Device and method for automatically aligning unmanned container truck and quay crane

Abstract

The invention provides an automatic alignment device for an unmanned container truck and a quay crane, and the device comprises a sensor module which is used for sensing and obtaining the surrounding environment information of the unmanned container truck; the information processing module is used for establishing a vehicle coordinate system taking the central point of the unmanned container truck as an original point according to the mounting position of the sensor module, and unifying the environment information acquired by the sensor module to the vehicle coordinate system; the calculation module is used for calculating the relative position L0 of the unmanned container truck and the front cross beam and the rear cross beam of the quay crane under the vehicle coordinate system so as to facilitate fuzzy alignment, and calculating the longitudinal deviation L of the unmanned container truck and the quay crane according to the function L = (Lfront-Lread) / 2 so as to facilitate accurate alignment; and the control module is used for controlling the unmanned container truck to move longitudinally and perform fuzzy alignment according to the relative position L0 output by the calculation module, and then controlling the unmanned container truck to move longitudinally by a corresponding distance according to the longitudinal deviation L output by the calculation module and perform accurate positioning.

Description

Technical Field

[0001] The present application relates to the technical field of driverless container trucks, and particularly to a device and method for automatically aligning a driverless container truck with a quay crane. Background Art

[0002] In the context of the global intelligent transformation of ports, the application of driverless technology has become one of the important symbols of the modernization and intelligent development of ports. The application of driverless container trucks can significantly reduce port operation costs. Driverless container trucks refer to container trucks without drivers, which are mainly used for container transportation within ports. Driverless container trucks are positioned through 5G network communication, receive and accurately execute transportation instructions in real time, and automatically make fine-tuning, avoidance and other actions according to the surrounding environment to ensure safety and efficiency during the transportation process. First of all, driverless container trucks can reduce labor costs. Traditional transport vehicles require drivers for driving and monitoring, while driverless container trucks do not require drivers, thus saving labor costs. They can also optimize the transport route according to real-time traffic information and cargo transport needs, avoid congestion and empty runs, and further improve logistics efficiency. Secondly, through an intelligent dispatching and management system, driverless container trucks can achieve precise dispatching and real-time monitoring of driverless container trucks. This can not only improve the utilization rate and operation efficiency of vehicles, but also dynamically adjust the number and type of vehicles according to actual needs, thereby optimizing resource allocation and reducing operation costs. In addition, driverless container trucks have functions such as autonomous obstacle avoidance and precise docking, which can significantly reduce the risk of traffic accidents. Through remote monitoring and intelligent management, potential safety hazards can be detected and handled in a timely manner, thus ensuring the safety and reliability of the transportation process. As an important part of the automation and intelligent development of ports, the application and development of driverless container trucks are of great significance for improving port operation efficiency, reducing operation costs, enhancing safety, etc. Currently, driverless container trucks mainly obtain the position information of quay cranes through RTK technology (RTK, Real-Time Kinematic, is a high-precision satellite navigation technology that uses the relative position differences between multiple GNSS receivers, such as GPS, Beidou, GLONASS, etc., to correct position data in real time. Through a high-precision carrier phase differential algorithm, the error of traditional satellite positioning is reduced from the meter level to the centimeter level), send the positioning coordinates of the target quay crane to the vehicle terminal of the driverless container truck, and the driverless container truck plans the driving route according to the positioning information of the current position and the target position, and drives to the position of the target quay crane along the planned path under the positioning of integrated navigation to achieve the alignment of the driverless container truck and the quay crane. However, the existing technology for achieving the alignment of the driverless container truck and the quay crane relies on the precise positioning of the quay crane RTK and the integrated navigation of the driverless container truck. When signal shielding occurs in scenarios such as the ionosphere and multi-quay crane combined transportation, it is easy to cause the positioning drift of the quay crane RTK and the failure of the integrated navigation of the driverless container truck, resulting in different degrees of longitudinal offset in the positioning of the driverless container truck and the quay crane, affecting the alignment accuracy of the driverless container truck and the quay crane. This will not only increase the burden on the subsequent alignment of the driverless container truck and the spreader, reduce the alignment efficiency, but in severe cases, it will also cause the longitudinal deviation to exceed the normal working range of the alignment of the driverless container truck and the spreader, requiring manual intervention in a timely manner, affecting the unmanned operation of the driverless container truck. Summary of the Invention

