Intelligent ship moving control method and system based on adaptive algorithm

Abstract

The invention relates to the technical field of intelligent ship control, in particular to an intelligent ship moving control method and system based on a self-adaptive algorithm. The method comprises the following steps: acquiring hardware equipment data required by ship movement control; configuring a network topology structure of a computer network system, winch equipment and an encoder according to hardware equipment data, and constructing an electrical ring network; carrying out initialization setting and communication test on the electrical ring network, and establishing a data sharing mechanism between devices to obtain system initial configuration data; collecting the winding and unwinding length and tension parameters of a steel wire rope through a sensor of winch equipment based on initial configuration data of the system, and constructing a real-time monitoring model of the position and posture of the ship; and performing ship state monitoring by using the real-time monitoring model to generate ship real-time state data. According to the invention, the electrical ring network with a redundant backup mechanism is adopted, the communication real-time performance and reliability of the system are improved, and the stability of control signal transmission is ensured.

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" usually refers to ship trajectory tracking, that is, controlling the ship to move precisely along a pre-set trajectory. This concept involves the ship's heading control and path tracking, with the goal of enabling the ship to automatically and safely sail along a designated route, even in a complex and uncertain marine environment. Trajectory tracking is an important research direction in the field of intelligent ships, which involves automatic control technology, enabling ships to make autonomous decisions and automatically navigate safely without human intervention.

[0003] However, traditional intelligent ship moving control methods often have the following problems: the control method relies on a fixed control strategy and cannot quickly respond to complex environmental changes, such as sudden wind direction, currents, and dynamic changes in equipment status, resulting in low efficiency during the ship moving process and even control errors. The linkage control between multiple winch systems is prone to uneven force or asynchronous action, resulting in deviations in the ship's attitude and position from the trajectory, affecting the accuracy of task 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 comprises the following steps:

[0006] Step S1: Obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network system, winch equipment and encoder according to the hardware equipment data, and 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 system initial configuration data;

[0007] Step S2: Based on the initial configuration data of the system, the retracted and extended length and tension parameters of the wire rope are collected through the sensors of the winch equipment, and a real-time monitoring model of the ship's position and posture is constructed; the ship's state is monitored using the real-time monitoring model to generate the ship's real-time state data; the ship's real-time state data is subjected to motion control analysis and processing to generate the ship's motion control parameters;

[0008] Step S3: establishing an adaptive control model according to 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 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, the ship is controlled and adjusted in real time to generate a correction control instruction, wherein the real-time control adjustment specifically includes performing PID control and constant torque control through the real-time state data of the ship, and performing speed adjustment and trajectory correction;

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

[0011] The present invention can build an electrical ring network by collecting the hardware equipment data required for ship movement control and configuring the network topology of the computer network system and equipment according to these 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 the initialization setting and communication test 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 sensor of the winch equipment collects the retracted and released length and tension parameters of the wire rope in real time, and combines the system 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 and processing are further converted into ship motion control parameters, laying a data foundation for the precise movement and construction trajectory control of the ship. 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 constructing an adaptive control model including anchor calculation model, position deviation correction model, angle deviation correction model and torque balance control model through ship motion control parameters, the dynamic position and attitude adjustment requirements of the ship can be fully handled. The parameter calibration and optimization steps improve the accuracy of each model, so that the generated adaptive control strategy data can flexibly adapt to different sea conditions and task requirements. The effect of this step is to improve the adaptability, control accuracy and response efficiency of the system through a highly flexible and accurate control model. Based on the adaptive control strategy data, PID control and constant torque control are performed using real-time status data, which not only realizes speed adjustment, but also accurately corrects the trajectory of the ship. Through this process, the correction control instruction can effectively reduce the deviation and maintain the smooth movement of the ship. The effect of this step is reflected in improving the accuracy and stability of the ship during movement, especially in complex construction environments, which can significantly reduce errors and improve work efficiency. According to the correction control instruction, the working status of each winch is coordinated and controlled, which can realize the ship's linkage ship moving function and construction trajectory line ship moving function. This winch linkage control method not only improves the coordination of the ship during 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 accurate execution of the trajectory, and reduce the impact of a single equipment failure on the entire system through linkage control.

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

[0013] The equipment configuration module is used to obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network system, winch equipment and encoder according to the hardware equipment data, and 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;

[0014] The status monitoring module is used to collect the length and tension parameters of the wire rope through the sensors of the winch equipment based on the initial configuration data of the system, and to build a real-time monitoring model for the position and posture of the ship; to monitor the ship status using the real-time monitoring model and generate the real-time status data of the ship; to perform motion control analysis and processing on the real-time status data of the ship and generate the motion control parameters of the ship;

[0015] The adaptive control module is used to establish an adaptive control model according to 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; perform parameter calibration and optimization on each model in the adaptive control model to generate adaptive control strategy data;

[0016] The control adjustment module is used to perform real-time control adjustment of the ship based on the adaptive control strategy data and generate correction control instructions, wherein the real-time control adjustment specifically includes PID control and constant torque control through the real-time status data of the ship, and speed adjustment and trajectory correction;

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

[0018] The present invention obtains the hardware equipment data required for ship movement control, configures the network topology of the computer network system, winch equipment and encoder according to these data, and establishes an electrical ring network. By initializing the electrical ring network and performing communication testing, it is ensured that the data sharing mechanism between devices is successfully established. This process provides a stable infrastructure, ensures the smooth operation of the ship movement control system, and reduces the possibility of hardware configuration errors and system communication failures. By optimizing the network topology, the reliability of the system and the efficiency of data exchange can be effectively improved. Based on the initial configuration data of the system, the sensor of the winch equipment is used to collect the retracted and released length and tension parameters of the wire rope, thereby constructing a real-time monitoring model of the ship's position and posture. The real-time monitoring model provides accurate data support for ship status monitoring, ensuring that the position, posture and dynamic changes of the ship are fully monitored. By analyzing the real-time status data of the ship, the ship motion control parameters can be further generated to ensure the accuracy of the motion control. This module helps to achieve efficient motion control and real-time adjustment by continuously monitoring and analyzing the ship status. In the adaptive control module, the ship motion control parameters are used to establish an adaptive control model, covering the anchor calculation model, the position deviation correction model, the angle deviation correction model and the torque balance control model. By calibrating and optimizing the parameters of these models, the adaptive adjustment of ships in different working environments can be achieved. The establishment of the adaptive control model ensures that the ship can flexibly adjust according to the changes in the environment and operating conditions in actual applications, thereby realizing accurate control strategies and helping to improve the stability and accuracy of ship operations. Based on the adaptive control strategy data, the control adjustment module performs PID control and constant torque control through the real-time status data of the ship to adjust the speed and correct the trajectory. This process realizes the fine adjustment of the ship's motion control and ensures that the ship can maintain a suitable posture and trajectory in a complex operating environment. By adjusting the control parameters of the ship in real time, the deviation can be effectively eliminated to ensure that the ship moves on the expected track and avoid deviation from the track or unstable posture. The collaborative ship moving module coordinates and controls the working status of each winch according to the correction control instructions to realize the ship's linkage ship moving and construction trajectory line ship moving functions. This module realizes the precise movement of the ship through the collaborative work of multiple winches and ensures that the ship always operates along the correct trajectory during the construction process. By optimizing the collaboration between winches, the movement of the ship is more efficient and stable, avoiding the problem of ship moving 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 from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0020] Figure 1The figure is a schematic flow chart of the steps of the ship intelligent 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 technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" 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 ship intelligent moving control method 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 system, winch equipment and encoder according to the hardware equipment data, and 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 system initial configuration data;

[0028] Step S2: Based on the initial configuration data of the system, the retracted and extended length and tension parameters of the wire rope are collected through the sensors of the winch equipment, and a real-time monitoring model of the ship's position and posture is constructed; the ship's state is monitored using the real-time monitoring model to generate the ship's real-time state data; the ship's real-time state data is subjected to motion control analysis and processing to generate the ship's motion control parameters;

