Multi-modal dynamic region chart generation loading and self-adaptive navigation method and multi-modal dynamic region chart generation loading and self-adaptive navigation system

Abstract

The invention relates to a multi-modal dynamic region chart generation loading and self-adaptive navigation method and system. The method comprises the following steps: acquiring multi-modal data information; generating a self-adaptive chart based on the static chart data and the dynamic environment information; based on the ship characteristic data and the navigation plan information, generating multi-target path information associated with a self-adaptive sea chart, and sending the multi-target path information to a ship end and a base shore end; when target path information fed back by the ship end and the base shore end is received, navigation information is generated based on the target path information; receiving feedback information from the ship end and the base shore end based on a preset receiving frequency, and updating navigation information; by integrating the static chart data, the dynamic environment information, the ship characteristic data and the navigation plan information, the navigation system with environment perception, path planning and closed-loop updating is constructed, the conversion from the static chart to dynamic adaptive navigation is realized, and the effect of improving the navigation safety and efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of nautical chart generation and navigation, and in particular to a multi-modal dynamic area nautical chart generation, loading and adaptive navigation method and system. Background Art

[0002] In the field of maritime navigation, traditional electronic nautical chart display and information systems have long served as the core technological support for ship navigation. Existing technologies primarily rely on static electronic nautical chart data, integrating GPS positioning and AIS vessel dynamic information to achieve basic navigation functions. While these systems can provide a visual display of fixed geographic information such as waterways and water depths, they exhibit significant limitations in practical applications.

[0003] Due to the lack of real-time integration capabilities for multi-source dynamic environmental information, existing electronic nautical chart navigation systems are unable to promptly reflect dynamic navigation risk factors such as tidal changes and sudden weather changes, resulting in navigation safety being highly dependent on the crew's manual judgment. Especially in complex waters or severe weather conditions, static nautical charts are unable to dynamically mark real-time obstacle information, increasing the lag in navigation decisions. At the same time, existing navigation path planning mostly uses fixed algorithms that fail to fully consider the differences in characteristics of different ships, making the generated routes often difficult to match actual navigation needs.

[0004] The above problems directly lead to the following defects in the existing electronic nautical chart display and navigation systems: the static environment perception leads to delayed navigation risk warnings, the differences in the adaptation of ship characteristics lead to insufficient practicality of path planning, and the weak system coordination restricts the improvement of global navigation efficiency. Summary of the Invention

[0005] In order to solve the above-mentioned defects, the present application provides a multi-modal dynamic area chart generation, loading and adaptive navigation method and system.

[0006] The above-mentioned invention objective of this application is achieved through the following technical solutions:

[0007] A multi-modal dynamic area chart generation, loading and adaptive navigation method comprises the following steps:

[0008] Acquiring multimodal data information, the multimodal data information including static chart data, dynamic environment information, ship characteristic data, and voyage plan information;

[0009] Generate adaptive charts based on static chart data and dynamic environmental information;

[0010] Generates multi-target path information associated with adaptive charts based on ship characteristic data and voyage plan information, and sends it to the ship and shore terminals;

[0011] When receiving the target path information fed back by the ship side and the shore side, the navigation information is generated based on the target path information and sent to the ship side and the shore side;

[0012] Feedback information is received from the ship end and the shore end based on a preset receiving frequency, and navigation information is updated based on the feedback information and sent to the ship end and the shore end.

[0013] By adopting the above technical solution, the present application integrates static nautical chart data, dynamic environmental information, ship characteristic data and navigation plan information to construct a navigation system with environmental perception, path planning and closed-loop update, realizing the transformation from static nautical charts to dynamic adaptive navigation, which has the effect of improving navigation safety and efficiency: by combining dynamic environmental information with static nautical chart data, the generated adaptive nautical chart can dynamically adapt to navigation risks and realize dynamic risk warning for ship navigation; by generating multi-target path information of the adaptive nautical chart based on ship characteristic data and navigation plan information, the route planning is matched with the actual characteristics of the ship, and a variety of multi-target paths are provided to the ship end and the base shore end; through the two-way data synchronization mechanism between the ship end and the shore end and the feedback optimization mechanism based on both ends, the dynamic refresh of navigation information and the coordinated optimization of ship navigation resources are realized.

