Unmanned ship navigation decision-making method and system, computer equipment and storage medium

Abstract

The invention discloses an unmanned ship navigation decision-making method and system, computer equipment and a storage medium, and relates to the technical field of unmanned ship navigation decision-making, and the method comprises the steps: obtaining feasible trajectory data corrected by wave interference based on a six-degree-of-freedom dynamic model; calculating the dynamic response characteristics of the unmanned ship based on the mass inertia matrix and the disturbance force parameters; propulsive efficiency factors under different navigational speeds and wave conditions are optimized through a propulsive force function, and real-time self-adaptive adjustment of propeller control parameters is carried out. According to the method, it is ensured that the unmanned ship can maintain an accurate sailing path in the complex marine environment, the motion response capacity of the unmanned ship is optimized, the adaptability and stability of the unmanned ship in the changing environment are improved, the efficiency problem of a traditional propelling system in the complex marine environment is solved, intelligent optimization of the propelling system is achieved, and the propelling system is suitable for being popularized and applied. The unmanned ship can dynamically adjust the propulsion strategy according to the real-time sea condition and the navigational speed change, the propulsion efficiency is improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat navigation decision-making, and specifically to an unmanned boat navigation decision-making method, system, computer device, and storage medium. Background Art

[0002] With the continuous development of unmanned boat technology, especially its increasingly wide application in fields such as marine surveying and mapping, patrol and surveillance, and environmental monitoring, researchers have proposed various navigation decision-making methods for unmanned boats. Early navigation decisions were mostly based on the Global Positioning System (GPS) and Inertial Navigation System (INS). However, these methods usually rely on simplified models and static environment assumptions and cannot cope with the dynamic changes in the marine environment. With the progress of sensor technology, navigation algorithms, and control theory, the trajectory planning method based on the six-degree-of-freedom dynamics model (6-DOF) has gradually become the research focus. Especially when dealing with complex factors such as waves, currents, and wind speeds, it demonstrates stronger adaptability and accuracy. However, the current related technologies still face multiple challenges and need to be further optimized to cope with the complex marine environment in practical applications.

[0003] The existing unmanned boat navigation decision-making methods generally have the following deficiencies. In a complex marine environment, the existing methods often cannot fully consider the influence of dynamic disturbance factors such as waves, flow velocities, and wind speeds, resulting in low accuracy of trajectory planning. Most methods are based on simplified models or static assumptions and cannot adjust or correct the navigation path in real time, easily deviating from the predetermined trajectory. Although the existing technology optimizes the propulsion system through the Computational Fluid Dynamics (CFD) model, it usually ignores complex environmental factors such as wave height and flow velocity. Although the propulsion efficiency can be optimized according to the ship speed, it cannot fully cope with the dynamic changes in different marine environments. Therefore, the propulsion system often fails to operate at the optimal energy efficiency in practical applications, resulting in energy waste. Most existing propulsion strategies rely on fixed environmental parameters and cannot adaptively adjust according to real-time changes in a complex marine environment. For example, the existing propulsion force optimization methods fail to consider the real-time changes in factors such as wave height and flow velocity, resulting in the thruster control system being unable to accurately respond to different conditions in a complex environment, reducing the energy efficiency and affecting the system stability. The present invention obtains the feasible trajectory data corrected by wave interference based on the six-degree-of-freedom dynamics model, and combines the mass inertia matrix and disturbing force parameters to calculate the dynamic response characteristics of the unmanned boat, thereby making up for the deficiencies of the existing technology. By optimizing the propulsion force function in real time and adjusting the thruster control parameters, the present invention can adaptively optimize the propulsion efficiency in a complex marine environment, improving the navigation accuracy and energy efficiency of the unmanned boat under different wave and flow velocity conditions. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is that the existing unmanned boat navigation decision-making technology has low accuracy, insufficient efficiency, poor adaptive adjustment ability, and how to cope with the influence of disturbance factors such as waves and flow velocity in a complex dynamic marine environment, and real-time optimize the propulsion force and navigation accuracy.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an unmanned boat navigation decision-making method, including obtaining feasible trajectory data corrected by wave interference based on a six-degree-of-freedom dynamics model; calculating the dynamic response characteristics of the unmanned boat based on the mass inertia matrix and disturbing force parameters; optimizing the propulsion efficiency factor under different speeds and wave conditions through a propulsion force function, and performing real-time adaptive adjustment of the thruster control parameters; obtaining the feasible trajectory data corrected by wave interference includes calculating the position, attitude and change rate of the unmanned boat in three-dimensional space, introducing the influence of waves, including wave amplitude, wave frequency, main frequency and roll angle, as interference input for dynamic correction, and performing trajectory stability calculation through a dynamic correction module to output a controllable trajectory data set under the influence of waves; optimizing the propulsion efficiency factor under different speeds and wave conditions includes dynamically correcting the propulsion efficiency factor, constructing a correction term based on the deviation between the wave height and the reference wave height, adjusting the propulsion efficiency factor, when the wave height exceeds the reference value, the adjustment term changes in a negative correlation exponential manner, reflecting the trend of the thruster efficiency decreasing under high sea conditions, and predicting the propulsion performance based on the corrected propulsion efficiency and speed deviation to adapt to different sea conditions and speed conditions.

[0007] As a preferred embodiment of the unmanned boat navigation decision-making method of the present invention, wherein: obtaining the feasible trajectory data corrected by wave interference includes establishing a six-degree-of-freedom dynamics model, modeling the position, attitude and change rate of the unmanned boat in three-dimensional space, and based on the Coriolis force, nonlinear resistance, added mass, gravity and buoyancy, constructing a wave disturbance term, mapping the current wave amplitude, main frequency, frequency and the roll response factor of the unmanned boat as interference input for dynamic correction, and calculating the trajectory stability under the influence of waves to output a trajectory data set.

