Unmanned ship adaptive wave interference control method and system based on digital twinning

Abstract

The invention belongs to the technical field of unmanned ship control, and particularly relates to an unmanned ship adaptive wave interference control method and system based on digital twin, and the system comprises a data acquisition module, a data processing module, a decision module and an output module. The method has the advantages that a self-adaptive multi-dimensional regression strategy is adopted, the difference between a virtual model and a physical model is evaluated, and dynamic adjustment is carried out according to prediction and feedback results. On the basis of ensuring the sailing safety and task execution of the unmanned ship, the task execution efficiency and the robustness of the system are improved, and the adaptability and continuity of the digital twin technology in the complex task environment of the unmanned ship are enhanced.

Description

Technical Field

[0001] The present application belongs to the field of unmanned ship control technology, and specifically relates to an unmanned ship adaptive wave interference control method and system based on digital twin. Background Art

[0002] An unmanned ship is a surface robot that can navigate on the water according to preset tasks without remote control, relying on precise satellite positioning and self-sensing. This "surface robot" integrates multiple technologies such as ships, communications, automatic control, remote monitoring and networked systems, and realizes multiple functions such as autonomous navigation, intelligent obstacle avoidance, long-distance communication, real-time video transmission and networked monitoring. As a new technical means, unmanned ships have been widely used in the fields of marine commissioning, maritime defense, etc.

[0003] During the operation of unmanned ships, they often encounter high sea conditions caused by wind and waves. In high sea conditions, unmanned ships are prone to capsizing, stalling or running away. Therefore, the control method of unmanned ships in high sea conditions is directly related to the driving safety and mission suitability of unmanned ships. At present, the control method of unmanned ships can ensure the smooth operation of unmanned ships in low sea conditions, but when encountering high sea conditions, the existing unmanned ship control method cannot adaptively control the unmanned ship according to the sea conditions, and thus cannot ensure the smooth operation of the unmanned ship in high sea conditions. Summary of the invention

[0004] Based on the above problems, the present application provides a method and system for adaptive wave interference control of an unmanned ship based on digital twins, and its technical solution is: An adaptive wave interference control method for an unmanned ship based on digital twins comprises the following steps: S1. Obtain wave information and perform wave frequency analysis; S2. Calculate response time; S3. Control the attitude and speed of the unmanned ship; S4. Use deep reinforcement learning to optimize the attitude and speed of unmanned ships.

[0005] Preferably, in step S1, S11. Use sensors to collect wave signals in real time , including wave height ,wavelength , wave speed and wave direction The time domain information, this time domain signal Reflects the changes of waves in the time dimension: ; S12. Using generalized Fourier transform to analyze wave signals Perform frequency domain analysis and convert it into wave spectrum in the frequency domain : ; is the time window function, is a frequency variable, different frequency components Corresponding to different oscillation periods in the wave; S13. Extracting the main frequencies through frequency spectrum analysis , that is, the frequency with the most concentrated energy in the spectrum. The main frequency is determined by finding the frequency peak with the largest amplitude in the spectrum: ; In the wave spectrum In the process, by scanning the frequency range, find the area with the largest energy, that is, the location of the main frequency; S14. Use filters to further filter out noise and adjust the filter coefficients based on real-time wave data , optimize the detection effect of wave signals: .

[0006] Preferably, in step S1, in the case of multi-frequency waves, the frequency bandwidth is calculated , which represents the frequency range between frequency components close to the main frequency, is used to capture the energy concentration part within a wider frequency band: ; and They are the upper and lower frequency limits, respectively, indicating the frequency range with greater fluctuation intensity in the wave spectrum.

[0007] Preferably, S21. according to the frequency of extraction Calculate the main angular frequencies of the waves : ; Calculate the dominant period of the wave : ; S22. Using the calculated main frequency and dominant cycle , calculate the response time window of the unmanned ship ,This window determines the shortest time the unmanned ship needs to complete the adjustment before the wave impact; ; in, is the safety factor.