[0014] In a preferred example, the present application may be further configured as follows: the step of generating an adaptive nautical chart based on static nautical chart data and dynamic environmental information includes the following steps:

[0015] Generate basic base maps based on static chart data;

[0016] Through dynamic environment information and navigation plan information, coordinate conversion and scale optimization processing are performed on the basic base map to obtain the target base map;

[0017] Generate dynamic risk layers based on dynamic environmental information, including tidal risk layers, meteorological risk layers, and traffic risk layers;

[0018] Generate adaptive charts based on dynamic risk layers and target basemaps.

[0019] By adopting the above technical solution, the adaptive nautical charts are processed and combined in layers to achieve the fusion of static and dynamic navigation data, and to construct an adaptive nautical chart system with environmental perception capabilities: a basic base map is constructed based on standardized static nautical chart data to ensure the integrity and accuracy of basic geographic information for navigation; then, through the coordinated processing of dynamic environmental data and navigation plan information, the coordinate system of the basic base map is normalized and the scale is adaptively adjusted to match the chart display range with current navigation needs; then, dynamic environmental data such as meteorology, hydrology and traffic collected in real time are used to generate a dynamic risk layer, and the optimized base map is integrated with the dynamic risk layer to form an adaptive nautical chart that reflects the state of the navigation environment; through the above modular processing flow, this application achieves the integration and visual expression of dynamic risk information while maintaining the stability of basic nautical chart data, providing reliable environmental perception support for navigation decisions, and has the effect of improving the accuracy and timeliness of navigation situation perception.

[0020] In a preferred example, the present application may be further configured as follows: the dynamic environmental information includes tidal data, meteorological data, and AIS ship information; and the step of generating a dynamic risk layer based on the dynamic environmental information, wherein the risk layer includes a tidal risk layer, a meteorological risk layer, and a traffic risk layer, comprises the following steps:

[0021] Generate tidal risk layers based on real-time tidal data;

[0022] Generate meteorological risk layers based on meteorological data;

[0023] Generate traffic risk layers based on AIS ship information;

[0024] The tidal risk layer, meteorological risk layer and traffic risk layer are spatially overlaid and analyzed to generate a dynamic risk layer.

[0025] By adopting the above technical solution, a classification processing and fusion mechanism for multi-source dynamic environmental data is constructed to achieve accurate identification of navigation risk factors; specifically, this application first generates and processes the associated risk layers of different types of dynamic environmental information: based on real-time tidal data, the impact of tidal height changes on ship draft is calculated to generate a tidal risk layer with tide time forecasting function; by analyzing factors such as wind speed and visibility through meteorological data, a meteorological risk layer reflecting the distribution characteristics of severe weather is established; AIS ship information is used to identify the density and encounter situation of surrounding ships to form a traffic risk layer that characterizes the degree of navigation traffic risk; the three types of risk layers are fused using a spatial overlay analysis method, which not only retains the characteristics of each type of risk, but also realizes the coordinated expression of risk factors through a unified spatial reference system; this application realizes the perception and early warning of navigation environment risks through the above-mentioned classification processing and comprehensive integration technology integration, and provides multi-dimensional environmental situation support for navigation decision-making, so that ship navigation can flexibly respond to the environmental characteristics of different sea areas, and has the effect of improving the accuracy and timeliness of navigation situation perception.

[0026] In a preferred example, the present application may be further configured as follows: the step of performing spatial overlay analysis on the tidal risk layer, the meteorological risk layer, and the traffic risk layer to generate a dynamic risk layer includes the following steps:

[0027] Unify the tidal risk layer, meteorological risk layer, and traffic risk layer into the same spatial coordinate system and construct a risk assessment matrix;

[0028] Calculate the comprehensive risk value of each spatial unit in the risk assessment matrix through a preset superposition algorithm;

[0029] The risk weight coefficient of each spatial unit in the risk assessment matrix is ​​set based on AIS ship information, and a dynamic risk layer is generated.

