Intelligent navigation system and method for ship

Abstract

The invention discloses an intelligent navigation system and method for a ship in the technical field of ship navigation, and the system comprises a sensing module which is used for collecting the surrounding environment information and the state information of the ship in real time; the data processing and fusing module is used for preprocessing, calibrating and fusing the surrounding environment information and the self state information of the sensing module; the route optimization module is used for formulating a navigation plan and a collision avoidance strategy; the navigation control module is used for autonomously controlling the ship according to the output of the ship route optimization module; and the communication monitoring module monitors the navigation state of the ship and the change of the surrounding environment through real-time communication between the ship and a shore-based control center, other ships and a maritime affair management department. According to the method, data of different sensing units are associated, and the associated data are fused, so that mutual verification and complementation can be realized, and the accuracy and reliability of target identification are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship navigation, and in particular relates to an intelligent navigation system and method for ships. Background Art

[0002] The ship intelligent navigation system refers to the automatic acquisition of data information on the ship itself, the marine environment, transportation, etc. through the use of the Internet, the Internet of Things, communications, sensors, etc., and based on computer technology, big data processing and analysis technology, and automatic control technology, to achieve intelligent operation in the navigation and overall management of ships, making the ship's navigation safer, more environmentally friendly, economical and reliable. With the continuous development and improvement of technology, the intelligent navigation system will play an increasingly important role in the shipping field.

[0003] Existing intelligent navigation systems usually collect relevant data through various sensors and then analyze the data to optimize the navigation path. These sensors continuously generate a large amount of data. The data collected by different sensors may have different quality and the data types are also more diverse, which increases the difficulty of data integration and processing.

[0004] In summary, how to solve the problem in the prior art that data processing is difficult due to the large number of data types and large amounts has become a difficult problem that needs to be solved urgently in this field. Therefore, it is necessary to propose an intelligent navigation system and method for ships. Summary of the invention

[0005] In order to solve the above problems, the purpose of the present invention is to provide an intelligent navigation system and method for ships, which can verify and supplement each other by associating data from different sensing units and fusing the associated data, thereby improving the accuracy and reliability of target recognition.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows: an intelligent navigation system for ships, comprising the following modules:

[0007] The perception module is used to collect the surrounding environment information and the vessel's own status information in real time; the surrounding environment information includes obstacle location, water depth, sea conditions and weather information; the own status information includes the vessel's performance, speed, heading and location information;

[0008] The data processing and fusion module is used to pre-process, calibrate and fuse the surrounding environment information and self-state information of the perception module; use the data processing algorithm to analyze the fused data information, extract key navigation information, and store the key navigation information;

[0009] The route optimization module is used to formulate several navigation plans and collision avoidance strategies based on the processed key navigation information, combined with navigation rules, ship dynamics model, expert system knowledge and real-time environmental information; and use optimization algorithms to evaluate and select multiple navigation plans and collision avoidance strategies to determine the initial navigation path;

[0010] The navigation control module is used to autonomously control the ship according to the output of the route optimization module, adjust the ship's speed, heading and position in real time, and navigate according to the preliminary navigation path;

[0011] The communication monitoring module is used to monitor the navigation status of the ship and changes in the surrounding environment, and to discover and report potential safety hazards of the ship through real-time communication between the ship and the shore-based control center, other ships and maritime management departments.

[0012] Furthermore, the perception module includes the following perception units:

[0013] A radar sensing unit, which is used to detect surrounding ships and obstacles by emitting electromagnetic waves and receiving their reflected signals;

[0014] The LiDAR sensing unit is used to calculate the distance to surrounding obstacles by emitting laser beams and measuring the time it takes for them to reflect back, and to construct a three-dimensional image of the surrounding environment;

[0015] Wind speed and direction sensing unit, used to detect wind speed and direction around the ship in real time and provide meteorological information;

[0016] A camera acquisition unit is used to capture images of the surrounding environment and process and analyze them through computer vision technology to identify objects in the surrounding environment;

[0017] Automatic identification unit, used to receive navigation information between ships and exchange information between ships and shore-based facilities;

[0018] Electronic chart display and information unit, used to provide comprehensive information on ship navigation and sailing, and provide electronic chart information;

[0019] Inertial Navigation Unit, which is used to measure and calculate the position, velocity and attitude of the vessel using inertial sensors of gyroscopes and accelerometers;

[0020] A satellite communication unit is used to determine the latitude, longitude, altitude and time information of the ship's position using satellite signals.

