Automatic ship driving control method and system based on artificial intelligence

Abstract

The invention discloses an automatic ship driving control method and system based on artificial intelligence, and relates to the technical field of ship control, and the driving control steps are as follows: S1, obtaining a current two-dimensional coordinate position of a ship based on a satellite map, and obtaining wind direction data of the current two-dimensional coordinate position in a ship route based on a weather bureau, obtaining a ship route state construction data set; and S2, establishing a ship running model, predicting position and attitude changes of the ship in a future route in combination with real-time wind direction data, calculating a ship displacement control quantity in advance through a control algorithm, and counteracting offset generated by wind direction factors. According to the invention, the ship can timely and accurately return to a correct route, the navigation accuracy of the ship in a complex environment is greatly improved, fine adjustment of ship navigation parameters is realized, unnecessary energy waste is avoided, deviation from a target route is avoided, and the risks of collision and stranding are increased.

Description

Technical Field

[0001] The present invention relates to the technical field of ship control, and in particular to an automatic ship driving control method and system based on artificial intelligence. Background Art

[0002] At present, with the continuous development of the global shipping industry, due to its large transportation capacity and low cost, ship transportation occupies a crucial position in the fields of cargo transportation and people's livelihood trade, undertaking 95% of the global crude oil transportation and 99% of the iron ore transportation volume. However, the traditional ship driving mode faces severe challenges in terms of safety, efficiency, and cost. To effectively solve the above problems, ship autonomous driving technology has emerged and become a research hotspot. Early ship autonomous driving mainly relied on traditional control methods such as PID control and fuzzy control. PID control calculates the output signal of the control rudder by the deviation between the ship's target heading and the actual heading and related change amounts to maintain the ship's heading, and combines the consideration of the wind direction change during the ship's travel to adjust and change the position during the ship's travel. However, due to the large inertia of the ship, when the autonomous driving system adjusts the control strategy according to the wind direction change, the ship cannot respond immediately. Especially in strong wind weather, after the wind direction suddenly changes, the ship may take a long time to adjust to the new heading. During this period, the ship may deviate from the target route, increasing the risks of collision, stranding, etc., and also increasing the power energy consumption of the ship to a certain extent. In view of this, we propose an automatic ship driving control method and system based on artificial intelligence. Summary of the Invention

[0003] To solve the above technical problems, an automatic ship driving control method and system based on artificial intelligence are provided. This technical solution solves the problems that the ship cannot respond immediately, the ship will deviate from the target route, and the risks of collision and stranding are increased.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: an automatic ship driving control method based on artificial intelligence, and the driving control steps are as follows: S1. Obtain the current two-dimensional coordinate position of the ship based on the satellite map, obtain the wind direction data at the current two-dimensional coordinate position in the route based on the meteorological bureau, and obtain the ship route status to construct a data set; S2. Establish a ship travel model, combine the real-time wind direction data to predict the position and attitude changes of the ship in the future route, and through the control algorithm, calculate the ship displacement control amount in advance to offset the deviation caused by the wind direction factor; S3. When encountering a sudden strong wind causing deviation from the route, generate a route correction path based on the A* algorithm; S4. Based on the current inertia state, steering situation and route safety of the ship, screen out the best correction path from the generated correction routes and drive into the route; S5. Based on the optimized control algorithm, taking the ship displacement control quantity, energy consumption, and structural stress as optimization objectives, establish the objective function and constraint conditions. Based on the particle swarm optimization algorithm, when the ship corrects its course, find a set of optimal control parameters and control the ship's navigation based on the optimal control parameters to reduce energy consumption.

[0005] Preferably, the steps for establishing the ship navigation model in step S2 are as follows: Collect the basic parameters of the ship itself and the wind direction data on the route. Based on Newton's laws of motion and fluid mechanics, construct the kinematic and dynamic equations of the ship. In the form of a state-space model, establish a mathematical relationship between the ship's motion state variables and input variables to form the framework of the ship navigation model. Input the real-time wind direction data into the model, calculate the force and moment of the wind on the ship, and combine with the marine environmental factors to establish the coupling of the hydrodynamic model and the ship motion model. Based on historical navigation data and test data, verify the established model, adjust the model parameters, and complete the construction of the ship navigation model. Preferably, the steps for calculating the ship displacement control quantity in advance through the control algorithm in step S2 are as follows: Based on the obtained ship navigation state and wind direction data, after filtering, input them into the ship navigation model. Combine the wind direction data to predict the future position and attitude of the ship, and determine the offset caused by the wind direction. Use the control algorithm to calculate the displacement control quantity, input the displacement control quantity into the ship control system to control the ship displacement and offset the offset. Monitor the error between the actual motion state and the ideal state, and dynamically adjust the algorithm parameters to form a closed-loop control.

