Ship automatic navigation control method and device and readable storage medium

Abstract

The invention provides a ship automatic navigation control method and device and a readable storage medium, and the method comprises the steps: obtaining a plurality of pieces of historical navigation data of at least one cargo-carrying ship passing through a to-be-detected section of a river; acquiring multiple pieces of coordinate data according to the multiple pieces of historical navigation data; according to the multiple pieces of coordinate data, a navigation center line and a navigation contour line are determined; according to the historical navigation data, multiple pieces of first navigation speed information of cargo ships passing through the river to-be-detected section are obtained; according to the multiple pieces of first navigational speed information, determining a speed limit value of the to-be-detected river section; and based on the satellite map data, the navigation center line, the navigation contour line and the speed limit value of the to-be-measured river section, determining a target navigation channel of the ship in the to-be-measured river section, and controlling the ship to navigate according to the target navigation channel. When the target channel sailing in the to-be-measured section of the river is obtained, no ship or vehicle is needed for field data acquisition, and manpower, material resources and time cost are saved.

Description

Technical Field

[0001] This application relates to the field of marine technology, and more specifically, to a control method, apparatus, and readable storage medium for automatic navigation of a ship. Background Technology

[0002] In related technologies, when ships navigate automatically, it is necessary to draw river maps for navigation reference. When obtaining river maps, it is necessary to collect the shoreline as the boundary line through lidar or camera. However, it is not easy to identify underwater reefs. When ships travel along the boundary line, reefs can easily cause ships to run aground. Therefore, underwater surveying instruments are needed for underwater mapping, which increases the difficulty of mapping and costs. Summary of the Invention

[0003] This application aims to address the technical problem that existing or related technologies require underwater surveying instruments for underwater mapping when drawing river maps, which increases the difficulty of the surveying process.

[0004] Therefore, the first aspect of this application proposes a control method for automatic navigation of ships.

[0005] The second aspect of this application proposes a control device for automatic navigation of a ship.

[0006] The third aspect of this application proposes a control device for automatic navigation of a ship.

[0007] The fourth aspect of this application proposes a readable storage medium.

[0008] In view of the above, according to the first aspect of this application, a control method for automatic navigation of a ship is provided, comprising: acquiring multiple historical navigation data of at least one cargo ship passing through a section of a river to be measured; acquiring multiple coordinate data based on the multiple historical navigation data; determining a navigation centerline and a navigation outline based on the multiple coordinate data; acquiring multiple first speed information of the cargo ship passing through the section of the river to be measured based on the historical navigation data; determining a speed limit value for the section of the river to be measured based on the multiple first speed information; determining a target channel for the ship to navigate within the section of the river to be measured based on satellite map data of the section of the river to be measured, the navigation centerline, the navigation outline, and the speed limit value for the section of the river to be measured, and controlling the ship to navigate according to the target channel.

[0009] This application provides a control method for automatic navigation of ships, which includes acquiring multiple historical navigation data of at least one cargo ship passing through a section of a river to be measured. The section of the river to be measured refers to the waterway or other water area where the ship is about to navigate, and the multiple historical navigation data are the historical AIS (Automatic Identification System) data of the waterway or other water area where the ship is about to navigate. The historical AIS data includes the positioning information (latitude and longitude data), heading information, and speed information of various types of ships in this section of the waterway or water area. Therefore, multiple coordinate data can be obtained based on the multiple historical navigation data. These multiple coordinate data represent the multiple coordinate data of the cargo ship navigating within the section of the river to be measured. Specifically, the time information of the data can be adjusted to improve data accuracy. Since the multiple coordinate data points represent the location points of multiple cargo vessels within the waterway, these points are all areas where vessels can navigate normally. Therefore, the navigation centerline can be derived from these multiple location points (i.e., multiple coordinate data points) using a fitting algorithm. Essentially, a centerline can be drawn at the midpoints between these multiple location points; this centerline serves as the navigation centerline. During autopilot operation, vessels can navigate along this centerline to avoid running aground. The edges of the dispersed areas of the multiple location points can be used to create the navigation outline, which is the boundary line along which the vessel can navigate. This application samples historical navigation data from cargo vessels. Because cargo vessels have a greater draft than empty vessels, using cargo vessels as sample data maximizes the detection of reefs, thereby improving the accuracy of the channel centerline and outline drawing. Therefore, extrapolating the channel centerline and outline from historical navigation data reduces the difficulty of mapping the area and allows for the elimination of reefs without the use of underwater detection instruments, saving surveying costs. Since historical navigation data records the speeds of multiple vessels within the river section to be measured, multiple initial speeds of cargo vessels passing through this section can also be obtained from this data. These initial speeds can be numerical values. The average of these speed values ​​can be used as the speed limit for that section of the river, thereby improving navigation safety. Based on satellite map data, the navigation centerline, the navigation outline, and the speed limit for the river section to be measured, the target channel for vessels within that section can be determined. This target channel can be displayed on a map of the river section, which also shows the navigation centerline, the navigation outline, and the speed limit. After obtaining the map of the river section to be measured, vessels can be controlled to navigate along the target channel shown on the map, eliminating the need for on-site data collection by vessels or vehicles, thus saving manpower, resources, and time.Because the proposed solution can extrapolate routes from data and thus avoid underwater obstacles such as reefs, it can create navigable map boundaries for cargo ships with deep drafts without the need for underwater surveying instruments, saving costs and reducing technical complexity. By utilizing historical navigation data, the resulting channel centerline closely matches human navigation, thereby improving the accuracy and safety of automated navigation. The map of the river section to be surveyed can simultaneously provide information such as the channel centerline, boundary lines, and speed limits, enhancing its practicality.

