Intelligent ship navigation control and adjustment system and method based on multi-sensor data

Abstract

The invention is suitable for the field of ship control, and provides an intelligent ship navigation control and adjustment method based on multi-sensor data, and the method comprises the steps: collecting the data of a plurality of preset layout sensors; acquiring data of a plurality of preset layout sensors, and generating a real-time ship navigation state based on a multi-sensor data fusion algorithm; ship navigation target information is imported, and navigation sea condition environment prediction information is generated; generating a navigation optimization instruction based on the real-time ship navigation state and the navigation sea condition environment prediction information in combination with a ship adaptive control algorithm; and generating a ship autonomous driving control command based on the navigation optimization instruction. According to the method and the system, through multi-sensor data fusion and an intelligent algorithm, the safety and the efficiency of ship navigation are remarkably improved. Through comprehensive sensor data acquisition and accurate analysis, ship state and environment information can be sensed in real time, and an obstacle avoidance strategy is optimized in advance. The self-adaptive control and autonomous driving strategy can dynamically adjust the ship attitude according to the real-time condition.

Description

Technical Field

[0001] The present invention belongs to the field of ship control, and particularly relates to an intelligent ship navigation control and regulation system and method based on multi-sensor data. Background Art

[0002] With the rapid development of the modern shipping industry, the requirements for ship safety, navigation efficiency, energy conservation and emission reduction are increasing day by day. Traditional ship navigation control methods mainly rely on single-sensor data and manual intervention, which have certain limitations. For example, in the complex marine navigation environment including adverse weather, complex sea conditions and other factors, the detection ability of a single sensor often fails to meet the actual needs, which easily leads to an increase in navigation safety risks.

[0003] To solve the above problems, in recent years, intelligent navigation control methods based on multi-sensor fusion have gradually become a research hotspot. The multi-sensor data fusion technology can utilize the advantages of different sensors to comprehensively obtain multi-dimensional information such as the ship's operating state and external environment, so as to achieve more accurate navigation control and regulation.

[0004] At present, although some studies have proposed navigation control methods based on sensor data fusion in the field of intelligent ships, there are still many problems. How to efficiently utilize multi-source sensor data and combine the ship's motion characteristics to achieve real-time, accurate and intelligent navigation control and regulation is still an important problem to be solved urgently. Based on this, it is urgent to develop an intelligent ship navigation control and regulation method and system based on multi-sensor data. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide an intelligent ship navigation control and regulation system and method based on multi-sensor data, aiming to solve the problems raised in the above background art.

[0006] The embodiments of the present invention are implemented as follows. On the one hand, an intelligent ship navigation control and regulation method based on multi-sensor data, the method includes: Collect data of a plurality of preset layout sensors, and the preset layout sensors include radar, sonar, global positioning system, inertial measurement unit, environmental sensor, as well as water flow sensor and air flow sensor; Obtain data of a plurality of preset layout sensors and generate a real-time ship navigation state based on a multi-sensor data fusion algorithm; Import ship navigation target information and generate predicted information on the navigation sea condition environment; Generate a navigation optimization instruction based on the real-time ship navigation state and the predicted information on the navigation sea condition environment, in combination with a ship adaptive control algorithm; Generate a ship autonomous driving control command based on the navigation optimization instruction.

[0007] As a further solution of the present invention, the multi-sensor data fusion algorithm is the Kalman filter algorithm, and the algorithm can adaptively adjust the weights according to the performance, accuracy and environmental factors of the preset layout sensors.

[0008] As a still further solution of the present invention, the obtaining of the data of a plurality of preset layout sensors and generating the real-time ship navigation state based on the multi-sensor data fusion algorithm specifically includes: Sending the acquisition data of a plurality of preset layout sensors; Based on the Kalman filter algorithm, performing fusion calculation on the acquisition data to generate the Kalman gain of the acquisition data; Based on the Kalman gain of the acquisition data, generating the real-time value of the ship's comprehensive sensing.

