A ship intelligent moving control method and system based on adaptive algorithm

Abstract

The present invention relates to the field of intelligent ship control technology, and in particular to a method and system for intelligent ship movement control based on an adaptive algorithm. The method comprises the following steps: obtaining hardware device data required for ship movement control; configuring the network topology of a computer network system, winch equipment, and encoder according to the hardware device data, and constructing an electrical ring network; initializing and setting up the electrical ring network and conducting communication tests, establishing a data sharing mechanism between devices, and obtaining the system's initial configuration data; based on the system's initial configuration data, collecting the wire rope's retraction and extension length and tension parameters through sensors of the winch equipment, and constructing a real-time monitoring model for the ship's position and posture; and utilizing the real-time monitoring model to monitor the ship's status and generate real-time ship status data. The present invention adopts an electrical ring network with a redundant backup mechanism, which improves the system's communication real-time performance and reliability, and ensures the stability of control signal transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of ship intelligent control, and in particular to a ship intelligent moving control method and system based on an adaptive algorithm. Background Art

[0002] "Ship movement" generally refers to ship trajectory tracking, which involves controlling a ship's precise movement along a pre-defined trajectory. This concept involves heading control and path tracking, enabling ships to automatically and safely navigate designated routes, maintaining accurate tracks even in complex and uncertain marine environments. Trajectory tracking is a key research area in the field of intelligent ships, involving automatic control technologies that enable ships to make autonomous decisions and navigate safely without human intervention.

[0003] However, traditional intelligent ship maneuvering control methods often suffer from the following problems: They rely on fixed control strategies and are unable to quickly respond to complex environmental changes, such as sudden changes in wind direction, currents, and dynamic changes in equipment status. This leads to inefficiencies in the maneuvering process and even control errors. The coordinated control between multiple winch systems is prone to uneven force distribution or asynchronous movements, resulting in deviations in the ship's attitude and trajectory, affecting the accuracy of mission execution. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a ship intelligent ship moving control method and system based on an adaptive algorithm to solve at least one of the above technical problems.

[0005] To achieve the above object, a ship intelligent moving control method based on an adaptive algorithm includes the following steps:

[0006] Step S1: Obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network, winch equipment and encoder according to the hardware equipment data to build an electrical ring network; initialize the electrical ring network and perform communication testing, establish a data sharing mechanism between devices, and obtain the system initial configuration data;

[0007] Step S2: Based on the initial system configuration data, the sensors of the winch equipment are used to collect the retracted and extended lengths and tension parameters of the wire rope, and a real-time monitoring model for the ship's position and attitude is constructed; the real-time monitoring model is used to monitor the ship's status and generate real-time ship status data; the real-time ship status data is subjected to motion control analysis and processing to generate ship motion control parameters;

[0008] Step S3: Establishing an adaptive control model based on the ship motion control parameters, wherein the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrating and optimizing the parameters of each model in the adaptive control model to generate adaptive control strategy data;

[0009] Step S4: Based on the adaptive control strategy data, real-time control adjustments are performed on the ship to generate correction control instructions. The real-time control adjustments specifically include performing PID control and constant torque control based on the real-time state data of the ship, as well as speed adjustment and trajectory correction.

[0010] Step S5: Coordinate and control the working status of each winch device according to the modified control instruction to realize the ship's linked ship movement and construction track line ship movement functions.

[0011] The present invention can build an electrical ring network by collecting the hardware device data required for ship movement control and configuring the network topology of the computer network and equipment based on this data. The design of the ring network ensures efficient data transmission and redundant backup, greatly improving the stability and fault tolerance of the system. The implementation of initialization settings and communication tests verifies the availability of the network, ensures the smoothness of data sharing between devices, and provides reliable basic configuration data for subsequent system operations. The effect of this step is to establish a stable and efficient network architecture and data communication mechanism, ensuring the real-time and safety of the entire system. The sensors of the winch equipment collect the retraction and tension parameters of the wire rope in real time, and combine the system's initial configuration data to build a real-time monitoring model of the ship's position and posture, which can accurately monitor the real-time status of the ship. The generated real-time status data of the ship provides high-precision input data for subsequent motion control analysis. The motion control analysis is further converted into ship motion control parameters, laying a data foundation for the precise movement of the ship and construction trajectory control. The effect of this step is reflected in improving the real-time and accuracy of ship status monitoring, supporting dynamic adjustment and intelligent decision-making. By using ship motion control parameters to construct an adaptive control model, including an anchor calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model, it can comprehensively address the ship's dynamic position and attitude adjustment requirements. Parameter calibration and optimization steps improve the accuracy of each model, enabling the generated adaptive control strategy data to flexibly adapt to varying sea conditions and mission requirements. This step improves the system's adaptability, control accuracy, and response efficiency through a highly flexible and precise control model. Based on the adaptive control strategy data, PID control and constant torque control are implemented using real-time status data, enabling not only speed adjustment but also precise correction of the ship's trajectory. Through this process, corrected control instructions effectively reduce deviations and maintain smooth ship movement. This step improves the accuracy and stability of ship movement, significantly reducing errors and improving work efficiency, especially in complex construction environments. Based on the corrected control instructions, the operating status of each winch device is coordinated to implement the ship's coordinated ship movement and construction trajectory movement. This winch device coordinated control method not only improves coordination during ship movement but also ensures the accuracy and controllability of the construction trajectory. The effect of this step is to improve the overall efficiency of the construction process, ensure the precise execution of the trajectory, and at the same time reduce the impact of a single equipment failure on the entire system through linkage control.

[0012] The present invention also provides a ship intelligent maneuvering control system based on an adaptive algorithm, which is used to execute the above-mentioned ship intelligent maneuvering control method based on an adaptive algorithm. The ship intelligent maneuvering control system based on an adaptive algorithm includes:

[0013] The device configuration module is used to obtain the hardware device data required for ship movement control; configure the network topology of the computer network, winch equipment and encoder according to the hardware device data, and build an electrical ring network; initialize the electrical ring network and perform communication testing, establish a data sharing mechanism between devices, and obtain the system initial configuration data;

[0014] The status monitoring module is used to collect the wire rope's retraction and extension length and tension parameters through the winch equipment's sensors based on the system's initial configuration data, and to build a real-time monitoring model for the ship's position and attitude. The real-time monitoring model is used to monitor the ship's status and generate real-time ship status data. The real-time ship status data is then analyzed and processed for motion control to generate ship motion control parameters.

[0015] The adaptive control module is used to establish an adaptive control model based on the ship motion control parameters, where the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrate and optimize the parameters of each model in the adaptive control model to generate adaptive control strategy data;

[0016] The control adjustment module is used to make real-time control adjustments to the ship based on the adaptive control strategy data and generate correction control instructions. The real-time control adjustment specifically includes PID control and constant torque control based on the real-time status data of the ship, as well as speed adjustment and trajectory correction.

[0017] The coordinated ship moving module is used to coordinate and control the working status of each winch equipment according to the correction control instructions, realizing the ship's linked ship moving and construction trajectory line ship moving functions.

[0018] This invention establishes an electrical ring network by acquiring hardware device data required for ship motion control and, based on this data, configuring the network topology of the computer network, winch equipment, and encoders. Initializing and testing the electrical ring network ensures the smooth establishment of a data sharing mechanism between devices. This process provides a stable infrastructure, ensuring the smooth operation of the ship motion control system and reducing the possibility of hardware configuration errors and system communication failures. By optimizing the network topology, system reliability and data exchange efficiency can be effectively improved. Based on the system's initial configuration data, sensors on the winch equipment collect the retracted and unretracted length and tension parameters of the wire rope, thereby constructing a real-time monitoring model for the ship's position and attitude. This real-time monitoring model provides accurate data support for ship status monitoring, ensuring comprehensive monitoring of the ship's position, attitude, and dynamic changes. By analyzing the ship's real-time status data, ship motion control parameters can be further generated to ensure accurate motion control. By continuously monitoring and analyzing the ship's status, this module facilitates efficient motion control and real-time adjustment. In the adaptive control module, the ship motion control parameters are used to establish an adaptive control model, which includes the anchor calculation model, position deviation correction model, angle deviation correction model, and torque balance control model. By calibrating and optimizing the parameters of these models, the vessel can achieve adaptive adjustment in different operating environments. The establishment of an adaptive control model ensures that the vessel can flexibly adjust to changing environmental and operating conditions in real-time, thereby implementing precise control strategies and improving the stability and accuracy of vessel operations. Based on the adaptive control strategy data, the control adjustment module uses real-time vessel status data to implement PID control and constant torque control, adjusting speed and correcting trajectory. This process enables fine-tuning of the vessel's motion control, ensuring that the vessel maintains the appropriate attitude and trajectory in complex operating environments. Real-time adjustment of the vessel's control parameters effectively eliminates deviations, ensuring that the vessel moves on the intended trajectory and avoiding deviations or unstable attitudes. The coordinated ship maneuvering module coordinates the operating status of each winch device based on corrected control commands, enabling coordinated ship maneuvering and construction trajectory-based ship maneuvering. This module achieves precise ship maneuvering through the coordinated operation of multiple winches, ensuring that the vessel always follows the correct trajectory during construction. By optimizing the coordination between winches, ship maneuvering becomes more efficient and stable, avoiding maneuvering errors or instability caused by improper operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0020] Figure 1Schematic diagram of the steps of the ship intelligent ship moving control method based on the adaptive algorithm of the present invention;

[0021] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0022] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0025] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for controlling ship intelligent movement based on an adaptive algorithm, the method comprising the following steps:

[0027] Step S1: Obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network, winch equipment and encoder according to the hardware equipment data to build an electrical ring network; initialize the electrical ring network and perform communication testing, establish a data sharing mechanism between devices, and obtain the system initial configuration data;

[0028] Step S2: Based on the initial system configuration data, the sensors of the winch equipment are used to collect the retracted and extended lengths and tension parameters of the wire rope, and a real-time monitoring model for the ship's position and attitude is constructed; the real-time monitoring model is used to monitor the ship's status and generate real-time ship status data; the real-time ship status data is subjected to motion control analysis and processing to generate ship motion control parameters;

[0029] Step S3: Establishing an adaptive control model based on the ship motion control parameters, wherein the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrating and optimizing the parameters of each model in the adaptive control model to generate adaptive control strategy data;

[0030] Step S4: Based on the adaptive control strategy data, real-time control adjustments are performed on the ship to generate correction control instructions. The real-time control adjustments specifically include performing PID control and constant torque control based on the real-time state data of the ship, as well as speed adjustment and trajectory correction.

[0031] Step S5: Coordinate and control the working status of each winch device according to the modified control instruction to realize the ship's linked ship movement and construction track line ship movement functions.