[0010] In some technical solutions, optionally, multiple historical navigation data of at least one cargo vessel passing through the river section to be measured can be obtained, including: obtaining multiple initial historical navigation data of multiple vessels passing through the river section to be measured; the initial historical navigation data includes: the first speed information of multiple vessels, vessel type, and navigation status within the section; based on the first speed information, vessel type, navigation status within the section, and preset filtering conditions, the initial historical navigation data is filtered to determine at least one cargo vessel passing through the river section to be measured; based on the historical navigation data, multiple historical navigation data of at least one cargo vessel passing through the river section to be measured are obtained; wherein, the preset filtering conditions include at least: the first speed information is zero, the vessel type is a non-cargo vessel, and the navigation status within the section is a moored state.

[0011] In this technical solution, multiple initial historical navigation data of multiple vessels passing through the river section to be measured are acquired. The initial historical navigation data includes: the first speed information, vessel type, and navigation status within the section of the multiple vessels. Based on the first speed information, vessel type, navigation status within the section, and preset filtering conditions, the initial historical navigation data is filtered to determine at least one cargo vessel passing through the river section to be measured. Based on the historical navigation data, multiple historical navigation data of at least one cargo vessel passing through the river section to be measured are acquired. The preset filtering conditions include at least: the first speed information is zero, the vessel type is a non-cargo vessel, and the navigation status within the section is a moored state. Alternatively, the historical navigation data can be further filtered by distinguishing between high-speed vessels (greater than 5 knots) and low-speed vessels (less than 5 knots) to select vessels suitable for sampling. By excluding data from vessels with zero speed (stationary), non-cargo vessels (shallow draft), and those in anchored positions, the actual navigation trajectories of cargo vessels are accurately selected. This avoids invalid or low-reference-value data (such as anchored vessels and passenger ships) interfering with the mapping of the river area under test, thus affecting the automatic navigation of ships. The filtered data comprehensively reflects the dynamic navigation status of cargo vessels, and their draft characteristics enable the fitted navigation centerline and contour line to more accurately avoid reefs. The generated boundary line directly corresponds to the safe navigation threshold for cargo vessels.

[0012] Meanwhile, the speed limits, centerlines, and boundary lines generated based on dynamic navigation data closely match actual navigation scenarios, avoiding the high costs of underwater surveying and providing a reliable navigation framework based on human driving experience for autonomous driving, significantly improving the safety and adaptability of autonomous navigation.

[0013] In some technical solutions, optionally, the navigation centerline is determined based on multiple coordinate data, including obtaining multiple incoming cargo coordinate data and multiple opposing cargo coordinate data; determining the incoming centerline based on the multiple incoming cargo coordinate data; determining the opposing centerline based on the multiple opposing cargo coordinate data; and determining the navigation centerline based on the incoming centerline and the opposing centerline.

[0014] In this technical solution, coordinate data is categorized according to the actual navigation direction of the vessel (coming and going) and fitted separately to obtain the actual track deviations of vessels in different directions due to water flow, avoidance habits, etc., thereby improving the accuracy of the course centerline. By fitting centerlines in different directions, the generated coming / going centerlines can be made more consistent with the physical scenario. The final navigation centerline determined by combining the centerlines in both directions not only preserves the safe passage boundary for vessels in both directions and avoids the risk of running aground due to one-way data deviation, but also avoids the path distortion problem of traditional single centerline models in bends and narrow waters through dynamic balancing, providing autonomous vessels with a globally optimal path that is closer to human navigation experience.

[0015] In some technical solutions, optionally, the navigation centerline is determined based on multiple coordinate data, including obtaining multiple incoming cargo coordinate data and multiple outgoing cargo coordinate data based on multiple cargo coordinate data; and fitting the navigation centerline based on the multiple incoming cargo coordinate data and multiple outgoing cargo coordinate data.

[0016] In this technical solution, the actual navigation coordinates of the incoming and outgoing cargo ships, i.e., multiple coordinate data, can be fitted in one step to generate a centerline that can take into account the safety of two-way navigation, thus avoiding the accumulation of errors from step-by-step fitting.

[0017] In some technical solutions, optionally, the speed limit value of the river section to be measured is determined based on multiple first speed information, including: the river section to be measured includes multiple speed limit sections; multiple second speed information passing through each speed limit section is obtained based on multiple first speed information; and the speed limit value of each speed limit section in the river section to be measured is determined based on multiple second speed information.