[0008] As a preferred embodiment of the unmanned boat navigation decision-making method of the present invention, wherein: calculating the dynamic response characteristics of the unmanned boat includes calculating the true inertial response of the unmanned boat after being subjected to external forces in a fluid environment by matrix combination of the structural mass of the boat body and the added mass generated by the fluid attachment effect in six directions; calculating the dynamic terms of the Coriolis inertial force, linear fluid resistance term and hydrodynamic buoyancy offset received by the unmanned boat according to the current position, speed and attitude change state of the unmanned boat; constructing a state resolver that simulates the dynamic behavior of the unmanned boat by comparing the balance relationship between the control input and the system response, and the state resolver outputs the acceleration, attitude change amount and energy distribution information at each time node based on the input change predicted by the trajectory point.

[0009] As a preferred solution of the unmanned boat navigation decision-making method described in the present invention, wherein: optimizing the propulsion efficiency factor under different speeds and wave conditions includes setting the optimal speed of the propulsion system operation as the efficiency reference value, and taking the deviation between the current speed of the unmanned boat and the optimal speed value as the main control parameter for the change of propulsion efficiency; establishing an exponential efficiency decay curve to simulate the non-linear characteristics of the propulsion efficiency changing with speed, and the greater the deviation of the speed, the lower the efficiency; based on the current speed of the unmanned boat, the structural parameters of the thruster and the medium density, by correlating the force-bearing area of the thruster with the real-time propulsion efficiency parameters, generating a control result for dynamically outputting the propulsion power.

[0010] As a preferred solution of the unmanned boat navigation decision-making method described in the present invention, wherein: optimizing the propulsion efficiency factor under different speeds and wave conditions further includes dynamically correcting the propulsion efficiency factor; constructing a sea condition disturbance characteristic model, and calculating the sea condition adjustment item of the propulsion efficiency according to the deviation between the current wave height and the relative design reference wave height; when the wave height exceeds the reference value, the adjustment item will show a negative exponential change, reflecting the increasing trend of the thruster failure rate under high sea conditions; based on the corrected propulsion efficiency, substituting it into the propulsion force function and the attenuation effect caused by the speed deviation, modeling the output ability of the propulsion system in a multi-dimensional disturbance environment; predicting the propulsion performance through the speed and sea conditions.

[0011] As a preferred solution of the unmanned boat navigation decision-making method described in the present invention, wherein: performing the propulsion performance prediction includes jointly judging the propulsion efficiency factor, the real-time speed and the sea wave height signal, and triggering the propulsion mode switching logic through comparison with a preset interval threshold; when the speed is close to the optimal interval and in a stable sea condition, the system enters the standard propulsion mode to maximize the propulsion efficiency output; if the speed deviates from the optimal range, the system will reduce the output power according to the deviation degree and make an adaptive adjustment based on the goals of energy conservation and stable heading; in the case where the wave height is higher than the reference, the system switches to the wave-resistant mode, and suppresses the thrust fluctuation caused by the wave by adjusting the thruster angle or the attitude control method; constructing a propulsion control strategy based on the periodic feedback and the state perception mechanism, dynamically judging the propulsion mode interval to which the current state belongs, and adjusting the propulsion parameters.

[0012] As a preferred solution of the unmanned boat navigation decision-making method described in the present invention, wherein: the propulsion control strategy includes collecting the real-time speed and sea condition data of the unmanned boat as input variables, and performing a complete propulsion force estimation in combination with a preset efficiency function and thrust model; judging the state interval matching of the calculation result to identify whether to trigger operations such as propulsion mode switching, thruster power adjustment or angle adjustment; if the system judges that the current state is in the boundary area, trend extension prediction is required to prevent frequent switching triggered by fluctuations; the propulsion control module continuously outputs control instructions in a closed-loop manner and supports the access of external control signals and dynamic update of algorithm parameters to determine the stable response of the propulsion strategy to environmental changes.

[0013] Another object of the present invention is to provide an unmanned boat navigation decision-making system, which can optimize the propulsion efficiency factor under different speeds and wave conditions through a propulsion force function and perform real-time adaptive adjustment of thruster control parameters, solving the problems of low propulsion efficiency and poor adaptability to dynamic environments in the current unmanned boat propulsion control technology.

[0014] As a preferred solution of the unmanned boat navigation decision-making system described in the present invention, it includes a trajectory planning module, a dynamic response calculation module, and a propulsion force optimization module. The trajectory planning module is used to calculate the movement trajectory of the unmanned boat under wave interference through a six-degree-of-freedom dynamics model, combining factors such as Coriolis force, non-linear resistance, added mass, and gravity buoyancy, and generate a stable and controllable navigation trajectory dataset. The dynamic response calculation module is used to calculate the dynamic response of the unmanned boat in a fluid environment by combining the mass and added mass of the hull, calculate the Coriolis inertial force, fluid resistance, and hydrodynamic buoyancy offset factors based on the current state of the unmanned boat, construct a state solver, and output acceleration, attitude change, and energy distribution information at each time node to simulate the dynamic behavior of the unmanned boat in real time. The propulsion force optimization module includes a control result generation module, a dynamic correction module, a propulsion performance prediction module, and a propulsion control strategy construction module. The control result generation module is used to set the optimal speed and calculate the speed deviation, optimize the propulsion efficiency factor, simulate the non-linear change of propulsion efficiency by establishing an efficiency decay curve, and dynamically adjust the propulsion force output in combination with the thruster structure parameters and environmental conditions. The dynamic correction module is used to optimize the propulsion force function and dynamically correct the propulsion efficiency factor, and adjust the propulsion efficiency in real time in combination with the sea condition disturbance characteristic model. The propulsion performance prediction module is used to dynamically switch the propulsion mode, including the standard propulsion mode, the energy-saving adjustment mode, and the wave-resistant mode, optimize the propulsion efficiency, save energy, and stabilize the heading by real-time monitoring of the speed and wave height and combining with a preset threshold to judge the trigger condition, and use a periodic feedback and state perception mechanism to adjust the thruster parameters in real time to achieve adaptive control. The propulsion control strategy construction module is used to estimate the propulsion force based on the real-time speed and sea condition data of the unmanned boat, combine with the efficiency function and the thrust model, judge and execute operations such as propulsion mode switching, power adjustment, and angle adjustment, prevent frequent switching through trend prediction, and support the access of external control signals and the dynamic update of algorithm parameters to determine the stable response of the propulsion strategy to environmental changes.