[0006] Preferably, the steps for generating the route correction path by the A* algorithm in step S3 are as follows: Collect the real-time state data of the ship, and the state data includes position and speed information. Obtain the surrounding wind direction, sea condition environment data, and geographical data, perform grid processing on the navigation area on the electronic nautical chart, and determine the starting and target nodes. Set the heuristic function that integrates environmental factors, set the parameters of the A* algorithm, use the A* algorithm to search for the path, start from the starting node, expand the nodes according to the total cost, avoid the areas with poor sea conditions, and generate the correction path by backtracking after finding the target node.

[0007] Preferably, the grid processing of the route area determines the side length of the grid on the electronic nautical chart based on the ship size and performs grid processing according to the side length. After gridification, determine the starting node and the target node. The starting node is the grid center corresponding to the actual position of the ship after deviating from the current route, and the target node is selected according to the original route plan as the grid center corresponding to the next key position point that the ship should reach without being affected. The steps of the A* algorithm for searching the path are as follows: Search for the path from the starting node, calculate the total cost of the adjacent passable nodes of the starting node, select the node with the minimum total cost as the next node to be expanded. When expanding the node, preferentially search in the area with good sea conditions, expand sequentially, continuously update the cost of the node and the information of the parent node, construct the search tree. During the search process, judge in real time whether the target node is reached. If the target node is reached, then by backtracking the information of the parent node, starting from the target node, gradually return to the starting node along the parent node pointer to generate a preliminary route correction path from the current position of the ship to the target position. The path includes several different options.

[0008] Preferably, the specific steps for screening out the best correction path in step S4 are as follows: Collect the real-time data of the ship and preprocess the collected data. Based on the preprocessed data, calculate the quantization values of the evaluation indicators, including: The inertial state indicator is obtained by calculating the matching degree between the correction path and the current inertial state of the ship. The steering situation indicator is obtained by calculating whether the requirements of the correction path for the ship's steering ability are within the operable range. If the steering angle exceeds the maximum steering ability of the ship, the score is low; if the steering requirement conforms to the steering performance of the ship and the ship can complete the steering operation smoothly, the score is high. The route safety indicator is obtained by evaluating the safety of the correction path during navigation, considering factors such as the distance between the path and obstacles, the probability of passing through dangerous areas, and the influence of meteorological conditions on the path.

[0009] Preferably, the specific steps for screening out the best correction path further include: Determine the weight values corresponding to the inertial state indicator, the steering situation indicator, and the route safety indicator based on the analytic hierarchy process. For each generated correction path, calculate the scores of each evaluation indicator on the path respectively. Adopt a scoring system and score based on the degree of compliance between the path and the indicator. Calculate the comprehensive score value of each indicator in the current correction path through the weighted average formula to obtain the quantitative evaluation results of each path. Compare the comprehensive score values of all correction paths and select the path with the highest score as the best correction path. If there are multiple paths with the same score, then further compare the scores of the paths on the key indicators, and preferentially select the path with a higher score on the key indicators to determine the best correction path. Send the optimal correction path information to the ship's navigation and control system to guide the ship to safely return to the predetermined route.

[0010] Preferably, the objective function established in step S5 includes: a displacement control objective, an energy consumption objective, and a structural stress objective; The displacement control objective quantifies the difference between the actual position and heading of the ship and the target value. The smaller the difference, the closer it is to the ideal route, and this difference is quantified as the displacement control cost. The energy consumption objective calculates the total energy consumption cost during navigation by establishing an energy consumption calculation model based on the characteristics of the ship's power system and considering the influence of the magnitude of the propulsion force on energy consumption. The structural stress objective estimates the stress borne by the ship's structure by analyzing the changes in the rudder angle and propulsion force. The higher the stress level, the greater the corresponding stress cost. Add the three objectives according to proportional weights to form an objective function for comprehensively measuring the operation effect of the ship.