[0029] Step S3: establishing an adaptive control model according to 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 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, the ship is controlled and adjusted in real time to generate a correction control instruction, wherein the real-time control adjustment specifically includes performing PID control and constant torque control through the real-time state data of the ship, and performing speed adjustment and trajectory correction;

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

[0032] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a ship intelligent ship moving control method based on an adaptive algorithm according to the present invention. In this example, the ship intelligent ship moving control method 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 system, winch equipment and encoder according to the hardware equipment data, and 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 system initial configuration data;

[0034] The hardware device data required for ship movement control obtained by the embodiment of the present invention includes retrieving the basic configuration data of the winch device, encoder and computer network system. First, the controller of the winch device is connected to the computer network through the communication interface of the hardware device, and the parameter information such as the IP address and gateway of each device is read. Secondly, according to the layout requirements of the ship construction site, the network topology planning software is used to configure the network topology structure of the winch device and the encoder to form a logical ring structure to ensure that each node can achieve data redundant transmission. Then, the constructed electrical ring network is initialized and set using PLC (programmable logic controller), including node priority setting, communication baud rate adjustment and network group numbering. Finally, the communication validity between devices is verified through the data packet test function, and the network monitoring tool is combined to ensure that there is no packet loss and no delay communication problem. At the same time, a sharing mechanism between devices is established, and the real-time status data generated by the node is saved to the computer main control end through the shared memory mechanism to form the system initial configuration data.

[0035] Step S2: Based on the initial configuration data of the system, the retracted and extended length and tension parameters of the wire rope are collected through the sensors of the winch equipment, and a real-time monitoring model of the ship's position and posture is constructed; the ship's state is monitored using the real-time monitoring model to generate the ship's real-time state data; the ship's real-time state data is subjected to motion control analysis and processing to generate the ship's motion control parameters;

[0036] The embodiment of the present invention collects the retracted length and tension parameters of the wire rope through the force sensor and displacement sensor of the winch equipment based on the system initial configuration data generated in step S1, wherein the retracted length of the wire rope is recorded by the pulse counting method, and the tension parameters are collected in real time by the force sensor, and the sampling frequency is set to 100Hz. In combination with the ship heading sensor and the inclination sensor, a real-time monitoring model of the ship's position and attitude based on the Kalman filter algorithm is constructed, and the model is used to continuously track the spatial position, heading angle and attitude angle of the ship to generate high-precision real-time status data of the ship. The real-time status data of the ship is further used to analyze the changes in the acceleration, speed and angular velocity of the ship in combination with the kinematic equation, and the variable step 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 according to 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 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 the anchor calculation model to calculate the optimal anchor according to the target offset; adjusting the error between the current position of the ship and the target position through the position deviation correction model; optimizing the ship's heading angle and attitude angle using the angle deviation correction model; and distributing the pulling force of each winch through the torque balance control model to achieve balanced control. The parameter calibration of each model is carried out by combining offline simulation with actual calibration. First, historical data under typical construction scenarios are input in the simulation environment for preliminary calibration, and then 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, the ship is controlled and adjusted in real time to generate a correction control instruction, wherein the real-time control adjustment specifically includes performing PID control and constant torque control through the real-time state data of the ship, and performing 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: input the ship status data collected in the real-time monitoring model into the PID controller for position and angle correction, and the PID control parameters are set to Kp=0.8, Ki=0.2, Kd=0.1; combine the constant torque control strategy to control the tension of each winch to keep the force of the wire rope uniform; at the same time, adjust the moving speed of the ship according to the corrected motion parameters, and set the speed range to 0.1~0.5m / s to avoid excessive displacement errors; finally, combine the path planning module to continuously correct the actual trajectory of the ship, and gradually approximate the offset trajectory through the incremental adjustment strategy to ensure that the ship trajectory is consistent with the construction requirements.

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

[0042] According to the modified control instruction generated in step S4, the embodiment of the present invention dispatches the working status of each winch device in real time through the control center. The specific operations include: first, the retracting and releasing speed and tension of each winch are allocated according to the control instruction, and the variable frequency drive is used to realize the precise control of the winch motor speed. The retracting and releasing speed range is 0.050.3m / s, and the tension range is 010kN; secondly, a distributed coordination algorithm is used to ensure the synchronization of each winch to avoid slack or overload of the wire rope; in the linkage ship moving operation, the coordination of each winch 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 equipment data required for ship movement control and configuring the network topology of the computer network system and equipment according to these 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 the initialization setting and communication test 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 sensor of the winch equipment collects the retracted and released length and tension parameters of the wire rope in real time, and combines the system 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 and processing are further converted into ship motion control parameters, laying a data foundation for the precise movement and construction trajectory control of the ship. 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 constructing an adaptive control model including anchor calculation model, position deviation correction model, angle deviation correction model and torque balance control model through ship motion control parameters, the dynamic position and attitude adjustment requirements of the ship can be fully handled. The parameter calibration and optimization steps improve the accuracy of each model, so that the generated adaptive control strategy data can flexibly adapt to different sea conditions and task requirements. The effect of this step is to improve the adaptability, control accuracy and response efficiency of the system through a highly flexible and accurate control model. Based on the adaptive control strategy data, PID control and constant torque control are performed using real-time status data, which not only realizes speed adjustment, but also accurately corrects the trajectory of the ship. Through this process, the correction control instruction can effectively reduce the deviation and maintain the smooth movement of the ship. The effect of this step is reflected in improving the accuracy and stability of the ship during movement, especially in complex construction environments, which can significantly reduce errors and improve work efficiency. According to the correction control instruction, the working status of each winch is coordinated and controlled, which can realize the ship's linkage ship moving function and construction trajectory line ship moving function. This winch linkage control method not only improves the coordination of the ship during 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 accurate execution of the trajectory, and reduce the impact of a single equipment failure on the entire system through linkage control.

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

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

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

[0047] Step S13: acquiring hardware equipment data required for ship movement control according to the hardware parameter data and the 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 system, winch equipment and encoder according to the hardware equipment data and the communication protocol configuration data, and constructing 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, thereby obtaining hardware parameter data, wherein the basic parameters include the load capacity, sensor accuracy, and communication interface type of the electric variable frequency winch;

[0052] The embodiment of the present invention collects the basic parameters of the hardware equipment related to the 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 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: acquiring real-time demand data for ship movement control, and selecting an industrial-grade real-time Ethernet communication protocol according to the real-time demand data, thereby obtaining communication protocol configuration data;

[0054] The acquisition of real-time demand data in the embodiment of the present invention can be completed by combining with construction demand analysis, specifically including the requirements for ship movement response time and the tolerance range of communication delay. For example, in high-precision construction, the real-time demand data sets the ship movement response time to be less than 100ms and the communication delay to be less than 10ms. According to the real-time demand data, an industrial-grade real-time Ethernet communication protocol is selected, and a protocol comparison analysis method is used in the operation to perform performance evaluation on common protocols (such as PROFINET, EtherCAT, Modbus TCP), and finally the EtherCAT protocol that meets the 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, and form communication protocol configuration data.