[0018] This technical solution divides the entire river into multiple speed-limited sections, specifically key areas such as bends, bridges, and shoals. Based on the actual speed data (secondary speed information) within each sub-section, the speed limit is calculated by averaging the data. Dividing the entire river into multiple speed-limited sections and calculating the speed limit for each segment independently avoids localized safety hazards caused by a uniform speed limit across the entire river. This improves the safety of vessel speed during automated navigation.

[0019] In some technical solutions, optionally, multiple historical navigation data of at least one cargo vessel passing through the section of the river to be measured can be obtained, including: obtaining navigation data stored locally on the vessel, and determining multiple historical navigation data based on the navigation data stored locally on the vessel; or receiving a dataset sent by a server, the dataset including multiple historical navigation data of at least one cargo vessel passing through the section of the river to be measured.

[0020] In this technical solution, the locally stored data can ensure that the ship's own historical navigation data can still be accessed in the event of network interruption or server failure, such as historical navigation data of the ship passing through the river section multiple times.

[0021] By aggregating historical navigation data from multiple ships on a server-side dataset, including ship type, speed, and location coordinates, the efficiency of data acquisition can be improved.

[0022] In some technical solutions, the historical navigation data of the river section to be measured can be updated according to a preset cycle.

[0023] This technical solution utilizes regularly collected, up-to-date ship navigation data to promptly capture dynamic risks such as river siltation, reef displacement, or changes in artificial navigation marks, preventing the failure of existing centerlines / outlines due to environmental changes. Periodically supplementing high-value cargo ship trajectory data (such as new routes and ship types) continuously corrects the fitting deviation between the navigation centerline and boundary lines, improving the accuracy of automated driving paths. A periodic update mechanism enables the system to self-evolve, integrating differences in channel depth between low and high water periods (i.e., changes in cargo ship draft directly reflect safe navigation ranges) and accumulating obstacle avoidance experience under extreme weather conditions. This provides ships with a real-time, reliable navigation decision-making basis, reducing the risk of grounding or deviation due to outdated data.

[0024] A second aspect of this application provides a control device for automatic navigation of a ship, comprising a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module, a fifth acquisition module, and a sixth acquisition module. The first acquisition module acquires multiple historical navigation data of at least one cargo ship passing through a section of river to be measured; the second acquisition module acquires multiple coordinate data based on the multiple historical navigation data; the third acquisition module determines a navigation centerline and a navigation outline based on the multiple coordinate data; the fourth acquisition module acquires multiple first speed information of the cargo ship passing through the section of river to be measured based on the historical navigation data; the fifth acquisition module determines the speed limit value of the section of river to be measured based on the multiple first speed information; and the sixth acquisition module determines the target channel for the ship's navigation within the section of river to be measured based on satellite map data of the section of river to be measured, the navigation centerline, the navigation outline, and the speed limit value of the section of river to be measured, and controls the ship to navigate according to the target channel.

[0025] In the automatic navigation control device for ships provided in this application, a first acquisition module acquires multiple historical navigation data of at least one cargo ship passing through a river section to be measured. The river section to be measured refers to the waterway or other water area where the ship is about to navigate, and the multiple historical navigation data are the historical AIS (Automatic Identification System) data of the waterway or other water area where the ship is about to navigate. The historical AIS data includes the positioning information (latitude and longitude data), heading information, and speed information of various types of ships in this section of waterway or water area. Therefore, based on the multiple historical navigation data, a second acquisition module can acquire multiple coordinate data, which are the coordinate data of the cargo ship navigating within the river section to be measured. Specifically, the time information of the data can be adjusted to improve data accuracy. Since the multiple coordinate data points represent the location points of multiple cargo vessels within the waterway, these points are all areas where vessels can navigate normally. Therefore, the navigation centerline can be obtained by fitting the multiple location points, i.e., the multiple coordinate data points, through the fitting algorithm of the third acquisition module. In essence, a centerline can be drawn at the midpoint between the multiple location points; this centerline is the navigation centerline. During autopilot operation, the vessel can navigate along the navigation centerline to avoid running aground. The edges of the dispersed areas of the multiple location points can be used to create the navigation outline, i.e., the boundary line where the vessel can navigate, which is the navigation outline. This application samples historical navigation data of cargo vessels. Because cargo vessels have a greater draft than empty vessels, using cargo vessels as sample data can maximize the detection of reefs, thereby improving the accuracy of drawing the waterway centerline and outline. Therefore, by using historical navigation data and the third acquisition module to deduce the waterway centerline and outline, the difficulty of drawing regional maps can be reduced, and reefs can be eliminated without the use of underwater detection instruments, saving surveying costs. Since historical navigation data records the speeds of multiple vessels within the river section to be measured, multiple first-speed information for cargo vessels passing through this section can be obtained through historical navigation data and the fourth acquisition module. This first-speed information can be a numerical value. The fifth acquisition module averages these speed values ​​to obtain the speed limit for the river section under test, thereby improving navigation safety. The sixth acquisition module, based on satellite map data, navigation centerline, navigation outline, and the speed limit for the river section under test, can determine the target channel for vessels navigating within that section. The target channel can be displayed on a map of the river section under test, which also displays the navigation centerline, navigation outline, and speed limit. After obtaining the map of the river section under test, vessels can be controlled to navigate along the target channel on the map, eliminating the need for on-site data collection by vessels or vehicles, thus saving manpower, resources, and time.Because the proposed solution can extrapolate routes from data and thus avoid underwater obstacles such as reefs, it can create navigable map boundaries for cargo ships with deep drafts without the need for underwater surveying instruments, saving costs and reducing technical complexity. By utilizing historical navigation data, the resulting channel centerline closely matches human navigation, thereby improving the accuracy and safety of automated navigation. The map of the river section to be surveyed can simultaneously provide information such as the channel centerline, boundary lines, and speed limits, enhancing its practicality.

