Container alignment control method, container alignment control device, container alignment control equipment and field bridge

Abstract

The embodiment of the invention provides a container alignment control method, device and equipment and a field bridge, and relates to the technical field of port equipment. The method comprises the following steps: receiving radar data acquired by all laser radar sensors, converting the radar data into a coordinate system where the center of a lifting appliance is located, and splicing the converted radar data to obtain three-dimensional point cloud data; based on the point cloud data, determining the height difference between the lifting appliance and the corresponding end point of the target container; and generating and outputting a lifting appliance control instruction based on the height difference. According to the method, the problem of insufficient alignment control accuracy based on the laser sensor is effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of port equipment, and in particular to a container alignment control method, device, equipment and yard crane. Background Art

[0002] In a container terminal or yard, a gantry crane is a main equipment for handling and stacking containers. The gantry crane moves horizontally on the yard through a yard crane trolley, grabs and unloads containers through a spreader, and drives the spreader to move horizontally and vertically in a direction perpendicular to the moving track of the yard crane trolley through a trolley. When grabbing and unloading containers, the control system in the trolley needs to control the movement state of the spreader to ensure the contact speed and moving distance between the spreader and the container, and between the clamped container and the target container to be loaded, so as to avoid possible collisions between the spreader and the container, and between the containers. This process is also the process of container alignment.

[0003] In the related art, the control of container alignment includes a method of monitoring by setting a laser sensor on the spreader. However, since there is usually a certain degree of shaking during the spreader container alignment process, the monitoring result is not accurate enough and the purpose of soft landing of the container cannot be achieved. Summary of the invention

[0004] The present application provides a container alignment control method, device, equipment, equipment and field bridge, which are used to solve the problem of insufficient accuracy of alignment control based on laser sensors in related technologies.

[0005] In a first aspect, the present application provides a container alignment control method, comprising:

[0006] Receive radar data acquired by all laser radar sensors, wherein the laser radar sensor is arranged at the bottom of the trolley corresponding to the sling, and there are at least two laser radar sensors which are respectively located at different positions at the bottom of the trolley;

[0007] The radar data is transformed into the coordinate system of the center of the spreader, and the transformed radar data is spliced ​​to obtain three-dimensional point cloud data;

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

[0009] Based on the height difference, a spreader control instruction is generated and output, and the spreader control instruction is used to control the spreader to complete the alignment process with the target container.

[0010] In a second aspect, the present application provides a container alignment control device, comprising:

[0011] Different positions of the parts;

[0012] A processing module is used to transform the radar data into the coordinate system of the center of the spreader, and to splice the transformed radar data to obtain three-dimensional point cloud data;

[0013] An analysis module for determining the height difference between corresponding end points of the spreader and the target container based on the point cloud data;

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

[0015] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0016] The memory stores computer-executable instructions;

[0017] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides a field crane, which includes a container alignment control device as described in the second aspect and / or various possible implementations of the second aspect.

[0019] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0020] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0021] The container alignment control method, device, equipment and yard bridge provided by the embodiment of the present disclosure receive radar data obtained by all laser radar sensors, transform the radar data to the coordinate system where the center of the spreader is located, and splice the transformed radar data to obtain three-dimensional point cloud data; based on the point cloud data, determine the height difference between the corresponding end points of the spreader and the target container; based on the height difference, generate and output the spreader control instruction. Therefore, through the setting and data processing of multiple laser radar sensors, the accuracy of the spreader alignment control is significantly improved. By transforming the data of multiple laser radar sensors to a unified spreader center coordinate system and splicing them 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 instruction generated based on this height difference ensures the precise adjustment of the spreader during the alignment process, reduces the monitoring error caused by shaking, and achieves the goal of soft landing. This solution not only improves the accuracy and reliability of alignment control, but also enhances the adaptability of the system in a dynamic environment and optimizes the operating efficiency of port logistics. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0023] Figure 1 A diagram of an application scenario of the container alignment control method provided in an embodiment of the present disclosure;

[0024] Figure 2 A flow chart of a container alignment control method provided by an embodiment of the present disclosure;

[0025] Figure 3 A flowchart of a container alignment control method provided by yet another embodiment of the present disclosure;

[0026] Figure 4 A schematic structural diagram of a container alignment control device provided by yet another embodiment of the present disclosure;

[0027] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure;

[0028] Figure 6 A schematic structural diagram of a field bridge provided in yet another embodiment of the present disclosure.

