A shore power wireless charging prediction control method and system based on a digital twin model

Abstract

This invention discloses a predictive control method and system for shore power wireless charging based on a digital twin model, relating to the field of shore power access for berthed vessels. The method first constructs a digital twin model of the target vessel, including a vessel kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. Real-time motion attitude data and environmental wave data of the target vessel are acquired. The acquired data is then input into the digital twin model to map the coupling coefficient change trajectory. Finally, the coupling coefficient change trajectory is used as a feedforward quantity, combined with current electrical state parameters, to construct a model predictive control optimization problem. The optimal control quantity sequence is solved, and the first control quantity is output to the shore power wireless charging device to compensate in advance for the impact of coil offset caused by vessel motion on transmission efficiency. This achieves accurate prediction and proactive compensation for coupling coefficient changes caused by vessel motion, significantly improving the transmission efficiency and dynamic stability of shore power wireless charging.

Description

Technical Field

[0001] This invention belongs to the field of shore power technology for ships docking at port, and specifically relates to a predictive control method and system for shore power wireless charging based on a digital twin model. Background Technology

[0002] Shore power technology for ships in port refers to the technology that allows ships to stop using auxiliary machinery for power generation while berthing in port and instead use shore-based power sources to supply power to the ship. With the development of wireless charging technology, contactless power transfer between shore power and the ship can be achieved through electromagnetic induction, which can effectively avoid problems such as plug-and-play wear, connector corrosion, and personnel safety risks associated with traditional wired charging methods. It is especially suitable for ship charging scenarios in humid and salt spray environments in ports.

[0003] In a shore power wireless charging system, ships experience six degrees of freedom motion (including roll, pitch, bow, sway, heave, and yaw) under wave action. This causes dynamic changes in the relative position between the receiving coil mounted on the ship's hull and the transmitting coil mounted on the shore base, resulting in fluctuations in the coupling coefficient. These fluctuations directly affect the transmission efficiency and output power stability of the wireless charging system, and in severe cases, may lead to system detuning or even a protective shutdown.

[0004] To address the above problems, existing technologies mainly adopt the following solutions: (1) Mechanical buffering scheme, which is to absorb the displacement deviation caused by ship rolling by setting up a movable mechanism in the charging equipment and using the passive deformation of springs, dampers or movable frames; for example, the transmitting coil is installed on a movable platform or suspension device, and the passive adaptability of the mechanical structure is used to maintain the relative position between the coils; however, this scheme is a passive response adjustment, which has limited adaptability to high frequency and large amplitude ship rolling, and the mechanical structure has reliability problems such as wear and jamming. (2) Mechanical active adjustment scheme, that is, the ship's position is detected by visual recognition or attitude sensor, and the position of the transmitting coil or receiving coil is actively adjusted by the motor drive device to achieve dynamic alignment of the coil; although this scheme can achieve active adjustment, the response speed of the mechanical adjustment mechanism is limited by the performance of the actuator, making it difficult to track the high-frequency movement of the ship in real time; at the same time, the complex mechanical structure increases the system cost and maintenance difficulty. (3) Static optimization control scheme, that is, the coupling coefficient between coils is detected before charging, and the system operating parameters (such as operating frequency and phase shift angle) are optimized in advance based on the detection results to improve charging efficiency; however, this scheme only performs parameter optimization once before charging, and cannot cope with the real-time changes in coupling coefficient caused by ship movement during charging, so the control effect is limited. In summary, existing technologies generally lack effective prediction and active compensation mechanisms for dynamic changes in coupling coefficients caused by ship motion, making it difficult to achieve efficient and stable wireless charging under complex sea conditions. Summary of the Invention

[0005] The purpose of this invention is to provide a predictive control method, system, computer equipment, computer-readable storage medium, and computer program product for shore power wireless charging based on a digital twin model, in order to solve the problem that existing shore power wireless charging technologies at docks generally lack effective prediction and active compensation mechanisms for dynamic changes in coupling coefficients caused by ship motion, making it difficult to achieve efficient and stable wireless charging in complex sea conditions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a predictive control method for shore power wireless charging based on a digital twin model is provided, including: A digital twin model of the target ship is constructed, wherein the digital twin model includes a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. The coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude and the relative position and coupling coefficient of the wireless charging coil. The motion attitude data and environmental wave data of the target vessel are acquired in real time. The motion attitude data is collected in real time by a sensor group deployed on the target vessel and / or on the target vessel's docked shore base. The environmental wave data is collected in real time by wave monitoring equipment deployed on the docked shore base. The motion attitude data and the environmental wave data are input into the digital twin model in real time. The wave disturbance sub-model generates a wave excitation sequence in the future time domain based on the environmental wave data. The ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. The motion attitude change trajectory is then mapped to the coupling coefficient change trajectory in the future time domain based on the coupling coefficient mapping sub-model. Using the trajectory of the coupling coefficient change as a feedforward quantity, combined with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, and the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain is obtained by solving the problem. The first control quantity in the optimal control quantity sequence is output to the shore power wireless charging device to adjust the wireless charging parameters, so that the impact of the charging coil offset caused by the ship's movement on the charging transmission efficiency can be compensated in advance.

[0007] Based on the above-mentioned invention, a novel scheme is provided that can effectively predict and actively compensate for the dynamic changes in the coupling coefficient of shore power wireless charging caused by ship motion. This involves first constructing a digital twin model of the target ship, including a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. Real-time data on the target ship's motion attitude and environmental wave data are then acquired. This acquired data is input into the digital twin model. The wave disturbance sub-model generates a future wave excitation sequence in the time domain. The ship kinematics sub-model predicts the trajectory of motion attitude changes based on the motion attitude data, historical motion trajectories, and the wave excitation sequence. Finally, the coupling coefficient mapping sub-model is used to predict the dynamic changes in the coupling coefficient. The mapping sub-model maps the trajectory of motion attitude change to the trajectory of coupling coefficient change. Finally, the trajectory of coupling coefficient change is used as a feedforward quantity. Combined with the current electrical state parameters, a model predictive control optimization problem is constructed. The optimal control quantity sequence is obtained by solving the problem. The first control quantity is output to the shore power wireless charging device to adjust the charging parameters and compensate in advance for the impact of coil offset caused by ship motion on transmission efficiency. Thus, through the deep integration of digital twin and model predictive control, accurate prediction and active compensation for the coupling coefficient change caused by ship motion are achieved, which significantly improves the transmission efficiency and dynamic stability of the shore power wireless charging system and facilitates practical application and promotion.

[0008] In one possible design, a digital twin model of the target ship is constructed, including: A six-degree-of-freedom motion mechanism model of the target ship is established based on its hydrodynamic parameters. The model parameters of the six-degree-of-freedom motion mechanism model are then corrected using a system identification method based on the historical operating data of the target ship, so as to construct a sub-model of the ship's kinematics. An initial wave disturbance model is established based on wave spectrum theory, and the model parameters of the initial wave disturbance model are calibrated using historical wave data obtained by wave monitoring equipment to construct a wave disturbance sub-model for the target vessel. The wave monitoring equipment is deployed on the berthed shore base of the target vessel. The mapping relationship between the ship attitude of the target ship and the relative position and coupling coefficient of the wireless charging coil of the shore power wireless charging device is established by finite element simulation or actual measurement fitting, so as to construct the coupling coefficient mapping sub-model of the target ship. The shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship.

[0009] In one possible design, the wave disturbance sub-model generates a future time-domain wave excitation sequence based on the environmental wave data, including: The wave disturbance sub-model utilizes its internal wave spectrum theory to invert the wave spectrum parameters of the target sea area based on the environmental wave data. Then, based on the wave spectrum parameters, a future wave time sequence is generated in the time domain using either the linear superposition method or the wave spectrum simulation method as the wave excitation sequence. The wave spectrum theory includes the JONSWAP spectrum theory or the Pierson-Moskowitz spectrum theory. The environmental wave data package contains wave height, wave period, and wave direction information. The target sea area refers to the sea area where the target vessel is located.

[0010] In one possible design, the ship kinematics sub-model predicts the trajectory of the target ship's change in attitude in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence, including: The ship kinematics sub-model takes the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence as inputs, and uses an internal time series prediction algorithm to predict the motion attitude change trajectory of the target ship in the future time domain containing N prediction steps based on the inputs. The time series prediction algorithm includes a long short-term memory network, a gated recurrent unit, and / or a Kalman filter. N represents a positive integer and is dynamically adjusted and determined according to the control cycle of the shore power wireless charging device and the motion intensity of the target ship. The shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship. The motion intensity is quantitatively evaluated and determined by the variance or rate of change of the motion attitude data.