[0032] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of a method for controlling an intelligent ship movement based on an adaptive algorithm according to the present invention. In this example, the method for controlling an intelligent ship movement based on an adaptive algorithm includes the following steps:

[0033] Step S1: Obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network, winch equipment and encoder according to the hardware equipment data to build an electrical ring network; initialize the electrical ring network and perform communication testing, establish a data sharing mechanism between devices, and obtain the system initial configuration data;

[0034] Acquiring hardware device data required for ship motion control in this embodiment involves retrieving basic configuration data for winches, encoders, and computer networks. First, the winch controller is connected to the computer network through the hardware device's communication interface, and parameters such as the IP address and gateway address of each device are retrieved. Next, network topology planning software is used to configure the network topology of the winches and encoders based on the ship's construction site layout requirements, forming a logical ring structure to ensure redundant data transmission at each node. A programmable logic controller (PLC) is then used to initialize the constructed electrical ring network, including setting node priorities, adjusting the communication baud rate, and performing network group numbering. Finally, data packet testing is used to verify inter-device communication effectiveness, and network monitoring tools are used to ensure no packet loss or delay. A sharing mechanism is also established between devices, and real-time status data generated by the nodes is stored in the computer host via shared memory, forming the system's initial configuration data.

[0035] Step S2: Based on the initial system configuration data, the sensors of the winch equipment are used to collect the retracted and extended lengths and tension parameters of the wire rope, and a real-time monitoring model for the ship's position and attitude is constructed; the real-time monitoring model is used to monitor the ship's status and generate real-time ship status data; the real-time ship status data is subjected to motion control analysis and processing to generate ship motion control parameters;

[0036] The embodiment of the present invention collects the wire rope's retraction and extension length and tension parameters through the winch equipment's built-in force sensor and displacement sensor based on the system initial configuration data generated in step S1, wherein the wire rope's retraction and extension length is recorded by the pulse counting method, and the tension parameters are collected in real time by the force sensor, with the sampling frequency set to 100Hz. In combination with the ship's heading sensor and inclination sensor, a real-time monitoring model for the ship's position and attitude based on the Kalman filter algorithm is constructed. This model is used to continuously track the ship's spatial position, heading angle, and attitude angle, generating high-precision real-time ship status data. The ship's real-time status data is further utilized, combined with the kinematic equations to analyze the changes in the ship's acceleration, velocity, and angular velocity, and a variable step-size interpolation method is used to calculate the motion control parameters, including the target position offset and attitude angle correction in the ship's moving path, to provide input parameters for subsequent control.

[0037] Step S3: Establishing an adaptive control model based on the ship motion control parameters, wherein the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrating and optimizing the parameters of each model in the adaptive control model to generate adaptive control strategy data;

[0038] The embodiment of the present invention uses an adaptive algorithm to construct a control model based on the ship motion control parameters generated in step S2, specifically including: using an anchor calculation model to calculate the optimal anchor position based on the target offset; adjusting the error between the current position of the ship and the target position through a position deviation correction model; optimizing the ship's heading angle and attitude angle using an angle deviation correction model; and distributing the pulling force of each winch device through a torque balance control model to achieve balanced control. The parameter calibration of each model is carried out using a method that combines offline simulation with actual calibration. First, historical data under a typical construction scenario is input into a simulation environment for preliminary calibration. Subsequently, real-time monitoring data of actual construction is used for dynamic optimization to adjust the control gain and constraints. Finally, adaptive control strategy data suitable for the current construction scenario is generated to ensure that the dynamic response performance and steady-state accuracy of the control model meet the construction requirements.

[0039] Step S4: Based on the adaptive control strategy data, real-time control adjustments are performed on the ship to generate correction control instructions. The real-time control adjustments specifically include performing PID control and constant torque control based on the real-time state data of the ship, as well as speed adjustment and trajectory correction.

[0040] The embodiment of the present invention controls and adjusts the ship in real time based on the adaptive control strategy data generated in step S3. The specific method is as follows: the ship status data collected in the real-time monitoring model is input into the PID controller for position and angle correction, and the PID control parameters are set to Kp=0.8, Ki=0.2, and Kd=0.1; the constant torque control strategy is combined to maintain uniform force on the wire rope by controlling the tension of each winch device; at the same time, the ship's movement speed is adjusted according to the corrected motion parameters, and the speed range is set to 0.1~0.5 m / s to avoid excessive displacement errors; finally, the path planning module is combined to continuously correct the actual trajectory of the ship, and the offset trajectory is gradually approximated through an incremental adjustment strategy to ensure that the ship's trajectory is consistent with construction requirements.

[0041] Step S5: Coordinate and control the working status of each winch device according to the modified control instruction to realize the ship's linked ship movement and construction track line ship movement functions.

[0042] In the embodiment of the present invention, the working status of each winch device is scheduled in real time through the control center according to the corrected control instructions generated in step S4. The specific operations include: first, allocating the retraction and extension speed and tension of each winch device according to the control instructions, and using a variable frequency drive to achieve precise control of the winch device motor speed, with the retraction and extension speed range of 0.050.3m / s and the tension range of 0.10kN; second, using a distributed coordination algorithm to ensure the synchronization of the operation of each winch device to avoid slack or overload of the wire rope; in the linked ship moving operation, the coordination of each winch device is adjusted according to the overall position and posture of the ship, and the tension is dynamically distributed; in the construction trajectory line ship moving operation, the path tracking algorithm and real-time correction control instructions are used to ensure that the ship moves smoothly along the set trajectory, and finally complete the high-precision ship displacement control task.

[0043] The present invention can build an electrical ring network by collecting the hardware device data required for ship movement control and configuring the network topology of the computer network and equipment based on this data. The design of the ring network ensures efficient data transmission and redundant backup, greatly improving the stability and fault tolerance of the system. The implementation of initialization settings and communication tests verifies the availability of the network, ensures the smoothness of data sharing between devices, and provides reliable basic configuration data for subsequent system operations. The effect of this step is to establish a stable and efficient network architecture and data communication mechanism, ensuring the real-time and safety of the entire system. The sensors of the winch equipment collect the retraction and tension parameters of the wire rope in real time, and combine the system's initial configuration data to build a real-time monitoring model of the ship's position and posture, which can accurately monitor the real-time status of the ship. The generated real-time status data of the ship provides high-precision input data for subsequent motion control analysis. The motion control analysis is further converted into ship motion control parameters, laying a data foundation for the precise movement of the ship and construction trajectory control. The effect of this step is reflected in improving the real-time and accuracy of ship status monitoring, supporting dynamic adjustment and intelligent decision-making. By using ship motion control parameters to construct an adaptive control model, including an anchor calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model, it can comprehensively address the ship's dynamic position and attitude adjustment requirements. Parameter calibration and optimization steps improve the accuracy of each model, enabling the generated adaptive control strategy data to flexibly adapt to varying sea conditions and mission requirements. This step improves the system's adaptability, control accuracy, and response efficiency through a highly flexible and precise control model. Based on the adaptive control strategy data, PID control and constant torque control are implemented using real-time status data, enabling not only speed adjustment but also precise correction of the ship's trajectory. Through this process, corrected control instructions effectively reduce deviations and maintain smooth ship movement. This step improves the accuracy and stability of ship movement, significantly reducing errors and improving work efficiency, especially in complex construction environments. Based on the corrected control instructions, the operating status of each winch device is coordinated to implement the ship's coordinated ship movement and construction trajectory movement. This winch device coordinated control method not only improves coordination during ship movement but also ensures the accuracy and controllability of the construction trajectory. The effect of this step is to improve the overall efficiency of the construction process, ensure the precise execution of the trajectory, and at the same time reduce the impact of a single equipment failure on the entire system through linkage control.

[0044] Preferably, step S1 includes the following steps:

[0045] Step S11: collecting basic parameters of hardware equipment related to ship movement control to obtain hardware parameter data, wherein the basic parameters include the load capacity of the winch equipment, sensor accuracy, and communication interface type;

[0046] Step S12: obtaining real-time demand data for ship movement control, and selecting an industrial-grade real-time Ethernet communication protocol based on the real-time demand data, thereby obtaining communication protocol configuration data;

[0047] Step S13: Acquire hardware equipment data required for ship movement control based on the hardware parameter data and real-time requirement data, wherein the hardware equipment data includes equipment serial numbers and parameters of winch equipment, encoders, sensor systems, communication interfaces, and control terminals;

[0048] Step S14: configuring a network topology structure based on a computer network, winch equipment, and encoder according to the hardware device data and the communication protocol configuration data, and building an electrical ring network with a redundant backup mechanism;

[0049] Step S15: Perform communication testing and initialization settings on the electrical ring network, establish a data sharing mechanism between devices, and obtain system initial configuration data.

[0050] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0051] Step S11: collecting basic parameters of hardware equipment related to ship movement control to obtain hardware parameter data, wherein the basic parameters include the load capacity of the winch equipment, sensor accuracy, and communication interface type;

[0052] The embodiment of the present invention collects the basic parameters of the hardware equipment related to ship movement control, which requires comprehensive data collection of the winch equipment, sensor system and communication interface. The specific method is: first, read the load capacity data of the winch equipment controller, the load capacity range is usually between 1 ton and 10 tons, and record the maximum tolerable tension, starting current and working voltage. Secondly, through the accuracy calibration of the sensor system (such as position sensor, tension sensor), record the range and resolution of the sensor. For example, the range of the tension sensor is 0~20kN and the accuracy is 0.1kN. Finally, combined with the interface type of the industrial field equipment, use the communication protocol analysis tool to confirm the interface specification of the equipment, such as RS485, CAN bus or industrial Ethernet interface, and record the specific model and communication baud rate requirements, and finally form the hardware parameter data.

[0053] Step S12: obtaining real-time demand data for ship movement control, and selecting an industrial-grade real-time Ethernet communication protocol based on the real-time demand data, thereby obtaining communication protocol configuration data;

[0054] The acquisition of real-time demand data in the embodiments of the present invention can be accomplished by combining it with construction demand analysis, specifically including requirements for ship movement response time and the tolerance range for communication delay. For example, in high-precision construction, the real-time demand data sets ship movement response time less than 100ms and communication delay less than 10ms. Based on the real-time demand data, an industrial-grade real-time Ethernet communication protocol is selected. During operation, a protocol comparison analysis method is used to evaluate the performance of common protocols (such as PROFINET, EtherCAT, and Modbus TCP). Ultimately, the EtherCAT protocol that meets real-time requirements is selected, and its periodic data synchronization function is configured to ensure that the communication protocol configuration can support fast and efficient data transmission, thus forming the communication protocol configuration data.

