number of outliers.

[0111] In its implementation, the system randomly selects a subset from the point cloud corresponding to the target container and fits a planar model. Next, the system calculates the distance (e.g., 3cm) from all points to this plane and considers points with distances less than a set threshold as inliers. Through multiple iterations, the system selects the plane with the most inliers as the final fitting result. To ensure the fitted plane is parallel to the horizontal plane, the system also checks whether the angle between the plane's normal vector and the horizontal plane is within the allowable range (e.g., 3°).

[0112] B2. Based on the raster scanning method, determine the edge point cloud in the corresponding point cloud of the top surface.

[0113] Specifically, the system uses a grid scanning method (also known as latitude and longitude scanning) to identify edge point clouds in the corresponding point cloud on the top surface.

[0114] In practice, the system first projects the point cloud data corresponding to the top surface onto a horizontal plane and establishes a regular two-dimensional grid. Next, the system scans each grid cell to examine the point cloud distribution within it. Grid cells with significant changes in point cloud density are marked as edge regions.

[0115] In some embodiments, to improve the accuracy of edge recognition, edge detection algorithms, such as the Canny or Sobel operators, can be combined to further optimize the recognition results.

[0116] B3. Determine the coordinates of the top end point of the container based on the edge endpoints.

[0117] Specifically, the system extracts potential endpoints from the edge point cloud, which can be achieved using geometric analysis and clustering algorithms. For example, geometric analysis is used to identify corner points and extreme points in the edge point cloud, while clustering algorithms are used to group adjacent edge points to identify continuous edge lines. Next, the system calculates the endpoint coordinates of each edge line and compares them with the boundary of the top surface plane to determine the final top surface endpoint.

[0118] In some embodiments, in order to improve the accuracy of endpoint coordinate determination, least squares fitting or surface reconstruction techniques can be used to optimize the endpoint positioning results.

[0119] B4. Determine the endpoint height value of the top surface based on its height.

[0120] Specifically, the system substitutes the horizontal coordinates of the top surface endpoints into the previously fitted top surface plane equation to calculate the height value of each endpoint. The top surface plane equation is typically obtained using the RANSAC plane fitting algorithm, which provides an accurate description of the top surface in three-dimensional space.

[0121] In some embodiments, the system may then perform statistical analysis on the height values ​​of all endpoints to identify possible outliers and errors.

[0122] In some embodiments, filtering techniques, such as Kalman filtering or mean filtering, can be used to smooth variations in height values ​​in order to improve the accuracy of height determination. Alternative implementations also include using machine learning techniques to train a model to predict and correct endpoint height values, thereby improving the robustness and adaptability of the system.

[0123] Through this process, the system can accurately determine the height of the top end of the container, providing the necessary input for subsequent determination of spreader height and calculation of height difference.

[0124] S311. Based on the height range of the spreader, perform a second block search on the point cloud data other than the target container in the target point cloud data to obtain the point cloud corresponding to the spreader.

[0125] Specifically, similar to determining the point cloud corresponding to the target container, the method for determining the point cloud corresponding to the spreader can be as follows: traverse the point cloud blocks above the target container from bottom to top along the height direction, and determine the point cloud blocks located within the height range of the spreader and whose number of three-dimensional points in the point cloud block is greater than a second set value (such as 2000) as the second target point cloud blocks; then extract the three-dimensional points in all the second target point cloud blocks to obtain the point cloud corresponding to the spreader.

[0126] Based on the height range of the spreader, the system performs a second block search on the point cloud data outside the target container within the target point cloud data, which can extract the point cloud data corresponding to the spreader. Since the block index has already been established, only the block search needs to be performed at this point.

[0127] In some embodiments, spatial partitioning algorithms, such as octrees or kd-trees, can be used to improve the efficiency and accuracy of block search.

[0128] S312. Based on the point cloud corresponding to the spreader, determine the height of the spreader's endpoint.

[0129] Specifically, the system needs to determine the height of each end point at the bottom of the spreader (or the four end points at the bottom of the container held by the spreader) based on the point cloud data corresponding to the spreader.

[0130] In some embodiments, the method for determining the height of the end point of the spreading device specifically includes the following steps:

[0131] C1. Project the point cloud corresponding to the lifting device along the height direction to generate a binary image of the point cloud corresponding to the lifting device.