[0029] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0030] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] In addition, this application involves conducting 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 using artificial intelligence technology to make automated decisions, and making technical solutions that have a significant impact on personal rights and interests based on the results of automated decisions. The application provides users with corresponding operation entrances for them to choose to agree or reject the results of automated decisions; if the user chooses to reject, the expert decision-making process will be entered.

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

[0034] Figure 1 A schematic diagram of an application scenario of the method for controlling the posture of a sling provided in this application, such as Figure 1 As shown, during the process of spreader posture control, when the gantry crane 100 grabs and releases the container, it will collect data of the spreader 120 and the target container 130 through the laser radar sensor 110 to control the movement of the spreader 120 and complete the alignment control of the spreader 120 and the target container 130.

[0035] It should be noted that Figure 1The scene shown includes only one or a specific number of gantry cranes, image sensors, lidar sensors, spreaders, and target containers for illustration, but the present disclosure is not limited to this. In other words, the number of gantry cranes, image sensors, lidar sensors, spreaders, and target containers can be arbitrary.

[0036] In the field of port logistics, gantry cranes are key equipment for container handling and stacking, and their efficient operation directly affects the overall operational efficiency of the terminal. The gantry crane moves horizontally on the yard through the yard crane, and grabs and unloads the container through the spreader. In order to ensure the safety and accurate placement of the container, the movement state of the spreader must be precisely controlled, especially during the container alignment process. During the alignment process, the spreader needs to maintain a suitable contact speed and moving distance with the target container to avoid collision and damage. However, since the spreader may shake during movement, the traditional alignment control method based on laser sensors is insufficient in accuracy. Laser sensors are usually installed on the spreader to monitor the distance between the spreader and the container. However, the shaking of the spreader will cause the measurement results of the laser sensor to be unstable, thus affecting the accuracy of the alignment control.

[0037] In practical applications, laser sensors perform well under static conditions, but their measurement results are easily disturbed when the spreader is shaking. Therefore, positioning problems based on lidar sensors are difficult to detect. At the same time, the difficulty in solving this technical problem lies in the need to develop a control system that can operate stably in complex environments while taking into account real-time performance and accuracy. Traditional laser sensor methods are difficult to deal with measurement errors caused by spreader shaking, so new technical means need to be introduced to improve the accuracy of positioning control, and efficient data processing capabilities and precise algorithm support are required. Therefore, the existing technology lacks a solution that can meet the needs of complex environments while maintaining the accuracy of calculation results.

[0038] The container alignment control method provided in this application generates high-precision three-dimensional point cloud data by transforming the data of multiple laser radar sensors to the center coordinate system of the spreader and performing splicing processing. Based on this data, the system can accurately calculate the height difference between the spreader and the target container. Subsequently, the corresponding control instructions are generated to ensure that the spreader is accurately adjusted during the alignment process. This method effectively overcomes the measurement error caused by the shaking of traditional laser sensors and achieves more stable alignment control.

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

[0040] Figure 2 Schematic diagram of the process 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] Among them, the laser radar sensor is arranged at the bottom of the trolley corresponding to the sling, and there are at least two laser radar sensors which are respectively located at different positions at 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] The executing entity in the disclosed embodiment is a controller in the gantry crane that controls the position and motion status of the yard bridge trolley, car and hoisting equipment and can obtain the laser radar sensor data set on the car. Specifically, it can be a control system set in the car, or it can be a cloud server that is communicatively connected to the control system in the gantry crane. For the convenience of explanation, it is collectively referred to as a control system or system here.