[0026] The third aspect of this application provides a control device for automatic navigation of a ship, which includes a processor and a memory. The memory stores programs or instructions, and the processor executes the programs or instructions in the memory to implement the steps of the control method for automatic navigation of a ship as described in any of the above technical solutions.

[0027] In some technical solutions, optionally, the control device for automatic ship navigation includes a processor and a memory. The memory stores programs or instructions, and when the processor executes the programs or instructions in the memory, it implements the steps of the automatic ship navigation control method as described in any of the above technical solutions. Therefore, this automatic ship navigation control device possesses all the beneficial effects of the automatic ship navigation control method in any of the above technical solutions, which will not be elaborated further here.

[0028] The fourth aspect of this application provides a readable storage medium having a program or instructions stored thereon, which, when executed by a processor, implements the steps of the control method for automatic navigation of a ship as described in any of the above technical solutions, and thus has all the beneficial technical effects of the control method for automatic navigation of a ship as described in any of the above technical solutions.

[0029] Additional aspects and advantages of this application will become apparent in the following description or may be learned by practice of this application. Attached Figure Description

[0030] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 A flowchart of a control method for automatic navigation of a ship provided in some embodiments of this application is shown; Figure 2 This illustration shows one of the schematic diagrams of the distribution of target channels on a satellite map in some embodiments of this application; Figure 3 This is shown as a second schematic diagram illustrating the distribution of target channels on a satellite map in some embodiments of this application; Figure 4 This is shown as the third schematic diagram of the distribution of target channels on a satellite map in some embodiments of this application; Figure 5 The following is a structural block diagram of a control device for automatic navigation of a ship, provided in some embodiments of this application; Figure 6 The second block diagram shows a structural block diagram of a control device for automatic navigation of a ship, provided in some embodiments of this application. Detailed Implementation

[0031] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, these embodiments and the features described herein can be combined with each other.

[0032] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0033] The following reference Figures 1 to 6 This application describes a control method, apparatus, and readable storage medium for automatic navigation of a ship according to some embodiments.

[0034] like Figure 1 As shown, this application provides a control method for automatic navigation of a ship, the steps of which include: Step 102: Obtain multiple historical navigation data of at least one cargo ship that has passed through the section of the river to be tested; Step 104: Obtain multiple coordinate data based on multiple historical navigation data; Step 106: Determine the navigation centerline and navigation outline based on multiple coordinate data; Step 108: Based on historical navigation data, obtain multiple first speed information of cargo ships passing through the section of the river to be measured; Step 110: Determine the speed limit value of the section of the river to be tested based on multiple first-speed information. Step 112: Based on the satellite map data of the river section to be measured, the navigation centerline, the navigation outline, and the speed limit value of the river section to be measured, determine the target channel for the ship to navigate within the river section to be measured, and control the ship to navigate according to the target channel.