[0015] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the unmanned boat navigation decision-making method.

[0016] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it implements the steps of the unmanned boat navigation decision-making method.

[0017] Advantages of the present invention: The unmanned boat navigation decision-making method provided by the present invention is based on a six-degree-of-freedom dynamics model, and obtains feasible trajectory data corrected by wave interference, ensuring the stable navigation of the unmanned boat in a complex marine environment, improving the accuracy and robustness of trajectory planning. Based on the mass inertia matrix and disturbing force parameters, the dynamic response characteristics of the unmanned boat are calculated, enabling the unmanned boat to efficiently respond to external disturbances in a complex fluid environment, optimizing the navigation stability and system control performance. By optimizing the propulsion efficiency factor under different speeds and wave conditions through the propulsion force function, and performing real-time adaptive adjustment of the thruster control parameters, it ensures the high-efficiency operation of the thruster under different sea wave conditions, automatically adjusts the thruster angle or power output according to real-time environmental changes, thereby achieving adaptive control. The present invention achieves better results in terms of unmanned boat trajectory planning accuracy, propulsion efficiency optimization, and dynamic adaptation ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 It is the overall flowchart of the unmanned boat navigation decision-making method provided by the first embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0021] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides an unmanned boat navigation decision-making method, including:

[0022] S1: Based on a six-degree-of-freedom dynamics model, obtain feasible trajectory data corrected by wave interference.

[0023] Furthermore, obtaining the feasible trajectory data corrected by wave interference includes establishing a six-degree-of-freedom dynamics model to model the position, attitude, and their change rates of the unmanned boat in three-dimensional space. Based on the Coriolis force, nonlinear resistance, added mass, gravity, and buoyancy, by constructing a wave perturbation term, mapping the current wave amplitude, main frequency, frequency, and the roll response factor of the unmanned boat into interference inputs for dynamic correction, calculating the trajectory stability under the influence of waves, and outputting a trajectory dataset.

[0024] It should be noted that a preferred solution for calculating the trajectory stability under the influence of waves and outputting a controllable trajectory dataset specifically includes using a six-degree-of-freedom (6-DOF) dynamics model to calculate the feasible trajectory and combining wave influence compensation, expressed as:

[0025]

[0026] where M represents the mass inertia matrix, C(v) represents the Coriolis force matrix, D(v) represents the resistance matrix, g(η) represents the gravity and buoyancy terms, W(f,φ) represents the wave disturbing force, and τ is the control input. represents the acceleration vector of the unmanned boat in the lateral direction, and v represents the velocity of the unmanned boat in the lateral direction.

[0027] Calculate the wave disturbing force W(f,φ), expressed as:

[0028]

[0029] where A w is the wave amplitude, f is the wave frequency, f0 is the main frequency, β is to control the influence of the wave on the unmanned boat, and φ is the roll angle.

[0030] It should also be noted that by obtaining the feasible trajectory data corrected by wave interference based on the six-degree-of-freedom dynamics model, the navigation accuracy of the unmanned boat in a complex marine environment is improved; traditional navigation methods usually rely on simplified models or static assumptions, ignoring the dynamic changes of environmental factors such as waves, wind speed, and water flow, resulting in large errors in trajectory planning; by using a six-degree-of-freedom (6-DOF) dynamics model, combined with actual hydrodynamic parameters, considering factors such as the Coriolis force, nonlinear resistance, and added mass, it can accurately simulate the motion state and changes of the unmanned boat in three-dimensional space; through wave perturbation correction, the model dynamically compensates for the influence of waves on the track, ensuring the stability of the sailing path of the unmanned boat. This correction not only improves the accuracy of trajectory planning but also provides reliable basic data for subsequent propulsion force optimization, avoiding the problem of insufficient response to ocean perturbations in traditional methods and enhancing the adaptability and robustness to environmental changes.

[0031] S2: Calculate the dynamic response characteristics of the unmanned boat based on the mass inertia matrix and disturbing force parameters.

[0032] Furthermore, calculating the dynamic response characteristics of the unmanned boat includes calculating the true inertial response of the unmanned boat after being subjected to external forces in the fluid environment by combining the structural mass of the boat body and the added mass generated by the fluid attachment effect in six directions; calculating the Coriolis inertial force, linear fluid resistance term, and dynamic term of the hydrodynamic buoyancy offset received by the unmanned boat according to the current position, speed, and attitude change state of the unmanned boat; constructing a state solver that simulates the dynamic behavior of the unmanned boat by comparing the balance relationship between the control input and the system response. The state solver outputs the acceleration, attitude change amount, and energy distribution information at each time node based on the input change under the trajectory point prediction.

[0033] It should be noted that a preferred solution for calculating the true inertial response of the unmanned boat after being subjected to external forces in the fluid environment specifically includes calculating the mass inertia matrix M of the unmanned boat according to the mass of the unmanned boat and the added mass (the mass attached to the boat body by the water body), expressed as:

[0034]

[0035] where m X 、m Y 、m Z are the masses of the unmanned boat in the front-back, lateral, and vertical directions, represents the added mass caused by factors such as water flow and wind speed, I xx is the moment of inertia of the unmanned boat about the longitudinal axis, usually corresponding to the roll motion, with the unit of kg·m 2 ,I yy is the moment of inertia of the unmanned boat about the lateral axis, usually corresponding to the pitch motion, with the unit of kg·m 2 ,I zz is the moment of inertia of the unmanned boat about the vertical axis, usually corresponding to the yaw motion, with the unit of kg·m 2 .

[0036] Calculate the acceleration vector of the unmanned boat is expressed as:

[0037]

[0038] where respectively represent the acceleration vectors of the unmanned boat in the front-back, lateral, and vertical directions, respectively represent the acceleration vectors of the roll, pitch, and yaw angles of the unmanned boat.

[0039] It should also be noted that a preferred solution for calculating the Coriolis inertial force, fluid resistance term, and dynamic term of hydrodynamic buoyancy offset acting on the unmanned boat specifically includes calculating the Coriolis inertial force acting on the unmanned boat, which includes calculating the Coriolis force and centrifugal force term matrix C(v)v, expressed as:

[0040]

[0041] where u, v, and w respectively represent the velocities of the unmanned boat in the fore-aft, lateral, and vertical directions, and p, q, and r respectively represent the roll, pitch, and yaw angular velocities of the unmanned boat.