[0011] Preferably, the step of finding the optimal control parameters in step S5 is as follows: Input the control parameters for adjusting the ship's rudder angle and main engine speed into the established objective function. Evaluate the fitness of the particles by simulating and calculating the energy consumption, introduce an inertia weight factor, find the ship's driving control parameters corresponding to the optimal particle position, and control the ship's driving based on the found ship's driving control parameters during the ship's driving process.

[0012] An automatic ship driving control system based on artificial intelligence, the driving control system includes: A data acquisition module configured to acquire the ship's driving state and wind direction data on the route; A wind direction cancellation control module configured to calculate the ship's displacement control amount in advance to cancel the offset caused by the wind direction factor; A route correction module configured to generate multiple route correction paths; A screening module configured to screen out the optimal correction path; A control optimization module configured to obtain the optimal ship driving parameters for driving.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: By combining the ship's travel model to predict future position and attitude changes and calculating the displacement control amount in advance, the present invention can effectively offset the deviation caused by the wind direction, making the ship's navigation trajectory closer to the predetermined route. Even when the ship deviates from the route due to a sudden strong wind, the A* algorithm is used to quickly generate a correction path, and then the best correction route is determined through screening to ensure that the ship can return to the correct route in a timely and accurate manner. This greatly improves the navigation accuracy of the ship in a complex environment, realizes the refined adjustment of the ship's navigation parameters, avoids unnecessary energy waste, avoids deviating from the target route, and reduces the risks of collision and grounding. Description of the Drawings

[0014] Figure 1 This is a flowchart of the driving control steps of the present invention.

[0015] Figure 2 This is a framework diagram of the driving control system of the present invention. Detailed Embodiments

[0016] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0017] Referring to Figure 1 As shown, an automatic ship driving control method based on artificial intelligence, the driving control steps are as follows: S1. Obtain the current two-dimensional coordinate position of the ship based on the satellite map, obtain the wind direction data at the current two-dimensional coordinate position in the route based on the meteorological bureau, and obtain the ship route state to construct a data set; S2. Establish a ship travel model, combine real-time wind direction data to predict the position and attitude changes of the ship in the future route, and calculate the ship displacement control amount in advance through a control algorithm to offset the deviation caused by the wind direction factor; S3. When a sudden strong wind causes deviation from the route, generate a route correction path based on the A* algorithm; S4. Based on the current inertial state, steering situation and route safety of the ship, screen out the best correction path from the generated correction routes and drive into the route; S5. Based on the optimization control algorithm, take the ship displacement control amount, energy consumption and structural stress as optimization objectives, establish an objective function and constraint conditions, and based on the particle swarm optimization algorithm, when the ship corrects and drives into the route, find a set of optimal control parameters and control the ship's driving based on the optimal control parameters to reduce energy consumption.

[0018] By combining satellite maps and meteorological data, this application can accurately obtain the current two-dimensional coordinate position of the ship and real-time wind direction data, and construct a dataset, which enables the ship to have a clear and accurate understanding of its own position and the surrounding environment, provides a solid foundation for subsequent driving control, and helps to plan and respond to various situations in advance; By establishing a ship motion model and combining real-time wind direction data, it is possible to predict the position and attitude changes of the ship in the future route, and calculate the ship displacement control amount in advance through a control algorithm to offset the wind direction deviation. This active control method can effectively reduce the impact of wind direction on ship navigation, improve the accuracy and stability of navigation, and reduce navigation errors caused by wind direction uncertainty; When encountering sudden strong winds that cause deviation from the route, using the A* algorithm to generate a route correction path can quickly find the best path from the current position back to the original route or adjusted to a new safe route, providing an effective response strategy for the ship in an emergency and enhancing the ship's ability to cope with sudden situations; screening the generated correction route based on the ship's current inertial state, steering situation and route safety can ensure that the selected best correction path can not only make the ship return to the route, but also fully consider the actual operating state and safety factors of the ship, avoiding dangers or damages to the ship caused by unreasonable correction paths; taking the ship displacement control amount, energy consumption and structural stress as optimization objectives, establishing an objective function and constraint conditions, and finding the optimal control parameters based on the particle swarm optimization algorithm. This method can effectively reduce energy consumption, lower operating costs, protect the ship structure, extend the ship's service life, and achieve multi-objective optimization of navigation control during the process of controlling the ship's travel.