[0035] This application provides a control method for automatic navigation of ships, which includes acquiring multiple historical navigation data of at least one cargo ship passing through a section of a river to be measured. The section of the river to be measured refers to the waterway or other water area where the ship is about to navigate, and the multiple historical navigation data are the historical AIS (Automatic Identification System) data of the waterway or other water area where the ship is about to navigate. The historical AIS data includes the positioning information (latitude and longitude data), heading information, and speed information of various types of ships in this section of the waterway or water area. Therefore, multiple coordinate data can be obtained based on the multiple historical navigation data. These multiple coordinate data represent the multiple coordinate data of the cargo ship navigating within the section of the river to be measured. Specifically, the time information of the data can be adjusted to improve data accuracy. Since the multiple coordinate data points represent the location points of multiple cargo vessels within the waterway, these points are all areas where vessels can navigate normally. Therefore, the navigation centerline can be derived from these multiple location points (i.e., multiple coordinate data points) using a fitting algorithm. Essentially, a centerline can be drawn at the midpoints between these multiple location points; this centerline serves as the navigation centerline. During autopilot operation, vessels can navigate along this centerline to avoid running aground. The edges of the dispersed areas of the multiple location points can be used to create the navigation outline, which is the boundary line along which the vessel can navigate. This application samples historical navigation data from cargo vessels. Because cargo vessels have a greater draft than empty vessels, using cargo vessels as sample data maximizes the detection of reefs, thereby improving the accuracy of the channel centerline and outline drawing. Therefore, extrapolating the channel centerline and outline from historical navigation data reduces the difficulty of mapping the area and allows for the elimination of reefs without the use of underwater detection instruments, saving surveying costs. Since historical navigation data records the speeds of multiple vessels within the river section to be measured, multiple initial speeds of cargo vessels passing through this section can also be obtained from this data. These initial speeds can be numerical values. The average of these speed values ​​can be used as the speed limit for that section of the river, thereby improving navigation safety. Based on satellite map data, the navigation centerline, the navigation outline, and the speed limit for the river section to be measured, the target channel for vessels within that section can be determined. This target channel can be displayed on a map of the river section, which also shows the navigation centerline, the navigation outline, and the speed limit. After obtaining the map of the river section to be measured, vessels can be controlled to navigate along the target channel shown on the map, eliminating the need for on-site data collection by vessels or vehicles, thus saving manpower, resources, and time.Because the proposed solution can extrapolate routes from data and thus avoid underwater obstacles such as reefs, it can create navigable map boundaries for cargo ships with deep drafts without the need for underwater surveying instruments, saving costs and reducing technical complexity. By utilizing historical navigation data, the resulting channel centerline closely matches human navigation, thereby improving the accuracy and safety of automated navigation. The map of the river section to be surveyed can simultaneously provide information such as the channel centerline, boundary lines, and speed limits, enhancing its practicality.

[0036] In some embodiments, optionally, acquiring multiple historical navigation data of at least one cargo vessel passing through the river section to be measured includes: acquiring multiple initial historical navigation data of multiple vessels passing through the river section to be measured; the initial historical navigation data includes: first speed information, vessel type, and navigation status within the section of multiple vessels; filtering the initial historical navigation data based on the first speed information, vessel type, navigation status within the section of multiple vessels, and preset filtering conditions to determine at least one cargo vessel passing through the river section to be measured; acquiring multiple historical navigation data of at least one cargo vessel passing through the river section to be measured based on the historical navigation data; wherein the preset filtering conditions include at least: the first speed information is zero, the vessel type is a non-cargo vessel, and the navigation status within the section is a moored state.

[0037] In this embodiment, multiple initial historical navigation data of multiple vessels passing through the river section to be measured are acquired. The initial historical navigation data includes: the first speed information of the multiple vessels, vessel type, and navigation status within the section. Based on the first speed information, vessel type, navigation status within the section, and preset filtering conditions, the initial historical navigation data is filtered to determine at least one cargo vessel passing through the river section to be measured. Based on the historical navigation data, multiple historical navigation data of at least one cargo vessel passing through the river section to be measured are acquired. The preset filtering conditions include at least: the first speed information is zero, the vessel type is a non-cargo vessel, and the navigation status within the section is a moored state. Alternatively, the historical navigation data can be further filtered by distinguishing between high-speed vessels (greater than 5 knots) and low-speed vessels (less than 5 knots) to select vessels suitable for sampling. By excluding data from vessels with zero speed (stationary), non-cargo vessels (shallow draft), and those in anchored positions, the actual navigation trajectories of cargo vessels are accurately selected. This avoids invalid or low-reference-value data (such as anchored vessels and passenger ships) interfering with the mapping of the river area under test, thus affecting the automatic navigation of ships. The filtered data comprehensively reflects the dynamic navigation status of cargo vessels, and their draft characteristics enable the fitted navigation centerline and contour line to more accurately avoid reefs. The generated boundary line directly corresponds to the safe navigation threshold for cargo vessels.

[0038] Meanwhile, the speed limits, centerlines, and boundary lines generated based on dynamic navigation data closely match actual navigation scenarios, avoiding the high costs of underwater surveying and providing a reliable navigation framework based on human driving experience for autonomous driving, significantly improving the safety and adaptability of autonomous navigation.

[0039] In some embodiments, optionally, determining the navigation centerline based on multiple coordinate data includes obtaining multiple incoming cargo coordinate data and multiple opposing cargo coordinate data based on the multiple coordinate data; determining the incoming centerline based on the multiple incoming cargo coordinate data; determining the opposing centerline based on the multiple opposing cargo coordinate data; and determining the navigation centerline based on the incoming centerline and the opposing centerline.

[0040] In this embodiment, coordinate data is categorized according to the actual navigation direction of the vessel (coming and going) and fitted separately to obtain the actual track deviations of vessels in different directions due to water flow, avoidance habits, etc., thereby improving the accuracy of the route centerline. Fitting centerlines in different directions makes the generated coming / going centerlines more consistent with the physical scenario. The final navigation centerline determined by combining the centerlines in both directions not only preserves the safe passage boundary for vessels in both directions and avoids the risk of running aground due to one-way data deviation, but also avoids the path distortion problem of traditional single centerline models in curves and narrow waters through dynamic balancing, providing autonomous vessels with a globally optimal path that is closer to human navigation experience.