[0011] In one possible design, the trajectory of the coupling coefficient change is used as a feedforward quantity. Combined with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, and the optimal control sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain is obtained by solving the problem. This sequence includes: The objective function is to maximize the wireless charging transmission efficiency and / or output power stability of the shore power wireless charging device, wherein the shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship. The inverter phase shift angle, operating frequency, and / or compensation network parameters of the shore power wireless charging equipment are used as optimization variables. The electrical safety limits of the shore power wireless charging equipment are used as constraints, wherein the electrical safety limits include input voltage limits, output current limits and / or output power limits; Using the trajectory of the coupling coefficient change as a feedforward quantity, and combining it with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, which includes the objective function, the optimization variables, and the constraints. Using the optimization of the objective function as the search objective, the model predictive control optimization problem is solved to obtain the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain.

[0012] In one possible design, the first control variable in the optimal control sequence is output to the shore power wireless charging device to adjust the wireless charging parameters, including: The first control quantity in the optimal control quantity sequence is parsed to obtain the corresponding control command, and the control command is output to the shore power wireless charging device to adjust at least one parameter among the following wireless charging parameters (a) to (c): (a) The phase shift angle of the inverter in the shore power wireless charging device; (b) The operating frequency of the inverter in the shore power wireless charging equipment; (c) The capacitance or inductance value of the adjustable compensation network in the shore power wireless charging device.

[0013] In one possible design, after outputting the first control quantity in the optimal control quantity sequence to the shore power wireless charging device, the method further includes: After each control cycle of the shore power wireless charging device is completed, the actual output electrical parameters of the shore power wireless charging device are obtained, wherein the actual output electrical parameters include the transmitter voltage, transmitter current, receiver voltage and / or receiver current. Based on the actual output electrical parameters, the actual coupling coefficient of the most recent historical control cycle is calculated by inversion. The actual coupling coefficient is compared with the coupling coefficient predicted by the coupling coefficient mapping sub-model in the most recent prediction before the current most recent historical control cycle, and the coupling coefficient prediction deviation is calculated. When the prediction deviation of the coupling coefficient exceeds a preset deviation threshold, the model parameters of the coupling coefficient mapping sub-model and / or the ship kinematics sub-model are corrected online using a recursive least squares algorithm or a Kalman filter algorithm, and the corrected model parameters are synchronously updated to the digital twin model.

[0014] In one possible design, during the operation of the shore power wireless charging device, the method further includes performing at least one of the following safety protection operations (A) to (C): (A) When the coupling coefficient predicted by the coupling coefficient mapping sub-model is lower than the first preset safety threshold, a power reduction command is generated and the power reduction command is output to the shore power wireless charging device to reduce the output power to a preset safety power value. When the coupling coefficient predicted by the coupling coefficient mapping sub-model is lower than the second preset safety threshold, a shutdown command is generated and the shutdown command is output to the shore power wireless charging device to suspend charging. The second preset safety threshold is lower than the first preset safety threshold. (B) When the prediction deviation of the coupling coefficient exceeds the preset alarm threshold and the duration exceeds the preset duration, a switching command is generated and the switching command is output to the shore power wireless charging device so as to switch from the model prediction control mode to the standby control mode, wherein the standby control mode includes a constant current charging mode or a constant voltage charging mode. (C) When the wave height in the wave excitation sequence predicted by the wave disturbance sub-model exceeds the preset safe wave height threshold, a pre-shutdown command is generated and the pre-shutdown command is output to the shore power wireless charging device so as to control the shore power wireless charging device to perform power reduction or shutdown operation before the target ship reaches the predicted extreme working condition.

[0015] Secondly, a shore power wireless charging predictive control system based on a digital twin model is provided, including a twin model construction unit, a real-time data acquisition unit, a trajectory prediction unit, a predictive control optimization unit, and an optimal control output unit. The twin model construction unit is used to construct a digital twin model of the target ship. The digital twin model includes a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. The coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude and the relative position and coupling coefficient of the wireless charging coil. The real-time data acquisition unit is used to acquire the motion attitude data and environmental wave data of the target vessel in real time. The motion attitude data is acquired in real time by a sensor group deployed on the target vessel and / or on the target vessel's docked shore base, and the environmental wave data is acquired in real time by a wave monitoring device deployed on the docked shore base. The trajectory prediction unit is communicatively connected to the twin model construction unit and the real-time data acquisition unit, respectively. It is used to input the motion attitude data and the environmental wave data into the digital twin model in real time. The wave disturbance sub-model generates a wave excitation sequence in the future time domain based on the environmental wave data. The ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. The motion attitude change trajectory is then mapped to the coupling coefficient change trajectory in the future time domain based on the coupling coefficient mapping sub-model. The predictive control optimization unit is communicatively connected to the trajectory prediction unit. It is used to take the trajectory of the coupling coefficient change as a feedforward quantity, combine it with the current electrical state parameters of the shore power wireless charging device, construct a model predictive control optimization problem, and solve it to obtain the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain. The optimal control output unit is communicatively connected to the predictive control optimization unit and is used to output the first control quantity in the optimal control quantity sequence to the shore power wireless charging device in order to adjust the wireless charging parameters and compensate in advance for the impact of the charging coil offset caused by the ship's movement on the charging transmission efficiency.

[0016] Thirdly, the present invention provides a computer device comprising a storage module, a processing module, and a transceiver module connected in sequence for communication, wherein the storage module is used to store a computer program, the transceiver module is used to send and receive messages, and the processing module is used to read the computer program and execute the shore power wireless charging predictive control method as described in the first aspect or any possible design in the first aspect.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the shore power wireless charging predictive control method as described in the first aspect or any possible design within the first aspect.

[0018] Fifthly, the present invention provides a computer program product, including a computer program or instructions, wherein the computer program or instructions, when executed by a computer, implement the shore power wireless charging predictive control method as described in the first aspect or any possible design in the first aspect.

[0019] The beneficial effects of the above scheme are: (1) This invention creatively provides a new scheme that can effectively predict and actively compensate for the dynamic changes in the coupling coefficient of shore power wireless charging caused by ship motion. That is, firstly, a digital twin model of the target ship is constructed, including a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. The motion attitude data and environmental wave data of the target ship are acquired in real time. Then, the acquired data is input into the digital twin model. The wave disturbance sub-model generates the wave excitation sequence in the future time domain. The ship kinematics sub-model predicts the motion attitude change trajectory based on the motion attitude data, historical motion trajectory, and wave excitation sequence. The coupling coefficient mapping sub-model maps the motion attitude change trajectory to the coupling coefficient change trajectory. Finally, the coupling coefficient change trajectory is used as a feedforward quantity. Combined with the current electrical state parameters, a model predictive control optimization problem is constructed. The optimal control quantity sequence is solved and the first control quantity is output to the shore power wireless charging device to adjust the charging parameters and compensate in advance for the impact of coil offset caused by ship motion on the transmission efficiency. Thus, through the deep integration of digital twin and model predictive control, accurate prediction and active compensation for the changes in coupling coefficient caused by ship motion are achieved, which significantly improves the transmission efficiency and dynamic stability of the shore power wireless charging system. (2) By constructing a digital twin model that includes ship kinematics, wave disturbance and electromagnetic coupling mapping, and integrating real-time data from multiple sources, the accurate prediction of the ship's motion attitude and the trajectory of the coupling coefficient change was realized. The prediction results were embedded as feedforward quantities into the model predictive control optimization problem, and the charging parameters were adjusted in advance. This fundamentally solved the technical bottleneck of the traditional solution that can only respond after the fact, and significantly improved the transmission efficiency under dynamic conditions. (3) By introducing a feedback correction mechanism, the actual coupling coefficient is inverted using the actual output electrical parameters and compared with the predicted value. When the deviation exceeds the threshold, the recursive least squares or Kalman filter is used to correct the model parameters online, so that the digital twin model has the self-learning ability to continuously evolve, forming a closed-loop adaptive control architecture that deeply integrates feedforward prediction and feedback correction. (4) A multi-level predictive safety protection system was constructed. Power reduction, mode switching and pre-shutdown operations were performed for low coupling coefficient, continuous exceeding of prediction deviation and extreme sea wave conditions, respectively. The safety protection was upgraded from post-event response to pre-event prediction, which effectively ensured the safe operation of the system under complex sea conditions. (5) This solution has achieved significant improvements in transmission efficiency, dynamic stability, adaptive capability and operational safety, providing a complete technical solution for the engineering application of shore power wireless charging technology in complex marine environments, which is convenient for practical application and promotion. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the shore power wireless charging predictive control method based on a digital twin model, provided in an embodiment of this application.