[0045] During the container alignment process, the system receives radar data from multiple LiDAR sensors installed on the bottom of the corresponding trolley of the spreader. LiDAR sensors are devices that can provide high-precision distance measurement, usually by emitting laser beams and detecting reflected signals to obtain three-dimensional information of the surrounding environment.

[0046] In this solution, at least two laser radar sensors are installed at different positions on the bottom of the trolley to ensure full coverage of the surrounding environment of the spreader. For example, there are four laser radar sensors, which are symmetrically installed at the bottom of the trolley near the four corners, or two laser radar sensors are grouped together, and the two groups of laser radar sensors are symmetrically installed at the bottom of the trolley near the edge or diagonal positions.

[0047] The process of receiving radar data needs to ensure the integrity and real-time nature of the data in order to respond quickly in a dynamic environment.

[0048] By receiving and processing these radar data, the foundation is laid for subsequent coordinate transformation and point cloud generation.

[0049] S202, transforming the radar data into the coordinate system where the center of the spreader is located, and splicing the transformed radar data to obtain three-dimensional point cloud data.

[0050] Specifically, the received radar data needs to be converted into the coordinate system where the center of the spreader is located to achieve unified data processing.

[0051] This process is called coordinate transformation, which is to convert the measurement data of different sensors into a common reference frame. This can be achieved by using rotation matrix and translation matrix. The rotation matrix is ​​used to adjust the direction of the radar data, while the translation matrix is ​​used to adjust its position. Through these matrix operations, the data of each sensor can be accurately aligned to the coordinate system of the center of the spreader. After the transformation is completed, the system splices the processed radar data to generate complete 3D point cloud data.

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

[0053] S203: Determine the height difference between corresponding end points of the spreader and the target container based on the point cloud data.

[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. This information is crucial for achieving precise alignment control.

[0055] In the specific implementation, the system needs to extract the key feature points of the spreader and container from the point cloud data, which usually involves the use of point cloud segmentation and feature extraction algorithms. The segmentation algorithm divides the point cloud data into different areas to identify the outlines of the spreader and container, and the feature extraction algorithm is used to identify the endpoints and edges of these objects.

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

[0057] In some embodiments, in order to improve the accuracy of calculation, multiple algorithms may 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, it provides the necessary input for generating control instructions.

[0059] S204: Generate and output a spreader control instruction based on the height difference, where the spreader control instruction is used to control the spreader to complete the alignment process with the target container.

[0060] Specifically, the system generates a spreader control instruction according to the calculated height difference, and can realize precise control of the container alignment process.

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

[0062] By combining multiple lidar sensors, a redundant design for data acquisition is provided to improve the robustness of the control system, thereby effectively responding to environmental changes and uncertainties and ensuring that the system can achieve stable and reliable spreader positioning control in complex port operating environments.

[0063] The container alignment control method provided in the embodiment of the present application receives radar data acquired by all laser radar sensors, transforms the radar data to the coordinate system where the center of the spreader is located, and splices the transformed radar data to obtain three-dimensional point cloud data; based on the point cloud data, determines the height difference between the corresponding end points of the spreader and the target container; based on the height difference, generates and outputs the spreader control instruction. Thus, through the setting and data processing of multiple laser radar sensors, the accuracy of the spreader alignment control is significantly improved. By transforming the data of multiple laser radar sensors to a unified spreader center coordinate system and splicing them 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 instruction generated based on this height difference ensures the precise adjustment of the spreader during the alignment process, reduces the monitoring error caused by shaking, and achieves the goal of soft landing. This solution not only improves the accuracy and reliability of alignment control, but also enhances the adaptability of the system in a dynamic environment and optimizes the operating efficiency of port logistics.

[0064] Figure 3 Schematic diagram of the process of container alignment control provided for this application Figure 2 ,like Figure 3 As shown, in this embodiment Figure 2 Based on the embodiment, the specific process in the container alignment control method is described in detail, and the method includes:

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

[0066] Specifically, this embodiment further illustrates the process of performing container alignment control by using multiple laser radar sensors.

[0067] Before carrying out specific container alignment control, first of all, each lidar needs to be synchronized in time and space. For those that require time synchronization, all lidar sensors need to be time synchronized to ensure that all lidar sensor data can be fused under the same time reference.