[0041] In some embodiments, optionally, determining the navigation centerline based on multiple coordinate data includes obtaining multiple incoming cargo coordinate data and multiple opposing cargo coordinate data based on multiple cargo coordinate data; and fitting the navigation centerline based on the multiple incoming cargo coordinate data and multiple opposing cargo coordinate data.

[0042] In this embodiment, the actual navigation coordinates of the incoming and outgoing cargo ships, i.e., multiple coordinate data, can be fitted in one step to generate a centerline that can take into account the safety of two-way navigation, thus avoiding the accumulation of errors from step-by-step fitting.

[0043] In some embodiments, optionally, determining the speed limit value of the river section to be measured based on multiple first speed information includes: the river section to be measured includes multiple speed limit sections; obtaining multiple second speed information passing through each speed limit section based on multiple first speed information; and determining the speed limit value of each speed limit section in the river section to be measured based on multiple second speed information.

[0044] In this embodiment, the entire river is divided into multiple speed-limited sections, specifically key areas such as bends, bridges, and shoals. The speed limit is calculated by averaging the actual speed data (second speed information) within each sub-section. Dividing the entire river into multiple speed-limited sections and calculating the speed limit for each segment independently avoids localized safety hazards caused by a uniform speed limit across the entire river. This improves the safety of vessel speed during automated navigation.

[0045] Specifically, zone speed limits can be directly embedded into the ship navigation system to automatically trigger deceleration control when passing through high-risk areas. For example, the speed limit for curves can be used as a speed planning constraint to achieve a balance between safety and traffic efficiency across the entire route.

[0046] In some embodiments, optionally, acquiring multiple historical navigation data of at least one cargo vessel passing through the section of the river to be measured includes: acquiring navigation data stored locally on the vessel, and determining multiple historical navigation data based on the navigation data stored locally on the vessel; or receiving a dataset sent by a server, the dataset including multiple historical navigation data of at least one cargo vessel passing through the section of the river to be measured.

[0047] In this embodiment, the locally stored data ensures that the ship's own historical navigation data, such as historical navigation data of the ship passing through the river section multiple times, can still be accessed in the event of network interruption or server failure.

[0048] By aggregating historical navigation data from multiple ships on a server-side dataset, including ship type, speed, and location coordinates, the efficiency of data acquisition can be improved.

[0049] In some embodiments, the historical navigation data of the river section to be measured may be updated at a preset period.

[0050] In this embodiment, by regularly collecting the latest ship navigation data, dynamic risks such as river siltation, reef displacement, or changes in artificial navigation marks can be promptly captured, preventing the original centerline / outline from becoming invalid due to environmental changes. By periodically supplementing high-value cargo ship trajectory data (such as new routes and new ship types), the fitting deviation between the navigation centerline and boundary lines is continuously corrected, improving the accuracy of the autonomous driving path. The periodic update mechanism enables the system to have self-evolution capabilities, integrating the differences in channel depth between dry and wet seasons (i.e., changes in cargo ship draft directly reflect the safe navigation range) and accumulating obstacle avoidance experience under extreme weather conditions. This provides ships with a real-time and reliable navigation decision-making basis, reducing the risk of grounding or deviation due to outdated data.

[0051] In some embodiments, optionally, such as Figure 2 , Figure 3 and Figure 4As shown, this application collects and organizes historical AIS data of inland waterway vessels. Outline A represents the outline of both sides of the inland waterway collected by radar and detection equipment. This application categorizes the AIS data of the same inland waterway, with the data mainly including latitude, longitude, heading, and speed. Data traveling in different directions are divided into two categories: incoming and outgoing. Data traveling at different speeds are divided into low-speed and high-speed categories with a 5-knot dividing line, thus further classifying the data. Simultaneously, stationary, non-cargo, berthed / unberthed, and non-cargo vessels are filtered out. The AIS positioning data of vessels traveling in different directions are plotted on a satellite map. a1 and a2 represent vessels traveling in different directions, showing that they are generally traveling slightly to the right of the center. An empirical high-precision map of the waterway centerline is fitted, and the waterway centerline, i.e., navigation centerline B, is drawn at the midpoint between a1 and a2. This can be done manually or automatically generated by calculating the average of points. The navigation outline C is the outer contour line drawn based on historical AIS positioning data. It represents the maximum range the vessel has historically navigated, and serves as the boundary line of the empirically-based high-precision map. Because this area represents the actual navigation route, the risk of grounding is significantly reduced. Therefore, the navigation centerline B and the navigation outline C can constitute the target channel for the vessel navigating within the measured section of the river.

[0052] By acquiring navigation data from empty vessels, the channel boundaries for empty vessels can also be drawn. Based on the different AIS data of empty and cargo ships, the average speed can be calculated to draw speed limits for vessels on different sections of the route, thereby restricting the speed of autonomous vessels to prevent them from exceeding the average speed for human pilots.