[0021] Furthermore, the data processing algorithm adopts a deep learning algorithm, which automatically predicts and identifies navigation risks and disaster warnings by analyzing historical navigation data, surrounding environment information and its own status information, and the communication monitoring module issues warnings to the crew through voice prompts.

[0022] Furthermore, the optimization algorithm adopts a neural network algorithm, which learns and optimizes the processed navigation data; the navigation control module automatically adjusts the navigation strategy and parameters according to the different water characteristics of rivers, oceans and lakes; and adjusts the speed of the ship in real time according to sea conditions, ship load and navigation mission; and automatically plans considering traffic rules and restrictions during navigation.

[0023] Furthermore, the route optimization module is also used to establish a navigation effect feedback mechanism, compare the actual navigation effect with the expected target, and continuously optimize the navigation strategy through reinforcement learning technology; the navigation control module is also used to monitor wind speed, wave height, wave direction and water current factors, monitor the changes in sea conditions in the navigation area in real time, and automatically adjust the navigation path; and based on the real-time sea condition change data, predict future sea condition data and plan response strategies in advance.

[0024] Furthermore, an intelligent navigation method for a ship comprises the following steps:

[0025] Step 1: Data collection and processing: collect real-time data through the perception module, pre-process, calibrate and fuse the real-time data, and analyze the processed real-time data;

[0026] Step 2: Navigation planning and decision-making: Use neural network algorithms to plan a preliminary navigation path from the starting point to the end point based on ship parameters, navigation environment, weather forecast and sea condition information;

[0027] Step 3: Intelligent perception and decision-making: judge the current navigation environment based on real-time data, and dynamically adjust the speed according to the ship's energy consumption optimization goals; if navigation risks and disaster warnings are detected, take collision avoidance measures and optimize the navigation path;

[0028] Step 4: End of voyage and recording: After the ship reaches the destination, record the voyage data; the voyage data includes voyage time, voyage energy consumption, voyage route and voyage risk.

[0029] Further, in step 1, data preprocessing includes the following steps:

[0030] S1, denoising and filtering: denoising the raw data collected by the perception module to eliminate the noise and interference of the raw data; and using filtering algorithm to smooth the signal;

[0031] S2, data compression: compress the data of large sensing units; retain key feature information and remove redundant data;

[0032] S3. Time synchronization: Perform time synchronization processing on data from different perception units; and keep the time and space consistency of data from various perception units.

[0033] Further, in step one, data calibration includes the following steps:

[0034] S1. Individual calibration: Calibrate each sensor unit individually; calibrate using known standard objects and environmental parameters;

[0035] S2. Fusion calibration: Set several target points for each type of perception unit for fusion calibration; configure corresponding correction parameters in the target points, calculate the deviation and error between the data of each type of perception unit; and keep the various perception units matching each other during fusion.

[0036] Further, in step 1, data fusion includes the following steps:

[0037] S1. Data association: Associating data from different perception units to identify the manifestations of the same target event in different perception units;

[0038] S2. Data fusion: Use fusion algorithm to fuse the associated data; select and optimize according to different application scenarios and requirements;

[0039] S3. Fusion evaluation: Evaluate and verify the fusion results by comparing with the actual environment and with data from other perception units.