[0068] In some embodiments, GPS clock timing can be adopted to synchronize the clock source of each lidar to the GPS clock, and when processing data, ensure that the timestamps of the four radar data are controlled within 10ms.

[0069] S302: Calibrate the external parameters of each laser radar sensor.

[0070] Among them, the external parameter calibration is used to obtain the external parameter matrix that transforms the 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 center of the sling, that is, external parameter calibration is required:

[0072] ;

[0073] Among them, x, y, z are the coordinate systems of the laser radar sensor itself, the matrix on the right side of the equal sign is the rotation transformation matrix, R and T are the rotation matrix and translation matrix of the laser radar respectively, and X, Y, Z are the coordinate systems based on the center of the sling after transformation. By calibrating the transformation method of each laser radar to the center of the sling, the coordinate systems of each laser radar can be synchronized in space.

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

[0075] Among them, the laser radar sensor is arranged at the bottom of the trolley corresponding to the sling, and there are at least two laser radar sensors which are respectively located at different positions at the bottom of the trolley.

[0076] Specifically, this step is Figure 2 The corresponding steps in the illustrated embodiment are the same in content and will not be described again here.

[0077] S304: Based on the external parameter matrix, transform the radar data to the coordinate system where the center of the spreader is located.

[0078] Specifically, each point cloud data can be rotated to the coordinate system where the center of the sling is located through the external parameter matrix, referring to the formula in the previous steps.

[0079] S305 , adding the converted radar data to obtain three-dimensional point cloud data.

[0080] Specifically, after the transformation is completed, each point cloud data is added together to obtain a fused point cloud (that is, three-dimensional point cloud data).

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

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

[0083] Specifically, the point clouds corresponding to the spreader and the target container can be determined from the three-dimensional point cloud data obtained by splicing in the above steps. Before the specific determination, the target area where the spreader and the target container are located needs to be determined.

[0084] The system will predetermine the specific container type in the container corresponding to the operation task (such as the size type of the target container into which the clamp needs to place the container being clamped or the size type of the target container to be clamped), and then determine the size of the target area based on the size type. 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 yard crane trolley and a range of -2 meters to 2 meters along the direction of travel of the trolley.

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

[0086] Specifically, the system may extract the three-dimensional point cloud data within the identified target area and determine it as the target point cloud data.

[0087] In some embodiments, during this process, the system also needs to screen the point cloud data in the target area to remove noise points and irrelevant point clouds, for example, using filtering techniques such as statistical filtering or conditional filtering to improve the quality and accuracy of the point cloud data.

[0088] S308: Determine the height range of the target container and the 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 the specific determination, the system will control the position of the spreader relative to the trolley through the microcontroller chip, so that the approximate height range of the spreader can be determined; similarly, the system will pre-determine the location of the target container to be processed according to the task, and then determine the height range of the target container based on this location.

[0090] The height range of the spreader and the height range of the target container obtained in the above steps are theoretical values ​​pre-entered or automatically calculated in the system. Since the spreader may actually shake or hold the container differently (such as there is 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 only by this value, the spreader (or the container held by the spreader) may easily collide with the target container. Therefore, further control based on point cloud data is required.

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

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

[0093] In some embodiments, the method for determining the target container point cloud 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] Among them, the block index includes the corresponding point cloud block.

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

[0097] In some embodiments, the selection of the block size may 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 sufficient point cloud information without being too sparse.

[0098] A2. Correspond the target point cloud data to each point cloud block of the block index.

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

[0100] In the specific process, 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 in the height range of the target container and have a number of three-dimensional points greater than a first set value as the first target point cloud blocks.

[0102] Specifically, when performing a block search, the system needs to traverse the point cloud blocks from bottom to top along the height direction to identify the point cloud blocks 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 the block is greater than the first set value (such as 3000), and the height of the block is consistent with the height range of the target container, it is the first target point cloud block to which the target container point cloud belongs.

[0104] A4. Extract all three-dimensional points in the first target point cloud block to obtain the point cloud corresponding to the target container.