[0022] Figure 2 This is a schematic diagram of the structure of a shore power wireless charging predictive control system based on a digital twin model, provided in an embodiment of this application.

[0023] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0025] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0026] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0027] Example like Figure 1 As shown, the shore power wireless charging predictive control method based on a digital twin model provided in the first aspect of this embodiment can be executed, but is not limited to, by a computer device with certain computing resources, such as an industrial-grade edge server deployed in the dock control room. This server is equipped with a multi-core CPU (clock speed not less than 2.5GHz) and a graphics processing unit (GPU) to accelerate deep learning calculations, as well as a large-capacity memory (not less than 32GB) and a solid-state drive to run a real-time operating system and interact with the shore power wireless charging equipment and sensor network via industrial Ethernet. Figure 1 As shown, the shore power wireless charging predictive control method includes, but is not limited to, the following steps S1 to S5.

[0028] S1. Construct a digital twin model of the target ship, wherein the digital twin model includes, but is not limited to, a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model, and the coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude and the relative position and coupling coefficient of the wireless charging coil.

[0029] In step S1, the target vessel is the object that is moored in port and undergoing shore wireless charging, such as a small yacht equipped with a wireless charging receiving coil on its hull to cooperate with the transmitting coil and related power conversion unit of the shore wireless charging equipment deployed on the shore base of the berth. Since the yacht will experience significant six-degree-of-freedom motion under wave action, the relative position of the receiving coil and transmitting coil will change significantly, causing significant fluctuations in the coupling coefficient. Therefore, it is necessary to construct the digital twin model for it. In the digital twin model, the ship kinematics sub-model is used to describe the motion response characteristics of the target vessel under external excitations (such as waves, wind, and currents), the wave disturbance sub-model is used to describe the wave characteristics of the sea area where the target vessel is located and their changes over time, and the coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude (such as roll angle, pitch angle, and heave displacement) and the relative position and coupling coefficient of the wireless charging coil. The aforementioned three sub-models together constitute the digital twin model of the target ship, enabling real-time mapping and prediction of the ship's motion state under complex sea conditions and its impact on the wireless charging coupling coefficient. Preferably, constructing the digital twin model of the target ship includes, but is not limited to, the following steps S11 to S13.

[0030] S11. Based on the hydrodynamic parameters of the target ship, establish a six-degree-of-freedom motion mechanism model of the ship, and use the historical operation data of the target ship to correct the model parameters of the six-degree-of-freedom motion mechanism model of the ship through a system identification method, so as to construct a ship kinematic sub-model of the target ship.

[0031] In step S11, the hydrodynamic parameters of the target ship include, but are not limited to, the ship's main dimensions (such as length, beam, and draft), hull form factor, added mass, damping coefficient, and restoring force coefficient. These parameters can be obtained through computational fluid dynamics simulation or ship model experiments. The six-degree-of-freedom motion mechanism model of the ship is based on the ship's motion differential equations (such as the MMG model) and can describe the motion response characteristics of the ship under external excitations (such as waves, wind, or currents). Since the initial parameters of the aforementioned mechanism model may deviate from those of the actual ship, this step further uses the historical operational data of the target ship (e.g., real motion attitude data collected by the inertial measurement unit during previous berthing periods) to correct the model parameters. Specifically, recursive least squares or subspace identification methods can be used to iteratively optimize the model parameters with the goal of minimizing the error between the model's predicted output and the actual observed values, ultimately obtaining the ship kinematic sub-model that accurately reflects the motion characteristics of the target ship.

[0032] S12. An initial wave disturbance model is established based on wave spectrum theory, and the model parameters of the initial wave disturbance model are calibrated using historical wave data obtained by wave monitoring equipment to construct a wave disturbance sub-model for the target vessel, wherein the wave monitoring equipment is deployed on the berthed shore base of the target vessel.

[0033] In step S12, the wave spectrum theory is actually an existing mathematical model describing the distribution of wave energy with frequency. As an example, the initial wave disturbance model can be constructed using the JONSWAP spectrum theory. This JONSWAP spectrum is applicable to sea areas with limited wind ranges, and its parameters include significant wave height, peak period, and peak shape parameters. The wave monitoring equipment is typically a wave radar or a pressure wave meter, capable of continuously collecting historical wave data (including wave height, wave period, and wave direction) near the berth over long periods. This step uses this historical wave data to calibrate the parameters of the initial wave disturbance model. For example, the peak period and significant wave height of the JONSWAP spectrum are obtained through least-squares fitting, ensuring the theoretical spectrum output by the model best matches the actual observed spectrum. Furthermore, the calibrated wave disturbance sub-model can dynamically generate an excitation sequence reflecting the current wave characteristics based on real-time input environmental wave data, providing accurate external input for ship motion prediction.

[0034] S13. Establish the mapping relationship between the ship attitude of the target ship and the relative position and coupling coefficient of the wireless charging coil of the shore power wireless charging device through finite element simulation or actual measurement fitting, so as to construct the coupling coefficient mapping sub-model of the target ship, wherein the shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship.

[0035] In step S13, the coupling coefficient refers to the ratio of the mutual inductance coefficient between the shore-based transmitting coil and the ship receiving coil to the geometric average of the self-inductance coefficients of the two coils. It is a key parameter for measuring the energy transmission efficiency of the wireless charging system. This step establishes the mapping relationship between the ship's attitude (including roll angle, pitch angle, heave displacement, and sway displacement) and the coupling coefficient through finite element simulation or experimental fitting. If finite element simulation is used, a three-dimensional model of the transmitting and receiving coils is established using electromagnetic field simulation software (such as ANSYS Maxwell). The relative positions of the coils under different ship attitudes are set, and the corresponding coupling coefficients are calculated using static magnetic field or transient field solvers, thereby generating an attitude-coupling coefficient mapping table. If experimental fitting is used, the attitude of the receiving coil is changed and the corresponding coupling coefficients are measured on a real or scaled-down experimental platform. A mapping function is established using polynomial fitting or interpolation methods. Furthermore, the finally constructed coupling coefficient mapping sub-model can quickly output the corresponding coupling coefficients based on the input ship attitude, realizing the mapping from motion attitude to charging parameters.

[0036] Based on the above steps S11 to S13, a complete digital twin model can be constructed, which includes a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. This model can accurately reflect the motion response characteristics of the target ship under real sea conditions and the mapping relationship between the ship's attitude and the wireless charging coupling coefficient. It provides an accurate model basis for predicting the trajectory of the coupling coefficient change and executing feedforward compensation control in subsequent steps, and significantly improves the adaptive capability and control accuracy of shore power wireless charging under complex sea conditions.

[0037] In addition, between step S11 (constructing a ship kinematics sub-model) and step S13 (constructing a coupling coefficient mapping sub-model), as a preferred embodiment, the edge server can also establish a ship digital twin model parameter library. This parameter library is stored on a local hard drive or in a cloud database and includes ship kinematics sub-model parameters (including added mass, damping coefficient, etc.) and coupling coefficient mapping sub-model parameters (including coil self-inductance, mutual inductance fitting coefficients as attitude change, etc.) corresponding to different ship types (such as container ships, bulk carriers, yachts), tonnage classes (such as 1,000 tons, 5,000 tons, 10,000 tons and above) and loading states (empty, half-loaded, fully loaded) under different ship conditions. When constructing the digital twin model of the target vessel, the edge server first obtains the vessel type, current tonnage, and loading status information (which can be obtained through the port scheduling system or manual input). Then, it matches the corresponding model parameters from the parameter library. If a vessel type and tonnage class that perfectly matches the target vessel exists in the parameter library, the corresponding model parameters are directly loaded as initial values. If no perfect match exists, an interpolation method is used to perform a weighted average of the model parameters for adjacent tonnage classes to generate initial model parameters. During subsequent charging, the initial model parameters can be dynamically optimized through the online correction mechanism in step S64, forming a hybrid modeling strategy of "prior knowledge + online adaptation." Therefore, the aforementioned solution significantly reduces model construction costs and improves the system's ability to quickly adapt to different vessel types.

[0038] S2. Real-time acquisition of motion attitude data and environmental wave data of the target vessel, wherein the motion attitude data is acquired in real time by a sensor array deployed on the target vessel and / or on the target vessel's docked shore base, and the environmental wave data is acquired in real time by wave monitoring equipment deployed on the docked shore base.