[0040] Furthermore, in step three, intelligent perception and decision-making includes the following steps:

[0041] S1. Real-time perception of navigation environment: Real-time perception of navigation risks around the ship based on the perception module;

[0042] S2. Obstacle detection and judgment: Identify and judge perceived obstacles and assess their impact on navigation safety;

[0043] S3. Disaster warning information processing: real-time acquisition and processing of ship disaster warning information, and assessment of its impact on the navigation path;

[0044] S4. Navigation strategy judgment: Based on the perceived navigation risks and disaster warning information, determine the navigation strategy to be adopted and optimize the navigation route.

[0045] The following beneficial effects are achieved by adopting the above scheme: 1. The present invention obtains detailed information about the ship's surrounding environment in real time through various sensing units, including obstacle locations, water depth, sea conditions and weather information, etc., to provide accurate data support for the route optimization module. By combining real-time environmental information and ship dynamics models, the route optimization module can formulate a more reasonable navigation path. Optimizing route planning can reduce navigation time and increase navigation speed, thereby improving overall efficiency; it can also reduce unnecessary navigation adjustments. The intelligent navigation system helps reduce fuel consumption and emissions of ships, which is in line with the development trend of green shipping.

[0046] 2. The present invention uses deep learning algorithms to analyze historical navigation data, meteorological information and ship performance parameters. The system can predict and identify potential risks in navigation in advance, such as shallow waters, obstacles, bad weather, etc. By providing real-time risk warnings and navigation suggestions to the crew, the decision-making quality and response speed of the crew are improved. By predicting and identifying potential navigation risks, preventive measures can be taken in advance to reduce the accident rate.

[0047] 3. The present invention uses a neural network algorithm to learn and optimize the processed navigation data to achieve intelligent adjustment of navigation strategies. According to different water characteristics, the navigation strategy and parameters are automatically adjusted to adapt to the best navigation effect in different environments. Traffic rules and restrictions during navigation are considered to ensure the safety of the route and avoid accidents such as collisions. By integrating sensor data such as wind speed, wave height, and water flow speed on the ship, the current sea conditions are evaluated in real time, and the route is planned in advance according to the predicted data to avoid adverse sea conditions, thereby improving the safety of navigation.

[0048] 4. During navigation, the data collected by various sensing units may be interfered by environmental noise and equipment noise. The present invention can eliminate these noises through denoising, thereby improving the accuracy of the data; improve the accuracy of the sensing data through filtering algorithms, reduce fluctuations and mutations in the signal, and make the signal more stable, which is conducive to the intelligent navigation system to accurately judge the ship status and environmental changes; retain key feature information and remove redundant data through data compression processing, thereby reducing the cost of data storage and transmission; and ensure that the data from different sensing units are consistent in time and space through time synchronization processing, providing an accurate data basis for the intelligent navigation system.

[0049] 5. The present invention calculates the measurement parameters of various sensing units, eliminates their inherent deviations and errors, and improves the accuracy and reliability of data. It keeps various sensing units matching each other during fusion, ensuring that data from different sources can be seamlessly integrated and work together. It helps to improve the overall performance and intelligence level of the intelligent navigation system, enabling the system to respond to environmental changes more quickly and accurately, and improving the safety and efficiency of ship navigation.

[0050] 6. The present invention ensures that the data of different sensing units are consistent with each other through data association and fusion, reducing the data inconsistency problem caused by the differences between different sensing units. By associating the data of different sensing units, they can verify and supplement each other, improving the accuracy and reliability of target recognition. When a sensing unit fails or the data is abnormal, the system can rely on the data of other sensing units to supplement and correct it to maintain the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The structure block diagram of the intelligent navigation system for ships according to an embodiment of the present invention.

[0052] Figure 2 This is a structural block diagram of a perception module in an intelligent navigation system for ships according to an embodiment of the present invention.

[0053] Figure 3 The figure is a flow chart of an intelligent navigation method for ships according to an embodiment of the present invention.

[0054] Figure 4 The figure is a flow chart of data collection and processing in the intelligent navigation method for ships according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following is further described in detail through specific implementation methods:

[0056] Embodiment 1:

[0057] Basically as attached Figure 1 , Figure 2 As shown:

[0058] An intelligent navigation system for ships, comprising the following modules:

[0059] The perception module is used to collect the ship's surrounding environment information and its own status information in real time; the surrounding environment information includes obstacle location, water depth, sea conditions and weather information; the own status information includes ship performance, speed, heading and location information.