[0019] The steps for establishing the ship motion model in step S2 are as follows: Collect the basic parameters of the ship itself and the wind direction data in the route; Based on Newton's laws of motion and fluid mechanics, construct the kinematic and dynamic equations of the ship, adopt the form of a state space model, establish a mathematical relationship between the ship's motion state variables and input variables, and form the framework of the ship motion model; Input the real-time wind direction data into the model, calculate the force and moment of the wind on the ship, and combine with marine environmental factors to establish the coupling of the hydrodynamic model and the ship motion model; Based on historical navigation data and test data, verify the established model, adjust the model parameters, and complete the construction of the ship motion model.

[0020] The model constructed by this application through Newton's laws of motion and fluid mechanics can accurately depict the motion state of a ship in a complex marine environment. After coupling real-time wind direction, sea waves, ocean currents and other data into the model, the ship can predict in advance the impact of the harsh environment on its own motion. For example, before strong winds or huge waves come, the crew can, based on the calculation results of the model, plan a safer route in advance, adjust the speed and heading, effectively avoid dangerous areas, and reduce the probability of accidents such as collisions and capsizes, providing a solid guarantee for the safety of the ship and personnel. The ship driving model framework established based on the state space model can clearly present the relationship between the ship motion state variables and the input variables, and different navigation schemes can be simulated using the model to analyze the most fuel-efficient and shortest-time-consuming routes and operation strategies.

[0021] In step S2, the steps of calculating the ship displacement control amount in advance through the control algorithm are as follows: Based on the obtained ship navigation state and wind direction data, after filtering processing, it is input into the ship driving model; Combined with the wind direction data, predict the future position and attitude of the ship, and determine the offset caused by the wind direction; Use the control algorithm to calculate the displacement control amount, input the displacement control amount into the ship control system, and control the ship displacement to offset the offset; Monitor the error between the actual motion state and the ideal state, dynamically adjust the algorithm parameters, and form a closed-loop control.

[0022] The specific calculation steps are as follows: Definition of state vector: ; Where is the ship position coordinate, is the ship heading angle, is the ship longitudinal and lateral speeds, is the ship turning angular velocity; The wind direction data vector is: ; Where is the wind speed magnitude, is the wind direction angle, is the wind angle; The prediction formula based on the state space model is: ; Where is the system matrix, is the control input matrix, is the disturbance input matrix, contains the disturbance vector affected by the wind direction; The formula for calculating the offset caused by the wind direction is: ; Where is the wind direction influence gain matrix, is the wind direction influence function, including: ; The formula for predicting the future position and attitude is: ; where is the prediction time domain length; The formula for calculating the displacement control amount is: ; where is the rudder angle control amount, is the propeller thrust control amount, is the calculated displacement control amount. By inputting the position control amount into the ship control system, the accurate control of the ship's displacement is used to offset the deviation caused by the wind direction in real time.

[0023] The steps of generating the route correction path by the A* algorithm in step S3 are: Collect the real-time state data of the ship, and the state data includes position and speed information; Obtain the surrounding wind direction, sea condition environment data and geographical data, perform grid processing on the navigation area on the electronic nautical chart, and determine the starting and target nodes; Set the heuristic function that combines environmental factors, set the parameters of the A* algorithm, use the A* algorithm to search for the path, start from the starting node, expand the nodes according to the total cost, avoid the areas with poor sea conditions, and generate the correction path by backtracking after finding the target node.

[0024] This application can dynamically generate a correction path according to the current actual situation by collecting the real-time state data of the ship and the surrounding wind direction, sea condition environment data and geographical data. Whether the ship encounters sudden bad sea conditions or the original route is no longer optimal due to the change of wind direction, the A* algorithm can adjust in time according to the latest information, so that the ship can always sail along the path most suitable for the current environment, improving the ship's ability to cope with complex and changeable marine environments. The A* algorithm itself has high efficiency. After performing grid processing on the navigation area on the electronic nautical chart, starting from the starting node and expanding the nodes according to the total cost, it can quickly search for the target node and generate the correction path. This search method avoids blind search and greatly reduces the search space and time.