[0039] In step S2, specifically, a high-precision inertial measurement unit (IMU) can be installed on the target vessel. This IMU includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, capable of real-time acquisition of the vessel's acceleration, angular velocity, and heading angle information at a sampling frequency of at least 100Hz. The data is then transmitted to a local device (i.e., the industrial-grade edge server) via a wireless communication module on the vessel (such as 5G, Wi-Fi, or a data radio). Alternatively, a lidar or vision camera can be deployed on the docked shore base to monitor the vessel's attitude changes in real-time via a non-contact method. The environmental wave data is acquired in real-time by wave monitoring equipment deployed on the docked shore base. This equipment can employ wave radar or a pressure wave meter, continuously acquiring parameters such as wave height, wave period, and wave direction at a frequency of 1Hz to 10Hz, and transmitting the data to the edge server via industrial Ethernet or fieldbus. Upon receiving the aforementioned data, the edge server aligns it with timestamps and stores it in a real-time database, providing real-time input for subsequent prediction calculations of the digital twin model.

[0040] S3. The motion attitude data and the environmental wave data are input into the digital twin model in real time. The wave disturbance sub-model generates a wave excitation sequence in the future time domain based on the environmental wave data. The ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. The motion attitude change trajectory is then mapped to the coupling coefficient change trajectory in the future time domain based on the coupling coefficient mapping sub-model.

[0041] In step S3, the wave excitation sequence is time-series data. Specifically, the wave disturbance sub-model generates the future time-domain wave excitation sequence based on the environmental wave data. This includes, but is not limited to: using its internal wave spectrum theory, the wave disturbance sub-model inverts the wave spectrum parameters of the target sea area based on the environmental wave data; then, based on the wave spectrum parameters, it generates the future time-domain wave excitation sequence using a linear superposition method or a wave spectrum simulation method. The wave spectrum theory includes, but is not limited to, JONSWAP spectrum theory or Pierson-Moskowitz spectrum theory, etc. The environmental wave data includes, but is not limited to, wave height, wave period, and wave direction information, etc. The target sea area refers to the sea area where the target vessel is located. The aforementioned JONSWAP spectrum is applicable to sea areas with limited wind zones, and its parameters include significant wave height, spectral peak period, and peak shape parameters; the aforementioned Pierson-Moskowitz spectrum is applicable to fully developed waves, and its parameters mainly include significant wave height and spectral peak period. The aforementioned inversion process involves using a least-squares fitting method to substitute measured wave height and wave period data into the selected wave spectrum model, solving for the spectral parameter values ​​that best match the theoretical and measured spectra. The basic principle of the aforementioned linear superposition method is to treat waves as multiple cosine waves with different frequencies, amplitudes, and initial phases superimposed. The amplitude of each frequency component is determined by the wave spectrum, and the initial phase is randomly selected within the range of 0 to 2π. After superposition, the wave height sequence in the time domain can be obtained. The aforementioned wave spectrum simulation method uses more efficient numerical methods (such as Fast Fourier Transform) to directly generate random time histories that conform to the target spectrum. Furthermore, the duration of the future time domain can be dynamically set according to the control cycle and prediction requirements of the shore power wireless charging equipment; for example, it can be set to 5 seconds in the future to ensure the timeliness and accuracy of the prediction.

[0042] In step S3, the motion attitude change trajectory is another time-series data. Specifically, the ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. This includes, but is not limited to, the ship kinematics sub-model taking the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence as input, and using an internal time-series prediction algorithm to predict the motion attitude change trajectory of the target ship in the future time domain containing N prediction steps based on the input. The time-series prediction algorithm includes a long short-term memory network, a gated recurrent unit, and / or a Kalman filter. N represents a positive integer and is dynamically adjusted and determined according to the control cycle of the shore power wireless charging device and the motion intensity of the target ship. The shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship. The motion intensity is quantitatively evaluated and determined by the variance or rate of change of the motion attitude data. Taking the aforementioned time-series prediction algorithm employing LSTM (Long Short-Term Memory) as an example, this LSTM has memory units and a gating mechanism, which can effectively handle long-term dependencies in time-series data, making it suitable for time-series prediction tasks such as ship motion, which are characterized by periodicity and nonlinearity. Before deployment, the LSTM network needs to be trained offline using the historical motion data of the target ship (including attitude records under different sea states), enabling the network to learn the mapping relationship between the ship's motion response and input excitations. During the application prediction phase, the ship motion sub-model takes the current motion attitude data, the most recent historical motion trajectory (e.g., the past 10 seconds), and the future wave excitation sequence generated by the wave disturbance sub-model as inputs, and gradually outputs the attitude parameters for each future prediction step through the trained LSTM network, including roll angle, pitch angle, and heave displacement. Specifically, the control period is the time interval between the output control quantities of the shore power wireless charging device, for example, it can be set to 0.05 seconds, and the total length of the prediction time domain is usually set to an integer multiple of the control period (e.g., 100 times, i.e., 5 seconds). When the target ship's motion is highly turbulent, the uncertainty of the prediction model increases. In this case, it is appropriate to reduce N (i.e., shorten the prediction time domain) to improve the prediction accuracy. Conversely, when the ship's motion is stable, it is appropriate to increase N (i.e., extend the prediction time domain) to obtain longer feedforward information, which makes it easier for the controller to respond in advance.The intensity of the motion is quantitatively assessed and determined by the variance or rate of change of the motion attitude data. For example, the variance of the roll angle data in the most recent second is calculated. If the variance exceeds a preset threshold (e.g., 0.5°²), the motion is considered intense, and the prediction step size N is reduced to 60% of its original value. If the variance is below another preset threshold (e.g., 0.1°²), the motion is considered stable, and N is restored to its default value. Through the aforementioned rolling prediction mechanism, the ship kinematics sub-model can output the trajectory of motion attitude changes within the next N steps in each control cycle, providing continuous and dynamically updated feedforward information for subsequent coupling coefficient prediction and model predictive control.

[0043] In step S3, the coupling coefficient mapping sub-model internally stores a pre-constructed attitude-coupling coefficient mapping relationship fitted through finite element simulation or actual measurement. This can be represented as a mapping table, an interpolation function, or a neural network model. After the ship kinematics sub-model predicts the trajectory of the motion attitude change at each predicted step in the future time domain, the coupling coefficient mapping sub-model performs mapping processing on each attitude sampling point in that trajectory. Specifically, for the attitude parameters (including roll angle, pitch angle, and heave displacement, etc.) at each predicted moment, the coupling coefficient mapping sub-model calculates the corresponding coupling coefficient based on the pre-established mapping relationship: if the mapping relationship is in the form of a mapping table, the coupling coefficient corresponding to the attitude parameter is obtained by looking up the table and combining it with linear interpolation; if the mapping relationship is in the form of a function fitting, the attitude parameters are directly substituted into the fitting function to calculate the coupling coefficient; if the mapping relationship is in the form of a neural network model, the attitude parameters are used as input, and the output coupling coefficient is calculated through forward propagation. Thus, by mapping point by point, the attitude parameters at each moment in the future time domain are converted into corresponding coupling coefficients, thereby forming a continuous and time-varying trajectory of coupling coefficient changes (which reflects the dynamic change trend of coupling coefficients caused by coil offset due to ship motion in the future).

[0044] S4. Using the trajectory of the coupling coefficient change as a feedforward quantity, and combining it with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, and the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain is obtained by solving the problem.

[0045] In step S4, in order to construct a model predictive control optimization framework with the predicted coupling coefficient as feedforward, efficiency / stability as objective, electrical safety as constraint, and physical executable parameters as variables, preferably, the trajectory of the coupling coefficient change is used as the feedforward quantity. Combined with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, and the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain is obtained by solving the problem. This includes, but is not limited to, the following steps S41 to S45.

[0046] S41. The objective function is to maximize the wireless charging transmission efficiency and / or output power stability of the shore power wireless charging device, wherein the shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship.

[0047] In step S41, the wireless charging transmission efficiency refers to the ratio of the input power of the shore-based transmitter to the output power of the ship receiver, reflecting the energy conversion efficiency of wireless charging; the output power stability refers to the degree of fluctuation in the output power of the receiver, usually quantified by the variance or standard deviation of the power. As an example, the goal of maximizing transmission efficiency can be achieved by establishing a mapping model between efficiency and optimization variables, and seeking the optimal combination of parameters during the optimization process; the goal of output power stability is achieved by minimizing the variance of power fluctuations in the prediction time domain. In addition, the use of "and / or" expressions allows for the selection of a single objective or the construction of a multi-objective weighted optimization function according to different operating conditions (such as the intensity of ship motion, battery state of charge, or grid load conditions), so that the shore power wireless charging scheme can maintain optimal operating status in different scenarios.

