Control method and system for international trade port system

Abstract

The invention discloses a control method and system for a system for an international trade port. The control method comprises the following steps: acquiring a hoisting video of a port crane in a running state; by setting a switching surface and identifying a sliding-mode dynamic area, a high-risk interval of swinging of a suspended object can be accurately positioned, and calculation of an active-disturbance-rejection closed-loop control swing angle error is performed, so that the system can quantify the swing amplitude in real time, the error control precision can be within + / -0.5 degrees, and an analytical function designed by the rotational inertia is combined, so that the accuracy of the system is improved. Uncertain factors such as load change and wind interference are effectively counteracted, the stability of the system is remarkably improved, a controller can quickly respond to external disturbance through inertial link tracking and discrete parameter calculation, a control error is continuously attenuated through a generation mechanism of an equivalent control law, and finally the swing angle of a hung object is stably controlled within + / -1 degree. The swing control precision is improved, the collision risk is directly reduced, the cargo loading and unloading speed is increased, and the requirements of workers are met.

Description

Technical Field

[0001] The present invention relates to the technical field of port system control, and in particular to a control method and system for an international trade port system. Background Art

[0002] In recent years, with the increase in international trade throughput, the throughput of logistics terminals has become a significant issue. Port terminals serve as the connection point between ground transportation and other ships. As part of the logistics process, all containers are stored in cargo yards. Port cranes are commonly used lifting devices in port systems. Port cranes are mechanical equipment with loading and unloading functions, primarily consisting of lifting, telescopic, and rotation systems. The lifting system uses electric or hydraulic mechanisms to drive a lifting hook or clamp vertically to lift cargo. The telescopic system uses electric or hydraulic mechanisms to adjust the crane's operating radius. The rotation system allows the crane to rotate horizontally to a certain angle, allowing operators to accurately place cargo in the designated location.

[0003] In the existing technology, cranes swing during the lifting process, resulting in wasted time, reduced crane efficiency, and lowered cargo loading and unloading speed. Conventional port crane anti-sway control uses PD / LQR to calculate control parameters, which is easily affected by changes in the sensitivity of the crane's drive degrees of freedom, resulting in poor anti-sway control effect. Summary of the Invention

[0004] In order to solve the above technical problems, a control method and system for an international trade port are provided. This technical solution solves the problem that the crane mentioned in the above background technology swings during the lifting process, resulting in time waste, reduced crane working efficiency, and reduced cargo loading and unloading speed. In addition, the conventional port crane anti-sway control adopts PD / LQR to calculate the control parameters, which is easily affected by the sensitivity changes of the crane drive degree of freedom, resulting in poor anti-sway control effect.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: In a first aspect of the present invention, a method for controlling a system for an international trade port is provided, comprising: Obtaining a lifting video of a port crane in operation, and preprocessing the lifting video, wherein the preprocessing includes image denoising and image enhancement, to obtain the preprocessed lifting video; Based on the YOLO computer vision algorithm, the pre-processed lifting video is analyzed to obtain a set of port crane lifting parameters, including the luffing driving force and the crane slewing direction angle. An original switching surface is set in the preprocessed lifting video. The motion points in the switching surface include three types: normal points, starting points, and end points. The area with all end points is recorded as the sliding mode dynamic area. The swing angle error of the active disturbance rejection closed-loop control is calculated. In the port crane anti-sway control system, proportional error superposition is performed to adjust the output variables so that they have an equivalent control relationship, and the crane sliding mode control formula is obtained; The Lyapunov sliding mode control analysis function is obtained by combining the moment of inertia to eliminate uncertainty interference; An anti-sway auto-disturbance rejection controller for crane is designed. The control inertia link is used to track the auto-disturbance rejection control signal and the discrete control parameters are calculated. The anti-sway auto-disturbance rejection controller for crane adjusts the original control error and generates an equivalent control law by combining the positive real number parameters of the controller to control the anti-sway of the crane.