[0008] Preferably, in step S3, the attitude control method is as follows: by using the optimal control theory, the optimal attitude adjustment strategy of the unmanned ship is calculated according to the wave interference to ensure that the parameters such as the forward tilt angle and the roll angle of the hull can minimize the influence of the waves, and the control equation is: ; in is the angular momentum of the unmanned ship, is the wave torque, Control torque for thrusters; By solving the Hamiltonian equation, the optimal control strategy of the unmanned ship is obtained: ; in, To solve the matrix, is the attitude control input, is the control input weight matrix, is the control gain matrix, is the attitude angle of the unmanned ship; The speed control method is as follows: Calculate the optimal output power of the thruster , so that the speed of the unmanned ship can smoothly cope with the impact of waves: ; in, For the quality of unmanned ships, is the wave impact force, The speed of the unmanned ship.

[0009] Preferably, in step S4, the attitude and speed of the unmanned boat are adaptively adjusted through a multi-objective optimization strategy, and the reward function Combined with wave impact , Posture Adjustment and energy consumption To optimize: ; , , Respectively represent and The attitude adjustment includes the attitude angle adjustment (including pitch angle, roll angle and yaw angle) and spatial position adjustment (including displacement and relative position) of the unmanned ship.

[0010] Preferably, it also includes an emergency avoidance and recovery strategy of a multi-objective evolutionary algorithm, the steps are as follows: S5.1 When the wave impact force Exceeding safety threshold When , a multi-objective evolutionary algorithm is used for emergency avoidance; the objective function includes wave intensity, avoidance path cost and task execution cost: ; and is the weight coefficient in multi-objective optimization; S5.2 is solved by a non-dominated sorting genetic algorithm or a fast non-dominated sorting genetic algorithm to select the optimal avoidance path to maximize safety and minimize mission loss.

[0011] Preferably, the actual state of the unmanned ship is reflected by the multi-dimensional real-time feedback of the digital twin and the high-dimensional Kalman filter. Feedback is sent to the virtual model, and the virtual model is adaptively corrected through a high-dimensional Kalman filter: ; in, is the Kalman gain, is the virtual model state estimation, is the control parameter of the system, The real-time measurement value of the physical world, that is, the actual state of the unmanned ship ; is the state transition matrix, is the measurement matrix, Control input matrix.

[0012] An unmanned ship adaptive wave interference control system based on digital twin, used to execute an unmanned ship adaptive wave interference control method based on digital twin, comprising a data acquisition module, a data processing module, a decision module and an output module; Data acquisition module: obtains physical information data of the electromechanical product under study through sensors, monitoring equipment or other data sources, including sensor data, operation records, and environmental parameter data; Data processing module: interacts the collected multi-source data into the digital space, performs data preprocessing and analysis, and obtains key modeling parameters of working conditions and constraints; Decision-making module: Based on the measured data set of the physical space and the simulated data set of the digital space provided, the deep learning model is used for training; the emergency avoidance and recovery strategy of the target optimization algorithm is constructed, and the solution is obtained through the non-dominated sorting genetic algorithm or the fast non-dominated sorting genetic algorithm to select the optimal avoidance path, maximize safety and minimize mission loss; Output module: Utilize the multi-dimensional real-time feedback of digital twins and high-dimensional Kalman filters to visualize the results.

[0013] Compared with the prior art, the present application has the following beneficial effects: 1. This application uses digital twin technology to achieve real-time synchronization between the physical system and the virtual model, and corrects the state of the physical unmanned ship through dynamic feedback from the virtual model. Compared with the control method in the prior art that only relies on physical sensors, the present invention can more accurately predict the impact of wave interference on the unmanned ship and take adjustment measures in advance, thereby improving the response speed and accuracy of the system.