[0025] The grid processing of the route area determines the side length of the grid on the electronic nautical chart based on the ship size and performs grid processing according to the side length; After the grid processing is completed, the starting node and the target node are determined. The starting node is the grid center corresponding to the actual position of the ship after deviating from the current route, and the target node is selected according to the original route plan as the grid center corresponding to the next key position point that the ship should reach without being affected; Among them, the A* algorithm is specifically expressed as: Divide the navigation area into grids. The position of each grid is a node. The current position of the ship is the starting node, and the destination position is the target node. All grid nodes form a set. To measure the cost from the starting node to each node, we define three costs: the actual cost from the starting node to a certain node is called the actual cost; the estimated cost from a certain node to the target node is called the heuristic estimated cost; the sum of the actual cost and the heuristic estimated cost is the total cost. Each node also has a sea condition score, and the larger the value, the better the sea condition in that area. At the same time, a sea condition weight coefficient is set to reflect the influence degree of the sea condition on the path planning; When calculating the node cost, first calculate the basic heuristic cost without considering the sea condition, which is represented by the straight-line distance from the node to the target node. Incorporate the sea condition factor. The better the sea condition, the lower the estimated cost from this node to the target node. Add the actual cost from the starting node to this node and the estimated cost considering the sea condition to obtain the total cost of this node; After starting to search for the path, first establish two lists. An open list stores the nodes waiting to be expanded, and a closed list stores the nodes that have been expanded. Put the starting node into the open list. At this time, set the actual cost of the starting node to 0, and calculate its basic heuristic cost and the total cost considering the sea condition; Find the node with the minimum total cost from the open list as the current node to be expanded. Move it from the open list to the closed list, and then find the adjacent and passable nodes of this node. Calculate the movement cost from the current node to the adjacent node. The better the sea condition, the lower the movement cost. If the newly calculated actual cost of the adjacent node is less than the previously recorded actual cost of this node, update the actual cost, total cost of this node, and set the current node as its parent node; Continuously repeat the above operations of selecting and expanding nodes. During this process, real-time judge whether the currently expanded node is the target node. If so, it means that the path has been found; After finding the target node, trace back to the starting node step by step through the parent node information recorded by the target node, so as to generate a preliminary route correction path from the current position of the ship to the target position; We find the nodes in the open list and the closed list whose total costs are not much different from the total cost of the target node. Using these nodes as the end points, trace back in the same way to generate different paths, and these paths constitute several route correction path options.

[0026] The specific steps for screening the best correction path in step S4 are as follows: Collect the real-time data of the ship and preprocess the collected data; Based on the preprocessed data, calculate the quantization values of the evaluation indexes, including: The inertial state index is obtained by calculating the matching degree between the corrected path and the current inertial state of the ship; The steering situation index is obtained by calculating whether the requirements of the corrected path for the ship's steering ability are within the operable range; if the steering angle exceeds the maximum steering ability of the ship, the score is low; if the steering requirements conform to the steering performance of the ship and the ship can complete the steering operation smoothly, the score is high; The route safety index is obtained by evaluating the safety of the corrected path during navigation, considering factors such as the distance between the path and obstacles, the probability of passing through dangerous areas, and the influence of meteorological conditions on the path.

[0027] This application calculates the route safety index, comprehensively considers factors such as the distance between the corrected path and obstacles, the probability of passing through dangerous areas, and meteorological conditions, can effectively avoid potential dangers, reduce the collision risk, ensure the safety of the ship and personnel, improve the reliability and stability of navigation, consider the inertial state index, make the corrected path match the current inertial state of the ship, can reduce unnecessary acceleration and deceleration and steering, reduce energy consumption, improve navigation efficiency, save time and fuel costs, evaluate the steering situation index, ensure that the requirements of the corrected path for the ship's steering ability are within the operable range, can enable the ship to turn smoothly, and avoid route deviation or navigation delay caused by difficult steering.

[0028] The specific steps for screening out the best corrected path further include: Determine the weight values corresponding to the inertial state index, steering situation index, and route safety index based on the analytic hierarchy process; For each generated corrected path, calculate the scores of each evaluation index on the path respectively, adopt a scoring system, score based on the degree of conformity between the path and the index, and calculate the comprehensive score value of each index in the current corrected path through the weighted average formula to obtain the quantitative evaluation results of each path; Compare the comprehensive score values of all corrected paths, and select the path with the highest score as the best corrected path; If there are multiple paths with the same score, then further compare the scores of the paths on the key indicators, and preferentially select the path with higher scores on the key indicators to determine the best corrected path; Send the information of the best corrected path to the navigation and control system of the ship to guide the ship to safely return to the predetermined route.