[0042] Calculating the fluid resistance term D(v)v, which includes linear fluid resistance and nonlinear resistance, is expressed as:

[0043]

[0044] where X u , Y v , Z w are the linear resistances of the unmanned boat in the fore-aft, lateral, and vertical directions, and X |u|u , Y |v|v , Z |w|w are the nonlinear resistance coefficients, and K p , M q , N r are the resistance coefficients in the rotational directions.

[0045] Calculating the dynamic term of hydrodynamic buoyancy offset includes the gravity and buoyancy term g(η), expressed as:

[0046]

[0047] where ρ is the density of seawater, V is the displacement volume of the unmanned boat, and g is the acceleration due to gravity.

[0048] It should also be noted that a preferred solution for constructing a state solver for simulating the dynamic behavior of the unmanned boat specifically includes simulating the influence of waves on the unmanned boat and calculating the wave disturbing force W(f, φ), expressed as:

[0049]

[0050] where A w represents the wave amplitude, which depends on the wind speed and wave height, f represents the current wave frequency, f0 represents the wave dominant frequency, β represents the amplitude for adjusting the influence of waves on the unmanned boat, and θ represents the pitch angle of the unmanned boat.

[0051] It should also be noted that by calculating the dynamic response characteristics of the unmanned boat based on the mass inertia matrix and disturbing force parameters, the problems of dynamic modeling and response of the unmanned boat in a complex fluid environment are solved; the external forces acting on the unmanned boat during navigation include factors such as water flow, waves, and wind speed, which not only affect the speed of the unmanned boat but also change its attitude and stability; the existing technologies usually ignore or simplify the influence of these external forces, resulting in the inability to accurately predict the motion state of the unmanned boat in a complex environment. By calculating the dynamic terms such as the mass inertia matrix, Coriolis force, nonlinear resistance, and buoyancy of the unmanned boat, an accurate simulation of the dynamic behavior of the unmanned boat is provided, enabling it to accurately predict and adjust its motion state under different ocean conditions, enabling the unmanned boat to make timely dynamic adjustments in the face of complex environmental changes, avoiding trajectory deviation caused by external force interference, and improving the stability and accuracy of navigation.

[0052] S3: Optimize the propulsion efficiency factor under different speeds and wave conditions through the propulsion force function, and perform real-time adaptive adjustment of the thruster control parameters.

[0053] Furthermore, optimizing the propulsion efficiency factor under different speeds and wave conditions includes setting the optimal speed of the propulsion system operation as the efficiency reference value, and taking the deviation between the current speed of the unmanned boat and the optimal speed value as the main control parameter for the change of the propulsion efficiency; establishing an exponential efficiency decay curve to simulate the non-linear characteristics of the propulsion efficiency varying with speed, where the greater the deviation of the speed, the lower the efficiency; based on the current speed of the unmanned boat, the thruster structure parameters, and the medium density, by correlating the force-bearing area of the thruster with the real-time propulsion efficiency parameters, generating a control result for dynamically outputting the propulsion power.

[0054] It should also be noted that a preferred solution for correlating the force-bearing area of the thruster with the real-time propulsion efficiency parameters based on the current speed of the unmanned boat, the thruster structure parameters, and the medium density specifically includes, based on computational fluid dynamics (CFD), calculating the propulsion force Expressed as:

[0055]

[0056] Wherein, represents the propulsion force of the unmanned boat in the current environment, ρ represents the seawater density, A represents the force-bearing area of the thruster, C T represents the thruster thrust coefficient, U represents the current speed of the unmanned boat, and the propulsion efficiency factor ζ wop is optimized according to the speed, expressed as:

[0057]

[0058] Wherein, γ represents the propulsion efficiency decay parameter, U optIndicates the optimal propulsion speed of the unmanned boat.

[0059] By calculating the thrust coefficient C of the thruster T , the propulsion efficiency ζ is accurately described prop , expressed as:

[0060]

[0061] where C T0 represents the basic thrust coefficient of the thruster, usually obtained from laboratory tests or CFD simulations, reflecting the thrust coefficient under the optimal operating conditions of the thruster; δ T is the thruster efficiency decay coefficient, dimensionless, describing the loss of thruster efficiency caused by speed changes, λ T is the thruster efficiency adjustment parameter, dimensionless, controlling the steepness of the efficiency change and determining the thrust decay characteristics at different speeds; U mid is the median speed in the thruster efficiency curve, in m / s, representing the symmetry point of the efficiency curve, generally taking the designed optimal speed of the thruster.

[0062] Considering the influence of the ocean environment, the propulsion efficiency factor ζ prop is further optimized, expressed as:

[0063]

[0064] where H s represents the current wave height, and H s0 is the wave height under the reference sea condition.

[0065] Finally, the complete formula for calculating the propulsion force of the unmanned boat is optimized as:

[0066]

[0067] where is the propulsion force of the unmanned boat in the current environment.

[0068] It should also be noted that optimizing the propulsion efficiency factor under different speeds and wave conditions also includes dynamically correcting the propulsion efficiency factor; constructing a sea condition disturbance characteristic model, calculating the sea condition adjustment term of the propulsion efficiency according to the deviation between the current wave height and the relative design reference wave height; when the wave height exceeds the reference value, the adjustment term will show a negative exponential change, reflecting the increasing trend of thruster inefficiency under high sea conditions; modeling the output ability of the propulsion system in a multi-dimensional disturbance environment based on the corrected propulsion efficiency and the attenuation effect caused by the speed offset; predicting the propulsion performance through speed and sea conditions.