[0053] like Figure 5 As shown, this application provides a control device 200 for automatic navigation of a ship, including a first acquisition module 210, a second acquisition module 220, a third acquisition module 230, a fourth acquisition module 240, a fifth acquisition module 250, and a sixth acquisition module 260. The first acquisition module 210 is used to acquire multiple historical navigation data of at least one cargo ship passing through a section of river to be measured; the second acquisition module 220 is used to acquire multiple coordinate data based on the multiple historical navigation data; the third acquisition module 230 is used to determine the navigation centerline and navigation outline based on the multiple coordinate data; the fourth acquisition module 240 is used to acquire multiple first speed information of the cargo ship passing through the section of river to be measured based on the historical navigation data; the fifth acquisition module 250 is used to determine the speed limit value of the section of river to be measured based on the multiple first speed information; and the sixth acquisition module 260 is used to determine the target channel for the ship to navigate within the section of river to be measured based on satellite map data of the section of river to be measured, the navigation centerline, the navigation outline, and the speed limit value of the section of river to be measured, and control the ship to navigate according to the target channel.

[0054] In the automatic navigation control device 200 provided in this application, the first acquisition module 210 acquires multiple historical navigation data of at least one cargo vessel passing through a river section to be measured. The river section to be measured refers to the waterway or other water area where the vessel is about to navigate, and the multiple historical navigation data are the historical AIS (Automatic Identification System) data of the waterway or other water area where the vessel is about to navigate. The historical AIS data includes the positioning information (latitude and longitude data), heading information, and speed information of various vessels in this section of the waterway or water area. Therefore, based on the multiple historical navigation data, the second acquisition module 220 can acquire multiple coordinate data, which are the coordinate data of the cargo vessel navigating within the river section to be measured. Specifically, the time information of the data can be adjusted to improve data accuracy. Since the multiple coordinate data points represent the location points of multiple cargo vessels within the waterway, these points are all areas where vessels can navigate normally. Therefore, the navigation centerline can be obtained from these multiple location points, i.e., the multiple coordinate data points, through the fitting algorithm of the third acquisition module 230. In essence, a centerline can be drawn at the midpoint between these multiple location points; this centerline is the navigation centerline. During autopilot operation, the vessel can navigate along this centerline to avoid running aground. The edges of the dispersed areas of the multiple location points can be used to create the navigation outline, i.e., the boundary line where the vessel can navigate. This application samples historical navigation data from cargo vessels. Because cargo vessels have a greater draft than empty vessels, using cargo vessels as sample data maximizes the detection of reefs, thereby improving the accuracy of the channel centerline and outline drawing. Therefore, by using historical navigation data and the third acquisition module 230 to deduce the channel centerline and outline, the difficulty of drawing regional maps can be reduced, and reefs can be eliminated without the use of underwater detection instruments, saving surveying costs. Since historical navigation data records the speeds of multiple vessels within the river section to be measured, multiple first speed information of cargo vessels passing through the river section to be measured can also be obtained through historical navigation data and the fourth acquisition module 240. The first speed information can be a numerical value. The fifth acquisition module 250 averages the speed values ​​to obtain the speed limit value of the river section to be measured, thereby improving navigation safety. The sixth acquisition module 260, based on satellite map data of the river section to be measured, the navigation centerline, the navigation outline, and the speed limit value of the river section to be measured, can determine the target channel for the vessel to navigate within the river section to be measured. The target channel can be displayed on the map of the river section to be measured, which specifically displays the navigation centerline, the navigation outline, and the speed limit value of the river section to be measured.After obtaining a map of the river section to be measured, vessels can be controlled to navigate along the target channel on the map without the need for on-site data collection by ships or vehicles, saving manpower, resources, and time. Since the proposed solution can calculate the route from data and thus avoid underwater obstacles such as reefs, it can draw the navigable map boundaries of deep-draft cargo ships without the need for underwater surveying instruments, saving costs and reducing technical complexity. Using historical navigation data, the drawn channel centerline has a higher degree of alignment with human driving, thereby improving the accuracy and safety of automated navigation. The map of the river section to be measured can simultaneously provide information such as the channel centerline, boundary lines, and speed limits, enhancing practicality.

[0055] like Figure 6 As shown, this application provides a control device 300 for automatic navigation of a ship. The control device 300 includes a processor 310 and a memory 320. The memory 320 stores programs or instructions. When the processor 310 executes the programs or instructions in the memory 320, it implements the steps of the control method for automatic navigation of a ship as described in any of the above embodiments.

[0056] In some embodiments, the ship automatic navigation control device optionally includes a processor and a memory, wherein the memory stores programs or instructions, and the processor, when executing the programs or instructions in the memory, implements the steps of the ship automatic navigation control method as described in any of the above embodiments. Therefore, this ship automatic navigation control device possesses all the beneficial effects of the ship automatic navigation control method in any of the above embodiments, which will not be elaborated further here.

[0057] This application provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the control method for automatic navigation of a ship as described in any of the above embodiments, and thus possesses all the beneficial technical effects of the control method for automatic navigation of a ship as described in any of the above embodiments.