[0009] As a yet further solution of the present invention, the importing of the ship navigation target information and generating the navigation sea condition environment prediction information specifically includes: Based on the ship navigation target information, obtaining the longitude and latitude information of the ship's departure place and destination, and obtaining the ship's departure time; Based on the longitude and latitude information of the ship's departure place and destination, generating the expected arrival times of a plurality of waypoint positions; Importing the expected arrival times of a plurality of waypoint positions into a preset sea condition model to generate segment sea condition prediction values.

[0010] As a further solution of the present invention, the generating of the navigation optimization instruction based on the real-time ship navigation state and the navigation sea condition environment prediction information and in combination with the ship adaptive control algorithm specifically includes: Matching the real-time value of the ship's comprehensive sensing and the segment sea condition prediction values to generate a segment matching result; Based on the segment matching result and the ship adaptive control algorithm, generating a ship attitude adjustment instruction and a navigation path adjustment instruction; The ship adaptive control algorithm is a model predictive control algorithm based on deep reinforcement learning.

[0011] As a further solution of the present invention, on the other hand, an intelligent ship navigation control and adjustment system based on multi-sensor data, the system includes: An acquisition module, configured to acquire the data of a plurality of preset layout sensors; A obtaining module, configured to obtain the data of a plurality of preset layout sensors; A first generation module, configured to generate a real-time ship navigation state based on the multi-sensor data fusion algorithm; An import module, configured to import ship navigation target information; A second generation module, configured to generate navigation sea condition environment prediction information; The third generation module is used to generate navigation optimization instructions based on the real-time ship navigation status and navigation sea condition environment prediction information, in combination with the ship adaptive control algorithm; The fourth generation module is used to generate ship autonomous driving control commands based on the navigation optimization instructions.

[0012] As a further solution of the present invention, the first generation module includes: The sending unit is used to send the acquisition data of several preset layout sensors; The fusion calculation unit is used to perform fusion calculation on the acquisition data based on the Kalman filtering algorithm; The first generation unit is used to generate the Kalman gain of the acquisition data; The second generation unit is used to generate the real-time value of the ship's comprehensive sensing based on the Kalman gain of the acquisition data.

[0013] As a further solution of the present invention, the second generation module includes: The acquisition unit is used to acquire the longitude and latitude information of the ship's departure place and destination based on the ship navigation target information, and acquire the ship's departure time; The third generation unit is used to generate the estimated arrival times of several waypoint positions based on the longitude and latitude information of the ship's departure place and destination; The import unit is used to import the estimated arrival times of several waypoint positions into the preset sea condition model; The fourth generation unit is used to generate segment sea condition prediction values.

[0014] As a further solution of the present invention, the third generation module includes: The matching unit is used to match the real-time value of the ship's comprehensive sensing and the segment sea condition prediction values; The fifth generation unit is used to generate segment matching results; The sixth generation unit is used to generate ship attitude adjustment instructions and navigation path adjustment instructions based on the segment matching results and the ship adaptive control algorithm.

[0015] The intelligent ship navigation control and adjustment method and system based on multi-sensor data provided by the embodiments of the present invention significantly improve the safety and efficiency of ship navigation through multi-sensor data fusion and intelligent algorithms. Comprehensive sensor data acquisition and accurate analysis can real-time perceive the ship's state and environmental information, early warning of potential dangers, and optimize obstacle avoidance strategies; Adaptive control and autonomous driving strategies can dynamically adjust the ship's attitude according to real-time situations, optimize the navigation path, and provide strong support for the development of intelligent shipping. Description of the Drawings

[0016] Figure 1It is the main flowchart of the intelligent ship navigation control and regulation method based on multi-sensor data.

[0017] Figure 2 It is the flowchart of obtaining data from several preset layout sensors in the intelligent ship navigation control and regulation method based on multi-sensor data and generating the real-time ship navigation state based on the multi-sensor data fusion algorithm.

[0018] Figure 3 It is the flowchart of importing ship navigation target information to generate navigation sea condition environment prediction information in the intelligent ship navigation control and regulation method based on multi-sensor data.