[0069] It should also be noted that the propulsion performance prediction includes jointly judging the propulsion efficiency factor with the real-time speed and wave height signal, and triggering the propulsion mode switching logic by comparing the preset interval threshold; when the speed is close to the optimal interval and is in a stable sea condition, the system enters the standard propulsion mode to maximize the propulsion efficiency output; if the speed deviates from the optimal range, the system will reduce the output power according to the degree of deviation, and make adaptive adjustments based on the goals of energy saving and stable heading; when the wave height is higher than the benchmark, the system switches to the anti-wave mode, and suppresses the thrust fluctuations caused by waves by adjusting the thruster angle or attitude control method; a propulsion control strategy is constructed based on periodic feedback and state perception mechanism, and the propulsion mode interval to which the current state belongs is dynamically judged, and the propulsion parameters are adjusted.

[0070] It should also be noted that a preferred solution for predicting propulsion performance by speed and sea conditions specifically includes: based on the current speed U of the unmanned boat and the current wave height H s Optimize the propulsion strategy of unmanned boats.

[0071] Case 1: When U = U opt When the unmanned boat sails at the optimal speed, the propulsion efficiency reaches the maximum valueζ prop =1, the propeller provides the maximum thrust, calculate the propulsion force of the unmanned boat in the current environment It is expressed as:

[0072]

[0073] Among them, the wave impact factor determines the fine-tuning of the thrust, but the overall propulsion system works in the best condition.

[0074] Case 2: When U>U opt When , it means that the speed of the unmanned boat is too fast, the propeller efficiency is reduced, resulting in a decrease in propulsion force. Calculate the propulsion force of the unmanned boat in the current environment. It is expressed as:

[0075]

[0076] If γ is too large, the propulsion force will drop sharply, which may make it difficult to maintain the speed of the unmanned boat, and the propulsion power needs to be adjusted.

[0077] Case 3: When U opt When the speed of the unmanned boat is too slow, the propulsion efficiency decreases when the speed is low. Calculate the propulsion force of the unmanned boat in the current environment. It is expressed as:

[0078]

[0079] Slow speed sailing is suitable for energy saving mode, but may be detrimental to sailing stability in high wave conditions. ​

[0080] Case 4: When H s > H s0 it indicates that the sea waves are large, and the wave height H s exceeds the design reference value H s0 , then the propulsion force is affected by the sea waves, and the propulsion force of the unmanned boat in the current environment is calculated It is expressed as:

[0081]

[0082] When H s - H s0 is too large, the efficiency of the thruster drops sharply, and it is recommended to switch to the wave-resistant mode (automatically adjust the thruster angle).

[0083] Case 5: When H s = H s0 it indicates that the unmanned boat is sailing on calm water, and the propulsion system is calculated completely according to the propulsion efficiency. The influence of sea waves on it is the smallest, and the propulsion force of the unmanned boat in the current environment is calculated It is expressed as:

[0084]

[0085] Among them, U represents the current speed of the unmanned boat, and U opt is the optimal propulsion speed of the unmanned boat.

[0086] It should also be noted that the propulsion control strategy includes collecting the real-time speed and sea condition data of the unmanned boat as input variables, performing a complete propulsion force estimation in combination with a preset efficiency function and thrust model; making a state interval matching judgment on the calculation results to identify whether to trigger propulsion mode switching, thruster power adjustment or angle adjustment operations; if the system judges that the current state is in the boundary area, trend extension prediction is required to prevent frequent switching caused by fluctuations; the propulsion control module continuously outputs control commands in a closed-loop manner and supports the access of external control signals and the dynamic update of algorithm parameters to determine that the propulsion strategy stably responds to environmental changes.

[0087] It should also be noted that by optimizing the propulsion efficiency factor under different speeds and wave conditions through the propulsion force function and making real-time adaptive adjustments to the thruster control parameters, the propulsion efficiency and energy utilization rate of the unmanned boat in complex marine environments have been improved. Traditional propulsion control systems are usually optimized based on fixed speeds and environmental parameters and cannot respond in real time to changes in dynamic factors such as waves and flow velocities, resulting in energy waste and reduced propulsion efficiency. This step optimizes the working state of the propulsion system by dynamically adjusting the propulsion efficiency factor and combining real-time speed and sea condition data, enabling the thruster to achieve the best efficiency under different sea conditions and speeds. At the same time, the power and angle of the thruster are automatically adjusted according to environmental changes to ensure optimal performance even in unstable environments. This real-time adaptive control mechanism avoids the shortcoming of static optimization in traditional methods that cannot cope with changes, enabling the unmanned boat to operate efficiently in complex sea conditions, reducing energy waste, and improving the economy and stability of navigation.

[0088] Embodiment 2 is an embodiment of the present invention, which provides a navigation decision-making method for an unmanned boat. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0089] First, the unmanned boat was tested and debugged in detail, especially for the influence of complex disturbance factors such as waves and flow velocities in the marine environment. During the test preparation stage, the unmanned boat was placed in a simulated sea condition environment that included external disturbance factors such as different wave heights and wind speeds. The unmanned boat was equipped with a high-precision navigation system and sensors for real-time monitoring of the speed, position, attitude, and sea condition changes of the unmanned boat. The data acquisition module recorded important parameters such as wave frequency, wave amplitude, and current speed in real time through the sensors, providing basic data for subsequent calculations. Then, based on the six-degree-of-freedom dynamics model and using the established wave disturbing force correction formula, the feasible trajectory data corrected by wave interference was obtained. This process considered factors such as Coriolis force, nonlinear resistance, added mass, and gravity buoyancy, and output a series of controllable trajectory data sets. To verify the dynamic response characteristics, the experimental team calculated the dynamic response characteristics of the unmanned boat under different sea conditions based on the mass inertia matrix and disturbing force parameters, and simulated the acceleration, attitude changes, and energy distribution of the unmanned boat in these complex environments through a state solver. Finally, in order to optimize the efficiency of the propulsion system, the propulsion force function was used to optimize the propulsion efficiency factor under different speeds and wave conditions, and the thruster control parameters were automatically adjusted according to real-time speed and sea condition data to ensure that the unmanned boat could always operate with optimal efficiency in a changing environment. Referring to Table 1, some experimental data was recorded and analyzed.