[0003] In view of this, the present invention provides a device and method for automatically aligning an unmanned container truck with a quay crane to solve the technical problem that in the prior art, when signal shielding occurs in scenarios such as the ionosphere and multi-quay crane combined transportation, the RTK positioning of the quay crane drifts and the integrated navigation of the unmanned container truck fails, resulting in the inability of the unmanned container truck to be properly aligned with the quay crane.

[0004] The present invention provides a device for automatically aligning an unmanned container truck with a quay crane. The device includes: a sensor module, including a camera and a lidar, which is arranged on the unmanned container truck and is used to sense and obtain the environmental information around the unmanned container truck; an information processing module, connected to the sensor module, which is used to establish a vehicle coordinate system with the center point of the unmanned container truck as the origin according to the installation position of the sensor module, and unify the environmental information obtained by the sensor module under the vehicle coordinate system; a calculation module, connected to the information processing module, which is used to calculate the relative positions L0 of the unmanned container truck and the front and rear crossbeams of the quay crane in the vehicle coordinate system for fuzzy alignment. According to the function L = (L front -L rear ) / 2, calculate the longitudinal deviation L between the unmanned container truck and the quay crane for precise alignment, where L front is the vertical distance from the center of the unmanned container truck to the front crossbeam of the quay crane, and L rear is the vertical distance from the center of the unmanned container truck to the rear crossbeam of the quay crane; a control module, connected to the calculation module, which is used to control the longitudinal movement of the unmanned container truck according to the relative position L0 output by the calculation module for fuzzy alignment, and then control the unmanned container truck to move a corresponding distance longitudinally according to the longitudinal deviation L output by the calculation module for precise positioning.

[0005] Further, the device further includes: a cloud platform, connected to the unmanned container truck through a network, which is used to issue the target quay crane number so that the unmanned container truck can be aligned with the target quay crane according to the number.

[0006] Further, the sensor module is arranged in front of and behind the unmanned container truck.

[0007] Further, the camera is used to identify the quay crane number and the front and rear crossbeams, and the obtained environmental information is output by establishing a coordinate system with the camera as the origin.

[0008] Further, the lidar is used to identify the inner sides of the front and rear crossbeams of the quay crane and the containers placed on the unmanned container truck, and the obtained environmental information is output by establishing a coordinate system with the lidar as the origin.

[0009] The present invention also provides a method for automatically aligning an unmanned container truck with a quay crane. The method includes: Step 1, the camera identifies the quay crane number and obtains the position information of the target quay crane. The position information of the target quay crane includes the position vectors P front of the front crossbeam of the target quay crane and the position vector P rear; Step 2, the lidar identifies the environmental information of the inner sides of the front and rear crossbeams of the quay crane and the containers placed on the automated guided vehicle (AGV), and the environmental information includes the position vector R of the inner side of the front crossbeam front and the position vector R of the inner side of the rear crossbeam rear ; Step 3, establish a vehicle coordinate system with the center point of the AGV as the origin, and unify the environmental information identified by the camera and the lidar under the vehicle coordinate system. Assuming the transformation matrix under the vehicle coordinate system is T, then: [P' front =TP front [P' rear =TP rear [R' front =TR front [R' rear =TR rear ; Step 4, calculate the value of the relative position L0 between the AGV and the front and rear crossbeams of the target quay crane under the vehicle coordinate system, and the relative position is the distance difference L0 from the center point of the AGV to the front and rear crossbeams; Step 5, the control module controls the AGV to move longitudinally between the two crossbeams according to the relative position L0 output by the calculation module for fuzzy alignment so that it is located between the two crossbeams; Step 6, according to the function L = (L front -L rear ) / 2, calculate the longitudinal deviation L between the AGV and the quay crane, where L front is the vertical distance from the center of the AGV to the front crossbeam of the quay crane, and L rear is the vertical distance from the center of the AGV to the rear crossbeam of the quay crane; Step 7, the control module controls the AGV to move longitudinally by the corresponding distance according to the longitudinal deviation L output by the calculation module to achieve the precise alignment of the AGV and the quay crane.