[0105] Specifically, the system needs to extract three-dimensional points from all blocks marked as first target point cloud blocks and merge them into a complete point cloud data set to generate point cloud data corresponding to the target container.

[0106] S310: Determine the top surface point cloud and the endpoint height value of the top surface of the target container based on the corresponding point cloud of the target container.

[0107] Specifically, the system needs to determine the top surface point cloud and the endpoint height value of the top surface 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. Based on the plane fitting algorithm of random sample consistency, the plane in the corresponding point cloud of the target container that is parallel to the horizontal plane and whose angle allowable range and distance threshold are both smaller than the corresponding threshold is determined as the top surface of the target container.

[0110] Specifically, the system can use a random sample consensus plane fitting algorithm (also known as the RANSAC algorithm) to identify the top surface in 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 the specific implementation, the system needs to randomly select a subset from the point cloud corresponding to the target container and fit a plane model. Next, the system calculates the distance from all points to the plane (such as 3cm), and regards points with a distance less than the set threshold as inliers. Through multiple iterations, the system selects the plane with the largest number of inliers as the final fitting result. In order to ensure that the fitted plane is parallel to the horizontal plane, the system also needs to check whether the angle between the normal vector of the plane and the horizontal plane is within the allowable range (such as 3°).

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

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

[0114] In specific implementation, the system first projects the point cloud data corresponding to the top surface onto the horizontal plane and establishes a regular two-dimensional grid. Next, the system scans each grid cell and checks the point cloud distribution therein. For those grid cells where the point cloud density changes significantly, the system marks them as edge areas.

[0115] In some embodiments, in order to improve the accuracy of edge recognition, an edge detection algorithm, such as a Canny or Sobel operator, may be combined to further optimize the recognition result.

[0116] B3. Based on the edge endpoints, determine the coordinates of the top endpoint of the container.

[0117] Specifically, the system extracts possible 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 technology may be used to optimize the endpoint positioning result.

[0119] B4. Based on the height of the top surface, determine the endpoint height value of the top surface.

[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 usually obtained through the RANSAC plane fitting algorithm, which can provide an accurate description of the top surface in three-dimensional space.

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

[0122] In some embodiments, in order to improve the accuracy of determining the height value, filtering techniques such as Kalman filtering or mean filtering can be used to smooth the changes in the height value. Optional implementations also include using machine learning techniques to predict and correct the endpoint height value by training the model, thereby improving the robustness and adaptability of the system.

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

[0124] S311. 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.

[0125] Specifically, similar to determining the point cloud corresponding to the target container, the method for determining the point cloud corresponding to the sling can be: 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 in the height range of the sling and have a number of three-dimensional points greater than a second set value (such as 2000) as the second target point cloud blocks; then extract all the three-dimensional points in the second target point cloud blocks to obtain the point cloud corresponding to the sling.

[0126] Based on the height range of the spreader, the system performs a second block search on the point cloud data other than the target container in the target point cloud data, and can extract the point cloud data corresponding to the spreader. Since the block index has been established, only a block search is required at this time.

[0127] In some embodiments, in order to improve the efficiency and accuracy of block search, a space partitioning algorithm, such as an octree or a kd tree, may be used to improve the efficiency and accuracy of the search.

[0128] S312: Determine the height of the end point of the spreader based on the corresponding point cloud of the spreader.

[0129] Specifically, the system needs to determine the heights of each endpoint of the bottom of the spreader (or the four endpoints of the bottom of the container clamped 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 spreader includes the following steps:

[0131] C1. 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.

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

[0133] In specific implementation, the system needs to project the point cloud data corresponding to the spreader onto a horizontal plane and map each projection point to a pixel in the image.

[0134] In order to generate a binary image, the system needs to set a threshold and mark the area with point cloud density higher than the threshold as the foreground (that is, the area where the sling is located), and mark other areas as the background.

[0135] In this way, the system is able to generate a clear binary image reflecting the distribution of the spreaders on the horizontal plane.