[0048] In step S41, the objective function is preferably a weighted combination of maximizing transmission efficiency and ensuring output power stability, as shown in the following equation:

[0049] In the formula, This represents the transmission efficiency target term. This represents the output power stability target term. and They represent dynamic weight coefficients and satisfy the following conditions: The edge server adaptively adjusts the dynamic weighting coefficients based on the target vessel's motion intensity, battery state of charge, and current load status of the shore power grid. Specifically, when the motion intensity is high (e.g., roll variance exceeds 0.5°²), the stability weight is increased. Prioritize ensuring stable output power; when the intensity of the exercise is low and the battery is in a constant current charging phase, increase the efficiency weight. Prioritize maximizing transmission efficiency; when the grid load is high, appropriately reduce the charging power target and increase the stability weight. The values ​​are adjusted to a higher level to avoid impacting the power grid. Through the aforementioned adaptive weight adjustment, the system can achieve an optimal trade-off between efficiency and stability under different operating conditions.

[0050] S42. The inverter phase shift angle, operating frequency, and / or compensation network parameters of the shore power wireless charging equipment are used as optimization variables.

[0051] In step S42, the inverter phase shift angle refers to the phase difference between the drive signals of the two bridge arms in the high-frequency inverter. Adjusting the phase shift angle changes the amplitude of the transmitting coil current, thereby adjusting the transmission power. The operating frequency refers to the switching frequency of the inverter. Adjusting the operating frequency allows the shore power wireless charging scheme to operate in the optimal resonance state, avoiding detuning problems caused by coil offset. The compensation network parameters refer to the values ​​of series compensation capacitors, parallel compensation capacitors, or compensation inductors. Adjusting these parameters enables dynamic impedance matching, compensating for impedance changes caused by variations in the coupling coefficient. Furthermore, the use of "and / or" allows for the selection of a single optimization variable or multiple variables for collaborative optimization based on the system hardware configuration.

[0052] S43. The electrical safety limits of the shore power wireless charging device are used as constraints, wherein the electrical safety limits include input voltage limits, output current limits and / or output power limits.

[0053] In step S43, the electrical safety limits refer to the maximum or minimum boundary values ​​of electrical parameters allowed for the shore power wireless charging equipment under normal operating conditions. The input voltage limits include the upper and lower limits of the DC bus voltage at the transmitting end, preventing damage to power devices due to excessive voltage or system malfunction due to insufficient voltage. The output current limits include the upper limit of the output current at the receiving end, preventing cable overheating or battery damage due to excessive current. The output power limits include the upper limit of the charging power, preventing exceeding the rated capacity of the shore power equipment or ship battery. These constraints are incorporated into the model predictive control optimization problem in the form of inequalities to ensure that the optimal control sequence obtained by the solution always remains within the safe operating range of the equipment.

[0054] S44. Using the trajectory of the coupling coefficient change as a feedforward quantity, and combining it with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, which includes the objective function, the optimization variables, and the constraints.

[0055] In step S44, the model predictive control optimization problem is constructed based on model predictive control theory. Specifically, the objective function determined in step S41, the optimization variables determined in step S42, and the constraints determined in step S43 are mathematically expressed to form the optimization problem shown in the following formula:

[0056] In the formula, J represents the objective function. η Indicates transmission efficiency. σ Indicates power fluctuation, λ Let represent the weighting coefficient, u represent the optimization variables (i.e., phase shift angle, operating frequency, and compensation network parameters), and x represent the charging state (i.e., voltage, current, and power). Simultaneously, the coupling coefficient change trajectory predicted in step S3 is introduced as a feedforward quantity into the prediction model, enabling this scheme to anticipate future coupling coefficient changes and thus adjust the control quantity in advance during the optimization process to cope with upcoming disturbances.

[0057] S45. Using the optimization of the objective function as the optimization objective, solve the model predictive control optimization problem to obtain the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain.

[0058] In step S45, the embedded optimization solver can be invoked to solve the model predictive control optimization problem constructed in step S44 online. Since both the objective function and constraints can be expressed in quadratic form, a quadratic programming algorithm can be used for efficient solution. For nonlinear objective functions, numerical optimization algorithms such as gradient descent or sequential quadratic programming can be used. The result obtained is an optimal control quantity sequence, containing the optimal control quantity values ​​(such as optimal phase shift angle, optimal operating frequency, and optimal compensation network parameters) for each time point within the next N control cycles. Furthermore, according to the theory of model predictive control, only the first control quantity in this sequence needs to be output to the shore power wireless charging device (specifically its power conversion unit) for execution. When the next control cycle arrives, the optimization problem is reconstructed and solved based on the new state measurement values, achieving rolling optimization.

[0059] Based on the above steps S41 to S45, the optimal control quantity sequence can be solved in a rolling manner according to the predicted future trend of coupling coefficient change, realizing "pre-compensation" rather than "post-response", effectively suppressing the transmission efficiency fluctuation caused by ship motion, while ensuring that the shore power wireless charging process always operates within the safety boundary, significantly improving the dynamic response capability and operational stability of the shore power wireless charging equipment.

[0060] S5. Output the first control quantity in the optimal control quantity sequence to the shore power wireless charging device in order to adjust the wireless charging parameters so that the impact of the charging coil offset caused by the ship's movement on the charging transmission efficiency can be compensated in advance.

[0061] In step S5, specifically, the first control quantity in the optimal control quantity sequence is output to the shore power wireless charging device to adjust the wireless charging parameters, including but not limited to: parsing the first control quantity in the optimal control quantity sequence to obtain the corresponding control command, and outputting the control command to the shore power wireless charging device to adjust at least one parameter among the following wireless charging parameters (a) to (c): (a) the phase shift angle of the inverter in the shore power wireless charging device (which changes the current amplitude of the transmitting coil); (b) the operating frequency of the inverter in the shore power wireless charging device (which makes the shore power wireless charging work in the optimal resonance state); (c) the capacitance or inductance value of the adjustable compensation network in the shore power wireless charging device (which realizes dynamic impedance matching). In order to upgrade the digital twin model from an "offline-built static model" to an "online self-learning dynamic model", a feedback correction mechanism is used to achieve deep integration of feedforward prediction and feedback control, forming a complete adaptive closed loop of "prediction-control-correction-update". Preferably, after outputting the first control quantity in the optimal control quantity sequence to the shore power wireless charging device, the method also includes, but is not limited to, the following steps S61 to S64.

[0062] S61. After each control cycle of the shore power wireless charging device ends, the actual output electrical parameters of the shore power wireless charging device are obtained, wherein the actual output electrical parameters include, but are not limited to, transmitter voltage, transmitter current, receiver voltage and / or receiver current.

[0063] In step S61, the transmitting end of the shore power wireless charging device is equipped with voltage and current sensors (such as Hall effect sensors) to collect parameters such as the DC bus voltage and high-frequency inverter output current in real time at a sampling frequency no less than twice the control cycle (e.g., 10kHz). The receiving end is also equipped with voltage and current sensors, and the collected voltage and current data are transmitted back to the shore-based edge server via a wireless communication module on the ship (such as 5G or Wi-Fi). The control cycle refers to the interval between one execution of the model prediction control, which can be set to, for example, 0.05 seconds. After each control cycle, the edge server reads the electrical parameters collected during that cycle and takes their average or final value as the actual output electrical parameters for that cycle, providing a data basis for subsequent inversion calculations.

[0064] S62. Based on the actual output electrical parameters, the actual coupling coefficient of the most recent historical control cycle is calculated by inversion.

[0065] In step S62, the inversion calculation of the actual coupling coefficient is based on the equivalent circuit model of the wireless charging system. Taking a series-to-series compensation topology as an example, the equivalent circuit of the system in the resonant operating state satisfies the following relationship:

[0066] In the formula, k represents the coupling coefficient. Vin Indicates the input voltage at the transmitting end. Vout Indicates the output voltage of the receiving end. ω Indicates the operating angular frequency. Lp and Ls These represent the self-inductance of the transmitting and receiving coils, respectively. The edge server substitutes the current cycle electrical parameters obtained in step S61 into the above formula to calculate the actual coupling coefficient of the most recent historical control cycle. For other compensation topologies (such as LCC type), the corresponding equivalent circuit model can be used for inversion calculation. The aforementioned inversion process has a small computational load and can be completed in real time after the end of each control cycle.

[0067] S63. Compare the actual coupling coefficient with the coupling coefficient predicted by the coupling coefficient mapping sub-model in the most recent prediction before the current most recent historical control cycle, and calculate the coupling coefficient prediction deviation.