[0006] Preferably, the image denoising of the lifting video specifically includes the following steps: Decompose high-frequency and low-frequency information in the lifting video; The low-frequency pixel points are processed into local pixel groups, and an unbiased estimation of the error is used to approximate the similarity between the local pixel block and the target pixel block, and a sample set of similar local pixel blocks is obtained; Traverse each sample set obtained and use the principal component analysis algorithm to denoise it in turn. By calculating the covariance matrix, the orthogonal transformation matrix is obtained. Combined with the eigenvalue matrix, the dimensions containing a small amount of information in the sample set are removed to obtain the reconstructed low-frequency components. The high-frequency information is decomposed into overlapping blocks of the same size. The Euclidean distance is calculated to group similar blocks. The adaptive learning dictionary of each group is learned using singular value decomposition. The sparse coding is calculated using the split Bregman iterative algorithm combined with a convex optimization algorithm. The high-frequency components are reconstructed using the sparse coding and the adaptive learning dictionary. The inverse wavelet transform aggregates the high-frequency components and the low-frequency components to obtain the denoised lifting video.

[0007] Preferably, the image enhancement of the lifting video specifically includes the following steps: Perform adaptive histogram equalization on the denoised lifting video; Determining a cumulative distribution function of a local area by calculating a grayscale histogram of the denoised lifting video; Based on the cumulative distribution function, the grayscale mapping relationship of the local area is adaptively adjusted to obtain an enhanced lifting video.

[0008] Preferably, the method of analyzing the pre-processed lifting video based on the YOLO computer vision algorithm to obtain the port crane lifting parameter set specifically includes the following steps: Based on the crane's motion trajectory, calculate its maximum swing distance; Calculate the luffing driving force based on the displacement change of the hook and the load condition of the crane; The slewing torque is calculated by using the crane's structural model and moment of inertia, combined with the slewing angular velocity; Calculate the swing angle of the crane through the tracked trajectory; Calculate the direction angle change during luffing from the hook path; The slewing direction angle is calculated from the crane's rotation center and current position.

[0009] Preferably, the calculation formula of the swing angle error of the active disturbance rejection closed-loop control is:

[0010] Where, To control the error, is the expected control value, is the closed-loop control angle, is the closed-loop matrix, is the control parameter.

[0011] Preferably, the crane sliding mode control formula is:

[0012] Where, is the external disturbance parameter, is the intrinsic disturbance parameter, and It is the luffing driving force of the crane.

[0013] Preferably, the Lyapunov sliding mode control analysis function is:

[0014] Where, is a positive diagonal matrix, To control the disturbance, the control parameters are adjusted in real time in the Lyapunov sliding mode control analysis function to fully suppress the crane buffeting.

[0015] Preferably, the calculation formula of the discrete control parameter is:

[0016] Where, is the discrete control solution, is the nonlinear differential control coefficient, is the representative tracking parameter; The equivalent control law is:

[0017] Where, To control for the equivalent estimate, represents the first-order control derivative, is the intrinsic disturbance parameter, is a positive diagonal matrix, It is the luffing driving force of the crane.

[0018] In a second aspect of the present invention, a control system for an international trade port system is provided, comprising: A preprocessing module, the preprocessing module is used to obtain a lifting video of the port crane in an operating state, and preprocess the lifting video, the preprocessing including image denoising and image enhancement, to obtain the preprocessed lifting video; An analysis module is configured to analyze the preprocessed lifting video based on the YOLO computer vision algorithm to obtain a set of lifting parameters of the port crane, including a luffing driving force, a crane slewing direction angle, and the like; A calculation module is used to set an original switching surface in the preprocessed lifting video. The motion points in the switching surface include three types: normal points, starting points, and end points. The area with all end points is recorded as the sliding mode dynamic area, and the swing angle error of the active disturbance rejection closed-loop control is calculated; A sliding mode control module is used to perform proportional error superposition in a port crane anti-sway control system, adjust output variables so that they have an equivalent control relationship, and obtain a sliding mode control formula for the crane; An analysis module, wherein the analysis module is used to eliminate uncertainty interference by combining the moment of inertia to obtain a Lyapunov sliding mode control analysis function; An active disturbance rejection controller design module is used to design an active disturbance rejection controller for crane anti-sway. The module uses a control inertia link to track the active disturbance rejection control signal and calculates discrete control parameters. The crane anti-sway active disturbance rejection controller adjusts the original control error and generates an equivalent control law based on the controller's positive real number parameters to control the crane's anti-sway.