[0055] Step S13: acquiring hardware equipment data required for ship movement control according to the hardware parameter data and the 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 the 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 system, winch equipment and encoder according to the hardware equipment data and the communication protocol configuration data, and constructing an electrical ring network with a redundant backup mechanism;

[0058] The embodiment of the present invention configures the network topology structure according to the hardware device data and the communication protocol configuration data. The specific method is as follows: first, design the network topology diagram according to the number of devices and the installation location, and adopt a ring redundant topology to enhance the 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 be maintained when any node fails. Secondly, use an Ethernet configuration tool (such as TwinCAT or ProfiNetConfigurator) to group and assign addresses to network nodes, set the IP address range to 192.168.1.1~192.168.1.255, and assign a unique identifier to each device. Finally, enable the redundant backup mechanism, set the main communication path and the backup communication path, test the communication reliability through the redundant switching function, ensure that the network can switch quickly when a failure occurs, and finally build 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, the communication delay between devices is tested by a network analyzer. The delay should be controlled within 10ms, and the integrity and packet loss rate of the data packet transmission are detected. Secondly, the PLC is used to initialize the settings of each node device, including the 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] The present invention collects basic parameters such as load capacity, sensor accuracy, and communication interface type of the electric variable frequency winch, and can fully understand the performance and functions of the equipment required for ship movement control. This process ensures that the actual capacity of the equipment is fully considered in the subsequent configuration and optimization, avoiding unnecessary waste of resources or system bottlenecks. By analyzing the real-time requirements of ship movement control and selecting an adapted industrial-grade real-time Ethernet communication protocol, the communication efficiency and stability of the system can be significantly improved. The protocol selection is customized based on the requirements to ensure the efficiency and timeliness of data transmission and meet the high real-time requirements in complex dynamic environments. The hardware parameter data and the real-time requirement data are integrated to further obtain the specific information of the equipment, including the equipment serial number and detailed parameters of the winch equipment, encoder, sensor system, communication interface, and control terminal. This link ensures that the system design stage has a global view, facilitates the implementation of accurate configuration, and provides reliable data support for subsequent system initialization and debugging. Combined with the hardware equipment data and the communication protocol configuration data, the network topology of the computer network system, winch equipment and encoder is configured, and an electrical ring network with a redundant backup mechanism is designed. Through the redundant design, the fault tolerance and stability of the system are enhanced, and the data transmission and equipment linkage can be quickly restored in the event of a fault to ensure continuity. Conduct communication tests and initialization settings on the electrical ring network, establish a data sharing mechanism between devices, and provide an efficient basic communication environment for subsequent operations. Verify the reliability and availability of the network through testing to avoid potential communication interruption risks, and ensure that all devices can be smoothly linked after initialization to form a stable operation foundation.

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

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

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

[0065] Step S153: Performing a network communication test on the electrical ring network according to the encoder configuration data and the winch parameter data through the computer network system, thereby obtaining network communication test data, wherein the network communication test includes a network connectivity test to verify the data exchange capability between various devices, a network delay and jitter test to ensure the real-time and stability of control signal transmission, and a simulation test of the anti-interference capability of the network under extreme working conditions;

[0066] Step S154: Designing a loosely coupled communication data interaction protocol between devices based on the publish-subscribe mode 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 to obtain the system initial configuration data.

[0068] The embodiment of the present invention accurately calibrates the mechanical parameters of the winch equipment according to the hardware parameter data. The specific operation is as follows: first, the winch equipment is connected to the load test system, and different tensions are gradually applied to measure its maximum load-bearing capacity, for example, loading step by step through an electronic tensile gauge, and recording the maximum tension value as 5000N. Next, the travel range of the winch wire rope is tested using a high-precision displacement sensor, and the wire rope is changed from a fully relaxed state to a fully tightened state, and the travel range is recorded as 50m. Then, the winch response speed is recorded by a high-speed data acquisition device, specifically the time required from receiving the control signal to starting to move, and the time from starting to reach the steady-state speed, which are calibrated as 10ms and 200ms respectively. Finally, the above parameters are organized into winch parameter data for subsequent configuration and control optimization. The encoder in the electrical ring network is configured for accuracy. The specific method is as follows: first, the encoder is connected to the winch equipment, and its displacement measurement accuracy is calibrated using a standard displacement ruler to ensure that the error of the encoder within the millimeter range is less than 0.1mm. For example, the winch wire rope is moved step by step, and the displacement value output by the encoder is recorded each time for comparison and calibration. Then, the encoder speed measurement accuracy is verified by a high-speed rotation tester, and the speed calculation error is ensured to be less than 0.5% within the millisecond range. Finally, specific parameters are set in the encoder configuration software according to the calibration results, such as a pulse frequency of 10kHz and a resolution of 4096 pulses / turn, and the encoder configuration data is finally generated to ensure that it is adapted to the ship's mobile control requirements. The electrical ring network is tested for network communication through a computer network system. The specific operations are as follows: First, a network connectivity test is performed, and the IP addresses of each device are pinged using a network test tool to verify whether the data packets are sent and received smoothly, and the packet loss rate is recorded to be less than 0.01%. Secondly, a network delay and jitter test is performed, and different network communication tasks are gradually loaded using a network performance analyzer to record the delay and jitter of the real-time signal, such as a maximum delay of 5ms and a jitter amplitude of less than 1ms. Finally, noise interference is introduced into the network to simulate extreme working conditions, and communication stability is recorded through anti-interference testing to ensure that data exchange can still be maintained between devices. After the test, all test data are collated to generate network communication test data for verifying network performance and stability. Based on the network communication test data, a loosely coupled publish-subscribe mode communication protocol between devices is designed. The specific operations are as follows: First, define topics (Topic) according to the communication requirements of each device in the test data, such as winch position topic, tension monitoring topic, etc. Next, design a publish-subscribe mechanism to ensure that each device only needs to subscribe to related topics without directly communicating with other devices, reducing the complexity of data exchange. Then, a lightweight communication protocol such as MQTT is used to implement topic publishing and subscription functions, and optimize protocol parameters, such as setting the packet transmission time interval to 50ms and the maximum packet size to 512 bytes to adapt to network delay and jitter data.Finally, the data sharing mechanism data is generated after verifying its stability through protocol testing. After completing the above tests and mechanism design, the data sharing mechanism data, network communication test data and electrical ring network are integrated. The specific method is as follows: First, the configuration parameters and test data of all devices are entered into the system initialization configuration database, including winch parameter data, encoder configuration data, network performance indicators, etc. Then, all communication protocols and sharing mechanisms are integrated through the control terminal, and the 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 conduct a comprehensive test of the system to confirm the integrity and consistency of the data integration, such as testing the delay and accuracy of data interaction between devices by simulating ship movement tasks, and finally generating the system initial configuration data to ensure that the ship movement control system has a stable operation foundation.

[0069] The present invention accurately calibrates the mechanical parameters such as the maximum pulling force, travel range and response speed of the winch equipment in the electrical ring network according to the hardware parameter data, ensuring that the equipment performance is consistent with the actual operation requirements. This process provides a reliable parameter basis for subsequent precise control and dynamic adjustment, and improves the control accuracy and operation safety of the system. The encoder in the electrical ring network is configured with millimeter-level and millisecond-level accuracy to adapt to the displacement and speed measurement requirements of the winch equipment. The high-precision configuration optimizes the performance of the encoder in real-time monitoring, enhances the system's ability to capture small changes, and provides support for high-precision calculation of position and speed. Through the computer network system, the electrical ring network is tested for multi-dimensional communication based on the encoder configuration data and the winch parameter data. The network connectivity test verifies the effectiveness of data exchange between devices; the network delay and jitter test ensures the real-time transmission of the control signal; and the anti-interference capability simulation test evaluates the stability of the system under extreme conditions. This process comprehensively improves the communication reliability and anti-interference capability of the system. Based on the network communication test data, the publish-subscribe mode is used to design a loosely coupled communication data interaction protocol between devices to achieve a flexible and efficient data sharing mechanism. This protocol reduces the direct dependence between devices, improves the scalability and maintenance efficiency of the system, and ensures the real-time and accuracy of communication between devices. The system initial configuration data is generated by combining the data sharing mechanism data, network communication test data, and the configuration of the electrical ring network. The integrated data not only ensures the stability of the network and the coordination of the equipment, but also provides a highly optimized initial environment for the subsequent operation of the system, ensuring the efficiency and reliability of the operation.