[0060] The perception module includes the following perception units:

[0061] Radar sensing unit, used to detect surrounding ships and obstacles by emitting electromagnetic waves and receiving their reflected signals.

[0062] The LiDAR sensing unit is used to calculate the distance to surrounding obstacles by emitting laser beams and measuring the time it takes for them to reflect back, and to construct a three-dimensional image of the surrounding environment.

[0063] The wind speed and direction sensing unit is used to detect the wind speed and direction around the ship in real time and provide meteorological information.

[0064] The camera acquisition unit is used to capture images of the surrounding environment and process and analyze them through computer vision technology to identify objects in the surrounding environment.

[0065] Automatic identification unit, used to receive navigation information between ships and exchange information between ships and shore-based facilities.

[0066] The electronic chart display and information unit is used to provide comprehensive information on ship navigation and sailing, and provide electronic chart information.

[0067] Inertial Navigation Unit, used to measure and calculate the position, velocity and attitude of the vessel using inertial sensors of gyroscopes and accelerometers.

[0068] A satellite communication unit is used to determine the latitude, longitude, altitude and time information of the ship's position using satellite signals.

[0069] The data processing and fusion module is used to preprocess, calibrate and fuse the surrounding environment information and self-state information of the perception module; use the data processing algorithm to analyze the fused data information, extract key navigation information, and store the key navigation information.

[0070] The route optimization module is used to formulate several navigation plans and collision avoidance strategies based on the processed key navigation information, combined with navigation rules, ship dynamics models, expert system knowledge and real-time environmental information; and use optimization algorithms to evaluate and select multiple navigation plans and collision avoidance strategies to determine the initial navigation path. Optimizing route planning can reduce navigation time and increase navigation speed, improve overall efficiency; and reduce unnecessary navigation adjustments, which helps reduce fuel consumption and emissions of ships.

[0071] The navigation control module is used to autonomously control the ship according to the output of the route optimization module; adjust the ship's speed, heading and position in real time to adapt to the best navigation effect in different environments. And sail according to the preliminary navigation path. By integrating the sensor data such as wind speed, wave height, water flow speed on the ship, the current sea conditions are evaluated in real time, and the route is planned in advance according to the predicted data to avoid adverse sea conditions, thereby improving the safety of navigation.

[0072] The communication monitoring module is used to communicate with the shore-based control center, other ships and maritime management departments in real time through the ship; monitor the navigation status of the ship and changes in the surrounding environment, discover and report ship safety hazards, and take preventive measures in advance to reduce the accident rate.

[0073] The data processing algorithm adopts a deep learning algorithm. The deep learning algorithm automatically predicts and identifies navigation risks and disaster warnings by analyzing historical navigation data, surrounding environment information and its own status information. The communication monitoring module also issues warnings to the crew through voice prompts, thereby improving the crew's decision-making quality and response speed.

[0074] For example, suppose a freighter is crossing a busy strait, which is often affected by bad weather and has a large number of ships passing through. The intelligent navigation system collects historical navigation data, weather forecasts, sea conditions and water characteristics of the sea area in the past month; during the voyage, through real-time data collection, the model predicts strong winds and waves ahead, and when unidentified objects are detected on the channel ahead, the system will issue an early warning to the crew through the communication monitoring module. The early warning information is sent to the monitoring center of the shipping company through the communication monitoring module to provide remote guidance and support to the freighter. The crew adjusts the course and speed according to the information received to avoid the risk area.

[0075] The optimization algorithm adopts a neural network algorithm, which learns and optimizes the processed navigation data; the navigation control module automatically adjusts the navigation strategy and parameters according to the different water characteristics of rivers, oceans and lakes; and adjusts the speed of the ship in real time according to sea conditions, ship load and navigation missions; and automatically plans considering traffic rules and restrictions during navigation.