[0019] In a third aspect of the present invention, an electronic device is further provided. The electronic device comprises at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the first aspect of the present invention.

[0020] Compared with the prior art, the present invention provides a control method and system for an international trade port system, which has the following beneficial effects: The present invention can accurately locate the high-risk area of the swing of the suspended object by setting the switching surface and identifying the dynamic area of the sliding mode. The calculation of the swing angle error of the self-anti-disturbance closed-loop control enables the system to quantify the swing amplitude in real time. The error control accuracy can reach within ±0.5°. In combination with the analysis function of the moment of inertia design, it effectively offsets the uncertainty factors such as load changes and wind interference, and the system stability is significantly improved. Through inertia link tracking and discrete parameter calculation, the controller can quickly respond to external disturbances. The generation mechanism of the equivalent control law makes the control error continuously attenuated, and finally the swing angle of the suspended object is stably controlled within ±1°. The improved swing control accuracy directly reduces the risk of collision, increases the loading and unloading speed of goods, and meets the needs of staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the control method of the system for international trade ports in the present invention; Figure 2 Schematic diagram of the method for image denoising of a lifting video according to the present invention; Figure 3 Schematic diagram of the method for image enhancement of a lifting video in the present invention; Figure 4 Schematic diagram of the method for obtaining a set of lifting parameters of a port crane in the present invention; Figure 5 Schematic diagram of the control system of the international trade port system in the present invention. DETAILED DESCRIPTION

[0022] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0023] Example 1 Please refer to Figure 1 As shown, in a first aspect of the present invention, a method for controlling a system for an international trade port is provided, comprising: S101. Obtain a lifting video of a port crane in operation, and preprocess the lifting video, wherein the preprocessing includes image denoising and image enhancement, to obtain the preprocessed lifting video; S102. Analyze the preprocessed lifting video based on the YOLO computer vision algorithm to obtain a set of lifting parameters of the port crane, including a luffing driving force, a crane slewing direction angle, etc. S103. An original switching surface is set in the pre-processed lifting video. The motion points in the switching surface include three types: normal points, starting points, and end points. The area containing all end points is recorded as the sliding mode dynamic area. The swing angle error of the active disturbance rejection closed-loop control is calculated. S104, performing proportional error superposition in the port crane anti-sway control system, adjusting the output variables so that they have an equivalent control relationship, and obtaining a sliding mode control formula for the crane; S105, Lyapunov sliding mode control analysis function is obtained by combining the moment of inertia to eliminate uncertainty interference; S106. Design a crane anti-sway self-disturbance rejection controller, use the control inertia link to track the self-disturbance rejection control signal, calculate the discrete control parameters, and use the crane anti-sway self-disturbance rejection controller to adjust the original control error. Combined with the positive real number parameters of the controller, an equivalent control law is generated to control the crane anti-sway.

[0024] Those skilled in the art will understand that the present invention effectively eliminates environmental interference (such as fog and lighting changes) in the lifting video through image denoising and enhancement technology, improving the clarity of target recognition, which provides a high-quality data foundation for subsequent parameter extraction based on the YOLO algorithm. By setting the switching surface and identifying the sliding mode dynamic area, the high-risk area of the swing of the suspended object can be accurately located. The calculation of the swing angle error through the self-disturbance rejection closed-loop control enables the system to quantify the swing amplitude in real time, and the error control accuracy can reach within ±0.5°. In combination with the analysis function designed for the moment of inertia, it effectively offsets uncertainties such as load changes (±20% fluctuations) and wind interference (below level 5 wind), significantly improving system stability. Through inertia link tracking and discrete parameter calculation, the controller can quickly respond to external disturbances (response time <0.2s). The generation mechanism of the equivalent control law continuously attenuates the control error, ultimately achieving stable control of the suspended object swing angle within ±1°. The improved swing control accuracy directly reduces the risk of collision, increases the loading and unloading speed of cargo, and meets the needs of staff.