[0019] Figure 4 It is the flowchart of generating navigation optimization instructions based on the real-time ship navigation state and navigation sea condition environment prediction information and combining with the ship adaptive control algorithm in the intelligent ship navigation control and regulation method based on multi-sensor data.

[0020] Figure 5 It is the main structure diagram of the intelligent ship navigation control and regulation system based on multi-sensor data.

[0021] Figure 6 It is the structural block diagram of the first generation module in the intelligent ship navigation control and regulation system based on multi-sensor data.

[0022] Figure 7 It is the structural block diagram of the second generation module in the intelligent ship navigation control and regulation system based on multi-sensor data.

[0023] Figure 8 It is the structural block diagram of the third generation module in the intelligent ship navigation control and regulation system based on multi-sensor data. Detailed implementation manners

[0024] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0025] The following describes in detail the specific implementation of the present invention with reference to specific embodiments.

[0026] The intelligent ship navigation control and regulation method and system based on multi-sensor data provided by the present invention solve the technical problems in the background art.

[0027] As Figure 1 shown, it is the main flowchart of the intelligent ship navigation control and regulation method based on multi-sensor data provided by an embodiment of the present invention. The intelligent ship navigation control and regulation method based on multi-sensor data includes: Step S100: Collect data from several pre-set layout sensors; the pre-set layout sensors include radar, sonar, global positioning system, inertial measurement unit, environmental sensors, as well as water flow sensors and air flow sensors; Step S200: Obtain data from several pre-set layout sensors and generate a real-time ship navigation state based on a multi-sensor data fusion algorithm; Step S300: Import ship navigation target information and generate predicted information on the navigation sea condition environment; Step S400: Based on the real-time ship navigation state and the predicted information on the navigation sea condition environment, and in combination with a ship adaptive control algorithm, generate a navigation optimization instruction; Step S500: Generate a ship autonomous driving control command based on the navigation optimization instruction; When this embodiment is applied, it gives full play to the unique advantages of various sensors. Radar uses the principle of electromagnetic wave reflection to accurately detect information such as the distance, azimuth, and speed of target objects around the ship, providing key data for ship obstacle avoidance and surrounding environment monitoring; sonar relies on the propagation characteristics of sound waves in water to effectively detect underwater obstacles, fish school distribution, and seabed topography and geomorphology, ensuring the safety of the ship's underwater navigation. The global positioning system (GPS) relies on satellite positioning technology to provide high-precision position coordinates for the ship, ensuring that the ship always knows its specific position in the vast ocean; the inertial measurement unit measures the acceleration and angular velocity of the ship in real time, thereby accurately perceiving the ship's motion state and attitude changes. The environmental sensors closely monitor the meteorological conditions around the ship, such as temperature, air pressure, humidity, etc.; the water flow sensors and air flow sensors respectively sensitively capture information such as the speed and direction of water flow and air flow. These data together constitute a comprehensive picture of the ship's navigation environment; in the data processing stage, an advanced multi-sensor data fusion algorithm is used to deeply fuse and analyze the massive and multi-source data collected, and combined with the real-time perceived environmental state, a highly accurate real-time ship navigation state is generated, comprehensively reflecting the key parameters of the ship's position, speed, heading, and attitude. Then, the real-time ship navigation state, real-time sea condition environment information, and ship navigation target information are organically integrated. On this basis, a ship adaptive control algorithm is used to dynamically generate and send accurate ship attitude adjustment instructions according to the current ship state, environmental factors, and navigation target, and at the same time optimize the navigation path and formulate a more reasonable obstacle avoidance strategy to ensure the safe navigation of the ship in complex sea conditions; finally, based on the dynamic ship attitude adjustment instructions, a scientific and reasonable ship autonomous driving control strategy is generated to achieve autonomous and intelligent control of ship navigation.

[0028] As a preferred embodiment of the present invention, the multi-sensor data fusion algorithm is a Kalman filtering algorithm, and the algorithm can adaptively adjust weights according to the performance, accuracy, and environmental factors of the pre-set layout sensors.