[0010] Furthermore, the method further includes: Step 0, the AGV obtains the target quay crane number issued by the cloud platform.

[0011] Furthermore, the sensor module is arranged in front of and behind the AGV.

[0012] Furthermore, the environmental information obtained by the camera is output with the camera as the origin to establish a coordinate system.

[0013] Furthermore, the environmental information obtained by the lidar is output with the lidar as the origin to establish a coordinate system.

[0014] The present invention provides a device and method for automatically aligning an unmanned container truck with a quay crane. Through cameras and lidar installed in front of and behind the unmanned container truck, environmental information around the vehicle is obtained, and then the environmental information is unified in the vehicle coordinate system. Next, the relative position L0 and the longitudinal deviation L are calculated, and the alignment of the unmanned container truck with the quay crane is achieved in two steps. According to the characteristics of the information obtained and perceived by the cameras and lidar, the information obtained by the cameras and lidar is used successively to complete fuzzy alignment and precise alignment. The present invention solves the problem that when signal shielding occurs in scenarios such as the ionosphere and multi-quay-crane combined transportation, the RTK positioning of the quay crane drifts and the integrated navigation of the unmanned container truck fails, resulting in the inability of the unmanned container truck to be normally aligned with the quay crane, further improving the alignment efficiency of the unmanned container truck with the quay crane and reducing the manual takeover rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 FIG. is a schematic diagram of the position of a target quay crane in front of an unmanned container truck provided by the present invention; Figure 2 FIG. is a schematic diagram of the position of a target quay crane behind an unmanned container truck provided by the present invention; Figure 3 FIG. is a schematic diagram of the position of an unmanned container truck between the two crossbeams of a target quay crane provided by the present invention; Figure 4 FIG. is a schematic diagram of the operation of an unmanned container truck for loading and unloading containers onto a ship provided by the present invention; Figure 5 FIG. is a schematic diagram of the operation of an unmanned container truck for unloading containers from a ship and loading them into a container provided by the present invention; Figure 6 FIG. is a schematic flow chart of a method for automatically aligning an unmanned container truck with a quay crane provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment of the device item: The present invention provides a device for automatically aligning an unmanned container truck with a quay crane, and the device includes: a sensor module, an information processing module, a calculation module, a control module, and a cloud platform.

[0018] The sensor module, including a camera and a lidar, is installed on the driverless container truck and is used to sense and obtain the environmental information around the driverless container truck. The sensor module is arranged at the front and rear of the driverless container truck, that is, cameras and lidars are installed at the front and rear positions of the driverless container truck, and it is ensured that the camera can identify the quay crane number and the front and rear crossbeams, and the lidar can identify the inner sides of the front and rear crossbeams of the quay crane and the containers placed on the driverless container truck. The camera is used to identify the quay crane number and the front and rear crossbeams, and the obtained environmental information is output with the camera as the origin to establish a coordinate system. The lidar is used to identify the inner sides of the front and rear crossbeams of the quay crane and the containers placed on the driverless container truck, and the obtained environmental information is output with the lidar as the origin to establish a coordinate system.