[0025] Please refer to Figure 2 As shown in FIG, image denoising for a lifting video specifically includes the following steps: S201, decomposing high-frequency information and low-frequency information in the lifting video; S202, performing local pixel grouping processing on the pixels of the low-frequency information, using an unbiased estimation of the error to approximately represent the similarity between the local pixel block and the target pixel block, and obtaining a sample set of similar local pixel blocks; S203, traversing each acquired sample set, performing denoising in turn using the principal component analysis algorithm, calculating the covariance matrix to obtain an orthogonal transformation matrix, and combining it with the eigenvalue matrix to remove dimensions containing a small amount of information in the sample set to obtain a reconstructed low-frequency component; S204, decomposing the high-frequency information into overlapping blocks of the same size, calculating the Euclidean distance to group similar blocks, using singular value decomposition to learn an adaptive learning dictionary for each group, calculating sparse coding using a split-Bregman iterative algorithm combined with a convex optimization algorithm, and reconstructing the high-frequency components using the sparse coding and the adaptive learning dictionary; S205 , performing inverse wavelet transform on the high-frequency components and the low-frequency components to obtain a denoised lifting video.

[0026] Please refer to Figure 3 As shown in FIG, image enhancement of the lifting video specifically includes the following steps: S301, performing adaptive histogram equalization processing on the denoised lifting video; S302, determining a cumulative distribution function of a local area by calculating a grayscale histogram of the denoised lifting video; S303 : Based on the cumulative distribution function, adaptively adjust the grayscale mapping relationship of the local area to obtain an enhanced lifting video.

[0027] Please refer to Figure 4 As shown in the figure, based on the YOLO computer vision algorithm, the pre-processed lifting video is analyzed to obtain the lifting parameter set of the port crane, which specifically includes the following steps: S401. Calculate the maximum swing distance of the crane based on its motion trajectory; S402. Calculate the luffing driving force based on the displacement change of the hook and the load condition of the crane; S403, using the structural model and rotational inertia of the crane in combination with the rotational angular velocity to calculate the rotational torque; S404, calculating the swing rotation angle of the crane based on the tracked trajectory; S405, calculating the direction angle change during the luffing process from the hook path; S406: Calculate the rotation direction angle based on the crane's rotation center and current position.

[0028] The calculation formula of the swing angle error of the active disturbance rejection closed-loop control is:

[0029] Where, To control the error, is the expected control value, is the closed-loop control angle, is the closed-loop matrix, is the control parameter.

[0030] The sliding mode control formula of the crane is:

[0031] Where, is the external disturbance parameter, is the intrinsic disturbance parameter, and It is the luffing driving force of the crane.

[0032] The Lyapunov sliding mode control analysis function is:

[0033] Where, is a positive diagonal matrix, To control the disturbance, the control parameters are adjusted in real time in the Lyapunov sliding mode control analysis function to fully suppress the crane buffeting.

[0034] The calculation formula of discrete control parameters is:

[0035] Where, is the discrete control solution, is the nonlinear differential control coefficient, is the representative tracking parameter; The equivalent control law is:

[0036] Where, To control for the equivalent estimate, represents the first-order control derivative, is the intrinsic disturbance parameter, is a positive diagonal matrix, It is the luffing driving force of the crane.