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

[0137] C2. Based on the edge detection algorithm and the raster scanning method, edge detection is performed on the binary image to determine the edge line of the hanger in the binary image.

[0138] Specifically, an edge detection algorithm is an image processing technique used to identify areas in an image where brightness changes significantly, which usually correspond to the boundaries of objects.

[0139] In specific implementation, the system needs to apply edge detection algorithms, such as Canny or Sobel operators, to the binary image to identify possible edge pixels. Next, the system can further process the identified edge pixels using a raster scanning method to connect adjacent edge pixels and form a continuous edge line. The raster scanning method establishes a regular grid in the image to quickly identify and connect edge features.

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

[0141] C3. Based on the edge line, determine the corresponding edge three-dimensional point cloud.

[0142] Specifically, the edge line obtained in the above steps is a two-dimensional line, so the system needs to map the edge line pixels in the value map back to the three-dimensional point cloud data to identify the corresponding three-dimensional edge line. 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 sling corresponding to the three-dimensional point cloud need to be used.

[0143] Therefore, it is convenient for the system to further process the identified three-dimensional edge points to extract the feature points corresponding to the edge of the spreader.

[0144] C4. Based on the plane fitting algorithm of random sample consistency, plane fitting processing is performed on the three-dimensional point cloud corresponding to the edge to obtain the straight line equation corresponding to the edge of the spreader.

[0145] Specifically, the system uses the RANSAC algorithm to process the three-dimensional point cloud corresponding to the edge to fit the straight line equation of the edge of the spreader.

[0146] In the specific implementation, the system first randomly selects a subset from the 3D point cloud corresponding to the edge and fits a plane model. Next, the system calculates the distance from all points to the plane and regards the points whose distance is less than the set threshold as inliers. Through multiple iterations, the system selects the plane with the largest number of inliers as the final fitting result. In order to ensure the accuracy of the fitting result, the system also needs to check whether the angle between the normal vector of the fitting plane and the expected direction is within the allowable range.

[0147] After obtaining the plane model, the three-dimensional point cloud corresponding to the edge is substituted into the plane model to obtain the straight line equation corresponding to each edge line.

[0148] In some embodiments, an optional implementation includes using an improved RANSAC algorithm, such as MLESAC or PROSAC, to improve the accuracy and efficiency of fitting.

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

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

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

[0152] For example, the coordinates of the top end point of the target container are recorded as [x, y] (because in this solution, it is assumed that the spreader and the target container have been aligned in the horizontal plane), and the coefficients of the straight line equations of the four edge lines are A, B, C, and D respectively, then the corresponding spreader edge points can be obtained:

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

[0154] Among them, Z is the required height value of the edge point of the sling.

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

[0156] The spreader control instruction is used to control the spreader to complete the alignment process with the target container.

[0157] Specifically, the system calculates the difference between the height values ​​of each container edge point and the corresponding spreader edge point to obtain the corresponding height difference. When the height difference of each end point is the same, it means that the bottom surface of the spreader is parallel to the top surface of the target container. Otherwise, the bottom surface of the spreader is not parallel to the top surface of the target container due to shaking.

[0158] S314: Generate and output a spreader control instruction based on the height difference.

[0159] Specifically, this step is Figure 2 The corresponding steps in the illustrated embodiment are the same in content and will not be described again here.

[0160] The container alignment control method provided by the disclosed embodiment significantly improves the accuracy and reliability of data fusion through time synchronization and external parameter calibration of multiple laser radar sensors. By establishing block indexes and performing block searches, the system can efficiently extract point cloud data of target containers and spreaders. Combined with the RANSAC plane fitting and raster scanning methods, the scheme can accurately identify the top surface of the container and the edge of the spreader to achieve accurate height difference calculation. This scheme not only overcomes the measurement error caused by shaking in traditional methods, but also achieves precise alignment of the spreader and the container through precise control command generation. Overall, the scheme improves the safety and efficiency of port operations and enhances the system's adaptability in complex environments.