[0029] As Figure 2 shown, as a preferred embodiment of the present invention, the obtaining data of several preset layout sensors and generating real-time ship navigation states based on a multi-sensor data fusion algorithm specifically includes: Step S201: Transmit the acquisition data of several preset layout sensors; Step S202: Based on the Kalman filtering algorithm, perform fusion calculation on the acquisition data to generate the Kalman gain of the acquisition data; Step S203: Generate the real-time value of the ship's comprehensive sensing based on the Kalman gain of the acquisition data; When this embodiment is applied, first, various sensors preset on the ship, such as radar, sonar, global positioning system, inertial measurement unit, etc., comprehensively collect key data during the ship's navigation, covering the ship's surrounding environment information, its own motion parameters, and meteorological and hydrological conditions. The acquisition data is transmitted to the data processing center in real time to provide a basis for subsequent analysis. Based on the Kalman filtering algorithm, fusion calculation is performed on the acquisition data to generate the Kalman gain of the acquisition data. This algorithm can effectively process the noise and uncertainty in the data, predict and update the real-time value of the ship's sensing, and provide strong support for the accurate calculation of the ship's navigation state.

[0030] As Figure 3 shown, as a preferred embodiment of the present invention, the importing ship navigation target information and generating navigation sea condition environment prediction information specifically includes: Step S301: Based on the ship navigation target information, obtain the longitude and latitude information of the ship's departure place and destination, and obtain the ship's departure time; Step S302: Based on the longitude and latitude information of the ship's departure place and destination, generate the estimated arrival times of several waypoint positions; Step S303: Import the estimated arrival times of several waypoint positions into a preset sea condition model to generate sectional sea condition prediction values; It should be understood that based on the ship navigation target information, the longitude and latitude information of the ship's departure place and destination is obtained, and the ship's departure time is obtained. Based on the longitude and latitude information of the ship's departure place and destination, the estimated arrival times of several waypoint positions are generated, segmenting the navigation target. There is a preset sea condition model. The preset sea condition model deeply learns and trains the historical sea condition data of each sea area on the navigation route collected from the ocean database, meteorological agencies, and relevant research reports, including information such as wave height, sea current speed and direction, sea wind strength and direction, water temperature, and tide in different seasons and time periods over the years. Importing the estimated arrival times of several waypoint positions into the preset sea condition model can generate sectional sea condition prediction values, and the sea condition prediction values include navigation sea condition environment prediction information with parameters such as waves, sea currents, sea winds, and water temperature.

[0031] AsFigure 4 As shown in the figure, as a preferred embodiment of the present invention, the generation of the navigation optimization command based on the real-time ship navigation state and the predicted information of the navigation sea condition environment, combined with the ship adaptive control algorithm, specifically includes: Step S401: Match the real-time value of the ship's comprehensive sensor and the predicted value of the sectional sea condition to generate a sectional matching result; Step S402: Based on the sectional matching result and the ship adaptive control algorithm, generate a ship attitude adjustment command and a navigation path adjustment command; Step S403: The ship adaptive control algorithm is a model predictive control algorithm based on deep reinforcement learning; When this embodiment is applied, the ship navigation target information is imported, the navigation sea condition environment is predicted, and the predicted value of the sectional sea condition is obtained. On this basis, the real-time value of the ship's comprehensive sensor and the predicted value of the sectional sea condition are matched to generate a sectional matching result, which intuitively reflects the degree of fit between the real-time state of the ship and the future sea condition, and evaluates the risk of the ship's navigation. Based on the above sectional matching result, according to the dynamic principle and kinematic characteristics of the ship, combined with the model predictive control algorithm based on deep reinforcement learning, which is a ship adaptive control algorithm, a ship attitude adjustment command and a navigation path adjustment command are generated. These commands include rudder angle adjustment commands, main engine speed adjustment commands, fin stabilizer control commands, etc. The calculation method of the specific commands depends on the control algorithm and the specific structure of the ship. This algorithm dynamically adjusts the ship's attitude and navigation path by continuously learning and predicting according to the real-time state of the ship and the change of the sea condition, ensuring that the ship always maintains the best navigation state under complex sea conditions, and then the ship attitude adjustment command and the navigation path adjustment command are imported into the preset autonomous driving system, and then the corresponding ship autonomous driving control command is generated.