[0055] Step S13: Acquire hardware equipment data required for ship movement control based on the hardware parameter data and real-time requirement data, wherein the hardware equipment data includes equipment serial numbers and parameters of winch equipment, encoders, sensor systems, communication interfaces, and control terminals;

[0056] Based on the data in steps S11 and S12, the embodiment of the present invention further determines the specific information of the hardware device. First, the specific model is matched according to the load capacity of the winch equipment, and its serial number and technical parameters are queried through the equipment management system. For example, an electric winch equipment with a load capacity of 5 tons is selected, and the serial number is "WHC-5000". Secondly, the corresponding encoder and tension sensor are matched according to the accuracy and interface specifications of the sensor system. For example, the resolution of the encoder is 0.01°, and the interface type is RS485. Finally, considering the real-time demand data and the performance requirements of the control terminal, a control terminal device that meets industrial-grade requirements is selected, and its model and configuration parameters are recorded. For example, the control terminal uses an Intel i7 processor, supports Gigabit Ethernet, and the device serial number is "CTRL-1001", and finally forms complete hardware device data.

[0057] Step S14: configuring a network topology structure based on a computer network, winch equipment, and encoder according to the hardware device data and the communication protocol configuration data, and building an electrical ring network with a redundant backup mechanism;

[0058] The embodiment of the present invention configures the network topology structure based on hardware device data and communication protocol configuration data. The specific method is as follows: First, a network topology diagram is designed based on the number of devices and installation locations, and a ring redundant topology is adopted to enhance system reliability. The winch equipment, encoder, sensor system and control terminal equipment are connected into a ring through an industrial switch to ensure that communication can still be maintained when any node fails. Secondly, an Ethernet configuration tool (such as TwinCAT or ProfiNet Configurator) is used to group and assign addresses to network nodes, setting the IP address range to 192.168.1.1 to 192.168.1.255, and assigning a unique identifier to each device. Finally, a redundant backup mechanism is enabled, the main communication path and the backup communication path are set, and the communication reliability is tested using the redundant switching function to ensure that the network can quickly switch when a failure occurs, ultimately constructing an electrical ring network with a redundant backup mechanism.

[0059] Step S15: Perform communication testing and initialization settings on the electrical ring network, establish a data sharing mechanism between devices, and obtain system initial configuration data.

[0060] After the electrical ring network is constructed, the embodiment of the present invention needs to perform communication testing and initialization settings. The specific operations include: first, testing the communication delay between devices through a network analyzer. The delay should be controlled within 10ms, and detecting the integrity and packet loss rate of data packet transmission. Secondly, using PLC to initialize each node device, including startup status, communication rate (for example, 1Gbps) and device priority configuration. Subsequently, the data sharing mechanism between devices is enabled, and a real-time data exchange area is established through the shared memory allocation function, such as allocating data address intervals for each winch device and sensor system to ensure that the control terminal can read and write status data in real time. Finally, the stability of the sharing mechanism is verified through multiple communication tests and data comparisons to ensure that the initial configuration is correct, generate the system initial configuration data, and lay the foundation for subsequent ship movement control.

[0061] This method collects basic parameters of winch equipment, such as load capacity, sensor accuracy, and communication interface type, enabling a comprehensive understanding of the performance and functionality of the equipment required for ship motion control. This process ensures that the actual capabilities of the equipment are fully considered during subsequent configuration and optimization, avoiding unnecessary resource waste or system bottlenecks. By analyzing the real-time requirements of ship motion control and selecting an appropriate industrial-grade real-time Ethernet communication protocol, the system's communication efficiency and stability can be significantly improved. Protocol selection is customized based on requirements, ensuring efficient and timely data transmission and meeting the high real-time requirements in complex and dynamic environments. Hardware parameter data is integrated with real-time requirement data to further obtain detailed equipment information, including serial numbers and detailed parameters for the winch equipment, encoder, sensor system, communication interface, and control terminal. This step ensures a global view during the system design phase, facilitates precise configuration, and provides reliable data support for subsequent system initialization and debugging. Combining hardware device data and communication protocol configuration data, the network topology of the computer network, winch equipment, and encoder is configured, and an electrical ring network with redundant backup mechanisms is designed. This redundant design enhances the system's fault tolerance and stability, enabling rapid recovery of data transmission and device linkage in the event of a failure, ensuring continuity. Conduct communication testing and initialization of the electrical ring network to establish a data sharing mechanism between devices and provide an efficient basic communication environment for subsequent operations. This testing verifies network reliability and availability, avoiding potential communication interruptions and ensuring that all devices can operate smoothly after initialization, forming a stable operational foundation.

[0062] Preferably, step S15 includes the following steps:

[0063] Step S151: Accurately calibrate the mechanical parameters of the winch equipment in the electrical ring network according to the hardware parameter data, thereby obtaining winch equipment parameter data, wherein the mechanical parameters include maximum pulling force, travel range, and response speed;

[0064] Step S152: performing precision configuration on the encoders in the electrical ring network based on the millimeter-level and millisecond-level precise measurement of the winch equipment displacement and speed, thereby obtaining encoder configuration data;

[0065] Step S153: Performing a network communication test on the electrical ring network based on the encoder configuration data and winch equipment parameter data via a computer network to obtain network communication test data. The network communication test includes a network connectivity test to verify the data exchange capability between devices, a network delay and jitter test to ensure the real-time and stability of control signal transmission, and a simulation test of the network's anti-interference capability under extreme working conditions.

[0066] Step S154: Designing a data interaction protocol for loosely coupled communication between devices based on the publish-subscribe model according to the network communication test data, thereby obtaining data sharing mechanism data;

[0067] Step S155: Integrate the data sharing mechanism data, the network communication test data, and the electrical ring network data to obtain the system initial configuration data.

[0068] An embodiment of the present invention accurately calibrates the mechanical parameters of a winch device based on hardware parameter data. Specifically, the following steps are performed: First, the winch device is connected to a load test system and gradually applied with varying tensions to measure its maximum load-bearing capacity. For example, loading is performed step by step using an electronic dynamometer, with the maximum tension value recorded as 5000N. Next, a high-precision displacement sensor is used to test the travel range of the winch device's wire rope, moving the wire rope from a fully relaxed state to a fully tightened state, with the travel range recorded as 50m. Next, a high-speed data acquisition device is used to record the winch device's response speed, specifically the time from receiving a control signal to starting movement and the time from starting movement to reaching steady-state speed, calibrated to 10ms and 200ms, respectively. Finally, these parameters are organized into winch device parameter data for subsequent configuration and control optimization. The encoder in the electrical ring network is precision-configured. Specifically, the encoder is connected to the winch device and its displacement measurement accuracy is calibrated using a standard displacement ruler to ensure that the encoder's error within the millimeter range is less than 0.1mm. For example, the winch wire rope is gradually moved, and the encoder displacement output is recorded each time for comparison and calibration. The encoder's speed measurement accuracy is then verified using a high-speed rotation tester, ensuring a speed calculation error of less than 0.5% within the millisecond range. Finally, based on the calibration results, specific parameters are set in the encoder configuration software, such as a pulse frequency of 10kHz and a resolution of 4096 pulses / revolution. This generates encoder configuration data to ensure it meets the ship's motion control requirements. Network communication testing of the electrical ring network is performed via a computer network. The following steps are performed: First, a network connectivity test is performed. Pinging the IP addresses of each device using a network test tool verifies that data packets are being sent and received smoothly, with a packet loss rate of less than 0.01%. Next, network latency and jitter testing is performed. Using a network performance analyzer, different network communication tasks are gradually loaded, and the latency and jitter of the real-time signal are recorded. For example, a maximum latency of 5ms and a jitter amplitude of less than 1ms are achieved. Finally, noise interference is introduced into the network to simulate extreme operating conditions. Interference resistance testing is performed to verify communication stability and ensure that data exchange between devices is maintained. After the test, all test data was collated to generate network communication test data for verifying network performance and stability. Based on this network communication test data, a loosely coupled publish-subscribe communication protocol was designed between devices. The specific steps were as follows: First, topics were defined based on the communication requirements of each device in the test data, such as winch equipment location topics and tension monitoring topics. Next, a publish-subscribe mechanism was designed to ensure that each device only needed to subscribe to the relevant topics, eliminating the need for direct communication with other devices, thus reducing data exchange complexity. Finally, a lightweight communication protocol such as MQTT was used to implement topic publishing and subscription functionality, and protocol parameters were optimized, such as setting the packet transmission interval to 50ms and the maximum packet size to 512 bytes to accommodate network latency and jitter.Finally, after verifying its stability through protocol testing, data sharing mechanism data is generated. After completing the aforementioned testing and mechanism design, data integration of the data sharing mechanism data, network communication test data, and the electrical ring network is performed. The specific method is as follows: First, all device configuration parameters and test data are entered into the system initialization configuration database, including winch equipment parameter data, encoder configuration data, network performance indicators, etc. Next, all communication protocols and sharing mechanisms are integrated through the control terminal, and a loosely coupled publish-subscribe mechanism is deployed to the network to ensure that all devices can share data in real time. Finally, the control terminal is used to execute the initialization program to fully test the system and confirm the integrity and consistency of the data integration. For example, the latency and accuracy of data exchange between devices are tested by simulating ship movement tasks. Finally, the system initial configuration data is generated to ensure the stable operation of the ship movement control system.

[0069] This invention precisely calibrates mechanical parameters such as maximum pulling force, travel range, and response speed for winch equipment in an electrical ring network based on hardware parameter data, ensuring that equipment performance is consistent with actual operational requirements. This process provides a reliable parameter foundation for subsequent precise control and dynamic adjustment, improving the system's control accuracy and operational safety. The encoders in the electrical ring network are configured with millimeter- and millisecond-level accuracy to meet the displacement and velocity measurement requirements of the winch equipment. This high-precision configuration optimizes the encoder's performance in real-time monitoring, enhances the system's ability to capture subtle changes, and supports high-precision position and velocity calculations. Multi-dimensional communication testing of the electrical ring network is performed over a computer network based on encoder configuration data and winch equipment parameter data. Network connectivity testing verifies the effectiveness of data exchange between devices; network latency and jitter testing ensures real-time transmission of control signals; and anti-interference simulation testing assesses system stability under extreme conditions. This process comprehensively improves the system's communication reliability and anti-interference capabilities. Based on network communication test data, a loosely coupled communication data exchange protocol between devices is designed using a publish-subscribe model, achieving a flexible and efficient data sharing mechanism. This protocol reduces direct inter-device dependencies, improves system scalability and maintenance efficiency, and ensures real-time and accurate inter-device communication. Initial system configuration data is generated by combining data from the data sharing mechanism, network communication test data, and the configuration of the electrical ring network. This integrated data not only ensures network stability and device interoperability but also provides a highly optimized initial environment for subsequent system operation, ensuring efficient and reliable operation.