[0030] By adopting the above technical solution, the tidal risk layer, meteorological risk layer and traffic risk layer are unified into the same geographic spatial coordinate system to ensure the consistency of spatial benchmarks; a risk assessment matrix based on grid units is constructed, and a preset superposition algorithm is used to perform weighted calculations on each risk factor, wherein a weight adjustment mechanism based on AIS ship dynamic information is introduced so that the traffic risk coefficient can be dynamically adjusted with changes in ship density and speed; and finally a dynamic risk layer with a unified risk dimension is generated; this application realizes the integration of risk factors of different natures through the above-mentioned standard and quantitative risk fusion method, which not only ensures the objectivity of risk assessment, but also reflects the actual impact of risk factors through the dynamic weight adjustment mechanism, so that the generated dynamic risk layer can accurately reflect the comprehensive safety status of the navigation environment.

[0031] In a preferred example, the present application can be further configured as follows: the step of generating multi-target path information associated with an adaptive nautical chart based on the ship characteristic data and the voyage plan information and sending the information to the ship end and the base shore end includes the following steps:

[0032] Determine a number of flight segments based on the voyage plan information;

[0033] Calculate the minimum safe water depth for each section based on the ship's characteristic data and generate water depth constraints;

[0034] Set ETA thresholds based on ship characteristics data and voyage plan information;

[0035] Establish a speed-fuel consumption model based on ship characteristic data;

[0036] Multi-objective path information is generated based on a speed-fuel consumption model, a water depth constraint, and an ETA threshold. The multi-objective path information includes the shortest path, the most economical path, and the safest path.

[0037] By adopting the above technical solutions, a path planning method for collaborative optimization of ship characteristics and navigation requirements is constructed to achieve the generation and decision-making of multi-dimensional navigation plans: the segment units are divided according to turning points and key areas in the navigation plan to provide a framework for subsequent calculations; the safe water depth threshold of each segment is calculated through ship characteristic data such as ship draft and maneuverability, and water depth constraints based on navigation safety are established; the time tolerance range ETA threshold is set based on the performance of the ship's main engine and the timeliness requirements of the plan; an accurate speed-fuel consumption correspondence model is established based on the ship's main engine bench test data; finally, a multi-objective optimization path covering the shortest sailing time, lowest fuel consumption and minimum navigation risk is generated by combining the speed-fuel consumption model, water depth constraints and ETA thresholds; this application realizes multi-faceted evaluation and comparison of navigation plans through the above-mentioned multi-factor collaborative optimization, which not only meets the requirements of ship maneuverability characteristics, but also takes into account the balance between navigation safety and economy, providing scientific and reliable solution support for ship navigation decisions, and has the effect of improving the scientific nature of navigation decisions, enhancing navigation safety margins and optimizing ship energy efficiency management.

[0038] In a preferred example, the present application may be further configured as follows: the step of generating multi-objective path information based on the speed-fuel consumption model, the water depth constraint condition, and the ETA threshold, wherein the multi-objective path information includes the shortest path, the most economical path, and the safest path, includes the following steps:

[0039] Construct a target vector including the total voyage time, total fuel consumption and cumulative risk value as the objective function;

[0040] Set speed feasible region constraints based on the speed-fuel consumption model, and load water depth constraints, ETA thresholds, and speed feasible region constraints to constrain the target vector;

[0041] The target vector is solved by a preset solution algorithm to obtain a basic solution set, and the shortest path, the most economical path, and the safest path are extracted.