[0132] Specifically, a binary image is a method of converting three-dimensional point cloud data into a two-dimensional image representation, which can simplify subsequent edge detection and feature extraction.

[0133] In practice, the system needs to project the point cloud data corresponding to the lifting device onto a horizontal plane and map each projected point to a pixel in the image.

[0134] To generate a binary image, the system needs to set a threshold, marking areas with point cloud density higher than the threshold as foreground (i.e., the area where the lifting device is located), and marking other areas as background.

[0135] In this way, the system can generate a clear binary image that reflects the distribution of the spreader on the horizontal plane.

[0136] In some embodiments, filtering techniques, such as Gaussian filtering or median filtering, can be used to improve the accuracy of binary image generation in order to remove noise that may be introduced during the projection process.

[0137] C2. Based on edge detection algorithms and grid scanning methods, perform edge detection on the binary image to determine the edge lines of the lifting device in the binary image.

[0138] Specifically, edge detection algorithms are image processing techniques used to identify regions in an image where brightness changes significantly; these regions typically correspond to the boundaries of objects.

[0139] In its implementation, the system needs to apply edge detection algorithms, such as the Canny or Sobel operators, to the binary image to identify potential edge pixels. Next, the system can use a raster scanning method to further process the identified edge pixels, connecting adjacent edge pixels to form continuous edge lines. The raster scanning method establishes a regular grid in the image, thereby enabling rapid identification and connection of edge features.

[0140] In some embodiments, in order to improve the accuracy of edge line recognition, multiple edge detection algorithms can be combined for verification and optimization.

[0141] C3. Based on the edge lines, determine the corresponding 3D point cloud of the edge.

[0142] Specifically, the edge lines obtained from the aforementioned steps are two-dimensional lines. Therefore, the system needs to map the edge line pixels in the value map back to three-dimensional point cloud data in order to identify the corresponding three-dimensional edge lines. That is, the two-dimensional coordinates of each edge line pixel are converted into position coordinates in three-dimensional space. At this time, the previous projection and the corresponding three-dimensional point cloud are required.

[0143] This allows the system to further process the identified three-dimensional edge points to extract feature points corresponding to the edge of the lifting device.

[0144] C4. A plane fitting algorithm based on random sample consistency is used to perform plane fitting on the 3D point cloud corresponding to the edge to obtain the straight line equation corresponding to the edge of the lifting device.

[0145] Specifically, the system uses the RANSAC algorithm to process the 3D point cloud corresponding to the edge in order to fit the straight line equation of the lifting device edge.

[0146] In its implementation, the system first randomly selects a subset from the 3D point cloud corresponding to the edge and fits a planar model. Next, the system calculates the distance from all points to this plane and considers points with a distance less than a set threshold as inliers. Through multiple iterations, the system selects the plane with the most inliers as the final fitting result. To ensure the accuracy of the fitting result, the system also checks whether the angle between the normal vector of the fitted plane and the expected direction is within the allowable range.

[0147] After obtaining the planar model, substituting the 3D point cloud corresponding to the edge into the planar model will yield the equations of the lines corresponding to each edge.

[0148] In some embodiments, alternative implementations include using improved RANSAC algorithms, such as MLESAC or PROSAC, to improve the accuracy and efficiency of the fit.

[0149] Through this process, the system can accurately fit the straight line equation of the spreader edge, providing a reliable basis for subsequent height calculations.

[0150] C5. Substitute the coordinates of the top end of the target container on the horizontal plane into the equation of the straight line to obtain the height value of the corresponding spreader end.

[0151] Specifically, the system needs to substitute the horizontal coordinates of the top end of the target container into the previously fitted equation of the spreader edge line to calculate the corresponding spreader end height value.

[0152] For example, let the coordinates of the top endpoint of the target container be [x, y] (because in this scheme it is assumed that the spreader and the target container are aligned in the horizontal plane), and let the coefficients of the straight line equations of the four edges be A, B, C, and D, respectively, then the corresponding spreader edge points can be obtained:

[0153] Ax + By + CZ + D = 0;

[0154] Where Z is the desired height of the edge point of the lifting device.