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

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

[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 according to 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, thereby obtaining coordinate reference data;

[0074] Step S24: performing differential operation on the retracted and extended length of the wire rope in the winch sensor data to obtain the wire rope length change rate; and performing 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 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 the real-time status data of the ship;

[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 unretracted length and tension parameters of the wire rope through the sensor of the winch equipment according to the system initial configuration data, thereby obtaining the winch sensor data;

[0080] The embodiment of the present invention collects the retracted and extended length and tension parameters of the wire rope through the sensor of the winch equipment according to the initial configuration data of the system. 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 retracted and extended 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 winch 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, and the specific operation is 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 dock test location, and the center of gravity coordinates are obtained to be (0, 0, -2.5) m. Secondly, the geometric dimensions of the hull are measured using a laser scanner to obtain geometric parameters of a length of 50 m, a width of 10 m, and a height of 6 m. Next, the standard displacement of the ship is measured to be 500 tons using a weighing device, and the weight distribution parameters are input based on a preset weight distribution model. All the 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 according to 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, thereby obtaining coordinate reference data;

[0084] The embodiment of the present invention establishes a right-handed rectangular coordinate system according to 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 to be forward along the length of the hull, the Y-axis direction is perpendicular to the X-axis and to the left along the width of the hull, 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, and 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 differential operation on the retracted and extended length of the wire rope in the winch sensor data to obtain the wire rope length change rate; and performing 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 sensor data, thereby obtaining attitude angle monitoring data;

[0086] The embodiment of the present invention performs differential operation on the wire rope retracted and released length in the winch sensor data to calculate the length change rate. The specific operation is as follows: First, the differential calculation formula is implemented by programming The data with a sampling frequency of 100Hz is input into the operation module to obtain the real-time data of the length change rate of the wire rope in m / s. Then, according to the collected tension parameters, the tension difference is calculated using the mechanical analysis model, and it is combined with the length change rate of the wire rope. The Kalman filter algorithm based on the attitude matrix is ​​used to estimate the roll angle, pitch angle and heading angle of the ship, which are calibrated to the accuracy range of ±2°, ±1° and ±3° respectively, and finally the attitude angle monitoring data is generated.

[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 position and attitude of a ship based on coordinate reference data and attitude angle monitoring data. The specific method is as follows: first, a mathematical model with the center of gravity of the ship as the origin is established using the coordinate reference data, the hull is divided into multiple reference areas, and their three-dimensional position relationship is recorded. Then, the attitude angle monitoring data is input into the model, and the real-time position and attitude of each part of the ship are calculated by the matrix transformation formula P'=R·P (where R is the attitude angle rotation matrix and P is the initial coordinate point). Finally, 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 the real-time status data of the ship;

[0090] The embodiment of the present invention uses a real-time monitoring model to monitor the state of a ship. The specific operation is as follows: first, the data collected by the sensor is input into the monitoring model in real time to generate the dynamic displacement, speed and attitude angle data of the ship. For example, when the ship moves sideways, the monitoring model can record in real time that the displacement of the hull along the X-axis direction is 0.02m / s, and the inclination change along the Z-axis is ±0.1°. Then, the monitoring data is compared with the preset safety range. If the deviation threshold is exceeded, the alarm mechanism is triggered. Finally, the real-time state data of the ship 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 state data of the ship. The specific operations are as follows: first, a kinematic analysis model is constructed based on the real-time state data, and the ship 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 correction 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 tension adjustment value, the wire rope retraction and release length, and the ship attitude adjustment angle, for precise control.

[0093] The present invention collects the retracted length and tension parameters of the wire rope through the sensor of the winch equipment to obtain the winch sensor data. This process provides the system with a dynamic information basis for the winch equipment, supports the real-time analysis and control of the subsequent ship status, and ensures the comprehensiveness and accuracy of the measurement data. The initial static parameters such as the center of gravity coordinates, hull geometry, standard displacement and weight distribution of the ship are recorded as the reference data for ship status monitoring. These parameters provide a static reference for the posture and position analysis of the ship in operation, ensuring the scientific nature of the model construction and analysis results. With the center of gravity of the ship as the origin, the X, Y, and Z axis directions are defined, a right-handed rectangular coordinate system is established, and the absolute and relative accuracy are calibrated. By constructing accurate coordinate reference data, a mathematical framework and data support are provided for the subsequent high-precision monitoring of position and attitude, ensuring the standardization and consistency of spatial analysis. The wire rope retracted length is differentially operated to obtain the wire rope length change rate, and the roll angle, pitch angle and heading angle are calculated in combination with the tension difference to generate attitude angle monitoring data. This process realizes 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 static and dynamic parameters of the ship, provides real-time and comprehensive monitoring capabilities for the ship's motion status, and supports multi-dimensional data analysis. Through the real-time monitoring model, the ship's status is continuously monitored to generate real-time status data for the ship. This status data provides real-time input for subsequent motion control and strategy adjustments, improving the system's dynamic response capability and operating efficiency. Motion control analysis is performed based on the ship's real-time status data to generate ship motion control parameters. These parameters provide key data support for ship control, achieve precise control of the operating path and attitude, and provide guarantees for 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, thereby obtaining 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 evaluation model based on fuzzy logic according to the real-time state characteristic data of the ship, and using the motion state evaluation model to perform nonlinear mapping and evaluation on the real-time state characteristic data of the ship, thereby obtaining fuzzy evaluation result data;

[0097] Step S273: generating a preliminary motion control parameter matrix including a speed adjustment coefficient, a direction correction gain, a posture 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] The embodiment of the present invention performs multi-dimensional feature extraction on the real-time state data of the ship, and the specific operation is as follows: first, real-time acquisition includes vertical displacement, horizontal offset angle, roll angle and pitch angle and other parameter data, for example, vertical displacement is collected by a laser rangefinder with an accuracy of ±1mm, the horizontal offset angle is recorded by the inertial navigation system with an angle change value with an accuracy of ±0.01°, and the roll angle and pitch angle are recorded by the attitude sensor with an accuracy of ±0.02°. Then, these data are extracted with the principal component analysis (PCA) method to reduce the interference of redundant information. Next, the extracted feature data are standardized, and the maximum and minimum normalization method is used to normalize all feature data to the [0,1] interval to obtain the real-time state feature data of the ship for subsequent analysis. A motion state evaluation model based on fuzzy logic is established according to the real-time state feature data of the ship. The specific method is as follows: first, the input variables of the fuzzy set are defined as vertical displacement, horizontal offset angle, roll angle and pitch angle, and the output variable is the motion state level (such as "stable", "offset" and "severe offset"). Then, the input variables are fuzzified using fuzzy membership functions, for example, the roll angle is divided into [0°, 1°] (small), [1°, 3°] (medium), and [3°, 5°] (large). Next, a fuzzy rule base is constructed, such as "if the roll angle is medium and the pitch angle is small, the motion state is offset". The real-time state feature data of the ship is input into the model, and nonlinear mapping is performed through fuzzy reasoning and weighted average method, and finally the fuzzy evaluation result data is generated, and the ship motion state level and the corresponding feature weight are output. The preliminary motion control parameter matrix is ​​generated according to the fuzzy evaluation result data. The specific operation is as follows: First, combined with the weight value of each state level in the fuzzy evaluation result, the speed adjustment coefficient, direction correction gain, attitude stability control parameter and trajectory tracking weight are defined. For example, when the motion state is "offset" and the roll angle weight is 0.6, the speed adjustment coefficient is calculated to be 0.8, the direction correction gain is 1.2, the attitude stability control parameter is 2.5, and the trajectory tracking weight is 1.5. Next, these parameters are combined into a preliminary motion control parameter matrix M = [0.8, 1.2, 2.5, 1.5], and the input format of the control model is adapted by matrix mapping for the subsequent generation of motion control parameters. The ship motion control parameters are generated according to the preliminary motion control parameter matrix. The specific operations are as follows: First, the preliminary motion control parameter matrix is ​​input into the control algorithm. For example, the proportional-integral-differential (PID) controller is used to adjust the direction correction gain to optimize the heading. At the same time, the attitude stability control parameter is used to adjust the real-time response of the winch tension to maintain the roll angle within ±0.5°. Then, the speed adjustment coefficient is used to dynamically adjust the wire rope retraction and release rate to ensure that 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 ship motion control parameters include the retraction and release speed of each winch, the tension value and the ship attitude adjustment target, which are used to achieve precise motion control.