[0032] As Figure 5 shown in the figure, on the other hand, as another preferred embodiment of the present invention, an intelligent ship navigation control and adjustment system based on multi-sensor data, the system includes: An acquisition module 100 for acquiring data of a plurality of preset layout sensors; An acquisition module 200 for acquiring data of a plurality of preset layout sensors; A first generation module 300 for generating a real-time ship navigation state based on a multi-sensor data fusion algorithm; An import module 400 for importing ship navigation target information; A second generation module 500 for generating predicted information on the navigation sea condition environment; A third generation module 600 for generating a navigation optimization command based on the real-time ship navigation state and the predicted information of the navigation sea condition environment, combined with a ship adaptive control algorithm; The fourth generation module 700 is configured to generate a ship autonomous driving control command based on a navigation optimization instruction.

[0033] When this embodiment is applied, the acquisition module 100 acquires data of a plurality of preset layout sensors, the acquisition module 200 acquires data of a plurality of preset layout sensors, based on a multi-sensor data fusion algorithm, the first generation module 300 generates a real-time ship navigation state, the import module 400 imports ship navigation target information, the second generation module 500 generates a navigation sea condition environment prediction information, based on the real-time ship navigation state and the navigation sea condition environment prediction information, in combination with a ship adaptive control algorithm, the third generation module 600 generates a navigation optimization instruction, and based on the navigation optimization instruction, the fourth generation module 700 generates a ship autonomous driving control command.

[0034] As Figure 6 shown, as another preferred embodiment of the present invention, the first generation module 300 includes: A sending unit 301, configured to send the acquisition data of a plurality of preset layout sensors; A fusion calculation unit 302, configured to perform fusion calculation on the acquisition data based on the Kalman filtering algorithm; A first generation unit 303, configured to generate a Kalman gain of the acquisition data; A second generation unit 304, configured to generate a real-time value of ship integrated sensing based on the Kalman gain of the acquisition data.

[0035] When this embodiment is applied, the sending unit 301 sends the acquisition data of a plurality of preset layout sensors, based on the Kalman filtering algorithm, the fusion calculation unit 302 performs fusion calculation on the acquisition data, the first generation unit 303 generates a Kalman gain of the acquisition data, and based on the Kalman gain of the acquisition data, the second generation unit 304 generates a real-time value of ship integrated sensing.

[0036] As Figure 7 shown, as another preferred embodiment of the present invention, the second generation module 500 includes: An acquisition unit 501, configured to acquire the longitude and latitude information of the ship's departure place and destination based on the ship navigation target information, and acquire the ship's departure time; A third generation unit 502, configured to generate the estimated arrival times of a plurality of waypoint positions based on the longitude and latitude information of the ship's departure place and destination; An import unit 503, configured to import the estimated arrival times of a plurality of waypoint positions into a preset sea condition model; A fourth generation unit 504, configured to generate a segmented sea condition prediction value.

[0037] When this embodiment is applied, based on the ship navigation target information, the acquisition unit 501 acquires the longitude and latitude information of the ship's departure place and destination, and acquires the ship's departure time. Based on the longitude and latitude information of the ship's departure place and destination, the third generation unit 502 generates the estimated arrival times of several waypoint positions, and the import unit 503 imports the estimated arrival times of several waypoint positions into a preset sea condition model, and the fourth generation unit 504 generates segmented sea condition prediction values.

[0038] As Figure 8 shown, as another preferred embodiment of the present invention, the third generation module 600 includes: A matching unit 601 for matching the real-time value of the ship's integrated sensor and the segmented sea condition prediction value; A fifth generation unit 602 for generating a segmented matching result; A sixth generation unit 603 for generating a ship attitude adjustment instruction and a navigation path adjustment instruction based on the segmented matching result and the ship adaptive control algorithm.