[0076] The route optimization module is also used to establish a navigation effect feedback mechanism, compare the actual navigation effect with the expected target, and continuously optimize the navigation strategy through reinforcement learning technology; the navigation control module is also used to monitor wind speed, wave height, wave direction and water current factors, monitor the changes in sea conditions in the navigation area in real time, and automatically adjust the navigation path; and based on the real-time sea condition change data, predict future sea condition data and plan response strategies in advance.

[0077] For example, suppose a cargo ship sails from Port A to Port B on an inland river, passing through multiple bends, bridges, and shoals. The intelligent navigation system first collects real-time information about the current waters through the data processing module, including water depth, current speed, wind direction and speed, and channel restrictions. Then, the route optimization module learns and predicts the best navigation path and speed control strategy based on this information and historical navigation data.

[0078] During the voyage, the navigation control module automatically adjusts the ship's course and speed according to the output of the neural network algorithm to avoid shoals and bends, while ensuring that the ship meets the height limit when passing the bridge. In addition, when encountering other ships or obstacles, the system will automatically adjust the navigation path according to the collision avoidance strategy to avoid collision. Intelligent adjustments are made based on the distance, navigation speed, and navigation direction from other ships. If a merchant ship appears in front of the cargo ship at this time, about 2km away from the ship, according to the radar data, the system calculates the relative distance between the two ships, and analyzes through the automatic identification unit that the speed of the other ship is 15 knots and the heading is 270°. The navigation control module decides to turn right, and according to the current sea conditions and distance, appropriately reduce the speed to 12 knots, and automatically adjust the heading to 280° to ensure safe avoidance of the merchant ship in front.

[0079] Embodiment 2:

[0080] Basically as attached Figure 3 , Figure 4 As shown:

[0081] The difference from the above embodiment is that a smart navigation method for a ship comprises the following steps:

[0082] Step 1: Data collection and processing: Collect real-time data through the perception module, pre-process, calibrate and fuse the real-time data, and analyze the processed real-time data.

[0083] Step 2: Navigation planning and decision-making: Use neural network algorithms to plan the initial navigation path from the starting point to the end point based on ship parameters, navigation environment, weather forecast and sea condition information.

[0084] Step three, intelligent perception and decision-making: judge the current navigation environment based on real-time data, and dynamically adjust the speed according to the ship's energy consumption optimization goals; if navigation risks and disaster warnings are detected, take collision avoidance measures and optimize the navigation path.

[0085] Step 4: End of voyage and recording: After the ship reaches the destination, record the voyage data; the voyage data includes voyage time, voyage energy consumption, voyage route and voyage risk.

[0086] In step 1, data preprocessing includes the following steps:

[0087] S1. Denoising and filtering: Denoising is performed on the raw data collected by the perception module to eliminate noise and interference in the raw data, thereby improving the accuracy of the data; and the Kalman filtering algorithm is used to smooth the signal to improve the accuracy of the perception data, reduce fluctuations and mutations in the signal, and make the signal more stable, which is conducive to the intelligent navigation system to accurately judge the ship status and environmental changes.

[0088] S2. Data compression: compress the data of large perception units; retain key feature information and remove redundant data, thereby reducing the cost of data storage and transmission.

[0089] S3. Time synchronization: Perform time synchronization processing on data from different sensing units; and keep the time and space consistency of data from various sensing units, providing an accurate data basis for the intelligent navigation system.

[0090] In step 1, data calibration includes the following steps:

[0091] S1. Individual calibration: Calibrate each sensing unit individually; calibrate with known standard objects and environmental parameters to improve data accuracy and reliability.

[0092] S2. Fusion calibration: Set several target points for each type of perception unit for fusion calibration; configure corresponding correction parameters in the target points, calculate the deviation and error between the data of each type of perception unit; and keep the various perception units matching each other during fusion to ensure that data from different sources can be seamlessly integrated and work together.