[0014] 2. Through the combination of generalized Fourier transform and adaptive filter, the present application can more accurately capture the main frequency components in complex wave environments and improve the prediction accuracy of wave interference. Compared with traditional time domain analysis, the present invention can more effectively deal with the interference of multi-frequency waves, especially in sudden and non-stationary wave environments.

[0015] 3. The present invention adopts a multi-objective evolutionary algorithm, which can simultaneously optimize the unmanned ship's avoidance path and task execution efficiency when facing wave interference. Different from the single-objective control in the prior art, it can dynamically balance the intensity of wave interference, the safety of the avoidance path, and the degree of task completion, so that the unmanned ship can maintain efficient operation in complex sea conditions.

[0016] 4. Through deep reinforcement learning technology, the present invention can adaptively optimize the attitude and speed adjustment strategy of the unmanned ship according to the real-time wave environment. Compared with the traditional preset control scheme, deep reinforcement learning can self-optimize according to the changing sea conditions, significantly improving the stability and energy efficiency of the unmanned ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is the flow chart of this application. DETAILED DESCRIPTION

[0018] The following will be combined with the embodiments of the present invention and the drawings of the specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] An adaptive wave interference control method for an unmanned ship based on digital twins comprises the following steps: S1. Obtain wave information and perform wave frequency analysis; Step S1 includes: S11. Use sensors to collect wave signals in real time , including wave height ,wavelength , wave speed and wave direction The time domain information, this time domain signal Reflects the changes of waves in the time dimension: ; S12. Using generalized Fourier transform to analyze wave signals Perform frequency domain analysis and convert it into wave spectrum in the frequency domain : ; is the time window function, is a frequency variable, different frequency components Corresponding to different oscillation periods in the wave; S13. Extracting the main frequencies through frequency spectrum analysis , that is, the frequency with the most concentrated energy in the spectrum. The main frequency is determined by finding the frequency peak with the largest amplitude in the spectrum: ; In the wave spectrum In the process, by scanning the frequency range, find the area with the largest energy, that is, the location of the main frequency; S14. Use filters to further filter out noise and adjust the filter coefficients based on real-time wave data , optimize the detection effect of wave signals: .

[0020] Calculate the frequency bandwidth in case of multi-frequency waves , which represents the frequency range between frequency components close to the main frequency, is used to capture the energy concentration part within a wider frequency band: ; and They are the upper and lower frequency limits, respectively, indicating the frequency range with greater fluctuation intensity in the wave spectrum.

[0021] S2. Calculate response time; S21. Based on the frequency of extraction Calculate the main angular frequencies of the waves : ; Calculate the dominant period of the wave : ; S22. Using the calculated main frequency and dominant cycle , calculate the response time window of the unmanned ship ,This window determines the shortest time the unmanned ship needs to complete the adjustment before the wave impact; ; in, is the safety factor.

[0022] S3. Control the attitude and speed of the unmanned ship; In step S3, the attitude control method is as follows: through the optimal control theory, the optimal attitude adjustment strategy of the unmanned ship is calculated according to the wave interference to ensure that the parameters such as the forward tilt angle and the side tilt angle of the hull can minimize the influence of the waves. The control equation is: ; in is the angular momentum of the unmanned ship, is the wave torque, Control torque for thrusters; By solving the Hamiltonian equation, the optimal control strategy of the unmanned ship is obtained: ; in, To solve the matrix, is the attitude control input, is the control input weight matrix, is the control gain matrix, is the attitude angle of the unmanned ship; The speed control method is as follows: Calculate the optimal output power of the thruster , so that the speed of the unmanned ship can smoothly cope with the impact of waves: ; in, For the quality of unmanned ships, is the wave impact force, The speed of the unmanned ship.

[0023] S4. Use deep reinforcement learning to optimize the attitude and speed of unmanned ships.