[0068] In step S63, the coupling coefficient predicted most recently by the coupling coefficient mapping sub-model before the current most recent historical control cycle refers to the coupling coefficient prediction result calculated by the coupling coefficient mapping sub-model at the time of the current control cycle, based on the motion attitude prediction result at that time during the prediction phase of the previous control cycle. The edge server reads this prediction value from memory and compares it with the actual coupling coefficient obtained by inversion in step S62 to calculate the coupling coefficient prediction deviation. The deviation can be calculated using absolute deviation (actual value minus the absolute value of the prediction) or relative deviation (absolute deviation divided by the prediction value), for example:

[0069] or

[0070] This deviation value reflects the prediction accuracy of the digital twin model within the current control cycle.

[0071] S64. When the prediction deviation of the coupling coefficient exceeds a preset deviation threshold, the model parameters of the coupling coefficient mapping sub-model and / or the ship kinematics sub-model are corrected online using a recursive least squares algorithm or a Kalman filter algorithm, and the corrected model parameters are synchronously updated to the digital twin model.

[0072] In step S64, the preset deviation threshold can be set based on engineering experience, for example, a relative deviation threshold of 5% or an absolute deviation threshold of 0.02. When the prediction deviation of the coupling coefficient exceeds this threshold, the edge server triggers a model parameter correction procedure: when using the Recursive Least Square (RLS) algorithm, the actual coupling coefficient of the current period is used as the observed value, and the current parameters of the coupling coefficient mapping sub-model are used as the estimated quantity. The model parameters are updated online through the RLS recursive formula, so that the model output gradually approaches the actual observed value; when using the Kalman filter algorithm, the model parameters are used as state variables, and the actual coupling coefficient is used as the observed quantity. The optimal estimation of the parameters is achieved through the prediction-update iteration of the Kalman filter. The corrected model parameters are synchronously updated in the digital twin model for prediction calculation in subsequent control periods, enabling the digital twin model to have a self-learning ability for continuous evolution.

[0073] Based on the above steps S61 to S64, online feedback correction and adaptive updating of the digital twin model can be realized. This mechanism enables the model to continuously optimize its own parameters according to actual operating data, effectively compensate for modeling errors and parameter drift, and significantly improve prediction accuracy. At the same time, it can form a two-layer architecture of "soft correction + hard protection" with the subsequent safety protection mechanism: when the deviation is small, it is gradually adjusted through model correction; when the deviation is too large, the safety protection mechanism intervenes, thereby achieving adaptive evolution of the model while ensuring system stability, and enabling the shore power wireless charging system to have the long-term operating advantage of "the more it is charged, the more accurate it becomes".

[0074] Therefore, based on the shore power wireless charging predictive control method described in steps S1 to S5 above, a new scheme is provided that can effectively predict and actively compensate for the dynamic changes in the shore power wireless charging coupling coefficient caused by ship motion. Specifically, a digital twin model of the target ship is first constructed, including a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. Real-time motion attitude data and environmental wave data of the target ship are then acquired. The acquired data is then input into the digital twin model. The wave disturbance sub-model generates a future wave excitation sequence in the time domain. The ship kinematics sub-model predicts the motion attitude based on the motion attitude data, historical motion trajectory, and wave excitation sequence. The trajectory of the change in motion attitude is mapped to the trajectory of the change in coupling coefficient by the coupling coefficient mapping sub-model. Finally, the trajectory of the change in coupling coefficient is used as a feedforward quantity. Combined with the current electrical state parameters, a model predictive control optimization problem is constructed. The optimal control quantity sequence is solved, and the first control quantity is output to the shore power wireless charging device to adjust the charging parameters and compensate in advance for the impact of coil offset caused by ship motion on transmission efficiency. Thus, through the deep integration of digital twin and model predictive control, accurate prediction and active compensation for the change in coupling coefficient caused by ship motion are achieved, which significantly improves the transmission efficiency and dynamic stability of the shore power wireless charging system and facilitates practical application and promotion.

[0075] Based on the technical solution of the first aspect, this embodiment also provides a possible design for how to perform safety protection during shore power wireless charging, that is, during the operation of the shore power wireless charging device, the method further includes performing at least one of the following safety protection operations (A) to (C).

[0076] (A) When the coupling coefficient predicted by the coupling coefficient mapping sub-model is lower than the first preset safety threshold, a power reduction command is generated and the power reduction command is output to the shore power wireless charging device to reduce the output power to a preset safety power value. When the coupling coefficient predicted by the coupling coefficient mapping sub-model is lower than the second preset safety threshold, a shutdown command is generated and the shutdown command is output to the shore power wireless charging device to suspend charging. The second preset safety threshold is lower than the first preset safety threshold. As an example, the first preset safety threshold can be set to 0.15 and the second preset safety threshold to 0.08: (1) When the predicted coupling coefficient is lower than the first preset safety threshold (e.g., 0.15) but not lower than the second preset safety threshold (e.g., 0.08), it indicates that the coupling coefficient has deviated from the optimal working range but the system still has basic transmission capability; at this time, the edge server generates a power reduction command and sends the command to the power conversion unit controller of the shore power wireless charging device through the communication interface. After receiving the command, the controller adjusts the phase shift angle or working frequency of the inverter to reduce the output power to the preset safe power value (e.g., 50% of the rated power); the power reduction operation helps to reduce the transmitter current. (1) To meet the system's reactive power requirements, prevent the risk of overcurrent or device overheating caused by excessively low coupling coefficient, and maintain basic charging continuity; (2) When the predicted coupling coefficient deteriorates further and falls below the second preset safety threshold (e.g., 0.08), it indicates that the coupling coefficient has fallen below the minimum requirement for stable system operation. Continuing to charge may lead to system detuning, damage to power devices, or charging interruption. At this time, the edge server generates a shutdown command and sends it to the power conversion unit controller through the communication interface. The controller immediately blocks the inverter's drive pulse, cuts off the power output, and causes the shore power wireless charging equipment to suspend charging. After the coupling coefficient is detected to return to a safe range, the charging process can be restarted manually or automatically. Thus, through the aforementioned two-level safety protection mechanism, proactive early warning and graded response to abnormal coupling coefficient are achieved, which avoids premature shutdown affecting charging efficiency and ensures equipment safety under extreme operating conditions.

[0077] (B) When the coupling coefficient prediction deviation exceeds a preset alarm threshold and the duration exceeds a preset duration, a switching command is generated and output to the shore power wireless charging device to switch from model predictive control mode to standby control mode. The standby control mode includes constant current charging mode or constant voltage charging mode. The coupling coefficient prediction deviation reflects the prediction accuracy of the digital twin model. When the deviation is excessively large, it indicates that the model parameters may drift significantly or encounter unmodeled disturbances. Continuing to rely on model predictive control mode may lead to a decrease in control effectiveness or even system instability. The preset alarm threshold can be set based on engineering experience, for example, a relative deviation alarm threshold of 10% or an absolute deviation alarm threshold of 0.03. The preset duration is used to prevent erroneous switching due to instantaneous noise or brief disturbances, for example, it can be set to three consecutive control cycles or 0.15 seconds. The edge server calculates the coupling coefficient prediction deviation after each control cycle and maintains a counter to record the number of consecutive times the deviation exceeds the alarm threshold. When the number of consecutive exceedances reaches the number of cycles corresponding to a preset duration (e.g., 3 cycles), it is determined to be a continuous anomaly, triggering a mode switch. At this time, the edge server generates a switching command and sends it to the controller of the shore power wireless charging device through the communication interface. After receiving the command, the controller executes the switch from the model predictive control mode to the backup control mode. The backup control mode includes a constant current charging mode and a constant voltage charging mode. These two modes are the basic control modes of the wireless charging system, which do not rely on the ship motion prediction model and have higher robustness. The constant current charging mode adjusts the output with a constant current as the target and is suitable for charging stages with a wide range of battery voltage variations. The constant voltage charging mode adjusts the output with a constant voltage as the target and is suitable for the float charging stage when the battery is close to full. The specific mode selected can be automatically selected according to the battery's state of charge and charging curve, or preset by the operator. Thus, through the aforementioned deviation monitoring and mode switching mechanism, when the prediction accuracy of the digital twin model decreases, the system can be automatically switched to a basic control mode that does not rely on the prediction model, ensuring the safety and continuity of the charging process, and complementing the online correction of the model: when the deviation is small, the model parameters are gradually corrected, and when the deviation continues to be too large, the control mode is switched, realizing a two-layer safety architecture of "soft correction + hard protection".