[0029] This application determines the weight values of each evaluation index through the analytic hierarchy process, can more scientifically and accurately reflect the relative importance of different indicators in path evaluation. For each corrected path, calculate the scores of each evaluation index and obtain the comprehensive score value through weighted average. This quantitative evaluation method can comprehensively and objectively measure the overall quality of the path, avoid the one-sidedness of single-index evaluation, and make the evaluation results more accurate and reliable. When there are multiple paths with the same score, further compare the scores of the paths on key indicators, and preferentially select the path with a higher score on key indicators. This ensures that when multiple choices seem similar, decisions can be made based on factors that are more critical to navigation, further improving the rationality and optimization level of the screening results, and making the selected optimal path better meet the actual navigation needs.

[0030] The objective function established in step S5 includes: displacement control objective, energy consumption objective, and structural stress objective; The displacement control objective quantifies the difference between the actual position and heading of the ship and the target values. The smaller the difference, the closer it is to the ideal route, and this difference is quantified as the displacement control cost. The energy consumption objective calculates the total energy consumption cost during navigation by establishing an energy consumption calculation model based on the characteristics of the ship's power system and considering the influence of the propulsive force magnitude on energy consumption. The structural stress objective estimates the stress borne by the ship's structure by analyzing the changes in rudder angle and propulsive force. The higher the stress level, the greater the corresponding stress cost. Add the three objectives according to proportional weights to form an objective function for comprehensively measuring the operation effect of the ship.

[0031] The specific steps for constructing the objective function are as follows: Displacement control objective ; Where is the control parameter vector, are respectively the actual abscissa position of the ship at time, the actual ordinate position of the ship at time, and the actual heading angle of the ship at time, , are respectively the abscissa position of the ship's target at time, the ordinate position of the ship's target at time, and the heading angle of the ship's target at time, are respectively the abscissa position error weight, the ordinate position error weight, and the heading angle error weight; Energy consumption objective ; Where is the water density, is the acceleration due to gravity, is the drag coefficient, related to the ship speed, is the propeller coefficient; Structural stress objective ; Where is the propulsive force, is the propeller diameter, is the propulsion coefficient, related to the advance coefficient and is the stress weight coefficient; Calculate the comprehensive objective function ; where are the objective weight coefficients of each item, satisfying , is the objective function obtained by comprehensive calculation.

[0032] The steps to find the optimal control parameters in step S5 are as follows: During the ship's navigation, precise and efficient control is crucial to ensure the ship's safe and economical arrival at the destination. The rudder angle and the main engine speed of the ship are two key control parameters, and their reasonable adjustment can significantly affect the ship's navigation trajectory, speed, and energy consumption performance in many aspects; Establish an objective function that comprehensively considers various factors. This objective function covers multiple dimensions such as displacement control objectives, energy consumption objectives, and structural stress objectives. The displacement control objective quantifies the difference between the actual position and heading of the ship and the target value as the displacement control cost by comparing them, so as to ensure that the ship sails along the ideal route as much as possible; the energy consumption objective establishes an energy consumption calculation model based on the characteristics of the ship's power system, fully considering the influence of factors such as the magnitude of the propulsion force on energy consumption, and accurately calculates the total energy consumption cost during navigation, so as to achieve energy conservation and consumption reduction; the structural stress objective estimates the stress borne by the ship's structure by analyzing the changes in the rudder angle and propulsion force. The higher the stress level, the greater the corresponding stress cost, thereby ensuring the safety of the ship's structure; Input the two control parameters of the ship's rudder angle and main engine speed into the above-established objective function. With the help of advanced simulation technology, simulate the ship's navigation state under different combinations of control parameters. During the simulation process, focus on calculating the ship's energy consumption situation, which is used as an important basis for evaluating the fitness of particles. Here, introduce the concept in the particle swarm optimization algorithm, and regard different combinations of control parameters as different positions of particles. Each particle has its corresponding fitness. The higher the fitness, the better the performance of the control parameter combination corresponding to the particle position in meeting the objective function; An inertia weight factor is introduced. The inertia weight factor can balance the global search and local search capabilities of particles. In the initial stage of the search, a larger inertia weight factor makes the particles more inclined to global search, enabling them to explore the possible solution space in a wider range and avoiding the algorithm from converging to the local optimal solution prematurely. As the search progresses, the inertia weight factor gradually decreases, enhancing the local search ability of the particles, enabling them to conduct more refined searches near the current optimal solution and increasing the probability of finding the global optimal solution. By continuously iteratively updating the positions and velocities of the particles and continuously calculating the fitness of the particles, the optimal particle position is finally found. The control parameters such as the ship's rudder angle and main engine speed corresponding to this optimal particle position are the parameter combinations that can make the ship operate optimally under the current objective function and constraint conditions.