[0024] The attitude and speed of the unmanned ship are adaptively adjusted through a multi-objective optimization strategy, and the reward function Combined with wave impact , Posture Adjustment and energy consumption To optimize: ; , , Respectively represent and The attitude adjustment includes the attitude angle adjustment (including pitch angle, roll angle and yaw angle) and spatial position adjustment (including displacement and relative position) of the unmanned ship.

[0025] S5. Emergency avoidance and recovery strategy of multi-objective evolutionary algorithm, the steps are as follows: S5.1 When the wave impact force Exceeding safety threshold When , a multi-objective evolutionary algorithm is used for emergency avoidance; the objective function includes wave intensity, avoidance path cost and task execution cost: ; and is the weight coefficient in multi-objective optimization.

[0026] S5.2 is solved by a non-dominated sorting genetic algorithm or a fast non-dominated sorting genetic algorithm to select the optimal avoidance path to maximize safety and minimize mission loss.

[0027] The actual state of the unmanned ship is reflected by the multi-dimensional real-time feedback of the digital twin and the high-dimensional Kalman filter. Feedback is sent to the virtual model, and the virtual model is adaptively corrected through a high-dimensional Kalman filter: ; in, is the Kalman gain, is the virtual model state estimation, is the control parameter of the system, The real-time measurement value of the physical world, that is, the actual state of the unmanned ship ; is the state transition matrix, is the measurement matrix, Control input matrix.

[0028] An unmanned ship adaptive wave interference control system based on digital twin, used for an unmanned ship adaptive wave interference control method based on digital twin, comprising a data acquisition module, a data processing module, a decision module and an output module; Data acquisition module: obtains physical information data of the electromechanical product under study through sensors, monitoring equipment or other data sources, including sensor data, operation records, and environmental parameter data; Data processing module: interacts the collected multi-source data into the digital space, performs data preprocessing and analysis, and obtains key modeling parameters of working conditions and constraints; Decision-making module: Based on the measured data set of the physical space and the simulated data set of the digital space provided, the deep learning model is used for training; the emergency avoidance and recovery strategy of the target optimization algorithm is constructed, and the solution is obtained through the non-dominated sorting genetic algorithm or the fast non-dominated sorting genetic algorithm to select the optimal avoidance path, maximize safety and minimize mission loss; Output module: Utilize the multi-dimensional real-time feedback of digital twins and high-dimensional Kalman filters to visualize the results.

[0029] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An adaptive wave disturbance control method for unmanned ships based on digital twins, characterized in that: The following steps are involved: S1. Obtain wave information and perform wave frequency analysis; S2. Calculate response time; S3. Control the attitude and speed of the unmanned ship; S4. Use deep reinforcement learning to optimize the attitude and speed of unmanned ships.

2. The method for adaptive wave interference control of unmanned ships based on digital twins according to claim 1 is characterized in that: In step S1, S11. Use sensors to collect wave signals in real time , including wave height ,wavelength , wave speed and wave direction The time domain information, this time domain signal Reflects the changes of waves in the time dimension: ； S12. Using generalized Fourier transform to analyze wave signals Perform frequency domain analysis and convert it into wave spectrum in the frequency domain : ； is the time window function, is a frequency variable, different frequency components Corresponding to different oscillation periods in the wave; S13. Extracting the main frequencies through frequency spectrum analysis , that is, the frequency with the most concentrated energy in the spectrum. The main frequency is determined by finding the frequency peak with the largest amplitude in the spectrum: ； In the wave spectrum In the process, by scanning the frequency range, find the area with the largest energy, that is, the location of the main frequency; S14. Use filters to further filter out noise and adjust the filter coefficients based on real-time wave data , optimize the detection effect of wave signals: 。 3. The method for adaptive wave interference control of unmanned ships based on digital twins according to claim 2 is characterized in that: In step S1, in the case of multi-frequency waves, the frequency bandwidth is calculated , represents the frequency range between frequency components close to the main frequency; ； and are the upper and lower frequency limits respectively.