[0078] (C) When the wave height in the wave excitation sequence predicted by the wave disturbance sub-model exceeds a preset safe wave height threshold, a pre-shutdown command is generated and output to the shore power wireless charging device. This allows the shore power wireless charging device to perform power reduction or shutdown operations before the target vessel reaches the predicted extreme operating condition. The wave disturbance sub-model generates a future wave excitation sequence based on real-time environmental wave data within each control cycle. This sequence describes the trend of wave height variation over time in the form of wave time histories. The edge server can extract the maximum wave height value (or the peak wave height value in the shortest future time domain) from this sequence as a basis for judging extreme operating conditions. The preset safe wave height threshold can be set comprehensively based on the target vessel's wave resistance capability, the mechanical design strength of the shore power wireless charging device, and charging safety requirements; for example, it can be set to 1.0 meter. When the predicted wave height exceeds a preset safe wave height threshold, it indicates that extreme sea conditions may occur in the near future. Continuing charging could lead to violent ship movement, severe coil misalignment, or even equipment damage. In this case, the edge server generates a pre-shutdown command, which includes the predicted time of the extreme condition (e.g., 3.5 seconds in the future). The edge server sends the pre-shutdown command to the controller of the shore power wireless charging device. After parsing the command, the controller executes a power reduction or shutdown operation before the predicted time of the extreme condition (e.g., 0.5 seconds in advance). The phrase "before the target ship reaches the predicted time of the extreme condition" reflects the core innovation of this safety operation—proactive safety protection. Unlike traditional "post-event response" protection (such as immediate action after detecting an anomaly), this safety operation utilizes wave prediction information to perform protective operations before the extreme condition actually occurs, avoiding equipment impact caused by system response delays in severe sea conditions. The power reduction operation can gradually reduce the output power, suitable for scenarios where the predicted wave height slightly exceeds the threshold; the shutdown operation directly cuts off the power output, suitable for scenarios where the predicted wave height far exceeds the threshold or extreme sea conditions are imminent. The aforementioned pre-shutdown mechanism enables proactive early warning and intervention for extreme sea wave conditions, effectively avoiding the risk of equipment damage that may occur during charging in severe sea conditions, and significantly improving the safety and reliability of the shore power wireless charging system.

[0079] Based on the aforementioned possible design one, a multi-level and predictive safety protection system can be constructed through safety protection operations (A) to (C). Among them, safety protection operation (A) uses coupling coefficient prediction to achieve graded protection (power reduction → shutdown) to avoid equipment damage; safety protection operation (B) achieves redundant switching of control mode through prediction deviation monitoring to ensure the basic operation of the system when the model fails; and safety protection operation (C) uses wave prediction to achieve pre-shutdown to actively avoid extreme working conditions. The three work together to improve safety protection from post-event response to pre-event prediction, significantly enhancing the safety, robustness and intelligence of the system under complex sea conditions.

[0080] Furthermore, as a preferred embodiment, after the security protection operation is performed, the edge server continuously monitors the elimination status of the abnormal triggering conditions. Specifically, for the coupling coefficient security protection in step (A), the edge server continuously monitors whether the predicted coupling coefficient recovers to above the first preset security threshold; for the prediction deviation security protection in step (B), the edge server continuously monitors whether the predicted deviation of the coupling coefficient falls back below the preset alarm threshold; for the extreme operating condition security protection in step (C), the edge server continuously monitors whether the predicted wave height falls back below the preset safe wave height threshold. When the abnormal triggering condition is detected to be eliminated and remains stable for a preset recovery time (e.g., 10 consecutive control cycles or 0.5 seconds), the edge server performs a self-healing recovery operation: (D1) If it is power reduction protection, the output power is gradually restored to the target value according to the preset soft start slope (e.g., 20% of rated power is restored per second); (D2) If it is shutdown protection, the charging restart process is executed. After restarting, a constant current mode soft start is adopted. After the system runs stably for a preset observation time (e.g., 5 seconds) and the prediction deviation is normal, it automatically switches back to the model predictive control mode; (D3) If it is mode switching protection (i.e., switching to constant current / constant voltage mode), the coupling coefficient prediction deviation is continuously monitored. When the prediction deviation recovers to the normal range and remains stable for a preset time, it automatically switches back to the model predictive control mode. Through the aforementioned self-healing recovery mechanism, the system can automatically restore normal operation after the abnormality is eliminated, reducing the need for manual intervention, improving the automation level and unmanned operation and maintenance capabilities of the shore power wireless charging system, which is in line with the development trend of smart ports.

[0081] like Figure 2 As shown, the second aspect of this embodiment provides a virtual system for implementing the shore power wireless charging predictive control method described in the first aspect or possible design, including a twin model construction unit, a real-time data acquisition unit, a trajectory prediction unit, a predictive control optimization unit, and an optimal control output unit. The twin model construction unit is used to construct a digital twin model of the target ship. The digital twin model includes a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. The coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude and the relative position and coupling coefficient of the wireless charging coil. The real-time data acquisition unit is used to acquire the motion attitude data and environmental wave data of the target vessel in real time. The motion attitude data is acquired in real time by a sensor group deployed on the target vessel and / or on the target vessel's docked shore base, and the environmental wave data is acquired in real time by a wave monitoring device deployed on the docked shore base. The trajectory prediction unit is communicatively connected to the twin model construction unit and the real-time data acquisition unit, respectively. It is used to input the motion attitude data and the environmental wave data into the digital twin model in real time. The wave disturbance sub-model generates a wave excitation sequence in the future time domain based on the environmental wave data. The ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. The motion attitude change trajectory is then mapped to the coupling coefficient change trajectory in the future time domain based on the coupling coefficient mapping sub-model. The predictive control optimization unit is communicatively connected to the trajectory prediction unit. It is used to take the trajectory of the coupling coefficient change as a feedforward quantity, combine it with the current electrical state parameters of the shore power wireless charging device, construct a model predictive control optimization problem, and solve it to obtain the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain. The optimal control output unit is communicatively connected to the predictive control optimization unit and is used to output the first control quantity in the optimal control quantity sequence to the shore power wireless charging device in order to adjust the wireless charging parameters and compensate in advance for the impact of the charging coil offset caused by the ship's movement on the charging transmission efficiency.

[0082] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the shore power wireless charging predictive control method described in the first aspect or possible design, and will not be repeated here.

[0083] like Figure 3As shown, the third aspect of this embodiment provides a computer device for executing the shore power wireless charging predictive control method as described in the first aspect or possible design one. The device includes a storage module, a processing module, and a transceiver module connected in sequence for communication. The storage module stores a computer program, the transceiver module sends and receives messages, and the processing module reads the computer program and executes the shore power wireless charging predictive control method as described in the first aspect or possible design one. Specifically, the storage module may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processing module may, but is not limited to, use a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power supply module, a display screen, and other necessary components.

[0084] The working process, working details and technical effects of the aforementioned computer device provided in the third aspect of this embodiment can be found in the shore power wireless charging predictive control method described in the first aspect or possible design, and will not be repeated here.

[0085] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising the shore power wireless charging predictive control method as described in the first aspect or possible design one. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the shore power wireless charging predictive control method as described in the first aspect or possible design one. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0086] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the shore power wireless charging predictive control method as described in the first aspect or possible design, and will not be repeated here.

[0087] This fifth aspect of the embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implement the shore power wireless charging predictive control method as described in the first aspect or possible design. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0088] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A shore power wireless charging prediction control method based on a digital twin model, characterized in that, include: A digital twin model of the target ship is constructed, wherein the digital twin model includes a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. The coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude and the relative position and coupling coefficient of the wireless charging coil. The motion attitude data and environmental wave data of the target vessel are acquired in real time. The motion attitude data is collected in real time by a sensor group deployed on the target vessel and / or on the target vessel's docked shore base. The environmental wave data is collected in real time by wave monitoring equipment deployed on the docked shore base. The motion attitude data and the environmental wave data are input into the digital twin model in real time. The wave disturbance sub-model generates a wave excitation sequence in the future time domain based on the environmental wave data. The ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. The motion attitude change trajectory is then mapped to the coupling coefficient change trajectory in the future time domain based on the coupling coefficient mapping sub-model. Using the trajectory of the coupling coefficient change as a feedforward quantity, combined with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, and the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain is obtained by solving the problem. The first control quantity in the optimal control quantity sequence is output to the shore power wireless charging device to adjust the wireless charging parameters, so that the impact of the charging coil offset caused by the ship's movement on the charging transmission efficiency can be compensated in advance.