[0093] In step 1, data fusion includes the following steps:

[0094] S1. Data association: Associating data from different perception units to identify the manifestations of the same target event in different perception units. By associating data from different perception units, they can verify and supplement each other, reducing data inconsistency problems caused by differences between different perception units.

[0095] S2. Data fusion: Use the Bayesian network fusion algorithm to fuse the associated data; select and optimize according to different application scenarios and needs to improve the accuracy and reliability of target recognition.

[0096] S3, Fusion evaluation: Evaluate and verify the fusion results to ensure their accuracy and reliability; evaluate by comparing with the actual environment and with the data of other perception units. When a perception unit fails or the data is abnormal, the system can rely on the data of other perception units to supplement and correct it to maintain the stable operation of the system.

[0097] In step three, intelligent perception and decision-making include the following steps:

[0098] S1. Real-time perception of navigation environment: Real-time perception of navigation risks around the ship based on the perception module.

[0099] S2. Obstacle detection and judgment: Identify and judge perceived obstacles and assess their impact on navigation safety.

[0100] S3. Disaster warning information processing: Real-time acquisition and processing of ship disaster warning information, and assessment of its impact on the navigation path.

[0101] S4. Navigation strategy judgment: Based on the perceived navigation risks and disaster warning information, determine the navigation strategy to be adopted and optimize the navigation route.

[0102] For example, the navigation strategy is as follows:

[0103] Strategy 1: If strong winds and waves are predicted, the system can adjust the course and speed to reduce the impact of wind and waves on the ship.

[0104] Strategy 2: When a strong countercurrent is detected ahead, the system can choose to detour or increase the speed appropriately to reduce additional energy consumption.

[0105] Strategy 3: In the face of severe weather, the system recommends entering the nearest port for shelter or adjusting the route to calmer waters.

[0106] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An intelligent navigation system for a ship, characterized in that: Includes the following modules: The perception module is used to collect the surrounding environment information and the vessel's own status information in real time; the surrounding environment information includes obstacle location, water depth, sea conditions and weather information; the own status information includes the vessel's performance, speed, heading and location information; The data processing and fusion module is used to pre-process, calibrate and fuse the surrounding environment information and self-state information of the perception module; use the data processing algorithm to analyze the fused data information, extract key navigation information, and store the key navigation information; The route optimization module is used to formulate several navigation plans and collision avoidance strategies based on the processed key navigation information, combined with navigation rules, ship dynamics model, expert system knowledge and real-time environmental information; And use optimization algorithms to evaluate and select multiple navigation plans and collision avoidance strategies to determine the initial navigation path; The navigation control module is used to autonomously control the ship according to the output of the route optimization module, adjust the ship's speed, heading and position in real time, and navigate according to the preliminary navigation path; The communication monitoring module is used to monitor the navigation status of the ship and changes in the surrounding environment, and to discover and report potential safety hazards of the ship through real-time communication between the ship and the shore-based control center, other ships and maritime management departments.

2. The intelligent navigation system for ships according to claim 1, characterized in that: The perception module includes the following perception units: A radar sensing unit, which is used to detect surrounding ships and obstacles by emitting electromagnetic waves and receiving their reflected signals; The LiDAR sensing unit is used to calculate the distance to surrounding obstacles by emitting laser beams and measuring the time it takes for them to reflect back, and to construct a three-dimensional image of the surrounding environment; Wind speed and direction sensing unit, used to detect wind speed and direction around the ship in real time and provide meteorological information; A camera acquisition unit is used to capture images of the surrounding environment and process and analyze them through computer vision technology to identify objects in the surrounding environment; Automatic identification unit, used to receive navigation information between ships and exchange information between ships and shore-based facilities; Electronic chart display and information unit, used to provide comprehensive information on ship navigation and sailing, and provide electronic chart information; Inertial Navigation Unit, which is used to measure and calculate the position, velocity and attitude of the vessel using inertial sensors of gyroscopes and accelerometers; A satellite communication unit is used to determine the latitude, longitude, altitude and time information of the ship's position using satellite signals.