[0161] Figure 4 The structural diagram of the container alignment control device provided in this application is as follows: Figure 4 As shown, the container alignment control device 400 provided in this embodiment includes:

[0162] A receiving module 410 is used to receive radar data acquired by all laser radar sensors, wherein the laser radar sensor is arranged at the bottom of the trolley corresponding to the sling, and there are at least two laser radar sensors which are respectively located at different positions at the bottom of the trolley;

[0163] The processing module 420 is used to transform the radar data into the coordinate system where the center of the spreader is located, and to splice the transformed radar data to obtain three-dimensional point cloud data;

[0164] An analysis module 430 for determining a height difference between corresponding end points of the spreader and the target container based on the point cloud data;

[0165] The output module 440 is used to generate and output a spreader control instruction based on the height difference, where the spreader control instruction is 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 a predetermined type of target container, the type of target container including corresponding size information; determine the three-dimensional point cloud data within the target area as the target point cloud data; determine the height range where the target container and the spreader are located; based on the height range where the target container is located, perform a first block search 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 surface point cloud and the endpoint height value of the top surface of the target container; based on the height range where the spreader is located, 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; based on the point cloud corresponding to the spreader, determine the height of the spreader endpoint; and determine the height difference based on the endpoint height value of the target container and the height of the spreader endpoint.

[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 block; correspond the target point cloud data 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 block that is located in the height range of the target container and has a number of three-dimensional points greater than a first set value in the point cloud block as the first target point cloud block; extract the three-dimensional points in all the first target point cloud blocks to obtain the point cloud corresponding to the target container.

[0168] In a possible implementation, the analysis module 430 is specifically used to, based on a plane fitting algorithm based on random sample consistency, determine 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 smaller than the corresponding threshold as the top surface of the target container; based on a raster scanning method, determine the edge point cloud in the point cloud corresponding to the top surface; based on the edge endpoints, determine the coordinates of the top surface endpoint of the container; based on the height of the top surface, determine the endpoint height value 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, and determine the point cloud blocks located in the height range of the sling and the number of three-dimensional points in the point cloud blocks is greater than the second set value as the second target point cloud blocks; extract the three-dimensional points in all the second target point cloud blocks to obtain the point cloud corresponding to the sling.

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

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

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

[0173] The container alignment 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 this embodiment will not be described in detail here.

[0174] Figure 5 This is 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 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0175] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0176] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment 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 (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0178] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[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. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0180] The present application also provides a computer program product, including a computer program, which implements the method in any of the above embodiments when executed by a processor.

[0181] Figure 6 The present application also provides a field crane 60, which includes the container alignment control device 610 in the above device embodiment.

[0182] The specific implementation process of the container alignment control device 610 can be found in the above-mentioned method embodiment and device embodiment. The implementation principle and technical effect are similar, and will not be repeated in this embodiment.

[0183] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method in any of the above embodiments is implemented.

[0184] The above-mentioned 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0185] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium 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 be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0186] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, 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 interface, device or unit, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0189] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0190] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain 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 optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0191] It should be further noted that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

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

[0193] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0194] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc.

[0195] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0196] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0198] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A container alignment control method, characterized in that: The steps include: Receiving radar data acquired by all laser radar sensors, wherein the laser radar sensor is arranged at the bottom of the trolley corresponding to the sling, and there are at least two laser radar sensors which are respectively located at different positions at the bottom of the trolley; The radar data is transformed into the coordinate system of the center of the spreader, and the transformed radar data is spliced ​​to obtain three-dimensional point cloud data; Determining, based on the point cloud data, a height difference between corresponding end points of the spreader and the target container; Based on the height difference, a spreader control instruction is generated and output, where the spreader control instruction is used to control the spreader to complete an alignment process with the target container.

2. The method according to claim 1, characterized in that Determining the height difference between the corresponding endpoints of the spreader and the target container based on the point cloud data includes: Determining a target area where the spreader and the target container are located based on a predetermined type of target container, the type of target container including corresponding size information; Determine the three-dimensional point cloud data in the target area as target point cloud data; Determine the height range of the target container and the spreader; Based on the height range of the target container, a first block search is performed on the target point cloud data to obtain a point cloud corresponding to the target container; Based on 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 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 a point cloud corresponding to the spreader; Based on the point cloud corresponding to the spreader, determine the height of the spreader endpoint; The height difference is determined based on the endpoint height value of the target container and the height of the spreader endpoint.