[0090] Table 1 Experimental data record form

[0091]

[0092] From the comparison between experimental cycles 1 and 2, it can be seen that as the ship speed increases from 2.5 m / s to 3.0 m / s, the propulsive force increases from 15.2 N to 18.1 N. Although the increase in ship speed will increase the propulsive force, when the wave height is relatively large (wave height of 1.0 m in cycle 2), the propulsive efficiency factor decreases slightly (from 0.95 to 0.92); this indicates that at higher ship speeds, the waves have a certain negative impact on the propulsive efficiency, but the propulsive force is still relatively large. In cycle 3 (ship speed of 2.0 m / s), due to the lower ship speed and smaller wave height (0.5 m), the propulsive efficiency factor reaches 0.98. Although the propulsive force is relatively low (12.6 N), the trajectory stability score reaches 9 points, indicating that at low speeds, the stability of the system is better guaranteed and it is suitable for use in the energy-saving mode. In experimental cycle 4, the wave height increases to 1.5 m, resulting in the propulsive efficiency factor decreasing to 0.89. At this time, the propulsive force is 19.5 N, but the trajectory stability score is relatively low (6 points), indicating that under high-wave conditions, the efficiency of the thruster drops significantly and the stability of the unmanned boat is greatly affected. Therefore, the anti-wave mode proposed by the present invention is particularly important in this case. By adjusting the thruster angle or attitude, the thrust fluctuation caused by the waves is suppressed and the stability of the system is improved. According to the data of experimental cycles 5 and 6, in the case of relatively small waves, the system can effectively adjust the propulsive force and efficiency factor, ensure operation in the standard mode, and at the same time ensure a relatively high propulsive force output and stability score (cycle 5: 16.3 N, stability 8 points). In cycle 6 with relatively large waves, although the ship speed remains at 2.4 m / s, the propulsive force can still be maintained within a reasonable range (14.7 N), and the stability score is 7 points, indicating that through adaptive adjustment, the system can respond to wave and ship speed changes in real time and optimize the propulsive efficiency and stability.

[0093] It can be seen from the analysis of experimental data that the present invention effectively optimizes the propulsive efficiency of the unmanned boat under different sea conditions by dynamically adjusting the propulsive force function and control mode, avoiding the problem in the prior art that it cannot respond to sea condition changes in real time. Especially in a high-wave environment, the anti-wave mode and adaptive adjustment strategy significantly improve the propulsive force output and system stability, which has advantages compared with the static optimization method of traditional technologies.

[0094] Embodiment 3, an embodiment of the present invention, provides an unmanned boat navigation decision-making system, including a trajectory planning module 100, a dynamic response calculation module 200, and a propulsive force optimization module 300.

[0095] Among them, S4: The trajectory planning module 100 is used to calculate the motion trajectory of the unmanned boat under wave interference through a six-degree-of-freedom dynamics model, combining factors such as Coriolis force, nonlinear resistance, added mass, and gravity buoyancy, and generate a stable and controllable navigation trajectory dataset.

[0096] It should be noted that the trajectory planning module 100 is mainly responsible for the navigation path planning of the unmanned boat in the marine environment. By using the six-degree-of-freedom dynamics model and combining factors such as Coriolis force, non-linear resistance, added mass, and gravity buoyancy, it accurately calculates the movement of the unmanned boat under wave interference. Through real-time correction of the current navigation state of the unmanned boat, the trajectory planning module 100 generates a stable and controllable navigation trajectory data set. These data sets not only consider the influence of waves but also ensure the path controllability of the unmanned boat in complex sea conditions. The generated trajectory data is then provided to the dynamic response calculation module and the propulsion force optimization module as the basic data for subsequent processing.

[0097] The trajectory planning module 100 generates a controllable navigation trajectory data set while considering the influence of waves on the movement of the unmanned boat. The trajectory planning module 100 generates preliminary theoretical trajectory data based on wave disturbing forces and sea condition information. However, the theoretical trajectory data may not fully reflect the behavior of the unmanned boat in actual sea conditions. Therefore, the output result of the trajectory planning module 100 needs to be combined with the calculation result of the dynamic response calculation module 200. In the dynamic response calculation module, the actual dynamic response of the unmanned boat (such as acceleration, attitude change, etc.) is corrected according to real-time influencing factors such as Coriolis force, fluid resistance, added mass, and buoyancy. The data provided by the dynamic response calculation module helps to further calibrate and optimize the navigation trajectory in the trajectory planning module, making the theoretical path better conform to the actual situation.

[0098] S5: The dynamic response calculation module 200 is used to calculate the dynamic response of the unmanned boat in the fluid environment by combining the mass of the hull and the added mass, calculate the Coriolis inertial force, fluid resistance, and hydrodynamic buoyancy offset factors based on the current state of the unmanned boat, construct a state solver, and output the acceleration, attitude change, and energy distribution information at each time node to real-time simulate the dynamic behavior of the unmanned boat.

[0099] It should be noted that the dynamic response calculation module is responsible for simulating the dynamic behavior of the unmanned boat in the fluid environment to ensure that the unmanned boat can accurately respond to changes in the marine environment. The dynamic response calculation module calculates the true inertial response of the unmanned boat according to the combination of the mass of the unmanned boat and the added mass; inputs the current state of the unmanned boat in real-time, such as position, speed, and attitude change, and calculates dynamic factors such as Coriolis inertial force, fluid resistance, and hydrodynamic buoyancy; through these calculations, the dynamic response calculation module outputs the acceleration, attitude change amount, and energy distribution information at each time node in real-time, providing accurate dynamic response data for the propulsion force optimization module. These data are crucial for predicting the future motion state of the unmanned boat, adjusting the propulsion system, and optimizing the navigation trajectory.

[0100] The dynamic response calculation module 200 outputs the dynamic response information of the unmanned boat to the propulsion force optimization module, including the current speed, attitude, acceleration, tilt angle, etc. In the propulsion force optimization module 300, the control result generation module 301 optimizes the propulsion efficiency factor by setting the optimal speed and calculating the speed deviation, and combines the sea condition influence to adjust the propulsion force in real time. The dynamic state data provided by the dynamic response calculation module helps to determine the actual propulsion force demand and efficiency change. When the unmanned boat is affected by external disturbances such as waves and flow velocity during driving, the propulsion force optimization module will correct the propulsion force output in real time based on the dynamic response data and adjust the thruster control parameters to ensure that the propulsion system maintains the best efficiency and stable heading.