[0039] When this embodiment is applied, the matching unit 601 matches the real-time value of the ship's integrated sensor and the segmented sea condition prediction value, the fifth generation unit 602 generates a segmented matching result, and based on the segmented matching result and the ship adaptive control algorithm, the sixth generation unit 603 generates a ship attitude adjustment instruction and a navigation path adjustment instruction.

[0040] In the above embodiments of the present invention, an intelligent ship navigation control and regulation method based on multi-sensor data is provided, and an intelligent ship navigation control and regulation system based on multi-sensor data is provided, giving full play to the unique advantages of various sensors. The radar uses the principle of electromagnetic wave reflection to accurately detect information such as the distance, azimuth, and speed of target objects around the ship, providing key data for ship obstacle avoidance and surrounding environment monitoring; the sonar relies on the propagation characteristics of sound waves in water to effectively detect underwater obstacles, fish school distribution, and seabed topography and geomorphology, ensuring the safety of the ship's underwater navigation. The Global Positioning System (GPS) relies on satellite positioning technology to provide high-precision position coordinates for the ship, ensuring that the ship always knows its specific position in the vast ocean; the inertial measurement unit measures the acceleration and angular velocity of the ship in real time, so as to accurately perceive the ship's motion state and attitude changes. The environmental sensor closely monitors the meteorological conditions around the ship, such as temperature, air pressure, humidity, etc.; the water flow sensor and the air flow sensor respectively sensitively capture information such as the speed and direction of the water flow and the air flow. These data together constitute a comprehensive picture of the ship's navigation environment; in the data processing stage, an advanced multi-sensor data fusion algorithm is used to deeply fuse and analyze the massive and multi-source data collected, and combined with the real-time perceived environmental state, a highly accurate real-time ship navigation state is generated, comprehensively reflecting the key parameters of the ship's position, speed, heading, and attitude. Then, the real-time ship navigation state, real-time sea condition environment information, and ship navigation target information are organically integrated. On this basis, the ship adaptive control algorithm is used to dynamically generate and send accurate ship attitude adjustment instructions according to the current ship state, environmental factors, and navigation target, and at the same time optimize the navigation path planning and formulate a more reasonable obstacle avoidance strategy to ensure the safe navigation of the ship in complex sea conditions; finally, based on the dynamic ship attitude adjustment instructions, a scientific and reasonable ship autonomous driving control strategy is generated to realize the autonomous and intelligent control of ship navigation. This method and system significantly improve the safety and efficiency of ship navigation through multi-sensor data fusion and intelligent algorithms. Comprehensive sensor data collection and accurate analysis can real-time perceive the ship state and environmental information, early warning of potential dangers, and optimize the obstacle avoidance strategy; the adaptive control and autonomous driving strategy can dynamically adjust the ship attitude according to the real-time situation, optimize the navigation path, and provide strong support for the development of intelligent shipping.

[0041] In order to enable the above method and system to run smoothly, in addition to including the above various modules, the system may also include more or fewer components than the above description, or combine some components, or different components. For example, it may include input and output devices, network access devices, buses, processors, and memories, etc.

[0042] The so-called processor may be a central processing unit, or may also be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned system, connecting each part through various interfaces and circuits.

[0043] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0044] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be subject to the appended claims.

[0045] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent ship navigation control and adjustment method based on multi-sensor data, characterized in that: The method comprises: Collecting data from a plurality of preset layout sensors, wherein the preset layout sensors include radar, sonar, global positioning system, inertial measurement unit, environmental sensor, water flow sensor, and air flow sensor; Acquire data from several preset layout sensors and generate real-time ship navigation status based on multi-sensor data fusion algorithm; Import ship navigation target information and generate navigation sea condition environment prediction information; Generate navigation optimization instructions based on real-time ship navigation status and navigation sea environment prediction information combined with ship adaptive control algorithm; Generate ship autonomous driving control commands based on navigation optimization instructions.