3. The method according to claim 2, characterized in that The first block search is performed on the target point cloud data based on the height range where the target container is located to obtain the point cloud corresponding to the target container, including: Based on the set block size, establishing a block index corresponding to the target point cloud data, wherein the block index includes a corresponding point cloud block; Corresponding the target point cloud data to each point cloud block of the block index; Traversing the point cloud blocks from bottom to top along the height direction, and determining a point cloud block located in the height range where the target container is located and in which the number of three-dimensional points in the point cloud block is greater than a first set value as a first target point cloud block; The three-dimensional points in all the first target point cloud blocks are extracted to obtain the point cloud corresponding to the target container.

4. The method according to claim 2, characterized in that: The step of determining the top surface point cloud and the top surface endpoint height value of the target container based on the corresponding point cloud of the target container includes: Based on the plane fitting algorithm of random sample consistency, a 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 smaller than the corresponding threshold is determined as the top surface of the target container; Based on a raster scanning method, determining an edge point cloud in a point cloud corresponding to the top surface; Based on the edge endpoints, determining the top surface endpoint coordinates of the container; Based on the height of the top surface, an endpoint height value of the top surface is determined.

5. The method according to claim 4, characterized in that Based on the height range of the spreader, performing 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, including: Traversing the point cloud blocks above the target container from bottom to top along the height direction, and determining the point cloud blocks located in the height range of the spreader and having a number of three-dimensional points greater than a second set value as the second target point cloud blocks; The three-dimensional points in all the second target point cloud blocks are extracted to obtain the corresponding point cloud of the spreader.

6. The method according to claim 5, characterized in that The step of determining the height of the end point of the spreader based on the corresponding point cloud of the spreader includes: Projecting the point cloud corresponding to the spreader along the height direction to generate a binary image of the point cloud corresponding to the spreader; Based on the edge detection algorithm and the raster scanning method, edge detection is performed on the binary image to determine the edge line of the spreader in the binary image; Based on the edge line, determining a corresponding edge three-dimensional point cloud; Based on the plane fitting algorithm of random sample consistency, plane fitting processing is performed on the three-dimensional point cloud corresponding to the edge to obtain the straight line equation corresponding to the edge of the spreader; Substitute the coordinates of the target container top surface endpoint in the horizontal plane into the straight line equation to obtain the height value of the corresponding spreader endpoint.

7. The method according to any one of claims 1 to 6, characterized in that Before receiving the radar data acquired by all the laser radar sensors, the method further includes: Time synchronization of all lidar sensors; Each laser radar sensor is calibrated with external parameters, and the external parameter calibration is used to obtain an external parameter matrix that transforms the radar data to the center of the spreader.

8. The method according to claim 7, characterized in that The radar data is transformed into the coordinate system where the center of the spreader is located, and the transformed radar data is spliced ​​to obtain three-dimensional point cloud data, including: Based on the external parameter matrix, transform the radar data to the coordinate system where the center of the spreader is located; The converted radar data are added to obtain the three-dimensional point cloud data.

9. A container alignment control device, characterized in that: include: A receiving module, used for receiving radar data acquired by all laser radar sensors, wherein the laser radar sensor is arranged at the bottom of the trolley corresponding to the sling, and there are at least two laser radar sensors which are respectively located at different positions at the bottom of the trolley; A processing module, used for transforming the radar data into a coordinate system where the center of the spreader is located, and splicing the transformed radar data to obtain three-dimensional point cloud data; an analysis module, for determining a height difference between corresponding end points of the spreader and a target container based on the point cloud data; The output module is used to generate and output a spreader control instruction based on the height difference, wherein the spreader control instruction is used to control the spreader to complete the alignment process with the target container.

10. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

11. A field bridge, characterized in that: The field crane includes the container alignment control device as claimed in claim 9.

Images