[0101] S6: The propulsion force optimization module 300 includes a control result generation module 301, a dynamic correction module 302, a propulsion performance prediction module 303, and a propulsion control strategy construction module 304. The control result generation module 301 is used to set the optimal speed and calculate the speed deviation, optimize the propulsion efficiency factor, simulate the non-linear change of the propulsion efficiency by establishing an efficiency decay curve, and dynamically adjust the propulsion force output in combination with the thruster structure parameters and environmental conditions. The dynamic correction module 302 is used to optimize the propulsion force function and dynamically correct the propulsion efficiency factor, and combine the sea condition disturbance characteristic model to adjust the propulsion efficiency in real time. The propulsion performance prediction module 303 is used to dynamically switch the propulsion mode, including the standard propulsion mode, the energy-saving adjustment mode, and the wave-resistant mode, by real-time monitoring the speed and wave height and combining the preset threshold to judge the trigger condition, optimize the propulsion efficiency, save energy and stabilize the heading, and use the periodic feedback and state perception mechanism to adjust the thruster parameters in real time to achieve adaptive control. The propulsion control strategy construction module 304 is used to estimate the propulsion force based on the real-time speed and sea condition data of the unmanned boat, combine the efficiency function and the thrust model to judge and execute the operations of propulsion mode switching, power adjustment, and angle adjustment, prevent frequent switching through trend prediction, and support the access of external control signals and the dynamic update of algorithm parameters to determine the propulsion strategy to stably respond to environmental changes.

[0102] It should be noted that the core function of the propulsion force optimization module is to ensure that the unmanned boat achieves the optimal propulsion efficiency under different sea conditions, avoid energy waste, and maintain a stable heading; this module consists of multiple sub-modules that work in coordination with each other. The control result generation module 301 sets the optimal speed and calculates the deviation of the current speed, thereby optimizing the propulsion efficiency factor; by establishing an efficiency decay curve, it simulates the non-linear characteristics of the propulsion efficiency varying with speed, and at the same time combines the thruster structure and the current environmental conditions to adjust the propulsion force output in real time; then, the dynamic correction module 302 further improves the energy efficiency of the propulsion system by optimizing the propulsion force function and dynamically correcting the propulsion efficiency factor; this module collaborates with the sea condition disturbance characteristic model to respond in real time to changes in environmental factors such as waves and flow velocity; the propulsion performance prediction module 303 determines when to switch the propulsion mode (such as the standard propulsion mode, energy-saving adjustment mode, or wave-resistant mode) by monitoring the speed and wave height of the unmanned boat in real time and combining with preset thresholds to judge the triggering conditions. This module ensures that the unmanned boat can dynamically adjust the propulsion mode under different sea conditions, optimize the propulsion efficiency, reduce energy consumption, and ensure a stable heading; through the periodic feedback and state perception mechanism, the propulsion performance prediction module can accurately predict the future navigation state and make adjustments in advance; finally, the propulsion control strategy construction module 304 synthesizes the output results of the foregoing modules, and based on the real-time speed and sea condition data of the unmanned boat, combines the efficiency function and the thrust model to estimate the propulsion force; this module judges and executes operations such as propulsion mode switching, power adjustment, and angle adjustment, avoiding frequent switching caused by environmental disturbances. In addition, module 304 also supports the access of external control signals and the dynamic update of algorithm parameters to ensure that the propulsion strategy can stably respond to environmental changes and further improve the robustness and intelligent control ability of the system.

[0103] The controllable navigation trajectory data output by the trajectory planning module 100 provides the target path and direction for the propulsion force optimization module 300. The propulsion force optimization module 300 needs to dynamically adjust the output of the propulsion system according to these path data to ensure that the unmanned boat can accurately and smoothly travel along the planned trajectory. During actual operation, the trajectory planning module 100 generates a navigation trajectory corrected based on ocean disturbances, and the propulsion force optimization module 300 needs to adjust control parameters such as the propulsion force output, power control, and the angle of the thruster in real time according to the requirements of this trajectory.

[0104] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0105] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0106] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0107] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. Unmanned boat navigation decision-making method, characterized in that including: Obtaining feasible trajectory data corrected by wave interference based on a six-degree-of-freedom dynamics model; Calculating the dynamic response characteristics of the unmanned boat based on the mass inertia matrix and disturbing force parameters; Optimizing the propulsion efficiency factor under different speeds and wave conditions through the propulsion force function and performing real-time adaptive adjustment of the thruster control parameters; Obtaining feasible trajectory data corrected by wave interference includes calculating the position, attitude, and rate of change of the unmanned boat in three-dimensional space, introducing the influence of waves, including wave amplitude, wave frequency, main frequency, and roll angle, as interference inputs for dynamic correction, and performing trajectory stability calculation through a dynamic correction module to output a controllable trajectory data set under the influence of waves; Optimizing the propulsion efficiency factor under different speeds and wave conditions includes dynamically correcting the propulsion efficiency factor, constructing a correction term based on the deviation between the wave height and the reference wave height, adjusting the propulsion efficiency factor, and when the wave height exceeds the reference value, the adjustment term changes exponentially in a negative correlation, reflecting the trend of the thruster efficiency decreasing under high sea conditions, and predicting the propulsion performance based on the corrected propulsion efficiency and speed deviation to adapt to different sea conditions and speed conditions.

2. The unmanned boat navigation decision-making method according to claim 1, characterized in that: The obtaining of feasible trajectory data corrected by wave interference includes Establishing a six-degree-of-freedom dynamics model, modeling the position, attitude, and rate of change of the unmanned boat in three-dimensional space, and based on the Coriolis force, nonlinear resistance, added mass, gravity buoyancy, by constructing a wave disturbance term, mapping the current wave amplitude, main frequency, frequency, and roll response factors of the unmanned boat as interference inputs for dynamic correction, and performing calculation on the trajectory stability under the influence of waves to output a trajectory data set.