[0155] S313. Determine the height difference based on the end height of the target container and the height of the spreader end.

[0156] Among them, the spreader control command is used to control the spreader to complete the alignment process with the target container.

[0157] Specifically, the system calculates the height difference by comparing the height values ​​of each container edge point with the corresponding spreader edge point. When the height differences at each endpoint are the same, it indicates that the bottom surface of the spreader is parallel to the top surface of the target container; otherwise, the spreader may be swaying, causing the bottom surface to be non-parallel to the top surface of the target container.

[0158] S314. Based on the height difference, generate and output the spreader control command.

[0159] Specifically, this step is related to Figure 2 The corresponding steps in the illustrated embodiments are the same and will not be repeated here.

[0160] The container positioning control method provided in this disclosure significantly improves the accuracy and reliability of data fusion through time synchronization and extrinsic parameter calibration of multiple lidar sensors. By establishing a block index and performing block search, the system can efficiently extract point cloud data of the target container and spreader. Combining RANSAC plane fitting and grid scanning methods, the scheme can accurately identify the top surface of the container and the edge of the spreader, achieving precise height difference calculation. This scheme not only overcomes the measurement errors caused by swaying in traditional methods, but also achieves precise positioning of the spreader and container through precise control command generation. Overall, this scheme improves the safety and efficiency of port operations and enhances the system's adaptability in complex environments.

[0161] Figure 4 This is a schematic diagram of the container positioning control device provided in this application, as shown below. Figure 4 As shown, the container alignment control device 400 provided in this embodiment includes:

[0162] The receiving module 410 is used to receive radar data acquired by all lidar sensors. The lidar sensors are set at the bottom of the trolley corresponding to the lifting device, and there are at least two lidar sensors located at different positions on the bottom of the trolley.

[0163] The processing module 420 is used to transform radar data to the coordinate system of the center of the lifting device, and to stitch the transformed radar data to obtain three-dimensional point cloud data.

[0164] Analysis module 430 is used to determine the height difference between the corresponding endpoints of the spreader and the target container based on point cloud data;

[0165] Output module 440 is used to generate and output spreader control commands based on the height difference. The spreader control commands are used to control the spreader to complete the alignment process with the target container.

[0166] In one possible implementation, the analysis module 430 is specifically used to: determine the target area where the spreader and the target container are located based on the pre-determined type of the target container, wherein the type of the target container includes corresponding size information; determine the three-dimensional point cloud data within the target area as target point cloud data; determine the height range of the target container and the spreader; perform a first block search on the target point cloud data based on the height range of the target container to obtain the point cloud corresponding to the target container; determine the top surface point cloud and the endpoint height value of the top surface of the target container based on the point cloud corresponding to the target container; perform a second block search on the point cloud data other than the target container in the target point cloud data based on the height range of the spreader to obtain the point cloud corresponding to the spreader; determine the height of the endpoint of the spreader based on the point cloud corresponding to the spreader; and determine the height difference based on the endpoint height value of the target container and the endpoint height of the spreader.

[0167] In one possible implementation, the analysis module 430 is specifically used to: establish a block index corresponding to the target point cloud data based on a set block size, the block index including the corresponding point cloud blocks; map the target point cloud data to each point cloud block in the block index; traverse the point cloud blocks from bottom to top along the height direction, and determine the point cloud blocks located within the height range of the target container and whose number of three-dimensional points is greater than a first set value as the first target point cloud blocks; extract the three-dimensional points from all the first target point cloud blocks to obtain the point cloud corresponding to the target container.

[0168] In one possible implementation, the analysis module 430 is specifically used to: determine the top surface of the target container as a plane that is parallel to the horizontal plane, has an allowable angle range, and a distance threshold that is less than the corresponding threshold in the point cloud corresponding to the target container, based on a plane fitting algorithm of random sample consistency; determine the edge point cloud in the point cloud corresponding to the top surface based on a grid scanning method; determine the coordinates of the top surface endpoints of the container based on the edge endpoints; and determine the endpoint height value of the top surface based on the height of the top surface.

[0169] In one possible implementation, the analysis module 430 is specifically used to traverse the point cloud blocks above the target container from bottom to top along the height direction, identify the point cloud blocks located within the height range of the spreader and whose number of three-dimensional points is greater than a second set value as the second target point cloud blocks, and extract the three-dimensional points from all the second target point cloud blocks to obtain the point cloud corresponding to the spreader.