[0037] In a second aspect of the present invention, a control system for an international trade port system is provided. The system 500 includes: A preprocessing module 510 is used to obtain a lifting video of a port crane in operation and preprocess the lifting video. The preprocessing includes image denoising and image enhancement to obtain the preprocessed lifting video. An analysis module 520 is used to analyze the pre-processed lifting video based on the YOLO computer vision algorithm to obtain a set of lifting parameters of the port crane, including a luffing driving force, a crane slewing direction angle, etc. Calculation module 530 is used to set an original switching surface in the pre-processed lifting video. The motion points in the switching surface include three types: normal points, starting points, and end points. The area with all end points is recorded as the sliding mode dynamic area. The swing angle error of the active disturbance rejection closed-loop control is calculated; Sliding mode control module 540, which is used to perform proportional error superposition in the port crane anti-sway control system and adjust the output variables so that they have an equivalent control relationship, thereby obtaining a sliding mode control formula for the crane; An analysis module 550 is used to eliminate uncertainty interference by combining the moment of inertia to obtain a Lyapunov sliding mode control analysis function; The auto-disturbance rejection controller design module 560 is used to design an auto-disturbance rejection controller for crane anti-sway. The auto-disturbance rejection control signal is tracked by the control inertia link, and discrete control parameters are calculated. The auto-disturbance rejection controller for crane anti-sway adjusts the original control error and generates an equivalent control law by combining the positive real number parameters of the controller to control the anti-sway of the crane.

[0038] In a third aspect of the present invention, an electronic device is also provided. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0039] Electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of electronic device 600. Computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0040] Multiple components in the electronic device 600 are connected to the I / O interface 605, including an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0041] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as methods S100 through S106. For example, in some embodiments, methods S100 through S106 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of methods S100 through S106 described above can be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute methods S100 - S106 in any other appropriate manner (eg, by means of firmware).

[0042] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0043] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0044] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0045] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0046] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0047] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0048] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for a system used in an international trade port, characterized in that: include: Obtaining a lifting video of a port crane in operation, and preprocessing the lifting video, wherein the preprocessing includes image denoising and image enhancement, to obtain the preprocessed lifting video; Based on the YOLO computer vision algorithm, the pre-processed lifting video is analyzed to obtain a set of port crane lifting parameters, including the luffing driving force and the crane slewing direction angle. An original switching surface is set in the preprocessed lifting video. The motion points in the switching surface include three types: normal points, starting points, and end points. The area with all end points is recorded as the sliding mode dynamic area. The swing angle error of the active disturbance rejection closed-loop control is calculated. In the port crane anti-sway control system, proportional error superposition is performed to adjust the output variables so that they have an equivalent control relationship, and the crane sliding mode control formula is obtained; The Lyapunov sliding mode control analysis function is obtained by combining the moment of inertia to eliminate uncertainty interference; An anti-sway auto-disturbance rejection controller for crane is designed. The control inertia link is used to track the auto-disturbance rejection control signal and the discrete control parameters are calculated. The anti-sway auto-disturbance rejection controller for crane adjusts the original control error and generates an equivalent control law by combining the positive real number parameters of the controller to control the anti-sway of the crane.

2. The control method for an international trade port system according to claim 1, characterized in that: The image denoising of the lifting video specifically includes the following steps: Decompose high-frequency and low-frequency information in the lifting video; The low-frequency pixel points are processed into local pixel groups, and an unbiased estimation of the error is used to approximate the similarity between the local pixel block and the target pixel block, and a sample set of similar local pixel blocks is obtained; Traverse each sample set obtained and use the principal component analysis algorithm to denoise it in turn. By calculating the covariance matrix, the orthogonal transformation matrix is obtained. Combined with the eigenvalue matrix, the dimensions containing a small amount of information in the sample set are removed to obtain the reconstructed low-frequency components. The high-frequency information is decomposed into overlapping blocks of the same size. The Euclidean distance is calculated to group similar blocks. The adaptive learning dictionary of each group is learned using singular value decomposition. The sparse coding is calculated using the split Bregman iterative algorithm combined with a convex optimization algorithm. The high-frequency components are reconstructed using the sparse coding and the adaptive learning dictionary. The inverse wavelet transform aggregates the high-frequency components and the low-frequency components to obtain the denoised lifting video.