3. The unmanned boat navigation decision-making method according to claim 1 or 2, characterized in that: The calculating of the dynamic response characteristics of the unmanned boat includes Calculating the true inertial response of the unmanned boat after being subjected to external forces in the fluid environment by combining the structural mass of the boat body and the added mass generated by the fluid attachment effect in six directions in a matrix; Calculating the dynamic terms of the Coriolis inertial force, linear fluid resistance term, and hydrodynamic buoyancy offset received by the unmanned boat according to the current position, speed, and attitude change state of the unmanned boat; By comparing the balance relationship between the control input and the system response, constructing a state solver that simulates the dynamic behavior of the unmanned boat, and the state solver outputs the acceleration, attitude change amount, and energy distribution information at each time node based on the input change under the trajectory point prediction.

4. The unmanned boat navigation decision-making method according to claim 1, characterized in that: The optimizing of the propulsion efficiency factor under different speeds and wave conditions includes Setting the optimal speed of the propulsion system operation as the efficiency reference value, and taking the deviation between the current speed of the unmanned boat and the optimal speed value as the main control parameter for the change of the propulsion efficiency; Establishing an exponential efficiency decay curve to simulate the non-linear characteristic of the propulsion efficiency changing with speed, and the greater the speed deviation, the lower the efficiency; Based on the current speed of the unmanned boat, the structural parameters of the thruster, and the medium density, by correlating the force-receiving area of the thruster with the real-time propulsion efficiency parameter, generating a control result for dynamically outputting the propulsion power.

5. The unmanned boat navigation decision-making method according to claim 4, characterized in that: The optimizing of the propulsion efficiency factor under different speeds and wave conditions further includes Dynamically correcting the propulsion efficiency factor; Build a sea condition disturbance feature model and calculate the sea condition adjustment term of the propulsion efficiency according to the deviation between the current wave height and the relative design reference wave height; When the wave height exceeds the reference value, the adjustment term will show a negatively correlated exponential change, reflecting the increasing trend of the propeller inefficiency under high sea conditions; Based on the corrected propulsion efficiency, substitute it into the propulsion force function and the attenuation effect caused by the speed offset to model the output capacity of the propulsion system in a multi-dimensional disturbance environment; Predict the propulsion performance through the speed and sea conditions.

6. The unmanned boat navigation decision-making method according to claim 5, characterized in that: The propulsion performance prediction includes, Jointly judge the propulsion efficiency factor, the real-time speed and the wave height signal, and trigger the propulsion mode switching logic through comparison with the preset interval threshold; When the speed is close to the optimal range and in a stable sea condition, the system enters the standard propulsion mode to maximize the propulsion efficiency output; If the speed deviates from the optimal range, the system will reduce the output power according to the deviation degree and make adaptive adjustments based on the goals of energy conservation and stable heading; In the case where the wave height is higher than the reference, the system switches to the anti-wave mode to suppress the thrust fluctuation caused by the wave by adjusting the propeller angle or the attitude control method; Build a propulsion control strategy based on the periodic feedback and state perception mechanism, dynamically judge the propulsion mode interval to which the current state belongs, and adjust the propulsion parameters.

7. The unmanned boat navigation decision-making method according to claim 1 or 6, characterized in that: The propulsion control strategy includes, Collect the real-time speed and sea condition data of the unmanned boat as input variables, and perform a complete propulsion force estimation in combination with the preset efficiency function and thrust model; Judge the state interval matching of the calculation result to identify whether to trigger the propulsion mode switching, the propeller power adjustment or the angle adjustment operation; If the system judges that the current state is in the boundary area, it is necessary to perform a trend extension prediction to prevent frequent switching caused by fluctuations; The propulsion control module continuously outputs control commands in a closed-loop manner and supports the access of external control signals and the dynamic update of algorithm parameters to determine the stable response of the propulsion strategy to environmental changes.

8. Unmanned boat navigation decision-making system, characterized in that: Including a trajectory planning module (100), a dynamic response calculation module (200), and a propulsion force optimization module (300); The trajectory planning module (100) is used to calculate the motion trajectory of the unmanned boat under wave interference through a six-degree-of-freedom dynamics model, combining the Coriolis force, non-linear resistance, added mass, and gravity buoyancy factors, and generate a stable and controllable navigation trajectory dataset; The dynamic response calculation module (200) is used to calculate the dynamic response of the unmanned boat in the fluid environment by combining the mass and added mass of the hull, calculate the Coriolis inertial force, fluid resistance, and hydrodynamic buoyancy offset factors based on the current state of the unmanned boat, construct a state solver, and output the acceleration, attitude change, and energy distribution information at each time node to simulate the dynamic behavior of the unmanned boat in real time; The propulsion force optimization module (300) includes a control result generation module (301), a dynamic correction module (302), a propulsion performance prediction module (303), and a propulsion control strategy construction module (304). The control result generation module (301) is used to set the optimal speed and calculate the speed deviation, optimize the propulsion efficiency factor, simulate the non-linear change of the propulsion efficiency by establishing an efficiency decay curve, and dynamically adjust the propulsion force output in combination with the thruster structure parameters and environmental conditions. The dynamic correction module (302) is used to optimize the propulsion force function and dynamically correct the propulsion efficiency factor, and adjust the propulsion efficiency in real time in combination with the sea condition disturbance characteristic model. The propulsion performance prediction module (303) is used to dynamically switch the propulsion mode, including the standard propulsion mode, the energy-saving adjustment mode, and the wave-resistant mode, by real-time monitoring the speed and wave height and judging the trigger condition in combination with a preset threshold, optimize the propulsion efficiency, save energy, and stabilize the course, and adopt a periodic feedback and state perception mechanism to adjust the thruster parameters in real time to achieve adaptive control. The propulsion control strategy construction module (304) is used to estimate the propulsion force based on the real-time speed and sea condition data of the unmanned boat, judge and execute the operations of propulsion mode switching, power adjustment, and angle adjustment, prevent frequent switching through trend prediction, support the access of external control signals and the dynamic update of algorithm parameters, and determine the propulsion strategy to stably respond to environmental changes.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the unmanned boat navigation decision method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the unmanned boat navigation decision method according to any one of claims 1 to 7 are implemented.

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