[0170] In one possible implementation, the analysis module 430 is specifically used to: project the point cloud corresponding to the spreader along the height direction to generate a binary image of the point cloud corresponding to the spreader; perform edge detection on the binary image based on an edge detection algorithm and a raster scanning method to determine the edge line of the spreader in the binary image; determine the corresponding three-dimensional point cloud based on the edge line; perform plane fitting processing on the three-dimensional point cloud corresponding to the edge based on a random sample consistency plane fitting algorithm to obtain the straight line equation corresponding to the edge of the spreader; and substitute the coordinates of the top end point of the target container on the horizontal plane into the straight line equation to obtain the height value of the corresponding spreader end point.

[0171] In one possible implementation, the receiving module 410 is further configured to perform time synchronization processing on all lidar sensors before receiving the radar data acquired by all lidar sensors; and to perform extrinsic parameter calibration on each lidar sensor, the extrinsic parameter calibration being used to obtain the extrinsic parameter matrix that transforms the radar data to the center of the spreader.

[0172] In one possible implementation, the processing module 420 is specifically used to transform the radar data to the coordinate system of the center of the lifting device based on the extrinsic parameter matrix; and to add the transformed radar data to obtain three-dimensional point cloud data.

[0173] The container positioning control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0174] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0175] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0176] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0177] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0178] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0179] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0180] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods in any of the above embodiments.

[0181] Figure 6 This is a schematic diagram of the structure of the yard crane provided in this application. This application also provides a yard crane 60, which includes the container positioning control device 610 from the above-described device embodiments.

[0182] The specific implementation process of the container alignment control device 610 can be found in the above-described method and device embodiments, which have similar implementation principles and technical effects. Therefore, they will not be repeated here.

[0183] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods in any of the above embodiments.

[0184] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0185] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0186] The division of units is merely a logical functional division; 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0187] 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 according to actual needs.

[0188] In addition, the functional units in the various embodiments of the present invention 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.

[0189] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes 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 of the various embodiments of this invention. 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.

[0190] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0191] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0192] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0193] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0194] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0195] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes 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 of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0196] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0197] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0198] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A container positioning control method, characterized in that, Includes the following steps: Receive radar data acquired by all lidar sensors, wherein the lidar sensors are disposed at the bottom of the trolley corresponding to the lifting device, and there are at least two lidar sensors located at different positions on the bottom of the trolley; The radar data is transformed to the coordinate system of the center of the lifting device, and the transformed radar data is stitched together to obtain three-dimensional point cloud data. Based on the point cloud data, the height difference between the corresponding endpoints of the spreader and the target container is determined; Based on the height difference, spreader control commands are generated and output, which are used to control the spreader to complete the alignment process with the target container; Determining the height difference between the corresponding endpoints of the spreader and the target container based on the point cloud data includes: Based on the pre-determined type of target container, the target area where the spreader and the target container are located is determined, and the type of target container includes corresponding size information; The three-dimensional point cloud data within the target area is identified as the target point cloud data; Determine the height range of the target container and spreader; Based on the height range of the target container, a first block search is performed on the target point cloud data to obtain the point cloud corresponding to the target container; Based on the point cloud corresponding to the target container, determine the top point cloud and the height value of the top endpoint of the target container; Based on the height range of the spreader, a second block search is performed on the point cloud data other than the target container in the target point cloud data to obtain the point cloud corresponding to the spreader; Based on the point cloud corresponding to the spreader, determine the height of the spreader's endpoint; The height difference is determined based on the end height value of the target container and the height of the spreader end.

2. The method according to claim 1, characterized in that, The step of performing a first block search on the target point cloud data based on the height range of the target container to obtain the point cloud corresponding to the target container includes: Based on a set block size, a block index corresponding to the target point cloud data is established, and the block index includes the corresponding point cloud block; The target point cloud data is mapped to each point cloud block of the block index; Traverse the point cloud blocks from bottom to top along the height direction, and determine the point cloud blocks that are located within the height range of the target container and have more than a first set value of three-dimensional points as the first target point cloud block; Extract all three-dimensional points from the first target point cloud block to obtain the point cloud corresponding to the target container.