[0033] An automatic ship driving control system based on artificial intelligence, the driving control system includes: A data acquisition module configured to acquire the ship's driving state and wind direction data in the route. A wind direction cancellation control module configured to calculate in advance the ship displacement control amount to cancel the offset caused by the wind direction factor. A route correction module configured to generate multiple route correction paths. A screening module configured to screen out the best correction path. A control optimization module configured to obtain the best ship driving parameters for driving.

[0034] By combining the ship driving model to predict future position and attitude changes and calculating in advance the displacement control amount, it can effectively cancel the offset caused by the wind direction, making the ship's navigation trajectory more conform to the predetermined route. Even when encountering a sudden strong wind and deviating from the route, through the A* algorithm, a correction path is quickly generated, and then the best correction route is determined through screening, ensuring that the ship can return to the correct route in a timely and accurate manner, greatly improving the navigation accuracy of the ship in complex environments, and realizing the refined adjustment of the ship's navigation parameters, avoiding unnecessary energy waste, avoiding deviating from the target route, and increasing the risks of collision and grounding.

[0035] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. An automatic ship driving control method based on artificial intelligence, characterized in that, The driving control steps are as follows: S1. Obtain the current two-dimensional coordinate position of the ship based on the satellite map, obtain the wind direction data at the current two-dimensional coordinate position in the shipping lane based on the meteorological bureau, and obtain the shipping lane status to construct a data set; S2. Establish a ship motion model, combine the real-time wind direction data to predict the position and attitude changes of the ship in the future shipping lane, and through the control algorithm, calculate the ship displacement control amount in advance to offset the deviation caused by the wind direction factor; S3. When encountering a sudden strong wind that causes deviation from the shipping lane, generate a shipping lane correction path based on the A* algorithm; S4. Based on the current inertial state, steering condition and shipping lane safety of the ship, screen out the best correction path from the generated correction routes and drive into the shipping lane; S5. Based on the optimal control algorithm, take the ship displacement control amount, energy consumption and structural stress as the optimization objectives, establish the objective function and constraint conditions, and based on the particle swarm optimization algorithm, when the ship corrects and drives into the shipping lane, find a set of optimal control parameters and control the ship's driving based on the optimal control parameters to reduce energy consumption.

2. The automatic ship driving control method based on artificial intelligence according to claim 1, wherein The steps for establishing the ship motion model in step S2 are as follows: Collect the basic parameters of the ship itself and the wind direction data in the shipping lane; Based on Newton's laws of motion and fluid mechanics, construct the kinematic and dynamic equations of the ship, adopt the form of the state space model, establish a mathematical relationship between the motion state variables and input variables of the ship, and form the framework of the ship motion model; Input the real-time wind direction data into the model, calculate the force and moment of the wind on the ship, and combine with the marine environmental factors to establish the coupling of the hydrodynamic model and the ship motion model; Based on the historical navigation data and test data, verify the established model, adjust the model parameters, and complete the construction of the ship motion model.

3. An automatic ship driving control method based on artificial intelligence according to claim 1, characterized in that, The steps for calculating the ship displacement control amount in advance through the control algorithm in step S2 are as follows: Based on the obtained ship navigation state and wind direction data, after filtering, input them into the ship motion model; Combine the wind direction data to predict the future position and attitude of the ship, and determine the deviation amount caused by the wind direction; Use the control algorithm to calculate the displacement control amount, input the displacement control amount into the ship control system, control the ship displacement, and offset the deviation; Monitor the error between the actual motion state and the ideal state, dynamically adjust the algorithm parameters, and form a closed-loop control.

4. The automatic ship driving control method based on artificial intelligence according to claim 1, characterized in that, The steps for generating the shipping lane correction path by the A* algorithm in step S3 are as follows: Collect the real-time state data of the ship, and the state data includes position and speed information; Obtain the surrounding wind direction, sea condition environment data and geographical data, perform grid processing on the navigation area on the electronic nautical chart, and determine the starting and target nodes; Set the heuristic function that combines environmental factors, set the A* algorithm parameters, use the A* algorithm to search for the path, start from the starting node, expand the nodes according to the total cost, avoid the areas with poor sea conditions, and generate the correction path by backtracking after finding the target node.