[0070] Preferably, step S2 includes the following steps:

[0071] Step S21: collecting the retracted and extended length and tension parameters of the wire rope through the sensors of the winch equipment according to the initial configuration data of the system, thereby obtaining the sensor data of the winch equipment;

[0072] Step S22: collecting initial static parameters of the ship, including coordinates of the center of gravity of the ship, geometric dimensions of the hull, standard displacement of the ship, and weight distribution parameters;

[0073] Step S23: Select the ship's center of gravity as the coordinate origin based on the ship's initial static parameters, define the directions of the X, Y, and Z axes, establish a right-handed rectangular coordinate system, and calibrate the absolute and relative accuracy of the coordinate system to obtain coordinate reference data;

[0074] Step S24: performing a differential operation on the retracted and extended length of the wire rope in the winch equipment sensor data to obtain a wire rope length change rate; and performing an attitude angle estimation based on the roll angle, pitch angle, and heading angle according to the wire rope length change rate and the tension difference in the winch equipment sensor data, thereby obtaining attitude angle monitoring data;

[0075] Step S25: constructing a real-time monitoring model of the ship's position and attitude based on the coordinate reference data and the attitude angle monitoring data;

[0076] Step S26: using the real-time monitoring model to monitor the ship status, thereby obtaining real-time ship status data;

[0077] Step S27: Perform motion control analysis on the real-time state data of the ship to generate ship motion control parameters.

[0078] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0079] Step S21: collecting the retracted and extended length and tension parameters of the wire rope through the sensors of the winch equipment according to the initial configuration data of the system, thereby obtaining the sensor data of the winch equipment;

[0080] According to the initial configuration data of the system, the embodiment of the present invention collects the retraction and extension length and tension parameters of the wire rope through the sensors of the winch equipment. The specific operations are as follows: First, an optical encoder is installed on the winch equipment to record the length change of the wire rope during each retraction and extension process. The sampling frequency is set to 100Hz to ensure the refinement of data collection. Secondly, a tension sensor is installed at the end of the wire rope of the winch equipment to record the tension value of the wire rope in real time. The sensor range is set to 0-10kN, and the accuracy is ±0.01kN. Then, the displacement data output by the above encoder and the stress data of the tension sensor are synchronously transmitted to the control terminal through the data acquisition module to generate the winch equipment sensor data for subsequent calculation and analysis.

[0081] Step S22: collecting initial static parameters of the ship, including coordinates of the center of gravity of the ship, geometric dimensions of the hull, standard displacement of the ship, and weight distribution parameters;

[0082] The embodiment of the present invention collects the initial static parameters of the ship. The specific operations are as follows: First, the center of gravity coordinates of the ship are measured and calculated using a ship balance tester. For example, by recording data using an inclination sensor and a laser rangefinder installed at a test location on the dock, the center of gravity coordinates are obtained as (0, 0, -2.5) m. Secondly, the geometric dimensions of the hull are measured using a laser scanner, and the geometric parameters of the ship are obtained as 50 m in length, 10 m in width, and 6 m in height. Next, the standard displacement of the ship is measured as 500 tons using a weighing device, and the weight distribution parameters are input based on a preset weight distribution model. All collected static parameters are summarized to generate the initial static parameters of the ship, which are used to establish a coordinate system reference.

[0083] Step S23: Select the ship's center of gravity as the coordinate origin based on the ship's initial static parameters, define the directions of the X, Y, and Z axes, establish a right-handed rectangular coordinate system, and calibrate the absolute and relative accuracy of the coordinate system to obtain coordinate reference data;

[0084] The embodiment of the present invention establishes a right-handed rectangular coordinate system based on the initial static parameters of the ship. The specific operation is as follows: first, the coordinates of the center of gravity of the ship are set as the origin of the coordinate system (0,0,0). Then, the X-axis direction is defined as forward along the length of the hull, the Y-axis direction is perpendicular to the X-axis and along the width of the hull to the left, and the Z-axis direction is perpendicular to the horizontal plane and downward, ensuring compliance with the right-hand rule. Next, a high-precision total station is used to measure the calibration points of the coordinate system. The average value is taken through multiple measurements to eliminate errors, ensuring that the absolute accuracy is within ±1mm and the relative accuracy is within ±0.5mm. Finally, the calibration results are recorded as coordinate reference data for ship attitude monitoring and trajectory analysis.

[0085] Step S24: performing a differential operation on the retracted and extended length of the wire rope in the winch equipment sensor data to obtain a wire rope length change rate; and performing an attitude angle estimation based on the roll angle, pitch angle, and heading angle according to the wire rope length change rate and the tension difference in the winch equipment sensor data, thereby obtaining attitude angle monitoring data;

[0086] The embodiment of the present invention performs differential operation on the wire rope retraction and extension length in the winch equipment sensor data to calculate the length change rate. The specific operation is as follows: First, the differential calculation formula is implemented by programming Data with a sampling frequency of 100 Hz is input into the calculation module to obtain real-time data on the rate of change of the wire rope length in meters per second. Next, based on the collected tension parameters, a mechanical analysis model is used to calculate the tension difference. This difference is combined with the rate of change of the wire rope length. A Kalman filter algorithm based on the attitude matrix is ​​used to estimate the ship's roll, pitch, and heading angles. These are calibrated to accuracy ranges of ±2°, ±1°, and ±3°, respectively, ultimately generating attitude angle monitoring data.

[0087] Step S25: constructing a real-time monitoring model of the ship's position and attitude based on the coordinate reference data and the attitude angle monitoring data;

[0088] The embodiment of the present invention constructs a real-time monitoring model of the ship's position and attitude based on the coordinate reference data and the attitude angle monitoring data. The specific method is as follows: First, the coordinate reference data is used to establish a mathematical model with the ship's center of gravity as the origin, the hull is divided into multiple reference areas, and their three-dimensional position relationships are recorded. Then, the attitude angle monitoring data is input into the model, and the matrix transformation formula is used to calculate the position and attitude of the ship. (where R is the attitude rotation matrix and P is the initial coordinate point) to calculate the real-time position and attitude of each part of the ship. Ultimately, the real-time data is integrated into the dynamic model and the model is optimized to ensure that the deviation between the model output and the actual state is less than 1%.

[0089] Step S26: using the real-time monitoring model to monitor the ship status, thereby obtaining real-time ship status data;

[0090] This embodiment of the present invention utilizes a real-time monitoring model to monitor ship status. Specifically, the model collects data from sensors and feeds it into the monitoring model in real time, generating data on the ship's dynamic displacement, velocity, and attitude angle. For example, when the ship is moving sideways, the monitoring model can record in real time the displacement of the hull along the X-axis at 0.02 m / s and the inclination along the Z-axis at ±0.1°. The monitoring data is then compared with a preset safety range. If the deviation exceeds a threshold, an alarm mechanism is triggered. Ultimately, real-time ship status data is generated to guide subsequent motion control analysis.

[0091] Step S27: Perform motion control analysis on the real-time state data of the ship to generate ship motion control parameters.

[0092] The embodiment of the present invention performs motion control analysis and processing on the real-time status data of the ship. The specific operations are as follows: first, a kinematic analysis model is constructed based on the real-time status data, and the ship's speed, displacement and attitude angle are input to calculate the control force requirements in each direction. For example, when the ship deviates from the target position by 0.5m, the analysis model calculates that the corrective force to be applied is 2kN. Then, the PID controller parameters are adjusted in combination with dynamic feedback to ensure the real-time and stability of the response. Finally, the ship motion control parameters are generated, including the winch equipment tension adjustment value, the wire rope retraction and release length, and the ship attitude adjustment angle, which are used to achieve precise control.

[0093] The present invention uses sensors on the winch equipment to collect the wire rope retraction and extension length and tension parameters, obtaining winch equipment sensor data. This process provides the system with dynamic information about the winch equipment, supporting subsequent real-time analysis and control of the ship's status while ensuring the comprehensiveness and accuracy of the measured data. Initial static parameters, such as the ship's center of gravity coordinates, hull geometry, standard displacement, and weight distribution, are recorded as baseline data for ship status monitoring. These parameters provide a static reference for analyzing the ship's attitude and position during operation, ensuring the scientific nature of model construction and analysis results. With the ship's center of gravity as the origin, the X, Y, and Z axes are defined, a right-handed rectangular coordinate system is established, and absolute and relative accuracy are calibrated. This precise coordinate reference data provides a mathematical framework and data support for subsequent high-precision position and attitude monitoring, ensuring standardized and consistent spatial analysis. The wire rope retraction and extension length is differentiated to obtain the rate of change of the wire rope length. This is then combined with the tension difference to calculate the roll, pitch, and heading angles to generate attitude monitoring data. This process achieves real-time estimation of the ship's attitude through dynamic parameter calculation, providing accurate attitude information for subsequent control. A real-time monitoring model for the ship's position and attitude is constructed using coordinate reference data and attitude angle monitoring data. This model integrates the ship's static and dynamic parameters, providing real-time, comprehensive monitoring of the ship's motion status and supporting multi-dimensional data analysis. Through the real-time monitoring model, the ship's status is continuously monitored, generating real-time status data. This status data provides real-time input for subsequent motion control and strategy adjustments, improving the system's dynamic responsiveness and operational efficiency. Motion control analysis is performed based on the ship's real-time status data to generate ship motion control parameters. These parameters provide critical data support for ship operation, enabling precise control of the ship's path and attitude, and ensuring efficient movement in complex operating environments.

[0094] Preferably, step S27 includes the following steps:

[0095] Step S271: extracting multi-dimensional features from the real-time state data of the ship and performing data standardization to obtain the real-time state feature data of the ship, wherein the multi-dimensional features include vertical displacement, horizontal offset angle, roll angle, and pitch angle;

[0096] Step S272: establishing a motion state assessment model based on fuzzy logic according to the real-time state characteristic data of the ship, and performing nonlinear mapping and assessment on the real-time state characteristic data of the ship using the motion state assessment model, thereby obtaining fuzzy assessment result data;

[0097] Step S273: generating a preliminary motion control parameter matrix including a speed adjustment coefficient, a direction correction gain, an attitude stabilization control parameter, and a trajectory tracking weight according to the fuzzy evaluation result data;

[0098] Step S274: Generate ship motion control parameters according to the preliminary motion control parameter matrix.