2. The intelligent ship navigation control and adjustment method based on multi-sensor data according to claim 1 is characterized in that: The multi-sensor data fusion algorithm is a Kalman filter algorithm, which can adaptively adjust weights according to the performance, accuracy and environmental factors of the preset layout sensors.

3. The intelligent ship navigation control and adjustment method based on multi-sensor data according to claim 2 is characterized in that: The step of acquiring data from a plurality of preset layout sensors and generating a real-time ship navigation status based on a multi-sensor data fusion algorithm specifically includes: Sending collected data from several preset layout sensors; Based on the Kalman filter algorithm, the collected data is fused and calculated to generate the Kalman gain of the collected data; Based on the Kalman gain of the collected data, the real-time value of the ship's comprehensive sensing is generated.

4. The intelligent ship navigation control and adjustment method based on multi-sensor data according to claim 3 is characterized in that: The importing of ship navigation target information and generating navigation sea environment prediction information specifically includes: Based on the ship's navigation target information, obtain the latitude and longitude information of the ship's departure and destination, and obtain the ship's departure time; Generate estimated arrival times for several route points based on the latitude and longitude information of the ship's departure and destination; Import the estimated arrival time of several path points into the preset sea condition model to generate segmented sea condition prediction values.

5. The intelligent ship navigation control and adjustment method based on multi-sensor data according to claim 4 is characterized in that: The generation of navigation optimization instructions based on the real-time ship navigation status and navigation sea environment prediction information, combined with the ship adaptive control algorithm, specifically includes: Match the ship's comprehensive sensor real-time value and the segmented sea condition prediction value to generate segmented matching results; Generate ship attitude adjustment instructions and navigation path adjustment instructions based on segment matching results and ship adaptive control algorithm; The ship adaptive control algorithm is a model predictive control algorithm based on deep reinforcement learning.

6. Intelligent ship navigation control and regulation system based on multi-sensor data, characterized in that: The method for intelligent ship navigation control and regulation based on multi-sensor data as described in claims 1 to 5 is applied, and the system comprises: A collection module, used to collect data from a number of preset layout sensors; An acquisition module, used to acquire data of a plurality of preset layout sensors; The first generation module is used to generate real-time ship navigation status based on a multi-sensor data fusion algorithm; Import module, used to import ship navigation target information; The second generation module is used to generate navigation sea condition environment prediction information; The third generation module is used to generate navigation optimization instructions based on the real-time ship navigation status and navigation sea environment prediction information combined with the ship adaptive control algorithm; The fourth generation module is used to generate a ship autonomous driving control command based on the navigation optimization instruction.

7. The intelligent ship navigation control and regulation system based on multi-sensor data according to claim 6 is characterized in that: The first generation module comprises: A sending unit, used for sending the collected data of a plurality of preset layout sensors; A fusion calculation unit, used for performing fusion calculation on the collected data based on a Kalman filter algorithm; A first generating unit, used for generating a Kalman gain of collected data; The second generating unit is used to generate a real-time value of the ship's integrated sensing based on the Kalman gain of the collected data.

8. The intelligent ship navigation control and regulation system based on multi-sensor data according to claim 7 is characterized in that: The second generation module comprises: An acquisition unit, used to acquire the latitude and longitude information of the ship's departure place and destination, and acquire the ship's departure time based on the ship's navigation target information; The third generating unit is used to generate the estimated arrival time of several path points based on the latitude and longitude information of the departure place and destination of the ship; An import unit is used to import the estimated arrival time of several path points into a preset sea condition model; The fourth generating unit is used to generate segmented sea condition prediction values.

9. The intelligent ship navigation control and regulation system based on multi-sensor data according to claim 8 is characterized in that: The third generation module comprises: A matching unit, used to match the real-time value of ship comprehensive sensing and the segmented sea condition prediction value; A fifth generating unit, used to generate segment matching results; The sixth generation unit is used to generate ship attitude adjustment instructions and navigation path adjustment instructions based on the segment matching results and the ship adaptive control algorithm.

Images