[0100] The present invention extracts key feature data such as vertical displacement, horizontal offset angle, roll angle and pitch angle by performing multi-dimensional feature extraction and standardization on the real-time state data of the ship. This step screens and normalizes the main factors affecting the motion of the ship, and improves the accuracy and consistency of the subsequent model evaluation. Using the real-time state feature data of the ship, a motion state evaluation model based on fuzzy logic is constructed, and the data is processed by nonlinear mapping to generate fuzzy evaluation result data. The introduction of fuzzy logic solves the uncertainty problem that traditional models are difficult to accurately describe in the complex motion state of the ship, so that the evaluation results are more in line with the actual operating conditions. Combined with the fuzzy evaluation result data, a preliminary motion control parameter matrix containing speed adjustment coefficient, direction correction gain, attitude stability control parameters and trajectory tracking weight is generated. This process provides a quantitative basis for dynamic motion adjustment by comprehensively evaluating multi-dimensional features, ensuring the pertinence and flexibility of control parameters. According to the preliminary motion control parameter matrix, the final ship motion control parameters are further optimized and calculated. Through this step, the control strategy adapted to the current state of the ship is dynamically adjusted to meet the real-time motion control requirements and achieve more efficient motion control.

[0101] Preferably, step S3 comprises 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, wind direction and wind speed according to 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 expected data of the ship moving, 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 winches work together according to 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 the ship motion control parameters, and the specific operation is as follows: first, collect the data such as the winch retracting and releasing speed, tension value, attitude adjustment target value in the motion control parameters. For example, the winch retracting and releasing speed range is between 0-2m / s, the tension value is between 50-300kN, and the attitude adjustment target angle accuracy is ±0.1°. Then, the descriptive statistical analysis method is used to extract the characteristics of each parameter, including calculating the mean, variance, maximum value and minimum value, and analyzing the stability and fluctuation range of each variable in the motion control process. Then, the correlation analysis method is used to evaluate the degree of association between the parameters. For example, the correlation coefficient between the retracting and releasing speed and the tension value reaches 0.85, indicating that the two are highly correlated. Finally, the analysis results are integrated into motion control parameter feature data for model construction. The anchor position calculation model is constructed according to the preset ship moving expected data and motion control parameter feature data. The specific operation is as follows: first, extract the target position coordinates (such as (50m, 30m)) and target heading (such as 90°) in the ship moving expected 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 the topographic profile; at the same time, the distribution data of the current velocity (such as 0.5m / s) and wind speed (such as 8m / s) are collected. Then, based on the above constraints, the anchor adaptability indicators are defined, such as anchor stability, the shortest path between the anchor and the target position, and the uniformity of the anchor force. Finally, the multi-objective optimization algorithm (such as genetic algorithm) is used to quantify the anchor adaptability, select the anchor with the highest adaptability, and construct the anchor calculation model. According to the expected data of the ship moving, the motion control parameter characteristic data is corrected for position deviation. The specific operation is as follows: first, the target position of the moving ship 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 values ​​ΔX and ΔY in the horizontal plane are calculated, which are 2m and -2m respectively; for the vertical deviation, the laser rangefinder is used to collect data and calculate the deviation value ΔZ (such as -0.5m). Then, the control algorithm based on deviation minimization (such as linear least squares method) is used to correct each position parameter, and a position deviation correction model is constructed to provide a reference for subsequent control. The ship attitude angle is optimized according to the characteristic data of the motion control parameters. The specific operations are as follows: First, based on the collected motion control parameters, the attitude angle related data are extracted, including the current roll angle (such as 3°), pitch angle (such as 1.5°) and heading angle (such as 92°). Then, an angle deviation correction decision tree is constructed to set the optimization target range of the roll angle, pitch angle and heading angle. For example, the optimization targets of 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 winch retraction and release speed in the motion control parameters are adjusted, and finally the angle deviation correction model is generated to ensure the stability of the ship's attitude.According to the motion control parameter characteristic data, a force balance model is constructed when multiple winches work together. The specific operation is as follows: First, the real-time tension value of each winch is collected (such as 200kN for winch A and 220kN for winch B), and its torque contribution in the ship motion is analyzed. Then, based on the force balance principle, the target torque balance formula is defined, such as Σ(Ti)≈0, where Ti is the torque generated by each winch. Next, the tension value distribution of each winch is adjusted by an optimization algorithm (such as the Lagrange multiplier method), for example, the tension of winch A is adjusted to 210kN, and the tension of winch B is adjusted to 210kN, to achieve torque balance, and finally construct a torque balance control model. The anchor calculation model, position deviation correction model, angle deviation correction model and torque balance control model are integrated into an adaptive control model. The specific operation is as follows: First, a data integration framework is designed to unify the input and output data formats of each model, such as the output deviation values ​​ΔX and ΔY of the position deviation correction model as the input parameters of the anchor calculation model. Then, a modular design method is adopted to encapsulate each model into an independent module and call it through a standardized interface. Then, an integrated algorithm (such as recursive neural network) is used to realize dynamic adaptation and parameter adjustment between multiple models, and finally an adaptive control model is integrated to support real-time update and control strategy optimization. The parameters of each model in the adaptive control model are calibrated and optimized. The specific operations are as follows: First, the model is preliminarily calibrated using historical experimental data and simulation data. For example, the adaptability weight in the anchor calculation model is set to anchor stability 0.4, path shortestness 0.3, and force uniformity 0.3. Then, by collecting data from the actual ship control process, the model parameters are adjusted to match the actual situation. For example, the attitude angle adjustment coefficient in the optimized angle deviation correction model is adjusted from 1.2 to 1.5 to improve the correction accuracy. Finally, the parameter set of each model is globally optimized using a genetic algorithm to generate adaptive control strategy data for practical application.

[0110] The present invention extracts relevant characteristic data by performing multi-dimensional statistical analysis on the ship motion control parameters. This step enables the control parameters to be not only quantitatively analyzed, but also to identify the motion mode under different conditions, providing basic data for subsequent optimization, thereby improving the accuracy of control decisions. According to the preset ship movement expectation data, and combined with factors such as seabed topography, current distribution, wind direction and wind speed, an anchor calculation model under multi-dimensional constraints is constructed. The adaptability index of anchor selection is quantified to ensure the selection of the best anchor in a complex environment, thereby improving the accuracy and stability of ship positioning. A position deviation correction model is constructed by correcting the displacement deviation in the horizontal plane and vertical direction of the ship. The introduction of this model effectively solves the problem of position offset caused by external factors (such as currents, wind speed, etc.) of the ship, thereby ensuring that the ship can be accurately operated at the predetermined position. The angle deviation correction decision tree is used to optimize the ship attitude angle, and an angle deviation correction model is established. This model can quickly respond to changes in the ship attitude in a dynamic marine environment, adjust the ship angle, avoid ship tilt or instability, and ensure safe and efficient control. Based on the force balance analysis when multiple winches work together, a torque balance control model is constructed. This model helps to coordinate the working status of each winch in a complex working environment, so that the torque distribution is balanced, unnecessary power waste is reduced, and the energy efficiency of the ship is optimized. 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. This integrated model improves the flexibility and responsiveness of the control system by integrating the advantages of each sub-model, 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, allowing the ship to make intelligent adjustments according to 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 sea area geographic information database, which stores seabed topography, ocean current distribution, and wind direction and speed information;

[0113] Step S322: using the sea area geographic information database to accurately locate the expected anchor position of the ship in the preset expected ship movement data, and generate seabed terrain constraint condition data, ocean current dynamic characteristic data and wind environment constraint parameters;