[0099] This embodiment of the present invention performs multi-dimensional feature extraction on real-time ship status data. Specifically, the following steps are performed: First, real-time data is collected, including vertical displacement, horizontal offset angle, roll angle, and pitch angle. For example, vertical displacement is collected with an accuracy of ±1 mm using a laser rangefinder, horizontal offset angle is recorded with an accuracy of ±0.01° using an inertial navigation system, and roll and pitch angles are recorded with an accuracy of ±0.02° using an attitude sensor. This data is then used to extract key features using principal component analysis (PCA) to reduce the interference of redundant information. Next, the extracted feature data is normalized using the maximum-minimum normalization method to normalize all feature data to the interval [0, 1], resulting in real-time ship status feature data for subsequent analysis. A fuzzy logic-based motion state assessment model is established based on the real-time ship status feature data. Specifically, the following steps are performed: First, the input variables of a fuzzy set are defined as vertical displacement, horizontal offset angle, roll angle, and pitch angle, and the output variable is the motion state level (e.g., "stable," "drifting," or "severely drifting"). Then, fuzzy membership functions are used to fuzzify the input variables. For example, the roll angle is categorized as [0°, 1°] (small), [1°, 3°] (medium), and [3°, 5°] (large). A fuzzy rule base is then constructed, such as "If the roll angle is medium and the pitch angle is small, the motion state is drift." The model then inputs the ship's real-time state characteristic data into the model. Fuzzy inference and weighted averaging are used for nonlinear mapping, ultimately generating fuzzy evaluation results and outputting the ship's motion state level and corresponding characteristic weights. A preliminary motion control parameter matrix is ​​generated based on the fuzzy evaluation results. The specific steps are as follows: First, the speed adjustment coefficient, heading correction gain, attitude stabilization control parameter, and trajectory tracking weight are defined based on the weight values ​​of each state level in the fuzzy evaluation results. For example, when the motion state is "drift" and the roll angle weight is 0.6, the speed adjustment coefficient is calculated to be 0.8, the heading correction gain is 1.2, the attitude stabilization control parameter is 2.5, and the trajectory tracking weight is 1.5. These parameters are then combined into a preliminary motion control parameter matrix. , adapting the input format of the control model through matrix mapping for subsequent generation of motion control parameters. The ship motion control parameters are generated based on the preliminary motion control parameter matrix. The specific operation is as follows: First, the preliminary motion control parameter matrix is ​​input into the control algorithm. For example, a proportional-integral-derivative (PID) controller is used to adjust the direction correction gain to optimize the heading. At the same time, the attitude stabilization control parameters are used to adjust the real-time response of the winch equipment tension to maintain the roll angle within ±0.5°. Next, the speed adjustment coefficient is used to dynamically adjust the wire rope retraction and release rate to ensure the displacement error is less than 5cm. In addition, the path planning algorithm is optimized in combination with the trajectory tracking weight to correct the target trajectory in real time. The final output of the ship motion control parameters includes the retraction and release speed of each winch equipment, the tension value, and the ship attitude adjustment target, which are used to achieve precise motion control.

[0100] This method extracts and standardizes multidimensional features from real-time ship state data to extract key feature data, including vertical displacement, horizontal offset angle, roll angle, and pitch angle. This step screens and normalizes the main factors affecting ship motion, improving the accuracy and consistency of subsequent model evaluation. Using this real-time ship state feature data, a motion state assessment model based on fuzzy logic is constructed. This data is processed through nonlinear mapping to generate fuzzy assessment results. The introduction of fuzzy logic addresses the uncertainty inherent in traditional models' complex ship motion, making the assessment results more consistent with actual operating conditions. Combining the fuzzy assessment results, a preliminary motion control parameter matrix is ​​generated, including the speed adjustment coefficient, heading correction gain, attitude stabilization control parameters, and trajectory tracking weights. This process, through comprehensive evaluation of multidimensional features, provides a quantitative basis for dynamic motion adjustment, ensuring targeted and flexible control parameters. Based on this preliminary motion control parameter matrix, the final ship motion control parameters are further optimized and calculated. This step dynamically adjusts the control strategy to the current state of the ship, meeting real-time motion control requirements and achieving more efficient motion control.

[0101] Preferably, step S3 includes the following steps:

[0102] Step S31: performing multi-dimensional statistical analysis on the ship motion control parameters to obtain motion control parameter characteristic data;

[0103] Step S32: constructing multi-dimensional constraint conditions based on seabed topography, ocean current distribution, and wind direction and speed according to the preset ship movement expectation data and motion control parameter characteristic data, quantifying the adaptability index of anchor position selection, and thus obtaining an anchor position calculation model;

[0104] Step S33: Correcting the position deviation of the ship in the horizontal plane and in the vertical direction of the motion control parameter characteristic data according to the ship movement expectation data, thereby constructing a position deviation correction model;

[0105] Step S34: optimizing the ship attitude angle based on the angle deviation correction decision tree according to the motion control parameter characteristic data, thereby constructing an angle deviation correction model;

[0106] Step S35: constructing a force balance model when multiple winch devices work together based on the motion control parameter characteristic data, thereby obtaining a torque balance control model;

[0107] Step S36: Integrate the anchor position calculation model, the position deviation correction model, the angle deviation correction model, and the torque balance control model into an adaptive control model;

[0108] Step S37: calibrate and optimize the parameters of each model in the adaptive control model to generate adaptive control strategy data.

[0109] The present invention performs multi-dimensional statistical analysis on ship motion control parameters. Specifically, the following steps are taken: First, data on the winch retraction and deployment speeds, tension values, and attitude adjustment target values ​​are collected. For example, the winch retraction and deployment speed range is 0-2 m / s, the tension value is between 50-300 kN, and the attitude adjustment target angle accuracy is ±0.1°. Descriptive statistical analysis is then used to extract features from each parameter, including calculating the mean, variance, maximum, and minimum values, to analyze the stability and fluctuation range of each variable during the motion control process. Correlation analysis is then used to assess the correlation between the parameters. For example, the correlation coefficient between the retraction and deployment speeds and tension values ​​reaches 0.85, indicating a high correlation between the two. Finally, the analysis results are integrated into motion control parameter feature data for model construction. An anchor position calculation model is constructed based on the preset expected ship movement data and the motion control parameter feature data. Specifically, the following steps are taken: First, the target position coordinates (e.g., (50 m, 30 m)) and target heading (e.g., 90°)) are extracted from the expected movement data. Combined with the seabed topography data of the area where the ship is located, the digital elevation model (DEM) analysis method is used to generate a topographic profile; at the same time, the distribution data of ocean current velocity (such as 0.5m / s) and wind speed (such as 8m / s) are collected. Then, based on the above constraints, anchor adaptability indicators are defined, such as anchor point stability, the shortest path between the anchor point and the target position, and the uniformity of anchor force. Finally, a multi-objective optimization algorithm (such as a genetic algorithm) is used to quantify the anchor adaptability, select the anchor point with the highest adaptability, and construct an anchor calculation model. According to the expected data of the ship's movement, the motion control parameter characteristic data is corrected for position deviation. The specific operations are as follows: First, the target position of the ship movement and the real-time ship position coordinates are extracted. For example, the target position is (50m, 30m) and the real-time position is (48m, 32m). Then, the deviation value in the horizontal plane is calculated. and , 2m and -2m respectively; for vertical deviation, the laser rangefinder is used to collect data and calculate the deviation value (e.g. -0.5m). Then, a control algorithm based on deviation minimization (e.g. linear least squares method) is used to correct each position parameter and construct a position deviation correction model to provide a reference for subsequent control. The ship's attitude angle is optimized based on the characteristic data of the motion control parameters. The specific operations are as follows: First, based on the collected motion control parameters, attitude angle related data is extracted, including the current roll angle (e.g. 3°), pitch angle (e.g. 1.5°) and heading angle (e.g. 92°). Then, an angle deviation correction decision tree is constructed to set the optimization target ranges for the roll angle, pitch angle and heading angle. For example, the optimization targets for the roll angle and pitch angle are within ±1°, and the heading angle deviation is controlled within ±0.5°. According to the correction strategy, the tension value distribution and the retraction and extension speed of the winch equipment in the motion control parameters are adjusted, and finally an angle deviation correction model is generated to ensure the stability of the ship's attitude. Based on the motion control parameter characteristic data, a force balance model for multiple winches working together is constructed. The specific operation is as follows: First, the real-time pulling force value of each winch is collected (for example, winch A is 200kN, winch B is 220kN), and its torque contribution to the ship's motion is analyzed. Then, based on the force balance principle, the target torque balance formula is defined, such as ,in The torque generated by each winch device. Then, the tension value distribution of each winch device is adjusted through the optimization algorithm (such as the Lagrange multiplier method). For example, the tension of winch device A is adjusted to 210kN, and the tension of winch device B is adjusted to 210kN to achieve torque balance, and finally a torque balance control model is constructed. The anchor position calculation model, position deviation correction model, angle deviation correction model and torque balance control model are integrated into an adaptive control model. The specific operations are as follows: First, a data integration framework is designed to unify the input and output data formats of each model. For example, the output deviation value of the position deviation correction model 、 The system uses the input parameters of the anchor calculation model. A modular design approach is then used to encapsulate each model into independent modules, accessible through standardized interfaces. Next, an integrated algorithm (such as a recurrent neural network) is used to achieve dynamic adaptation and parameter adjustment between the multiple models. Ultimately, an adaptive control model is integrated to support real-time updates and control strategy optimization. Parameter calibration and optimization are performed on each model in the adaptive control model. The specific steps are as follows: First, a preliminary calibration of the model parameters is performed using historical experimental and simulation data. For example, the adaptability weights in the anchor calculation model are set to 0.4 for anchor stability, 0.3 for path shortestness, and 0.3 for force uniformity. Then, by collecting data from actual ship control processes, the model parameters are adjusted to match the actual situation. For example, the attitude angle adjustment coefficient in the angle deviation correction model is adjusted from 1.2 to 1.5 to improve correction accuracy. Finally, a genetic algorithm is used to globally optimize the parameter sets of each model to generate adaptive control strategy data for practical application.

[0110] This invention extracts relevant characteristic data through multi-dimensional statistical analysis of ship motion control parameters. This step not only enables quantitative analysis of control parameters but also identifies motion patterns under different conditions, providing foundational data for subsequent optimization, thereby improving the accuracy of control decisions. Based on preset expected ship movement data and incorporating factors such as seabed topography, current distribution, and wind direction and speed, a multi-dimensional anchor position calculation model is constructed. This quantifies the adaptability of anchor position selection, ensuring optimal anchor position selection in complex environments, thereby improving the accuracy and stability of ship positioning. A position deviation correction model is constructed by correcting for ship displacement deviations in the horizontal and vertical directions. This model effectively addresses the problem of ship position deviation caused by external factors (such as currents and wind speed), ensuring that the ship can accurately operate at the intended position. An angle deviation correction model is established by optimizing the ship's attitude angle using an angle deviation correction decision tree. This model can rapidly respond to changes in ship attitude in dynamic ocean environments, adjusting the ship's angle to prevent tilting or instability, ensuring safe and efficient control. A torque balance control model is constructed based on force equilibrium analysis when multiple winches operate in coordination. This model helps coordinate the operating conditions of various winch equipment in complex operating environments, ensuring balanced torque distribution, reducing unnecessary power waste, and optimizing the ship's energy efficiency. The anchor calculation model, position deviation correction model, angle deviation correction model, and torque balance control model are integrated into a unified adaptive control model. By leveraging the strengths of each sub-model, this integrated model improves the flexibility and responsiveness of the control system, enabling the ship to adapt to different operating environments and changes. By calibrating and optimizing the parameters of each model in the adaptive control model, adaptive control strategy data is generated. This process provides the system with precise control parameters, enabling the ship to make intelligent adjustments based on real-time environmental conditions, further improving the ship's operating efficiency and stability.