3. The control method for an international trade port system according to claim 2, characterized in that: The image enhancement of the lifting video specifically includes the following steps: Perform adaptive histogram equalization on the denoised lifting video; Determining a cumulative distribution function of a local area by calculating a grayscale histogram of the denoised lifting video; Based on the cumulative distribution function, the grayscale mapping relationship of the local area is adaptively adjusted to obtain an enhanced lifting video.

4. The control method for an international trade port system according to claim 3, characterized in that: The method of analyzing the pre-processed lifting video based on the YOLO computer vision algorithm to obtain the port crane lifting parameter set specifically includes the following steps: Based on the crane's motion trajectory, calculate its maximum swing distance; Calculate the luffing driving force based on the displacement change of the hook and the load condition of the crane; The slewing torque is calculated by using the crane's structural model and moment of inertia, combined with the slewing angular velocity; Calculate the swing angle of the crane through the tracked trajectory; Calculate the direction angle change during luffing from the hook path; The slewing direction angle is calculated from the crane's rotation center and current position.

5. The control method for an international trade port system according to claim 4, characterized in that: The calculation formula of the swing angle error of the active disturbance rejection closed-loop control is: ； Where, To control the error, is the expected control value, is the closed-loop control angle, is the closed-loop matrix, is the control parameter.

6. The control method for an international trade port system according to claim 5, characterized in that: The sliding mode control formula of the crane is: ； Where, is the external disturbance parameter, is the intrinsic disturbance parameter, and It is the luffing driving force of the crane.

7. The control method for an international trade port system according to claim 6, characterized in that: The Lyapunov sliding mode control analysis function is: ； Where, is a positive diagonal matrix, To control the disturbance, the control parameters are adjusted in real time in the Lyapunov sliding mode control analysis function to fully suppress the crane buffeting.

8. The control method for an international trade port system according to claim 7, characterized in that: The calculation formula of the discrete control parameter is: ； Where, is the discrete control solution, is the nonlinear differential control coefficient, is the representative tracking parameter; The equivalent control law is: ； Where, To control for the equivalent estimate, represents the first-order control derivative, is the intrinsic disturbance parameter, is a positive diagonal matrix, It is the luffing driving force of the crane.

9. A control system for an international trade port system, used to implement the control method for an international trade port system according to any one of claims 1 to 8, characterized in that: include: A preprocessing module, the preprocessing module is used to obtain a lifting video of the port crane in an operating state, and preprocess the lifting video, the preprocessing including image denoising and image enhancement, to obtain the preprocessed lifting video; An analysis module is configured to analyze the preprocessed lifting video based on the YOLO computer vision algorithm to obtain a set of lifting parameters of the port crane, including a luffing driving force, a crane slewing direction angle, and the like; A calculation module is used to set an original switching surface in the preprocessed lifting video. The motion points in the switching surface include three types: normal points, starting points, and end points. The area with all end points is recorded as the sliding mode dynamic area, and the swing angle error of the active disturbance rejection closed-loop control is calculated; A sliding mode control module is used to perform proportional error superposition in a port crane anti-sway control system, adjust output variables so that they have an equivalent control relationship, and obtain a sliding mode control formula for the crane; An analysis module, wherein the analysis module is used to eliminate uncertainty interference by combining the moment of inertia to obtain a Lyapunov sliding mode control analysis function; An active disturbance rejection controller design module is used to design an active disturbance rejection controller for crane anti-sway. The module uses a control inertia link to track the active disturbance rejection control signal and calculates discrete control parameters. The crane anti-sway active disturbance rejection controller adjusts the original control error and generates an equivalent control law based on the controller's positive real number parameters to control the crane's anti-sway.

10. An electronic device comprising at least one processor; and a memory communicatively connected to the at least one processor; characterized in that: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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