[0019] The information processing module is connected to the sensor module and is used to establish a vehicle coordinate system with the center point of the driverless container truck as the origin according to the installation position of the sensor module, and unify the environmental information obtained by the sensor module under the vehicle coordinate system. Since the surrounding environmental information obtained by the camera and the lidar is output with the camera and the lidar as the origin respectively, the difference in installation positions results in the information they obtain not being in the same coordinate system, and the relative position cannot be directly judged and calculated. Therefore, according to the installation positions of the camera and the lidar on the driverless container truck, all the information obtained by the cameras and lidars is uniformly converted to the vehicle coordinate system with the center point of the driverless container truck as the origin, so as to calculate the relative position relationship in the later stage. Since the camera cannot accurately sense the distance from surrounding objects, the spatial position information obtained by the camera is used for preliminary fuzzy alignment. The lidar can accurately sense the distance from surrounding objects, so the spatial position information obtained by the lidar is used for final accurate alignment.

[0020] The calculation module is connected to the information processing module and is used to calculate the relative position L0 between the driverless container truck and the front and rear crossbeams of the quay crane in the vehicle coordinate system for fuzzy alignment. According to the function L = (L front - L rear ) / 2, the longitudinal deviation L between the driverless container truck and the quay crane is calculated for accurate alignment, where L front is the vertical distance from the center of the driverless container truck to the front crossbeam of the quay crane, and L rear is the vertical distance from the center of the driverless container truck to the rear crossbeam of the quay crane.

[0021] The control module is connected to the calculation module and is used to control the longitudinal movement of the driverless container truck for fuzzy alignment according to the relative position L0 output by the calculation module, and then control the longitudinal movement of the driverless container truck by a corresponding distance according to the longitudinal deviation L output by the calculation module for precise positioning.

[0022] The cloud platform is connected to the driverless container truck through the network and is used to issue the target quay crane number so that the driverless container truck can align with the target quay crane according to the number.

[0023] The present invention provides a device and method for automatic alignment of an unmanned container truck and a quay crane. Through cameras and lidar installed in front of and behind the unmanned container truck, environmental information around the vehicle is obtained, and then the environmental information is unified in the vehicle coordinate system, and then the relative position L0 and the longitudinal deviation L are calculated, and fuzzy positioning and precise positioning are respectively performed. The present invention solves the problem that when signal shielding occurs in scenarios such as the ionosphere and multi-quay crane intermodal transportation, the RTK positioning of the quay crane drifts and the combined navigation of the unmanned container truck fails, resulting in the inability of the unmanned container truck and the quay crane to be normally aligned, further improving the alignment efficiency of the unmanned container truck and the quay crane and reducing the manual takeover rate.

[0024] Method embodiment: The present invention provides a method for an automatic alignment device of an unmanned container truck and a quay crane, as Figure 6 shown, the method includes the following steps.

[0025] Step 0, the unmanned container truck obtains the target quay crane number issued by the cloud platform.

[0026] First, obtain the target quay crane number issued by the cloud platform from the vehicle terminal of the unmanned container truck.

[0027] Step 1, the camera identifies the quay crane number and obtains the target quay crane position information, where the target quay crane position information includes the position vector P front of the front crossbeam of the target quay crane and the position vector P rear of the rear crossbeam; Step 2, the lidar identifies the environmental information of the inner sides of the front and rear crossbeams of the quay crane and the container placed on the unmanned container truck, where the environmental information includes the position vector R front of the inner side of the front crossbeam and the position vector R rear of the inner side of the rear crossbeam; Step 3, establish a vehicle coordinate system with the center point of the unmanned container truck as the origin, and unify the environmental information identified by the camera and the lidar in the vehicle coordinate system. Assuming that the transformation matrix in the vehicle coordinate system is T, then there are: [P' front = TP front [P' rear = TP rear [R' front = TR front [R' rear = TR rear ; ​​​As described above, the environmental information obtained by the camera is output with the camera as the origin to establish a coordinate system, and the environmental information obtained by the lidar is output with the lidar as the origin to establish a coordinate system. Therefore, it is necessary to unify the environmental information identified by the camera and the lidar to the vehicle coordinate system for information processing.