[0114] Step S323: Perform multi-dimensional correlation analysis on the seabed topography constraint condition data, the ocean current dynamic characteristic data and the wind environment constraint parameters, so as to construct 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, screening the anchor position data that meet the expected ship movement requirements, 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] The embodiment of the present invention obtains a marine geographic information database, and the specific operation is: through the integration of satellite remote sensing data, sonar detection results and data recorded by meteorological stations, a marine geographic information database is established. The data stored in the database include seabed topography information (such as depth, slope, geological characteristics), ocean current distribution data (such as speed range 0.1-2m / s, direction change angle 0-45°) and wind direction and speed information (such as wind speed range 5-15m / s, wind direction angle ±15°). These data are classified and indexed through spatial data management tools (such as PostGIS) to form a multidimensional geographic information data set that is easy to query and retrieve quickly, providing basic support for subsequent anchor positioning. The expected anchor position of the ship in the preset expected ship movement data is accurately located using the marine geographic information database. The specific operation is as follows: the geographic coordinates of the expected anchor position (such as (125.5°E, 38.2°N)) are used as query input, and the relevant modules in the database are called to retrieve the seabed terrain data (such as slope of 5°, water depth of 50m), ocean current dynamic characteristics data (such as speed of 0.5m / s, direction of southeast), and wind environment parameters (such as wind speed of 10m / s, wind direction of 15° north by east) in the surrounding area of ​​the anchor position. The geographic information system (GIS) is used for spatial analysis to extract the above data and generate seabed terrain constraint condition data, ocean current dynamic characteristics data and wind environment constraint parameters. The seabed terrain constraint condition data, ocean current dynamic characteristics data and wind environment constraint parameters are subjected to multi-dimensional correlation analysis. The specific operation is as follows: the constraint condition data is reduced in dimension based on multivariate statistical analysis methods (such as principal component analysis, PCA) to extract key features (such as terrain stability factor, flow velocity change amplitude). These features are trained using machine learning models (such as random forests) to evaluate the weight of each feature on the adaptability of the anchor position, for example, the weight of terrain stability is 0.4, the weight of ocean current change is 0.35, and the weight of wind impact is 0.25. Combine the weights to calculate the comprehensive adaptability evaluation index to form a unified adaptability evaluation system to provide a quantitative basis for anchor selection. According to the comprehensive adaptability evaluation index system, the motion control parameter feature data is comprehensively evaluated for adaptability. The specific operation is as follows: first, the key data in the motion control parameters (such as the maximum winch pull of 300kN and the maximum lateral displacement of the ship of 3m) are extracted, and the evaluation index is adapted and analyzed. For example, the seabed terrain constraint condition is combined with the winch pull data to calculate the terrain adaptability score, which is 85; the dynamic characteristics of the ocean current are combined with the lateral displacement of the ship to calculate the velocity adaptability score, which is 80; the wind environment constraint parameters are combined with the ship attitude stability to calculate the wind adaptability score, which is 90. Finally, the weighted sum of each score is generated to generate the anchor position adaptability score data (such as 86.5 points).The confidence interval analysis of the anchor adaptability score data is performed to screen the data that meet the expected data requirements for ship movement. The specific operation is: the Monte Carlo simulation method is used to randomly sample and estimate the score data, and the mean and confidence interval of the anchor adaptability score are calculated (such as the mean 86.5, the 95% confidence interval is 84.2-88.8). According to the adaptability score threshold in the expected data of ship movement (such as ≥85 points), the anchor positions that meet the conditions (such as anchor A, anchor B) are screened out. The relevant data of these anchors are sorted into anchor screening result data to provide input for the establishment of subsequent mathematical models. The mathematical model is established based on the anchor screening result data. The specific operation is: the screened anchor data is used as the input variable (such as the terrain factor of anchor A is 0.85, the flow factor is 0.8, and the wind factor is 0.9), and the mathematical model based on multi-objective optimization is constructed in combination with the characteristic data of ship motion control parameters. The model takes the maximization of the anchor adaptability score as the objective function, and the constraints include the terrain stability of the anchor position greater than 0.8, the velocity change less than 0.5m / s, and the wind impact angle deviation less than 15°. The optimization algorithm (such as particle swarm optimization algorithm, PSO) is used to solve and finally generate the anchor calculation model to provide calculation support for the precise positioning of the ship during the ship moving process.

[0119] The present invention provides basic data for subsequent ship anchor selection by acquiring a sea area geographic information database containing information such as seabed topography, current distribution, wind direction and wind speed. These detailed geographic information provides a key environmental background for anchor calculation, ensuring the accuracy and safety of operations in complex marine environments. The desired anchor position of the ship is accurately located using the sea area geographic information database, and the seabed topography constraint condition data, current dynamic characteristics data and wind environment constraint parameters related thereto are generated. This process ensures that the selection of the ship anchor is based on accurate environmental data, avoiding selection errors caused by topography or environmental factors. A comprehensive adaptability evaluation index system is constructed by performing multi-dimensional correlation analysis on the seabed topography, current dynamic characteristics and wind environment constraint parameters. The system can comprehensively evaluate the environmental adaptability of different anchor selections, ensuring that all possible factors are taken into account, thereby improving the scientificity and effectiveness of anchor selection. According to the constructed comprehensive adaptability evaluation index system, the motion control parameter characteristic data is comprehensively evaluated for adaptability, and the anchor adaptability score data is obtained. This scoring system provides a clear fitness indicator for each anchor position, so that the anchor position selection does not only rely on a single factor, but also takes into account multiple aspects of data to optimize the accuracy of the selection. The confidence interval analysis of the anchor position adaptability score data is carried out to screen out the anchor positions that meet the expected requirements of ship movement. Through this analysis, those anchor positions that may not have good adaptability in actual operation can be effectively excluded, ensuring that the final selected anchor position has high statistical reliability and accuracy, further reducing operational risks. A mathematical model is established based on the screening results and an anchor position calculation model is generated. This step ensures that the anchor position selection not only relies on qualitative environmental data analysis, but also performs precise calculations through mathematical models, providing scientific decision support for subsequent ship movements, and improving the systematicness 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 expected movement data and the motion control parameter feature data, including the real-time deviation values ​​of the roll angle, pitch angle, and yaw angle and their change rates;

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

[0123] Step S343: construct a multi-level decision tree according to the angle deviation level data, and set 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 attitude angle deviation based on the ship movement expected data and the motion control parameter characteristic data. 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 by time series analysis (such as 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 standardized according to the time step to eliminate the influence of unit differences, and are combined into a multidimensional feature vector of 2.5, 1.8, 3.2, 0.5, 0.3, 0.8 as the input for the subsequent angle deviation evaluation. According to the multi-dimensional feature vector, the angle deviation classification criterion based on fuzzy logic is designed. The specific operation is as follows: by analyzing the historical ship operation data and empirical rules, the deviation ranges of the roll angle, pitch angle and yaw angle are divided into four levels: normal, slight, significant and severe (for example, the classification standard of the roll angle is: normal: 0°-1°, slight: 1°-3°, significant: 3°-5°, severe: >5°). The fuzzy membership function (such as the triangular membership function) is used to calculate the classification membership of each angle. For example, the membership of the roll angle of 2.5° is slight 0.7 and significant 0.3, the membership of the pitch angle of 1.8° is slight 1.0, and the membership of the yaw angle of 3.2° is significant 0.6 and severe 0.4. Combined with the contribution factor of the change rate, quantitative angle deviation grade data is formed (such as the roll angle is slight, the pitch angle is slight, and the yaw angle is significant). A multi-level decision tree is constructed based on the angle deviation level data. The specific operation is as follows: the decision tree is designed with the attitude correction threshold, correction direction and correction strength as the main decision nodes. For example, the first-level node determines whether the deviation level reaches the correction threshold (such as significant or severe); the second-level node selects the correction direction according to the specific angle deviation direction (such as positive correction of roll angle deviation and negative correction of pitch angle deviation); the third-level node allocates the correction strength according to the degree of deviation (such as slight correction 0.2° / s, significant correction 0.5° / s, and severe correction 0.8° / s). The decision tree finally constructed can output correction strategies under different levels of conditions and form an attitude angle correction scheme corresponding to the actual operating conditions. The multi-level decision tree is simulated and verified. The specific operation is as follows: the ship's motion data and angle deviation level data are imported through the simulation platform (such as MATLAB / Simulink) to simulate the ship's attitude adjustment process under different environmental conditions (such as wind speed 10m / s, wave height 2m). Verify the correction effect of the decision tree in various situations. For example, under conditions of slight deviation, the roll angle is maintained within 0.5° after correction, and under conditions of significant deviation, the yaw angle is reduced from 4° to within 2° after correction.The simulation results are subjected to error analysis (such as correction effect deviation less than 10%) and optimization (adjustment of correction strength or branching rules), and finally an angle deviation correction strategy based on a multi-level decision tree is generated and integrated into an angle deviation correction model to describe the detailed process of ship attitude angle optimization.