2. The shore power wireless charging prediction control method of claim 1, wherein, Constructing a digital twin model of the target ship includes: A six-degree-of-freedom motion mechanism model of the target ship is established based on its hydrodynamic parameters. The model parameters of the six-degree-of-freedom motion mechanism model are then corrected using a system identification method based on the historical operating data of the target ship, so as to construct a sub-model of the ship's kinematics. An initial wave disturbance model is established based on wave spectrum theory, and the model parameters of the initial wave disturbance model are calibrated using historical wave data obtained by wave monitoring equipment to construct a wave disturbance sub-model for the target vessel. The wave monitoring equipment is deployed on the berthed shore base of the target vessel. The mapping relationship between the ship attitude of the target ship and the relative position and coupling coefficient of the wireless charging coil of the shore power wireless charging device is established by finite element simulation or actual measurement fitting, so as to construct the coupling coefficient mapping sub-model of the target ship. The shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship.

3. The shore power wireless charging prediction control method of claim 1, wherein, The wave disturbance sub-model generates a future time-domain wave excitation sequence based on the environmental wave data, including: The wave disturbance sub-model utilizes its internal wave spectrum theory to invert the wave spectrum parameters of the target sea area based on the environmental wave data. Then, based on the wave spectrum parameters, a future wave time sequence is generated in the time domain using either the linear superposition method or the wave spectrum simulation method as the wave excitation sequence. The wave spectrum theory includes the JONSWAP spectrum theory or the Pierson-Moskowitz spectrum theory. The environmental wave data package contains wave height, wave period, and wave direction information. The target sea area refers to the sea area where the target vessel is located.

4. The shore power wireless charging prediction control method of claim 1, wherein, Based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence, the ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain, including: The ship kinematics sub-model takes the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence as inputs, and uses an internal time series prediction algorithm to predict the motion attitude change trajectory of the target ship in the future time domain containing N prediction steps based on the inputs. The time series prediction algorithm includes a long short-term memory network, a gated recurrent unit, and / or a Kalman filter. N represents a positive integer and is dynamically adjusted and determined according to the control cycle of the shore power wireless charging device and the motion intensity of the target ship. The shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship. The motion intensity is quantitatively evaluated and determined by the variance or rate of change of the motion attitude data.

5. The shore power wireless charging predictive control method according to claim 1, characterized in that, Using the trajectory of the coupling coefficient change as a feedforward quantity, and combining it with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed. The optimal control sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain is then obtained, including: The objective function is to maximize the wireless charging transmission efficiency and / or output power stability of the shore power wireless charging device, wherein the shore power wireless charging device is deployed on the docked shore base of the target ship and is used to wirelessly charge the target ship. The inverter phase shift angle, operating frequency, and / or compensation network parameters of the shore power wireless charging equipment are used as optimization variables. The electrical safety limits of the shore power wireless charging equipment are used as constraints, wherein the electrical safety limits include input voltage limits, output current limits and / or output power limits; Using the trajectory of the coupling coefficient change as a feedforward quantity, and combining it with the current electrical state parameters of the shore power wireless charging device, a model predictive control optimization problem is constructed, which includes the objective function, the optimization variables, and the constraints. Using the optimization of the objective function as the search objective, the model predictive control optimization problem is solved to obtain the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain.

6. The shore power wireless charging predictive control method according to claim 1, characterized in that, The first control quantity in the optimal control quantity sequence is output to the shore power wireless charging device to adjust the wireless charging parameters, including: The first control quantity in the optimal control quantity sequence is parsed to obtain the corresponding control command, and the control command is output to the shore power wireless charging device to adjust at least one parameter among the following wireless charging parameters (a) to (c): (a) The phase shift angle of the inverter in the shore power wireless charging device; (b) The operating frequency of the inverter in the shore power wireless charging equipment; (c) The capacitance or inductance value of the adjustable compensation network in the shore power wireless charging device.

7. The shore power wireless charging predictive control method according to claim 1, characterized in that, After outputting the first control quantity in the optimal control quantity sequence to the shore power wireless charging device, the method further includes: After each control cycle of the shore power wireless charging device is completed, the actual output electrical parameters of the shore power wireless charging device are obtained, wherein the actual output electrical parameters include the transmitter voltage, transmitter current, receiver voltage and / or receiver current. Based on the actual output electrical parameters, the actual coupling coefficient of the most recent historical control cycle is calculated by inversion. The actual coupling coefficient is compared with the coupling coefficient predicted by the coupling coefficient mapping sub-model in the most recent prediction before the current most recent historical control cycle, and the coupling coefficient prediction deviation is calculated. When the prediction deviation of the coupling coefficient exceeds a preset deviation threshold, the model parameters of the coupling coefficient mapping sub-model and / or the ship kinematics sub-model are corrected online using a recursive least squares algorithm or a Kalman filter algorithm, and the corrected model parameters are synchronously updated to the digital twin model.

8. The shore power wireless charging predictive control method according to claim 7, characterized in that, During the operation of the shore power wireless charging device, the method further includes performing at least one of the following safety protection operations (A) to (C): (A) When the coupling coefficient predicted by the coupling coefficient mapping sub-model is lower than the first preset safety threshold, a power reduction command is generated and the power reduction command is output to the shore power wireless charging device to reduce the output power to a preset safety power value. When the coupling coefficient predicted by the coupling coefficient mapping sub-model is lower than the second preset safety threshold, a shutdown command is generated and the shutdown command is output to the shore power wireless charging device to suspend charging. The second preset safety threshold is lower than the first preset safety threshold. (B) When the prediction deviation of the coupling coefficient exceeds the preset alarm threshold and the duration exceeds the preset duration, a switching command is generated and the switching command is output to the shore power wireless charging device so as to switch from the model prediction control mode to the standby control mode, wherein the standby control mode includes a constant current charging mode or a constant voltage charging mode. (C) When the wave height in the wave excitation sequence predicted by the wave disturbance sub-model exceeds the preset safe wave height threshold, a pre-shutdown command is generated and the pre-shutdown command is output to the shore power wireless charging device so as to control the shore power wireless charging device to perform power reduction or shutdown operation before the target ship reaches the predicted extreme working condition.

9. A shore power wireless charging predictive control system based on a digital twin model, characterized in that, It includes a twin model construction unit, a real-time data acquisition unit, a trajectory prediction unit, a predictive control optimization unit, and an optimal control output unit; The twin model construction unit is used to construct a digital twin model of the target ship. The digital twin model includes a ship kinematics sub-model, a wave disturbance sub-model, and a coupling coefficient mapping sub-model. The coupling coefficient mapping sub-model is used to establish the mapping relationship between the ship's attitude and the relative position and coupling coefficient of the wireless charging coil. The real-time data acquisition unit is used to acquire the motion attitude data and environmental wave data of the target vessel in real time. The motion attitude data is acquired in real time by a sensor group deployed on the target vessel and / or on the target vessel's docked shore base, and the environmental wave data is acquired in real time by a wave monitoring device deployed on the docked shore base. The trajectory prediction unit is communicatively connected to the twin model construction unit and the real-time data acquisition unit, respectively. It is used to input the motion attitude data and the environmental wave data into the digital twin model in real time. The wave disturbance sub-model generates a wave excitation sequence in the future time domain based on the environmental wave data. The ship kinematics sub-model predicts the motion attitude change trajectory of the target ship in the future time domain based on the motion attitude data, the historical motion trajectory of the target ship, and the wave excitation sequence. The motion attitude change trajectory is then mapped to the coupling coefficient change trajectory in the future time domain based on the coupling coefficient mapping sub-model. The predictive control optimization unit is communicatively connected to the trajectory prediction unit. It is used to take the trajectory of the coupling coefficient change as a feedforward quantity, combine it with the current electrical state parameters of the shore power wireless charging device, construct a model predictive control optimization problem, and solve it to obtain the optimal control quantity sequence for the shore power wireless charging device to wirelessly charge the target ship in the future time domain. The optimal control output unit is communicatively connected to the predictive control optimization unit and is used to output the first control quantity in the optimal control quantity sequence to the shore power wireless charging device in order to adjust the wireless charging parameters and compensate in advance for the impact of the charging coil offset caused by the ship's movement on the charging transmission efficiency.

10. A computer device, characterized in that, The device includes a storage module, a processing module, and a transceiver module that are sequentially connected in communication. The storage module is used to store a computer program, the transceiver module is used to send and receive messages, and the processing module is used to read the computer program and execute the shore power wireless charging predictive control method as described in any one of claims 1 to 8.

Images