[0028] Step 4, calculate the value of the relative position L0 between the unmanned container truck and the front and rear crossbeams of the target quay crane in the vehicle coordinate system. The relative position is the distance difference L0 from the center point of the unmanned container truck to the front and rear crossbeams. The camera and the lidar obtain the spatial position information of the unmanned container truck (or container) and the two crossbeams of the target quay crane. After the calculation module obtains the spatial position information, it processes the obtained information and calculates the positional relationship between the unmanned container truck and the two crossbeams.

[0029] Step 5, according to the relative position L0 output by the calculation module, the control module controls the unmanned container truck to longitudinally move between the two crossbeams for fuzzy alignment so that it is located between the two crossbeams. The control module controls the unmanned container truck to longitudinally move between the two crossbeams to ensure that the two radars installed in front of and behind the unmanned container truck can respectively and stably identify the inner sides of the front crossbeam and the rear crossbeam of the quay crane close to the unmanned container truck, so as to realize the fuzzy alignment between the unmanned container truck and the quay crane. In the above vehicle coordinate system, calculate the spatial position relationship between the front and rear lidars installed on the unmanned container truck and the front and rear crossbeams of the quay crane respectively. If the front lidar of the unmanned container truck cannot stably identify the inner side of the front crossbeam of the target quay crane close to the unmanned container truck, or the rear lidar cannot stably identify the inner side of the rear crossbeam close to the unmanned container truck, it is easy to cause inaccurate longitudinal deviation calculation, then preliminary fuzzy alignment is required. If the front crossbeam of the target quay crane is in front of the front lidar of the unmanned container truck, and the rear crossbeam is in front of the rear lidar, as Figure 1 shown, at this time the unmanned container truck has not yet driven under the target quay crane, and the unmanned container truck is controlled to move forward until the front and rear lidars can respectively and stably identify the inner sides of the front and rear crossbeams close to the unmanned container truck. If the front crossbeam of the target quay crane is behind the front lidar of the unmanned container truck, and the rear crossbeam is behind the rear lidar, as Figure 2 shown, at this time the unmanned container truck has driven past the target quay crane, and the unmanned container truck is controlled to move backward until the front and rear lidars can respectively and stably identify the inner sides of the front and rear crossbeams close to the unmanned container truck. However, if the front crossbeam of the target quay crane is in front of the front lidar of the unmanned container truck, and the rear crossbeam is behind the rear lidar, as Figure 3 shown, at this time the front and rear lidars of the unmanned container truck can respectively and stably identify the inner sides of the front and rear crossbeams close to the unmanned container truck, then no fuzzy alignment is required. The camera is used to realize the preliminary fuzzy alignment between the unmanned container truck and the target quay crane, so that the front and rear lidars can respectively and stably identify the inner sides of the front and rear crossbeams close to the unmanned container truck, preparing for subsequent precise alignment.

[0030] Step 6, according to the function L = (L front - L rear ) / 2, calculate the longitudinal deviation L between the driverless yard truck and the quay crane, where L front is the vertical distance from the center of the driverless yard truck to the front crossbeam of the quay crane, and L rear is the vertical distance from the center of the driverless yard truck to the rear crossbeam of the quay crane; Based on the spatial position coordinates of the inner sides of the two crossbeams of the quay crane obtained by the lidar, calculate the vertical distances L front and L rear from the center of the driverless yard truck (or container) to the sides of the front and rear crossbeams of the quay crane respectively, and further calculate the longitudinal deviation L = (L front - L rear ) / 2. Obtain the ship loading / unloading or ship unloading / loading operation instructions sent by the cloud platform from the driverless yard truck terminal, and perform precise alignment according to different operation instructions. When loading the ship and unloading the container, the lidar on the driverless yard truck identifies the front and rear end faces of the container. According to the dimensions of the two end faces of the container and the relative spatial positions of the two end faces in the above vehicle coordinate system, the position of the center point of the container can be determined. Then, based on the spatial coordinates of the inner sides of the front and rear crossbeams obtained by the front and rear lidars in the above vehicle coordinate system, calculate the vertical distance Lfront from the center point of the container to the inner side of the front crossbeam of the quay crane, and the vertical distance Lrear from the center point of the container to the inner side of the rear crossbeam of the quay crane, and further calculate the longitudinal deviation L = (L front - L rear ) / 2. When unloading the ship and loading the container, according to the spatial coordinates of the inner sides of the front and rear crossbeams obtained by the front and rear lidars in the above vehicle coordinate system, calculate the vertical distance Lfront from the center point of the driverless yard truck to the inner side of the front crossbeam of the quay crane, and the vertical distance Lrear from the center point of the driverless yard truck to the inner side of the rear crossbeam of the quay crane, and further calculate the longitudinal deviation L = (L front - L rear ) / 2. Use the lidar to identify the inner sides of the two crossbeams of the target quay crane close to the driverless yard truck, accurately calculate the longitudinal error, and realize the precise alignment between the driverless yard truck (or container) and the target quay crane.