5. The automatic ship driving control method based on artificial intelligence according to claim 4, wherein, The grid processing of the shipping lane area determines the side length of the grid on the electronic nautical chart based on the ship size and performs grid processing according to the side length; After the grid processing is completed, determine the starting node and the target node. The starting node is the grid center corresponding to the actual position of the ship after deviating from the current shipping lane, and the target node is selected according to the original shipping lane plan as the grid center corresponding to the next key position point that the ship should reach without being affected. The steps of the A* algorithm for searching a path are as follows: Search for a path from the starting node, calculate the total cost of the adjacent passable nodes of the starting node, select the node with the minimum total cost as the next node to be expanded. When expanding nodes, preferentially search in areas with good sea conditions, expand sequentially, continuously update the cost of the nodes and the information of the parent nodes, and construct a search tree. During the search process, determine in real time whether the target node is reached. If the target node is reached, then by backtracking the parent node information, starting from the target node, gradually return to the starting node along the parent node pointer to generate a preliminary route correction path from the current position of the ship to the target position. The path includes several different options.

6. The automatic ship driving control method based on artificial intelligence according to claim 1, characterized in that, The specific steps for screening the best correction path in step S4 are as follows: Collect real-time data of the ship and preprocess the collected data; Based on the preprocessed data, calculate the quantification values of the evaluation indicators, including: The inertial state indicator, which is obtained by calculating the matching degree between the correction path and the current inertial state of the ship; The steering condition indicator, which is obtained by calculating whether the requirements of the correction path for the ship's steering ability are within the operable range; if the steering angle exceeds the maximum steering ability of the ship, the score is low; if the steering requirements conform to the steering performance of the ship and the ship can complete the steering operation smoothly, the score is high; The route safety indicator, which is obtained by evaluating the safety of the correction path during navigation, considering factors such as the distance from the path to obstacles, the probability of passing through dangerous areas, and the influence of meteorological conditions on the path.

7. An automatic ship driving control method based on artificial intelligence according to claim 6, characterized in that, The specific steps for screening the best correction path further include: Based on the analytic hierarchy process, determine the weight values corresponding to the inertial state indicator, the steering condition indicator, and the route safety indicator; For each generated correction path, calculate the scores of each evaluation indicator on the path respectively. Adopt a scoring system and score based on the degree of conformity between the path and the indicators. Calculate the comprehensive score value of each indicator in the current correction path through the weighted average formula to obtain the quantitative evaluation results of each path; Compare the comprehensive score values of all correction paths and select the path with the highest score as the best correction path; If there are multiple paths with the same score, then further compare the scores of the paths on the key indicators, and preferentially select the path with a higher score on the key indicators to determine the best correction path; Send the information of the best correction path to the navigation and control system of the ship to guide the ship to safely return to the predetermined route.

8. An automatic ship driving control method based on artificial intelligence according to claim 1, characterized in that The objective function established in step S5 includes: the displacement control objective, the energy consumption objective, and the structural stress objective; The displacement control objective is obtained by comparing the differences between the actual traveling position and heading of the ship and the target values. The smaller the difference, the closer it is to the ideal route. Quantify this difference as the displacement control cost; The energy consumption objective is obtained by establishing an energy consumption calculation model based on the characteristics of the ship's power system, considering the influence of the magnitude of the propulsion force on energy consumption, and calculating the total energy consumption cost during navigation; The structural stress objective is obtained by analyzing the changes in the rudder angle and propulsion force to estimate the stress borne by the ship's structure. The higher the stress level, the greater the corresponding stress cost; Add the three objectives according to the proportional weights to form an objective function for comprehensively measuring the operation effect of the ship.

9. An automatic ship driving control method based on artificial intelligence according to claim 1, characterized in that The steps for finding the optimal control parameters in step S5 are as follows: Input the control parameters for adjusting the ship's rudder angle and main engine speed into the established objective function, evaluate the fitness of particles for energy consumption through simulation calculations, introduce an inertia weight factor, find the ship's driving control parameters corresponding to the optimal particle position, and control the ship's driving based on the found ship's driving control parameters during the ship's voyage.

10. An automatic ship driving control system based on artificial intelligence, characterized in that, The driving control system includes: A data acquisition module configured to acquire the ship's driving state and wind direction data in the route; A wind direction cancellation control module configured to calculate the ship's displacement control amount in advance to cancel the offset caused by the wind direction factor; A route correction module configured to generate multiple route correction paths; A screening module configured to screen out the best correction path; A control optimization module configured to obtain the best ship driving parameters for driving.

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