[0111] Preferably, step S32 includes the following steps:

[0112] Step S321: Acquire a marine geographic information database, which stores information on seabed topography, ocean current distribution, and wind direction and speed;

[0113] Step S322: using the maritime geographic information database to accurately locate the desired anchorage of the ship in the preset desired ship movement data, and generate seabed terrain constraint data, ocean current dynamic characteristic data, and wind environment constraint parameters;

[0114] Step S323: Perform multi-dimensional correlation analysis on the seabed topography constraint data, the ocean current dynamic characteristic data, and the wind environment constraint parameters, thereby constructing a comprehensive adaptability evaluation index system;

[0115] Step S324: performing a comprehensive adaptability evaluation on the motion control parameter feature data according to the comprehensive adaptability evaluation index system, thereby obtaining anchor position adaptability score data;

[0116] Step S325: performing confidence interval analysis on the anchor position adaptability score data to screen out anchor position data that meet the expected data requirements for ship movement, thereby obtaining anchor position screening result data;

[0117] Step S326: Establish a mathematical model based on the anchor position screening result data and generate an anchor position calculation model.

[0118] This embodiment of the present invention acquires a marine geographic information database by integrating satellite remote sensing data, sonar detection results, and meteorological station data. The database stores information such as seafloor topography (such as depth, slope, and geological characteristics), ocean current distribution data (such as speeds ranging from 0.1 to 2 m / s and directional variations of 0 to 45°), and wind direction and speed information (such as wind speeds ranging from 5 to 15 m / s and wind direction angles of ±15°). This data is categorized and indexed using spatial data management tools (such as PostGIS), creating a multidimensional geographic information dataset that facilitates rapid query and retrieval, providing fundamental support for subsequent anchor positioning. The desired anchorage in the pre-set ship movement data was precisely located using a maritime geographic information database. Specifically, the desired anchorage coordinates (e.g., 125.5°E, 38.2°N) were used as query input. The database's relevant modules were then invoked to retrieve seafloor topography data (e.g., 5° slope, 50m water depth), current dynamic characteristics data (e.g., 0.5m / s, southeast direction), and wind environment parameters (e.g., wind speed 10m / s, wind direction 15° north-east) for the area surrounding the anchorage. Spatial analysis was performed using a geographic information system (GIS) to extract these data and generate seafloor topography constraint data, current dynamic characteristics data, and wind environment constraint parameters. Multidimensional correlation analysis was performed on these constraint data, using multivariate statistical analysis methods (e.g., principal component analysis (PCA)) to reduce the dimensionality of the constraint data and extract key features (e.g., topographic stability factor, current velocity variation). Machine learning models (such as random forests) are used to train these features to assess the weight of each feature's influence on anchoring adaptability. For example, terrain stability is weighted at 0.4, current fluctuations at 0.35, and wind influence at 0.25. These weights are combined to calculate a comprehensive adaptability evaluation index, forming a unified adaptability evaluation system that provides a quantitative basis for anchoring selection. A comprehensive adaptability evaluation of motion control parameter feature data is performed based on this comprehensive adaptability evaluation index system. The specific steps are as follows: First, key motion control parameter data (such as the maximum winch pull force of 300kN and the maximum lateral displacement of the ship of 3m) are extracted and matched to the evaluation indexes for adaptability analysis. For example, the terrain adaptability score is calculated by combining seabed topography constraints with winch pull data, resulting in a score of 85; the current dynamic characteristics are combined with the ship's lateral displacement to calculate the flow velocity adaptability score, resulting in a score of 80; and the wind environment constraints are combined with the ship's attitude stability to calculate the wind adaptability score, resulting in a score of 90. Finally, the weighted sum of these scores is used to generate the anchoring adaptability score (e.g., 86.5).Confidence interval analysis was performed on the anchor adaptability score data to screen for anchors that met the ship maneuvering requirements. Specifically, Monte Carlo simulation was used to randomly sample and estimate the score data, calculating the mean and confidence interval of the anchor adaptability score (e.g., a mean of 86.5 with a 95% confidence interval of 84.2-88.8). Based on the adaptability score threshold (e.g., ≥85) in the ship maneuvering requirements, anchor locations that met the requirements were screened (e.g., anchor A and anchor B). The relevant data for these anchors was compiled into anchor screening results data, providing input for the subsequent mathematical model development. The mathematical model was developed based on the anchor screening results data. Specifically, the selected anchor data was used as input variables (e.g., anchor A had a terrain factor of 0.85, a flow factor of 0.8, and a wind factor of 0.9). Combined with the characteristic data of the ship motion control parameters, a mathematical model based on multi-objective optimization was constructed. The model's objective function is to maximize the anchor adaptability score. Constraints include terrain stability greater than 0.8, velocity variation less than 0.5 m / s, and wind impact angle deviation less than 15°. Using optimization algorithms (such as particle swarm optimization (PSO)), the model generates an anchor calculation model, providing computational support for precise positioning during ship maneuvers.

[0119] This invention acquires a marine geographic information database containing information such as seabed topography, current distribution, wind direction and speed, providing basic data for subsequent anchorage selection. This detailed geographic information provides critical environmental context for anchorage calculations, ensuring operational accuracy and safety in complex marine environments. The marine geographic information database is used to precisely locate the vessel's desired anchorage and generate associated seabed topography constraint data, current dynamic characteristics data, and wind environment constraint parameters. This process ensures that anchorage selection is based on accurate environmental data, avoiding errors caused by topographic or environmental factors. By performing a multi-dimensional correlation analysis of seabed topography, current dynamic characteristics, and wind environment constraint parameters, a comprehensive adaptability evaluation index system is constructed. This system comprehensively evaluates the environmental adaptability of different anchorage options, ensuring that all possible factors are taken into account, thereby improving the scientific and effective nature of anchorage selection. Based on this comprehensive adaptability evaluation index system, motion control parameter characteristic data is subjected to a comprehensive adaptability evaluation to generate an anchorage adaptability score. This scoring system provides a clear fitness indicator for each anchorage, ensuring that anchorage selection relies not only on a single factor but also on a comprehensive consideration of multiple data points to optimize selection accuracy. Confidence interval analysis is performed on the anchorage adaptability score data to screen anchorages that meet the desired requirements for ship movement. This analysis effectively eliminates anchorages that may not be well-adapted in actual operations, ensuring that the final selected anchorage has high statistical reliability and accuracy, further reducing operational risks. A mathematical model is established based on the screening results, and an anchorage calculation model is generated. This step ensures that anchorage selection relies not only on qualitative environmental data analysis, but also on precise calculations through mathematical models, providing scientific decision-making support for subsequent ship movements and improving the systematicity and operability of the entire decision-making process.

[0120] Preferably, step S34 includes the following steps:

[0121] Step S341: constructing a multi-dimensional feature vector of the ship's attitude angle deviation based on the ship's desired movement data and the motion control parameter characteristic data, including the real-time deviation values ​​and change rates of the roll angle, pitch angle, and yaw angle;

[0122] Step S342: Designing an angle deviation classification criterion based on fuzzy logic according to the multidimensional feature vector, establishing a quantitative evaluation standard for the severity of the angle deviation, and thereby obtaining angle deviation grade data;

[0123] Step S343: constructing a multi-level decision tree based on the angle deviation level data, and setting decision nodes including attitude correction threshold, correction direction, and correction strength;

[0124] Step S344: Simulate and verify the multi-level decision tree, and generate an angle deviation correction strategy, thereby forming an angle deviation correction model that describes the ship attitude angle optimization process.

[0125] The embodiment of the present invention constructs a multidimensional feature vector of the ship's attitude angle deviation based on the expected data of the ship's movement and the characteristic data of the motion control parameters. The specific operation is as follows: first, the real-time deviation values ​​of the roll angle, pitch angle, and yaw angle are extracted from the motion control parameters (for example, the roll angle is 2.5°, the pitch angle is 1.8°, and the yaw angle is 3.2°), and their change rates are calculated through time series analysis (for example, the roll angle change rate is 0.5° / s, the pitch angle change rate is 0.3° / s, and the yaw angle change rate is 0.8° / s). These data are normalized according to the time step to eliminate the influence of unit differences and combined into a multidimensional feature vector. , serving as input for subsequent angular deviation assessment. A fuzzy logic-based angular deviation classification criterion is designed based on the multidimensional feature vector. Specifically, by analyzing historical ship operation data and empirical rules, the deviation ranges of roll, pitch, and yaw angles are classified into four levels: normal, minor, significant, and severe. (For example, the roll angle classification criteria are: normal: 0°-1°, minor: 1°-3°, significant: 3°-5°, and severe: >5°). A fuzzy membership function (such as a triangular membership function) is used to calculate the grading degree of each angle. For example, a roll angle of 2.5° has a membership of 0.7 for minor and 0.3 for significant; a pitch angle of 1.8° has a membership of 1.0 for minor; and a yaw angle of 3.2° has a membership of 0.6 for significant and 0.4 for severe. Combined with the contribution factor of the rate of change, this generates quantified angular deviation grading data (e.g., roll angle is minor, pitch angle is minor, and yaw angle is significant). A multi-level decision tree was constructed based on the angular deviation level data. Specifically, the tree was designed with the attitude correction threshold, correction direction, and correction intensity as key decision nodes. For example, the first-level nodes determined whether the deviation level reached the correction threshold (e.g., significant or severe); the second-level nodes selected the correction direction based on the specific angular deviation direction (e.g., positive correction for roll angle deviation, negative correction for pitch angle deviation); and the third-level nodes assigned the correction intensity based on the deviation severity (e.g., 0.2° / s for minor correction, 0.5° / s for significant correction, and 0.8° / s for severe correction). The resulting decision tree outputs correction strategies for different levels of conditions, generating attitude angle correction plans that correspond to actual operating conditions. The multi-level decision tree was validated through simulations. Specifically, the ship's motion data and angular deviation level data were imported into a simulation platform (e.g., MATLAB / Simulink) to simulate the ship's attitude adjustment process under different environmental conditions (e.g., wind speed of 10 m / s, wave height of 2 m). The decision tree's correction effectiveness was verified in various scenarios. For example, under mild deviation conditions, the roll angle remained within 0.5° after correction, while under significant deviation conditions, the yaw angle was reduced from 4° to within 2° after correction. The simulation results were analyzed for errors (e.g., correction effect deviation less than 10%) and optimized (adjusting correction strength or branching rules). Ultimately, a multi-level decision tree-based angle deviation correction strategy was generated and integrated into an angle deviation correction model, describing the detailed process of optimizing the ship's attitude angle.