[0031] Step 7, the control module controls the driverless yard truck to longitudinally move a corresponding distance according to the longitudinal deviation L output by the calculation module, so as to realize the precise alignment between the driverless yard truck and the quay crane.

[0032] As Figure 4 and Figure 5 shown, the calculation module outputs the longitudinal deviation L to the control module, and the control module controls the driverless yard truck to longitudinally move a corresponding distance, so as to realize the precise positioning between the driverless yard truck and the quay crane during ship loading / unloading and ship unloading / loading operations.

[0033] The present invention provides a device and method for automatic alignment of an unmanned container truck and a quay crane. Through cameras and lidar installed in front of and behind the unmanned container truck, environmental information around the vehicle is obtained, and then the environmental information is unified in the vehicle coordinate system. Subsequently, the relative position L0 and the longitudinal deviation L are calculated, and fuzzy positioning and precise positioning are performed respectively. The present invention solves the problem that when signal shielding occurs in scenarios such as the ionosphere and multi-quay-crane combined transportation, the RTK positioning of the quay crane drifts and the combined navigation of the unmanned container truck fails, resulting in the inability of the unmanned container truck and the quay crane to be properly aligned. Furthermore, it improves the alignment efficiency of the unmanned container truck and the quay crane and reduces the manual takeover rate.

[0034] In summary, the embodiment of the present invention provides a device and method for automatic alignment of an unmanned container truck and a quay crane. When signal shielding occurs in scenarios such as the ionosphere and multi-quay-crane combined transportation, or due to other reasons, a longitudinal deviation occurs between the actual operating position of the quay crane and the position where the unmanned container truck reaches the target position, this technical solution starts to operate. It can enhance the operating ability of the unmanned container truck when signal shielding occurs in scenarios such as the ionosphere and multi-quay-crane combined transportation, or due to other reasons, a longitudinal deviation occurs between the actual operating position of the quay crane and the position where the unmanned container truck reaches the target position. It improves the alignment efficiency and accuracy of the unmanned container truck and the quay crane, reduces the takeover rate of the unmanned container truck, and enhances the unmanned operation ability.

[0035] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A device for automatically aligning an unmanned container truck with a quay crane, characterized in that: The device comprises: The sensor module, including a camera and a laser radar, is installed on the unmanned container truck to sense and obtain the surrounding environment information of the unmanned container truck; The information processing module is connected to the sensor module and is used to establish a vehicle coordinate system with the center point of the unmanned container truck as the origin according to the installation position of the sensor module, and unify the environmental information obtained by the sensor module into the vehicle coordinate system; The calculation module is connected to the information processing module and is used to calculate the relative position L0 of the unmanned container truck and the front and rear beams of the quay crane in the vehicle coordinate system, so as to perform fuzzy alignment. front -L rear ) / 2, calculate the longitudinal deviation L between the unmanned container truck and the quay crane for accurate alignment, where L front L is the vertical distance from the center of the unmanned container to the front beam of the quay crane. rear It is the vertical distance from the unmanned truck center to the rear beam of the quay crane; The control module is connected to the calculation module and is used to control the longitudinal movement of the unmanned container truck according to the relative position L0 output by the calculation module, perform fuzzy alignment, and then control the longitudinal movement of the unmanned container truck according to the longitudinal deviation L output by the calculation module to perform precise positioning.