[0058] It should be clarified that in the claims, description, and accompanying drawings of this application, the term "multiple" refers to two or more objects. Unless otherwise explicitly defined, the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description process, not to indicate or imply that the device or element referred to must have the described specific orientation, or be constructed and operated in a specific orientation. Therefore, these descriptions should not be construed as limitations on this application. The terms "connection," "installation," "fixing," etc., should be interpreted broadly. For example, "connection" can be a fixed connection between multiple objects, a detachable connection between multiple objects, or an integral connection; it can be a direct connection between multiple objects or an indirect connection between multiple objects through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in this application can be understood based on the specific circumstances of the above data.

[0059] In the claims, description, and accompanying drawings of this application, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In the claims, description, and accompanying drawings of this application, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0060] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A control method for automatic navigation of a ship, characterized in that, include: Obtain multiple historical navigation data points for at least one cargo vessel that has passed through the section of the river to be measured; Based on the aforementioned historical navigation data, multiple coordinate data are obtained; Based on the coordinate data, determine the navigation centerline and navigation outline; Based on the historical navigation data, obtain multiple first speed information of the cargo vessel passing through the section of the river to be measured; Based on multiple sets of the first speed information, the speed limit value of the section of the river to be tested is determined; Based on satellite map data of the river section to be measured, the navigation centerline, the navigation outline, and the speed limit value of the river section to be measured, the target channel for the vessel to navigate within the river section to be measured is determined, and the vessel is controlled to navigate according to the target channel.

2. The control method for automatic navigation of ships according to claim 1, characterized in that, The acquisition of multiple historical navigation data points for at least one cargo vessel passing through the section of the river to be measured includes: Acquire multiple initial historical navigation data of multiple vessels passing through the section of the river to be measured; The initial historical navigation data includes: the first speed information of multiple vessels, vessel type, and navigation status within the segment; Based on the first speed information of multiple vessels, vessel type, navigation status within the section and preset screening conditions, the initial historical navigation data is filtered to determine at least one cargo vessel that has passed through the river section to be tested. Based on the historical navigation data, obtain multiple historical navigation data of at least one cargo ship that has passed through the section of the river to be measured; The preset screening conditions include at least the following: the first speed information is zero, the vessel type is a non-cargo vessel, and the navigation status within the section is a moored state.

3. The control method for automatic navigation of ships according to claim 1, characterized in that, Determining the navigation centerline based on multiple coordinate data includes: Based on the multiple coordinate data, obtain multiple inbound cargo coordinate data and multiple outbound cargo coordinate data; Based on multiple incoming cargo coordinate data, the incoming centerline is determined; Based on multiple opposing cargo coordinate data, the opposing centerline is determined; Determine the navigation centerline based on the incoming centerline and the opposing centerline.

4. The control method for automatic navigation of ships according to claim 1, characterized in that, Determining the navigation centerline based on multiple coordinate data includes: Based on the multiple coordinate data, obtain multiple inbound cargo coordinate data and multiple outbound cargo coordinate data; Based on multiple incoming cargo coordinate data and multiple opposing cargo coordinate data, a navigation centerline is fitted.

5. The control method for automatic navigation of ships according to claim 1, characterized in that, The step of determining the speed limit value of the river section to be tested based on multiple first speed information includes: The river section to be tested includes multiple speed-limited sections; Based on multiple first speed information, obtain multiple second speed information for each speed-limited section; Based on multiple pieces of the second speed information, the speed limit value of each speed-limited section in the river section to be tested is determined.

6. The control method for automatic navigation of ships according to claim 1, characterized in that, The acquisition of multiple historical navigation data points for at least one cargo vessel passing through the section of the river to be measured also includes: Obtain the navigation data stored locally on the vessel, and determine multiple historical navigation data based on the navigation data stored locally on the vessel; or Receive a dataset sent by the server, the dataset including multiple historical navigation data of at least one cargo vessel that has passed through the section of the river to be measured.

7. The control method for automatic navigation of a ship according to any one of claims 1 to 6, characterized in that, The historical navigation data of the river section to be tested is updated according to a preset cycle.

8. A control device for automatic navigation of a ship, characterized in that, include: The first acquisition module is used to acquire multiple historical navigation data of at least one cargo ship that has passed through the section of the river to be measured. The second acquisition module is used to acquire multiple coordinate data based on the multiple historical navigation data. The third acquisition module is used to determine the navigation centerline and navigation outline based on multiple coordinate data. The fourth acquisition module is used to acquire multiple first speed information of cargo ships passing through the section of the river to be measured, based on the historical navigation data. The fifth acquisition module is used to determine the speed limit value of the river section to be measured based on multiple first speed information. The sixth acquisition module is used to determine the target channel for the ship to navigate within the river section to be measured based on satellite map data of the river section to be measured, the navigation centerline, the navigation outline, and the speed limit value of the river section to be measured, and to control the ship to navigate according to the target channel.

9. A control device for automatic navigation of a ship, characterized in that, include: processor; A memory storing programs or instructions, wherein the processor, when executing the programs or instructions in the memory, implements the steps of the control method for automatic navigation of a ship as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the control method for automatic navigation of a ship as described in any one of claims 1 to 7.

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