4. The method for adaptive wave interference control of unmanned ships based on digital twins according to claim 1 is characterized in that: S21. Based on the frequency of extraction Calculate the main angular frequencies of the waves : ; Calculate the dominant period of the wave : ； S22. Using the calculated main frequency and dominant cycle , calculate the response time window of the unmanned ship ,This window determines the shortest time the unmanned ship needs to complete the adjustment before the wave impact; ； in, is the safety factor.

5. The method for adaptive wave interference control of unmanned ships based on digital twins according to claim 1 is characterized in that: In step S3, the attitude control method is as follows: the optimal attitude adjustment strategy of the unmanned ship is calculated according to the wave interference to ensure that the forward tilt angle and the side tilt angle parameters of the hull can minimize the influence of the waves. The control equation is: ； in is the angular momentum of the unmanned ship, is the wave torque, Control torque for thrusters; By solving the Hamiltonian equation, the optimal control strategy of the unmanned ship is obtained: ； in, To solve the matrix, is the attitude control input, is the control input weight matrix, is the control gain matrix, is the attitude angle of the unmanned ship; The speed control method is as follows: Calculate the optimal output power of the thruster , so that the speed of the unmanned ship can smoothly cope with the impact of waves: ； in, For the quality of unmanned ships, is the wave impact force, The speed of the unmanned ship.

6. The method for adaptive wave interference control of unmanned ships based on digital twins according to claim 1 is characterized in that: In step S4, the attitude and speed of the unmanned boat are adaptively adjusted through the multi-objective optimization strategy, and the reward function Combined with wave impact , Posture Adjustment and energy consumption To optimize: ； , , Respectively represent and The weight coefficient of .

7. The method for adaptive wave disturbance control of unmanned ships based on digital twins according to claim 1 is characterized in that: It also includes the emergency avoidance and recovery strategy of the multi-objective evolutionary algorithm. The steps are as follows: S5.1 When the wave impact force Exceeding safety threshold When , a multi-objective evolutionary algorithm is used for emergency avoidance; the objective function includes wave intensity, avoidance path cost and task execution cost: Y= ； and is the weight coefficient in multi-objective optimization; S5.2 is solved by a non-dominated sorting genetic algorithm or a fast non-dominated sorting genetic algorithm to select the optimal avoidance path to maximize safety and minimize mission loss.

8. The method for adaptive wave interference control of unmanned ships based on digital twins according to claim 1 is characterized in that: The actual state of the unmanned ship is reflected by the multi-dimensional real-time feedback of the digital twin and the high-dimensional Kalman filter. Feedback is sent to the virtual model, and the virtual model is adaptively corrected through a high-dimensional Kalman filter: ； in, is the Kalman gain, is the virtual model state estimation, is the control parameter of the system, The real-time measurement value of the physical world, that is, the actual state of the unmanned ship ; is the state transition matrix, is the measurement matrix, Control input matrix.

9. An unmanned ship adaptive wave interference control system based on digital twin, used to execute the unmanned ship adaptive wave interference control method based on digital twin according to any one of claims 1 to 8, characterized in that: It includes data acquisition module, data processing module, decision module and output module; Data acquisition module: obtains physical information data of the electromechanical product under study through sensors, monitoring equipment or other data sources, including sensor data, operation records, and environmental parameter data; Data processing module: Exchanges the collected multi-source data into the digital space, performs data preprocessing and analysis, and obtains key modeling parameters for working conditions and constraints; Decision-making module: Based on the measured data set of the physical space and the simulated data set of the digital space provided, the deep learning model is used for training; the emergency avoidance and recovery strategy of the target optimization algorithm is constructed, and the solution is obtained through the non-dominated sorting genetic algorithm or the fast non-dominated sorting genetic algorithm to select the optimal avoidance path, maximize safety and minimize mission loss; Output module: Utilize the multi-dimensional real-time feedback of digital twins and high-dimensional Kalman filters to visualize the results.

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