[0126] The present invention constructs a multidimensional feature vector containing real-time deviation values ​​and their change rates of roll angle, pitch angle and yaw angle by combining the expected data of ship movement and the characteristic data of motion control parameters. This feature vector can accurately reflect the dynamic changes of the ship's attitude and provide necessary data support for subsequent attitude correction. By analyzing the real-time deviation and its change rate, the slight deviation of the ship's attitude can be captured in time to ensure that the correction strategy can respond and adjust quickly. Based on the multidimensional feature vector, a grading criterion for angle deviation is designed, and a quantitative evaluation standard for the severity of angle deviation is established. This standard divides the angle deviation into different levels, and can make a refined judgment based on the real-time attitude deviation and change rate of the ship. Through the introduction of fuzzy logic, the uncertainty and complexity in reality can be effectively handled, making the judgment of angle deviation more flexible and in line with the actual situation. According to the angle deviation level data, a multi-level decision tree is constructed, and the decision nodes of attitude correction threshold, correction direction and correction strength are set at each level. This decision tree can formulate a reasonable correction strategy according to different deviation levels and actual conditions. By hierarchical and detailed correction strategies, it is ensured that the ship's attitude correction can make accurate adjustments to different degrees of deviation, thereby improving the ship's control accuracy and safety. By simulating and verifying the multi-level decision tree, the effectiveness of the angle deviation correction strategy in different scenarios was tested, and finally an angle deviation correction model describing the ship's attitude angle optimization process was generated. The simulation verification process can simulate various changes and interferences in actual operations to ensure that the correction strategy can be adjusted stably and effectively under different circumstances. The model provides a systematic framework for ship attitude correction, ensuring the stable operation of the ship in various complex environments.

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

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

[0129] Step S352: establishing a three-dimensional spatial geometric model of each winch force point relative to the ship's center of gravity according to the center of gravity moment component data;

[0130] Step S353: performing moment balance analysis on the gravity 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 based on the force balance of the ship according to the force imbalance data, thereby obtaining the pulling force data of the equilibrium state;

[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 through the sensor of the multi-winch system. For example, the winch located on the left side of the bow measures a tension of 2000N, the winch on the right side of the bow measures 2200N, and the winch at the center of the stern measures 1800N. Based on this real-time tension data and in combination with the coordinates of the center of gravity of the ship, the tension of each winch is decomposed into the torque component acting on the center of gravity. For example, the horizontal distance of the left winch relative to the center of gravity is 10m, and the vertical distance is 2m. The torque generated by the winch on the center of gravity is 10×2000=20000Nm (horizontal component), 2×2000=4000Nm (vertical component). The torque components of other winches are calculated in turn, and finally a data set containing the torque components of all winches is formed as the basis for subsequent analysis. According to the center of gravity torque component data obtained in step S351, a three-dimensional spatial geometric model of the multi-winch system is established. The specific method is: using the three-dimensional coordinates of the center of gravity of the ship (for example, X=0, Y=0, Z=0) as the reference point, combined with the spatial distribution coordinates of each winch (such as the left winch is located at X=-10m, Y=5m, Z=2m, and the right winch is located at X=10m, Y=5m, Z=2m), draw a geometric distribution diagram of the winch relative to the center of gravity of the ship. Through geometric modeling tools (such as MATLAB or CAD software), the spatial relationship between the force points of each winch and the center of gravity of the ship is visualized to provide an intuitive geometric basis for moment balance analysis. Using the three-dimensional spatial geometric model, the moment balance analysis is performed on the gravity center moment component data obtained in step S351. The specific operation is: according to the moment component of each winch in the geometric model, the total moment acting on the center of gravity is calculated. For example, the total moment in the X direction is 20000Nm (left winch)-22000Nm (right winch) =-2000Nm, indicating that there is an unbalanced moment to the left in the X direction. The unbalanced moments in the Y and Z directions are calculated in sequence, and the results are quantified as unbalanced coefficients, such as X=-0.1, Y=0.05, and Z=0.02. These unbalanced coefficients are used to describe the degree of deviation of the ship's force state to form the final unbalanced force data. According to the unbalanced force data calculated in step S353, the pull of each winch is adjusted to achieve the force balance of the ship. The specific method is: first set the torque balance threshold in each direction (for example, the unbalanced moment allowed in the X direction is ±500Nm). By adjusting the winch tension, for example, increasing the left winch tension to 2100N and reducing the right winch tension to 2100N, the sum of the moments in the X direction approaches zero and reaches a balanced state. At the same time, adjust the winch tension in other directions to ensure that the ship is in a force balance state in all three directions, and finally generate the equilibrium state tension data, and record the adjusted pull value of each winch. 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 as follows: the balanced tension value of each winch and its corresponding geometric parameters are input into the control algorithm, and a mathematical model is established using the torque balance equation Σ(Mx, My, Mz) = 0. The input variables are defined as real-time tension and geometric coordinates, and the output is the adjustment recommendation value. The response speed and stability of the control model are optimized through multiple simulation tests to ensure that the model can make accurate adjustments to the changes in tension data in real time. Finally, a torque balance control model with real-time correction capability is obtained to guide the coordinated work of multiple winches of ships in complex environments.

[0134] The present invention collects real-time tension data of multiple winch systems, and calculates the moment component of each winch tension on the ship's center of gravity based on these data, so as to understand in detail the influence of each winch on the ship's balance. This process can provide basic data for subsequent force balance analysis by accurately calculating the moment component, ensuring a comprehensive grasp of the force situation of the ship's center of gravity. According to the gravity center moment component data, a three-dimensional spatial geometric model of each winch force point relative to the ship's center of gravity is established, and this model can intuitively display the spatial relationship between each winch force point and the ship's center of gravity. Through the three-dimensional model, the force situation of each winch and its influence on the balance of the ship can be analyzed and calculated more accurately, thereby providing a geometric basis for the moment balance analysis. On the basis of the three-dimensional spatial geometric model, a moment balance analysis is performed, and the force imbalance coefficient in each direction is calculated. This analysis helps identify the moment imbalance of the ship in different directions, and provides key data for subsequent moment correction. By calculating the force imbalance coefficient, the mechanical state of the ship can be more clearly grasped, and it is clear which directions have potential problems of uneven force. According to the force imbalance data, the pulling force of each winch is adjusted to ensure that the force of the ship reaches a balanced state. This adjustment process can eliminate the imbalance of pulling force between the winches, thereby reducing the deviation of the ship's posture and force, and ensuring the stability of the ship. By adjusting the pulling force in real time, the ship's force balance in complex environments is ensured. According to the adjusted balanced state pulling force data, a torque balance control model based on tension balance is constructed. This model can monitor the changes in the pulling force of each winch of the ship in real time, and dynamically adjust the torque distribution to ensure the posture and stability of the ship during navigation. This control model provides a systematic torque balance control method, which helps the ship maintain stable operation under various working conditions.