2. The device for automatically aligning an unmanned container truck with a quay crane according to claim 1, characterized in that: The device also includes: a cloud platform, which is connected to the unmanned container truck through a network and is used to issue a target quay crane number so that the unmanned container truck is aligned with the target quay crane according to the number.

3. The device for automatically aligning an unmanned container truck with a quay crane according to claim 1, characterized in that: The sensor modules are arranged in front and rear of the unmanned container truck.

4. The device for automatically aligning an unmanned container truck with a quay crane according to claim 1, characterized in that: The camera is used to identify the quay crane number and the front and rear beams, and the acquired environmental information is output by establishing a coordinate system with the camera as the origin.

5. The device for automatically aligning an unmanned container truck with a quay crane according to claim 1, characterized in that: The laser radar is used to identify the inner side surfaces of the front and rear beams of the quay crane and the containers placed on the unmanned container trucks. The acquired environmental information is output by establishing a coordinate system with the laser radar as the origin.

6. A method using the automatic alignment device for unmanned container trucks and quay cranes according to claims 1-5, characterized in that: The method comprises: Step 1: The camera identifies the quay crane number and obtains the target quay crane position information, wherein the target quay crane position information includes the position vector P of the front beam of the target quay crane. front and the position vector P of the rear crossbeam rear ; Step 2: The laser radar identifies the inner side of the two beams at the front and rear of the quay crane and the environmental information of the container placed on the unmanned container truck. The environmental information includes the position vector R of the inner side of the front beam. front and the position vector R of the inner side of the rear cross beam rear ; Step 3: Establish a vehicle coordinate system with the center point of the unmanned truck as the origin, and unify the environmental information recognized by the camera and lidar into the vehicle coordinate system. Assuming that the transformation matrix in the vehicle coordinate system is T, we have: [P' front =TP front ] [P' rear =TP rear ] [R' front =TR front ] [R' rear =TR rear ]; Step 4, calculating the relative position L0 between the unmanned container truck and the front and rear beams of the target quay crane in the vehicle coordinate system, wherein the relative position is the distance difference L0 from the center point of the unmanned container truck to the front and rear beams; Step 5: The control module controls the unmanned container truck to move longitudinally to between the two beams according to the relative position L0 output by the calculation module, and performs fuzzy alignment so that it is located between the two beams; Step 6, according to the function L=(L front -L rear ) / 2, calculate the longitudinal deviation L between the unmanned container truck and the quay crane, where L front L is the vertical distance from the center of the unmanned container to the front beam of the quay crane. rear It is the vertical distance from the unmanned truck center to the rear beam of the quay crane; Step 7: The control module controls the unmanned container truck to move longitudinally by a corresponding distance according to the longitudinal deviation L output by the calculation module, so as to achieve precise alignment between the unmanned container truck and the quay crane.

7. The method for automatically aligning an unmanned container truck with a quay crane according to claim 6, characterized in that: The method also includes: step 0, the unmanned container truck obtains the target quay crane number issued by the cloud platform.

8. The method for automatically aligning an unmanned container truck with a quay crane according to claim 6, characterized in that: The sensor modules are arranged in front and rear of the unmanned container truck.

9. The method for automatically aligning an unmanned container truck with a quay crane according to claim 6, characterized in that: The environmental information acquired by the camera is output by establishing a coordinate system with the camera as the origin.

10. The method for automatically aligning an unmanned container truck with a quay crane according to claim 6, characterized in that: The environmental information acquired by the laser radar is output by establishing a coordinate system with the laser radar as the origin.

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