[0126] By combining expected ship movement data with motion control parameter characteristic data, the present invention constructs a multidimensional feature vector containing the real-time deviation values ​​and their rates of change for roll, pitch, and yaw angles. This feature vector accurately reflects the dynamic changes in the ship's attitude, providing the necessary data support for subsequent attitude correction. By analyzing the real-time deviations and their rates of change, even minor deviations in the ship's attitude can be promptly captured, ensuring that the correction strategy can be rapidly adapted and adjusted. Based on the multidimensional feature vector, a grading criterion for angle deviation is designed, and a quantitative assessment standard for angle deviation severity is established. This standard classifies angle deviations into different levels, enabling refined judgment based on the ship's real-time attitude deviation and rate of change. By introducing fuzzy logic, it effectively handles the uncertainty and complexity of reality, making the determination of angle deviation more flexible and accurate. Based on the angle deviation level data, a multi-level decision tree is constructed, with decision nodes at each level defining the attitude correction threshold, correction direction, and correction strength. This decision tree enables the formulation of appropriate correction strategies based on different deviation levels and actual conditions. By layering and refining the correction strategy, the ship's attitude correction can be precisely adjusted to varying degrees of deviation, thereby improving the ship's control accuracy and safety. Simulations of a multi-level decision tree verified the effectiveness of the angle deviation correction strategy in various scenarios, ultimately generating an angle deviation correction model that describes the ship's attitude angle optimization process. The simulation process simulated the various changes and disturbances encountered in actual operations, ensuring that the correction strategy can be adjusted stably and effectively under different circumstances. This model provides a systematic framework for ship attitude correction, ensuring stable operation of ships in various complex environments.

[0127] Preferably, step S35 includes the following steps:

[0128] Step S351: collecting real-time tension data of the multi-winch equipment system, and calculating the moment component of the tension of each winch equipment on the center of gravity of the ship based on the real-time tension data, thereby obtaining center of gravity moment component data;

[0129] Step S352: establishing a three-dimensional spatial geometric model of the force points of each winch device relative to the center of gravity of the ship based on the center of gravity moment component data;

[0130] Step S353: performing moment balance analysis on the gravity center moment component data according to the three-dimensional space geometric model, and calculating the force imbalance coefficient in each direction, thereby obtaining force imbalance data;

[0131] Step S354: adjusting the pulling force of each winch device based on the force balance of the ship according to the force imbalance data, thereby obtaining the balanced state pulling force data;

[0132] Step S355: constructing a torque balance control model based on tension balance according to the equilibrium state tension data.

[0133] The embodiment of the present invention collects the real-time tension data of each winch device through the sensors of the multi-winch device system. For example, the tension of the winch device located on the left side of the bow is The winch equipment on the right side of the bow is The winch equipment at the stern center is Based on this real-time tension data and combined with the coordinates of the ship's center of gravity, the tension of each winch device is decomposed into the torque components acting on the center of gravity. For example, the horizontal distance of the left winch device relative to the center of gravity is , the vertical distance is , then the moment it generates on the center of gravity is (horizontal component), (vertical component). Calculate the torque components of other winch equipment in sequence, and finally form a data set containing the torque components of all winch equipment, which serves as the basis for subsequent analysis. Based on the gravity center torque component data obtained in step S351, establish a three-dimensional spatial geometric model of the multi-winch equipment system. The specific method is: use the three-dimensional coordinates of the ship's center of gravity (for example ) as the reference point, combined with the spatial distribution coordinates of each winch equipment (such as the winch equipment on the left is located at , the right winch equipment is located at ), draw a geometric distribution diagram of the winch equipment relative to the ship's center of gravity. Use geometric modeling tools (such as MATLAB or CAD software) to visualize the spatial relationship between the force points of each winch equipment and the ship's center of gravity, providing an intuitive geometric basis for moment balance analysis. Using the three-dimensional spatial geometric model, perform moment balance analysis on the center of gravity moment component data obtained in step S351. The specific operation is: based on the moment component of each winch equipment in the geometric model, calculate the total moment acting on the center of gravity point, for example The total torque in the direction is , indicating that There is an unbalanced moment to the left. Calculate and The unbalanced moment in the direction and quantify the result as the unbalance coefficient, such as These imbalance coefficients are used to describe the degree of deviation of the ship's stress state and form the final stress imbalance data. According to the stress imbalance data calculated in step S353, the pulling force of each winch device is adjusted to achieve the ship's stress balance. The specific method is: first set the torque balance threshold in each direction (for example The unbalanced moment allowed in the direction is ±500Nm). By adjusting the tension of the winch equipment, for example, increasing the tension of the left winch equipment to , reduce the right winch equipment pulling force to , so that The sum of the torques in the three directions approaches zero, and the equilibrium state is reached. At the same time, the tension of the winch equipment in other directions is adjusted to ensure that the ship is in a state of force balance in the three directions. Finally, the equilibrium state tension data is generated, and the adjusted tension value of each winch equipment is recorded. According to the equilibrium state tension data generated in step S354, a torque balance control model based on tension balance is constructed. The specific operation is: the equilibrium tension value of each winch equipment and its corresponding geometric parameters are input into the control algorithm, and the torque balance equation is used to calculate the torque balance. A mathematical model was established, defining the input variables as real-time tension and geometric coordinates, and the output as recommended adjustment values. Through multiple simulation tests, the control model's response speed and stability were optimized to ensure that the model could accurately adjust to changes in tension data in real time. The result was a torque balance control model with real-time correction capabilities, which can be used to guide the coordinated operation of multiple winches on ships in complex environments.

[0134] By collecting real-time tension data from multiple winch systems and calculating the torque component of each winch's tension on the ship's center of gravity based on this data, the present invention provides a detailed understanding of the impact of each winch on the ship's balance. This process, through the precise calculation of torque components, provides basic data for subsequent force balance analysis, ensuring a comprehensive understanding of the forces acting on the ship's center of gravity. Based on the center of gravity torque component data, a three-dimensional geometric model of each winch's force point relative to the ship's center of gravity is constructed. This model intuitively displays the spatial relationship between each winch's force point and the ship's center of gravity. This three-dimensional model enables more precise analysis and calculation of the forces acting on each winch and their impact on the ship's balance, providing a geometric basis for moment balance analysis. Based on the three-dimensional geometric model, a moment balance analysis is performed, and the force imbalance coefficients in each direction are calculated. This analysis helps identify moment imbalances in different directions, providing key data for subsequent torque corrections. By calculating the force imbalance coefficients, the ship's mechanical state can be more clearly understood, identifying potential directions with uneven force. Based on the force imbalance data, the pulling force of each winch is adjusted to ensure that the ship's forces are balanced. This adjustment process eliminates any imbalances in pulling force between the winches, thereby reducing deviations in the ship's posture and force, and ensuring the ship's stability. Real-time adjustment of pulling force ensures balanced forces on the ship in complex environments. Based on the adjusted balanced pulling force data, a torque balance control model based on tension balance is constructed. This model monitors the changes in pulling force of each winch in real time and dynamically adjusts torque distribution to ensure the ship's posture and stability during navigation. This control model provides a systematic torque balance control method, helping the ship maintain stable operation under various operating conditions.

[0135] The present invention also provides a ship intelligent maneuvering control system based on an adaptive algorithm, which is used to execute the above-mentioned ship intelligent maneuvering control method based on an adaptive algorithm. The ship intelligent maneuvering control system based on an adaptive algorithm includes:

[0136] The device configuration module is used to obtain the hardware device data required for ship movement control; configure the network topology of the computer network, winch equipment and encoder according to the hardware device data, and build an electrical ring network; initialize the electrical ring network and perform communication testing, establish a data sharing mechanism between devices, and obtain the system initial configuration data;

[0137] The status monitoring module is used to collect the wire rope's retraction and extension length and tension parameters through the winch equipment's sensors based on the system's initial configuration data, and to build a real-time monitoring model for the ship's position and attitude. The real-time monitoring model is used to monitor the ship's status and generate real-time ship status data. The real-time ship status data is then analyzed and processed for motion control to generate ship motion control parameters.

[0138] The adaptive control module is used to establish an adaptive control model based on the ship motion control parameters, where the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrate and optimize the parameters of each model in the adaptive control model to generate adaptive control strategy data;

[0139] The control adjustment module is used to make real-time control adjustments to the ship based on the adaptive control strategy data and generate correction control instructions. The real-time control adjustment specifically includes PID control and constant torque control based on the real-time status data of the ship, as well as speed adjustment and trajectory correction.

[0140] The coordinated ship moving module is used to coordinate and control the working status of each winch equipment according to the correction control instructions, realizing the ship's linked ship moving and construction trajectory line ship moving functions.

[0141] This invention establishes an electrical ring network by acquiring hardware device data required for ship motion control and, based on this data, configuring the network topology of the computer network, winch equipment, and encoders. Initializing and testing the electrical ring network ensures the smooth establishment of a data sharing mechanism between devices. This process provides a stable infrastructure, ensuring the smooth operation of the ship motion control system and reducing the possibility of hardware configuration errors and system communication failures. By optimizing the network topology, system reliability and data exchange efficiency can be effectively improved. Based on the system's initial configuration data, sensors on the winch equipment collect the retracted and unretracted length and tension parameters of the wire rope, thereby constructing a real-time monitoring model for the ship's position and attitude. This real-time monitoring model provides accurate data support for ship status monitoring, ensuring comprehensive monitoring of the ship's position, attitude, and dynamic changes. By analyzing the ship's real-time status data, ship motion control parameters can be further generated to ensure accurate motion control. By continuously monitoring and analyzing the ship's status, this module facilitates efficient motion control and real-time adjustment. In the adaptive control module, the ship motion control parameters are used to establish an adaptive control model, which includes the anchor calculation model, position deviation correction model, angle deviation correction model, and torque balance control model. By calibrating and optimizing the parameters of these models, the vessel can achieve adaptive adjustment in different operating environments. The establishment of an adaptive control model ensures that the vessel can flexibly adjust to changing environmental and operating conditions in real-time, thereby implementing precise control strategies and improving the stability and accuracy of vessel operations. Based on the adaptive control strategy data, the control adjustment module uses real-time vessel status data to implement PID control and constant torque control, adjusting speed and correcting trajectory. This process enables fine-tuning of the vessel's motion control, ensuring that the vessel maintains the appropriate attitude and trajectory in complex operating environments. Real-time adjustment of the vessel's control parameters effectively eliminates deviations, ensuring that the vessel moves on the intended trajectory and avoiding deviations or unstable attitudes. The coordinated ship maneuvering module coordinates the operating status of each winch device based on corrected control commands, enabling coordinated ship maneuvering and construction trajectory-based ship maneuvering. This module achieves precise ship maneuvering through the coordinated operation of multiple winches, ensuring that the vessel always follows the correct trajectory during construction. By optimizing the coordination between winches, ship maneuvering becomes more efficient and stable, avoiding maneuvering errors or instability caused by improper operation.