3. The intelligent navigation system for ships according to claim 2, characterized in that: The data processing algorithm adopts a deep learning algorithm, which automatically predicts and identifies navigation risks and disaster warnings by analyzing historical navigation data, surrounding environment information and its own status information. The communication monitoring module then issues warnings to the crew through voice prompts.

4. The intelligent navigation system for ships according to claim 3, characterized in that: The optimization algorithm adopts a neural network algorithm, which learns and optimizes the processed navigation data; the navigation control module automatically adjusts the navigation strategy and parameters according to the different water characteristics of rivers, oceans and lakes; and adjusts the speed of the ship in real time according to sea conditions, ship load and navigation missions; and automatically plans considering traffic rules and restrictions during navigation.

5. The intelligent navigation system for ships according to claim 4, characterized in that: The route optimization module is also used to establish a navigation effect feedback mechanism, compare the actual navigation effect with the expected target, and continuously optimize the navigation strategy through reinforcement learning technology; the navigation control module is also used to monitor wind speed, wave height, wave direction and water current factors, monitor the changes in sea conditions in the navigation area in real time, and automatically adjust the navigation path; and based on the real-time sea condition change data, predict future sea condition data and plan response strategies in advance.

6. An intelligent navigation method for a ship, characterized in that: The following steps are involved: Step 1: Data collection and processing: collect real-time data through the perception module, pre-process, calibrate and fuse the real-time data, and analyze the processed real-time data; Step 2: Navigation planning and decision-making: Use neural network algorithms to plan a preliminary navigation path from the starting point to the end point based on ship parameters, navigation environment, weather forecast and sea condition information; Step 3: Intelligent perception and decision-making: judge the current navigation environment based on real-time data, and dynamically adjust the speed according to the ship's energy consumption optimization goals; if navigation risks and disaster warnings are detected, take collision avoidance measures and optimize the navigation path; Step 4: End of voyage and recording: After the ship reaches the destination, record the voyage data; the voyage data includes voyage time, voyage energy consumption, voyage route and voyage risk.

7. The intelligent navigation method for a ship according to claim 6, characterized in that: In step 1, data preprocessing includes the following steps: S1, denoising and filtering: denoising the raw data collected by the perception module to eliminate the noise and interference of the raw data; and using filtering algorithm to smooth the signal; S2, data compression: compress the data of large sensing units; retain key feature information and remove redundant data; S3. Time synchronization: Perform time synchronization processing on data from different perception units; and keep the time and space consistency of data from various perception units.

8. The intelligent navigation method for ships according to claim 7, characterized in that: In step 1, data calibration includes the following steps: S1. Individual calibration: Calibrate each sensor unit individually; calibrate using known standard objects and environmental parameters; S2. Fusion calibration: Set several target points for each type of perception unit for fusion calibration; configure corresponding correction parameters in the target points, calculate the deviation and error between the data of each type of perception unit; and keep the various perception units matching each other during fusion.

9. The intelligent navigation method for ships according to claim 8, characterized in that: In step 1, data fusion includes the following steps: S1. Data association: Associating data from different perception units to identify the manifestations of the same target event in different perception units; S2. Data fusion: Use fusion algorithm to fuse the associated data; select and optimize according to different application scenarios and requirements; S3. Fusion evaluation: Evaluate and verify the fusion results by comparing with the actual environment and with data from other perception units.

10. The intelligent navigation method for ships according to claim 9, characterized in that: In step three, intelligent perception and decision-making include the following steps: S1. Real-time perception of navigation environment: Real-time perception of navigation risks around the ship based on the perception module; S2. Obstacle detection and judgment: Identify and judge perceived obstacles and assess their impact on navigation safety; S3. Disaster warning information processing: real-time acquisition and processing of ship disaster warning information, and assessment of its impact on the navigation path; S4. Navigation strategy judgment: Based on the perceived navigation risks and disaster warning information, determine the navigation strategy to be adopted and optimize the navigation route.

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