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

[0136] The equipment configuration module is used to obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network system, winch equipment and encoder according to the hardware equipment data, and 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;

[0137] The status monitoring module is used to collect the length and tension parameters of the wire rope through the sensors of the winch equipment based on the initial configuration data of the system, and to build a real-time monitoring model for the position and posture of the ship; to monitor the ship status using the real-time monitoring model and generate the real-time status data of the ship; to perform motion control analysis and processing on the real-time status data of the ship and generate the motion control parameters of the ship;

[0138] The adaptive control module is used to establish an adaptive control model according to 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; perform parameter calibration and optimization on each model in the adaptive control model to generate adaptive control strategy data;

[0139] The control adjustment module is used to perform real-time control adjustment of the ship based on the adaptive control strategy data and generate correction control instructions, wherein the real-time control adjustment specifically includes PID control and constant torque control through the real-time status data of the ship, and speed adjustment and trajectory correction;

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

[0141] The present invention obtains the hardware equipment data required for ship movement control, configures the network topology of the computer network system, winch equipment and encoder according to these data, and establishes an electrical ring network. By initializing the electrical ring network and performing communication testing, it is ensured that the data sharing mechanism between devices is successfully established. This process provides a stable infrastructure, ensures the smooth operation of the ship movement control system, and reduces the possibility of hardware configuration errors and system communication failures. By optimizing the network topology, the reliability of the system and the efficiency of data exchange can be effectively improved. Based on the initial configuration data of the system, the sensor of the winch equipment is used to collect the retracted and released length and tension parameters of the wire rope, thereby constructing a real-time monitoring model of the ship's position and posture. The real-time monitoring model provides accurate data support for ship status monitoring, ensuring that the position, posture and dynamic changes of the ship are fully monitored. By analyzing the real-time status data of the ship, the ship motion control parameters can be further generated to ensure the accuracy of the motion control. This module helps to achieve efficient motion control and real-time adjustment by continuously monitoring and analyzing the ship status. In the adaptive control module, the ship motion control parameters are used to establish an adaptive control model, covering the anchor calculation model, the position deviation correction model, the angle deviation correction model and the torque balance control model. By calibrating and optimizing the parameters of these models, the adaptive adjustment of ships in different working environments can be achieved. The establishment of the adaptive control model ensures that the ship can flexibly adjust according to the changes in the environment and operating conditions in actual applications, thereby realizing accurate control strategies and helping to improve the stability and accuracy of ship operations. Based on the adaptive control strategy data, the control adjustment module performs PID control and constant torque control through the real-time status data of the ship to adjust the speed and correct the trajectory. This process realizes the fine adjustment of the ship's motion control and ensures that the ship can maintain a suitable posture and trajectory in a complex operating environment. By adjusting the control parameters of the ship in real time, the deviation can be effectively eliminated to ensure that the ship moves on the expected track and avoid deviation from the track or unstable posture. The collaborative ship moving module coordinates and controls the working status of each winch according to the correction control instructions to realize the ship's linkage ship moving and construction trajectory line ship moving functions. This module realizes the precise movement of the ship through the collaborative work of multiple winches and ensures that the ship always operates along the correct trajectory during the construction process. By optimizing the collaboration between winches, the movement of the ship is more efficient and stable, avoiding the problem of ship moving errors or instability caused by improper operation.

[0142] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0143] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A ship intelligent moving control method based on adaptive algorithm, characterized in that: The following steps are involved: Step S1: Acquire the hardware equipment data required for ship movement control; Configure the network topology of the computer network system, winch equipment and encoder according to the hardware equipment data, and 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 configuration data of the system, the retracted and extended length and tension parameters of the wire rope are collected through the sensors of the winch equipment, and a real-time monitoring model of the ship's position and posture is constructed; the ship's state is monitored using the real-time monitoring model to generate the ship's real-time state data; the ship's real-time state data is subjected to motion control analysis and processing to generate the ship's motion control parameters; Step S3: establishing an adaptive control model according to 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 parameters of each model in the adaptive control model to generate adaptive control strategy data; Step S4: Based on the adaptive control strategy data, the ship is controlled and adjusted in real time to generate a correction control instruction, wherein the real-time control adjustment specifically includes performing PID control and constant torque control through the real-time state data of the ship, and performing speed adjustment and trajectory correction; Step S5: Coordinate and control the working status of each winch according to the modified control instruction to realize the ship's linkage 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, thereby obtaining hardware parameter data, wherein the basic parameters include the load capacity, sensor accuracy, and communication interface type of the electric variable frequency winch; Step S12: acquiring real-time demand data for ship movement control, and selecting an industrial-grade real-time Ethernet communication protocol according to the real-time demand data, thereby obtaining communication protocol configuration data; Step S13: acquiring hardware equipment data required for ship movement control according to the hardware parameter data and the 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 system, winch equipment and encoder according to the hardware equipment data and the communication protocol configuration data, and constructing 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 calibrating the mechanical parameters of the winch equipment in the electrical ring network according to the hardware parameter data, thereby obtaining winch parameter data, wherein the mechanical parameters include maximum pulling force, travel range and response speed; Step S152: performing precision configuration on the encoder in the electrical ring network based on the millimeter-level and millisecond-level precise measurement of the winch displacement and speed, thereby obtaining encoder configuration data; Step S153: Performing a network communication test on the electrical ring network according to the encoder configuration data and the winch parameter data through the computer network system, thereby obtaining network communication test data, wherein the network communication test includes a network connectivity test to verify the data exchange capability between various devices, a network delay and jitter test to ensure the real-time and stability of control signal transmission, and a simulation test of the anti-interference capability of the network under extreme working conditions; Step S154: Designing a loosely coupled communication data interaction protocol between devices based on the publish-subscribe mode 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 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 unretracted length and tension parameters of the wire rope through the sensor of the winch equipment according to the system initial configuration data, thereby obtaining the winch sensor data; 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 according to 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, thereby obtaining coordinate reference data; Step S24: performing differential operation on the retracted and extended length of the wire rope in the winch sensor data to obtain the wire rope length change rate; and performing 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 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 the real-time status data of the ship; 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 S27 includes the following steps: Step S271: extracting multi-dimensional features from the real-time state data of the ship, and performing data standardization, thereby obtaining 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; Step S272: establishing a motion state evaluation model based on fuzzy logic according to the real-time state characteristic data of the ship, and using the motion state evaluation model to perform nonlinear mapping and evaluation on the real-time state characteristic data of the ship, thereby obtaining fuzzy evaluation result data; Step S273: generating a preliminary motion control parameter matrix including a speed adjustment coefficient, a direction correction gain, a posture stabilization control parameter, and a trajectory tracking weight according to the fuzzy evaluation result data; Step S274: Generate ship motion control parameters according to the preliminary motion control parameter matrix.

6. The ship intelligent moving control method based on adaptive algorithm according to claim 5 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, wind direction and wind speed according to 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 expected data of the ship moving, 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 winches work together according to 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.

7. The ship intelligent moving control method based on adaptive algorithm according to claim 6 is characterized in that: Step S32 includes the following steps: Step S321: Acquire a sea area geographic information database, which stores seabed topography, ocean current distribution, and wind direction and speed information; Step S322: using the sea area geographic information database to accurately locate the expected anchor position of the ship in the preset expected ship movement data, and generate seabed terrain constraint condition data, ocean current dynamic characteristic data and wind environment constraint parameters; Step S323: Perform multi-dimensional correlation analysis on the seabed topography constraint condition data, the ocean current dynamic characteristic data and the wind environment constraint parameters, so as to construct 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, screening the anchor position data that meet the expected ship movement requirements, 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.

8. The ship intelligent moving control method based on adaptive algorithm according to claim 7 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 expected movement data and the motion control parameter feature data, including the real-time deviation values ​​of the roll angle, pitch angle, and yaw angle and their change rates; Step S342: designing an angle deviation classification criterion based on fuzzy logic according to the multi-dimensional feature vector, establishing a quantitative evaluation standard for the severity of the angle deviation, and thus obtaining angle deviation grade data; Step S343: construct a multi-level decision tree according to the angle deviation level data, and set 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.

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

10. 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 equipment configuration module is used to obtain the hardware equipment data required for ship movement control; configure the network topology of the computer network system, winch equipment and encoder according to the hardware equipment data, and 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; The status monitoring module is used to collect the length and tension parameters of the wire rope through the sensors of the winch equipment based on the initial configuration data of the system, and to build a real-time monitoring model for the position and posture of the ship; to monitor the ship status using the real-time monitoring model and generate the real-time status data of the ship; to perform motion control analysis and processing on the real-time status data of the ship and generate the motion control parameters of the ship; The adaptive control module is used to establish an adaptive control model according to 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; perform parameter calibration and optimization on each model in the adaptive control model to generate adaptive control strategy data; The control adjustment module is used to perform real-time control adjustment of the ship based on the adaptive control strategy data and generate correction control instructions, wherein the real-time control adjustment specifically includes PID control and constant torque control through the real-time status data of the ship, and speed adjustment and trajectory correction; The coordinated ship moving module is used to coordinate and control the working status of each winch according to the correction control instructions, so as to realize the ship's linkage ship moving and construction trajectory line ship moving functions.

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