[0142] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0143] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A ship intelligent moving control method based on adaptive algorithm, characterized in that: The following steps are involved: Step S1: Acquire hardware equipment data required for ship movement control; Configure the network topology of the computer network, winch equipment, and encoder according to the hardware equipment data to build an electrical ring network; perform initialization settings and communication tests on the electrical ring network, establish a data sharing mechanism between devices, and obtain the initial configuration data of the system; Step S2: Based on the initial system configuration data, the sensors of the winch equipment are used to collect the retracted and extended lengths and tension parameters of the wire rope, and a real-time monitoring model for the ship's position and attitude is constructed; the real-time monitoring model is used to monitor the ship's status and generate real-time ship status data; motion control analysis and processing are performed on the real-time ship status data to generate ship motion control parameters, wherein the motion control analysis is specifically as follows: Extract multi-dimensional features from the ship's real-time status data and perform data standardization to obtain the ship's real-time status feature data, where the multi-dimensional features include vertical displacement, horizontal offset angle, roll angle, and pitch angle; According to the real-time state characteristic data of the ship, a motion state evaluation model based on fuzzy logic is established, and the motion state evaluation model is used to perform nonlinear mapping and evaluation on the real-time state characteristic data of the ship, thereby obtaining fuzzy evaluation result data; Generate a preliminary motion control parameter matrix including speed adjustment coefficient, direction correction gain, attitude stabilization control parameter and trajectory tracking weight according to the fuzzy evaluation result data; generating ship motion control parameters according to a preliminary motion control parameter matrix; Step S3: Establishing an adaptive control model based on the ship motion control parameters, wherein the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrating and optimizing the parameters of each model in the adaptive control model to generate adaptive control strategy data; Step S4: Based on the adaptive control strategy data, real-time control adjustments are performed on the ship to generate correction control instructions. The real-time control adjustments specifically include performing PID control and constant torque control based on the real-time state data of the ship, as well as speed adjustment and trajectory correction. Step S5: Coordinate and control the working status of each winch device according to the modified control instruction to realize the ship's linked ship movement and construction track line ship movement functions.

2. The ship intelligent moving control method based on adaptive algorithm according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting basic parameters of hardware equipment related to ship movement control to obtain hardware parameter data, wherein the basic parameters include the load capacity of the winch equipment, sensor accuracy, and communication interface type; Step S12: acquiring real-time demand data for ship movement control, and selecting an industrial-grade real-time Ethernet communication protocol based on the real-time demand data, thereby obtaining communication protocol configuration data; Step S13: Acquire hardware equipment data required for ship movement control based on the hardware parameter data and real-time requirement data, wherein the hardware equipment data includes equipment serial numbers and parameters of winch equipment, encoders, sensor systems, communication interfaces, and control terminals; Step S14: configuring a network topology structure based on a computer network, winch equipment, and encoder according to the hardware device data and the communication protocol configuration data, and building an electrical ring network with a redundant backup mechanism; Step S15: Perform communication testing and initialization settings on the electrical ring network, establish a data sharing mechanism between devices, and obtain system initial configuration data.

3. The ship intelligent moving control method based on adaptive algorithm according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: Accurately calibrate the mechanical parameters of the winch equipment in the electrical ring network according to the hardware parameter data, thereby obtaining winch equipment parameter data, wherein the mechanical parameters include maximum pulling force, travel range, and response speed; Step S152: performing precision configuration on the encoders in the electrical ring network based on the millimeter-level and millisecond-level precise measurement of the winch equipment displacement and speed, thereby obtaining encoder configuration data; Step S153: Performing a network communication test on the electrical ring network based on the encoder configuration data and winch equipment parameter data via a computer network to obtain network communication test data. The network communication test includes a network connectivity test to verify the data exchange capability between devices, a network delay and jitter test to ensure the real-time and stability of control signal transmission, and a simulation test of the network's anti-interference capability under extreme working conditions. Step S154: Designing a data interaction protocol for loosely coupled communication between devices based on the publish-subscribe model according to the network communication test data, thereby obtaining data sharing mechanism data; Step S155: Integrate the data sharing mechanism data, the network communication test data, and the electrical ring network data to obtain the system initial configuration data.

4. The ship intelligent moving control method based on adaptive algorithm according to claim 3 is characterized in that: Step S2 includes the following steps: Step S21: collecting the retracted and extended length and tension parameters of the wire rope through the sensors of the winch equipment according to the initial configuration data of the system, thereby obtaining the sensor data of the winch equipment; Step S22: collecting initial static parameters of the ship, including coordinates of the center of gravity of the ship, geometric dimensions of the hull, standard displacement of the ship, and weight distribution parameters; Step S23: Select the ship's center of gravity as the coordinate origin based on the ship's initial static parameters, define the directions of the X, Y, and Z axes, establish a right-handed rectangular coordinate system, and calibrate the absolute and relative accuracy of the coordinate system to obtain coordinate reference data; Step S24: performing a differential operation on the retracted and extended length of the wire rope in the winch equipment sensor data to obtain a wire rope length change rate; and performing an attitude angle estimation based on the roll angle, pitch angle, and heading angle according to the wire rope length change rate and the tension difference in the winch equipment sensor data, thereby obtaining attitude angle monitoring data; Step S25: constructing a real-time monitoring model of the ship's position and attitude based on the coordinate reference data and the attitude angle monitoring data; Step S26: using the real-time monitoring model to monitor the ship status, thereby obtaining real-time ship status data; Step S27: Perform motion control analysis on the real-time state data of the ship to generate ship motion control parameters.

5. The ship intelligent moving control method based on adaptive algorithm according to claim 4 is characterized in that: Step S3 includes the following steps: Step S31: performing multi-dimensional statistical analysis on the ship motion control parameters to obtain motion control parameter characteristic data; Step S32: constructing multi-dimensional constraint conditions based on seabed topography, ocean current distribution, and wind direction and speed according to the preset ship movement expectation data and motion control parameter characteristic data, quantifying the adaptability index of anchor position selection, and thus obtaining an anchor position calculation model; Step S33: Correcting the position deviation of the ship in the horizontal plane and in the vertical direction of the motion control parameter characteristic data according to the ship movement expectation data, thereby constructing a position deviation correction model; Step S34: optimizing the ship attitude angle based on the angle deviation correction decision tree according to the motion control parameter characteristic data, thereby constructing an angle deviation correction model; Step S35: constructing a force balance model when multiple winch devices work together based on the motion control parameter characteristic data, thereby obtaining a torque balance control model; Step S36: Integrate the anchor position calculation model, the position deviation correction model, the angle deviation correction model, and the torque balance control model into an adaptive control model; Step S37: calibrate and optimize the parameters of each model in the adaptive control model to generate adaptive control strategy data.

6. The ship intelligent moving control method based on adaptive algorithm according to claim 5 is characterized in that: Step S32 includes the following steps: Step S321: Acquire a marine geographic information database, which stores information on seabed topography, ocean current distribution, and wind direction and speed; Step S322: using the maritime geographic information database to accurately locate the desired anchorage of the ship in the preset desired ship movement data, and generate seabed terrain constraint data, ocean current dynamic characteristic data, and wind environment constraint parameters; Step S323: Perform multi-dimensional correlation analysis on the seabed topography constraint data, the ocean current dynamic characteristic data, and the wind environment constraint parameters, thereby constructing a comprehensive adaptability evaluation index system; Step S324: performing a comprehensive adaptability evaluation on the motion control parameter feature data according to the comprehensive adaptability evaluation index system, thereby obtaining anchor position adaptability score data; Step S325: performing confidence interval analysis on the anchor position adaptability score data to screen out anchor position data that meet the expected data requirements for ship movement, thereby obtaining anchor position screening result data; Step S326: Establish a mathematical model based on the anchor position screening result data and generate an anchor position calculation model.

7. The ship intelligent moving control method based on adaptive algorithm according to claim 6 is characterized in that: Step S34 includes the following steps: Step S341: constructing a multi-dimensional feature vector of the ship's attitude angle deviation based on the ship's desired movement data and the motion control parameter characteristic data, including the real-time deviation values ​​and change rates of the roll angle, pitch angle, and yaw angle; Step S342: Designing an angle deviation classification criterion based on fuzzy logic according to the multidimensional feature vector, establishing a quantitative evaluation standard for the severity of the angle deviation, and thereby obtaining angle deviation grade data; Step S343: constructing a multi-level decision tree based on the angle deviation level data, and setting decision nodes including attitude correction threshold, correction direction, and correction strength; Step S344: Simulate and verify the multi-level decision tree and generate an angle deviation correction strategy, thereby forming an angle deviation correction model that describes the ship attitude angle optimization process.

8. The ship intelligent maneuvering control method based on the adaptive algorithm according to claim 7 is characterized in that Step S35 includes the following steps: Step S351: collecting real-time tension data of the multi-winch equipment system, and calculating the moment component of the tension of each winch equipment on the center of gravity of the ship based on the real-time tension data, thereby obtaining center of gravity moment component data; Step S352: establishing a three-dimensional spatial geometric model of the force points of each winch device relative to the center of gravity of the ship based on the center of gravity moment component data; Step S353: performing moment balance analysis on the gravity center moment component data according to the three-dimensional space geometric model, and calculating the force imbalance coefficient in each direction, thereby obtaining force imbalance data; Step S354: adjusting the pulling force of each winch device based on the force balance of the ship according to the force imbalance data, thereby obtaining the balanced state pulling force data; Step S355: constructing a torque balance control model based on tension balance according to the equilibrium state tension data.

9. A ship intelligent ship moving control system based on adaptive algorithm, characterized in that: The method for controlling the intelligent ship movement based on the adaptive algorithm according to claim 1 is used to execute the method, wherein the intelligent ship movement control system based on the adaptive algorithm comprises: The device configuration module is used to obtain the hardware device data required for ship movement control; configure the network topology of the computer network, winch equipment and encoder according to the hardware device data, and build an electrical ring network; initialize the electrical ring network and perform communication testing, establish a data sharing mechanism between devices, and obtain the system initial configuration data; The status monitoring module is used to collect the wire rope's retraction and extension length and tension parameters through the winch equipment's sensors based on the system's initial configuration data, and to build a real-time monitoring model for the ship's position and attitude. The real-time monitoring model is used to monitor the ship's status and generate real-time ship status data. The real-time ship status data is then analyzed and processed for motion control to generate ship motion control parameters. The adaptive control module is used to establish an adaptive control model based on the ship motion control parameters, where the adaptive control model includes an anchor position calculation model, a position deviation correction model, an angle deviation correction model, and a torque balance control model; calibrate and optimize the parameters of each model in the adaptive control model to generate adaptive control strategy data; The control adjustment module is used to make real-time control adjustments to the ship based on the adaptive control strategy data and generate correction control instructions. The real-time control adjustment specifically includes PID control and constant torque control based on the real-time status data of the ship, as well as speed adjustment and trajectory correction. The coordinated ship moving module is used to coordinate and control the working status of each winch equipment according to the correction control instructions, realizing the ship's linked ship moving and construction trajectory line ship moving functions.

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