3. The method according to claim 1, characterized in that, The step of determining the top surface point cloud and the endpoint height value of the top surface of the target container based on the point cloud corresponding to the target container includes: Based on the random sample consistency plane fitting algorithm, the plane in the point cloud corresponding to the target container that is parallel to the horizontal plane and whose angle allowable range and distance threshold are both less than the corresponding threshold are determined as the top surface of the target container. Based on the grid scanning method, the edge point cloud in the point cloud corresponding to the top surface is determined; Based on the edge endpoints, determine the coordinates of the top surface endpoints of the container; Based on the height of the top surface, determine the endpoint height value of the top surface.

4. The method according to claim 3, characterized in that, Based on the height range of the spreader, a second block search is performed on the point cloud data other than the target container in the target point cloud data to obtain the point cloud corresponding to the spreader, including: Traverse the point cloud blocks above the target container from bottom to top along the height direction, and determine the point cloud blocks that are located within the height range of the spreader and have more than the second set value of three-dimensional points as the second target point cloud blocks; Extract all three-dimensional points from the point cloud of the second target to obtain the point cloud corresponding to the lifting device.

5. The method according to claim 4, characterized in that, Determining the height of the end point of the spreader based on the point cloud corresponding to the spreader includes: Project the point cloud corresponding to the lifting device along the height direction to generate a binary image of the point cloud corresponding to the lifting device; Based on edge detection algorithms and grid scanning methods, edge detection is performed on the binary image to determine the edge lines of the lifting device in the binary image; Based on the edge lines, the corresponding three-dimensional point cloud of the edge is determined; A plane fitting algorithm based on random sample consistency is used to perform plane fitting on the three-dimensional point cloud corresponding to the edge to obtain the straight line equation corresponding to the edge of the lifting device. Substitute the coordinates of the top end of the target container on the horizontal plane into the equation of the straight line to obtain the height value of the corresponding spreader end.

6. The method according to any one of claims 1 to 5, characterized in that, Before receiving radar data acquired by all lidar sensors, the process also includes: Time synchronization processing is performed on all lidar sensors; Extrinsic parameter calibration is performed on each lidar sensor. The extrinsic parameter calibration is used to obtain the extrinsic parameter matrix that transforms the lidar data to the center of the lifting device.

7. The method according to claim 6, characterized in that, The process of transforming the radar data to the coordinate system of the lifting device center and then stitching the transformed radar data to obtain three-dimensional point cloud data includes: Based on the extrinsic parameter matrix, the radar data is transformed to the coordinate system of the center of the lifting device; The converted radar data are summed to obtain the three-dimensional point cloud data.

8. A container positioning control device, characterized in that, include: A receiving module is used to receive radar data acquired by all lidar sensors, wherein the lidar sensors are disposed on the bottom of the trolley corresponding to the lifting device, and there are at least two lidar sensors located at different positions on the bottom of the trolley; The processing module is used to transform the radar data to the coordinate system of the center of the lifting device, and to stitch the transformed radar data to obtain three-dimensional point cloud data. The analysis module is used to determine the height difference between the corresponding endpoints of the spreader and the target container based on the point cloud data; The output module is used to generate and output spreader control commands based on the height difference. The spreader control commands are used to control the spreader to complete the alignment process with the target container. The analysis module is specifically used for: Based on the pre-determined type of target container, the target area where the spreader and the target container are located is determined, and the type of target container includes corresponding size information; The three-dimensional point cloud data within the target area is identified as the target point cloud data; Determine the height range of the target container and spreader; Based on the height range of the target container, a first block search is performed on the target point cloud data to obtain the point cloud corresponding to the target container; Based on the point cloud corresponding to the target container, determine the top point cloud and the height value of the top endpoint of the target container; Based on the height range of the spreader, a second block search is performed on the point cloud data other than the target container in the target point cloud data to obtain the point cloud corresponding to the spreader; Based on the point cloud corresponding to the spreader, determine the height of the spreader's endpoint; The height difference is determined based on the end height value of the target container and the height of the spreader end.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A field bridge, characterized in that, The yard crane includes the container positioning control device as described in claim 8.

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