[0042] By adopting the above technical solution, a path solving mechanism of multi-objective optimization and constraint collaboration is established to realize the decision-making and optimization selection of navigation paths: first, a multi-objective function vector including total navigation time, total fuel consumption and cumulative risk value is constructed to characterize the key performance indicators of navigation in many aspects; based on the speed-fuel consumption model, the feasible range constraints of the ship's speed in each section are determined, and at the same time, navigation conditions such as water depth safety constraints and ETA time window restrictions are loaded to form a complete constraint system; a preset optimization solution algorithm is used to solve the multi-objective problem, obtain the basic solution set, and extract the shortest path, the most economical path and the safest path; this application realizes the quantitative evaluation and trade-off of navigation performance indicators through the above-mentioned multi-objective collaborative optimization method, which not only ensures the feasibility of the path plan, but also provides a variety of navigation strategy options, so that the ship can flexibly adjust the navigation plan according to the actual navigation environment and mission requirements, and provide a scientific decision-making basis for the intelligent navigation of the ship.

[0043] The second object of the present invention is achieved through the following technical solutions:

[0044] A multi-modal dynamic regional chart generation, loading and adaptive navigation system, comprising:

[0045] A data acquisition module, configured to acquire multimodal data information, wherein the multimodal data information includes static chart data, dynamic environment information, ship characteristic data, and voyage plan information;

[0046] A chart generation module for generating adaptive charts based on static chart data and dynamic environmental information;

[0047] A path generation module is used to generate multi-target path information associated with an adaptive chart based on the ship's characteristic data and the voyage plan information, and send it to the ship and the shore terminal;

[0048] The navigation generation module is used to generate navigation information based on the target path information fed back by the ship end and the base shore end, and send it to the ship end and the base shore end;

[0049] The navigation update module is used to receive feedback information from the ship end and the base shore end based on a preset receiving frequency, update the navigation information based on the feedback information and send it to the ship end and the base shore end.

[0050] By adopting the above technical solution, a data acquisition module is used to acquire multimodal data information, wherein the multimodal data information includes static nautical chart data, dynamic environmental information, ship characteristic data and navigation plan information; a nautical chart generation module is used to generate an adaptive nautical chart based on the static nautical chart data and dynamic environmental information; a path generation module is used to generate multi-target path information associated with the adaptive nautical chart based on the ship characteristic data and the navigation plan information, and send it to the ship end and the base shore end; a navigation generation module is used to generate navigation information based on the target path information when receiving target path information fed back from the ship end and the base shore end, and send it to the ship end and the base shore end; a navigation update module is used to receive feedback information from the ship end and the base shore end based on a preset receiving frequency, update the navigation information based on the feedback information, and send it to the ship end and the base shore end.

[0051] In a preferred example, the present application may be further configured as follows: the nautical chart generation module includes:

[0052] Base map generation submodule, used to generate basic base maps based on static chart data;

[0053] The base map optimization submodule is used to perform coordinate conversion and scale optimization on the basic base map through dynamic environment information and navigation plan information to obtain the target base map;

[0054] A layer generation submodule is used to generate dynamic risk layers based on dynamic environmental information, wherein the risk layers include a tidal risk layer, a meteorological risk layer, and a traffic risk layer;

[0055] The chart synthesis submodule is used to generate adaptive charts based on dynamic risk layers and target base maps.

[0056] By adopting the above technical solution, the nautical chart generation module includes: a base map generation submodule, which is used to generate a basic base map based on static nautical chart data; a base map optimization submodule, which is used to perform coordinate conversion and scale optimization processing on the basic base map through dynamic environmental information and navigation plan information to obtain a target base map; a layer generation submodule, which is used to generate dynamic risk layers through dynamic environmental information, and the risk layers include tidal risk layers, meteorological risk layers, and traffic risk layers; and a nautical chart synthesis submodule, which is used to generate an adaptive nautical chart based on the dynamic risk layers and the target base map.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. The application integrates static chart data, dynamic environmental information, ship characteristic data and navigation plan information to build a navigation system with environmental perception, path planning and closed-loop updating, realizes the transition from static chart to dynamic adaptive navigation, and has the effect of improving maritime safety and efficiency: through the combination of dynamic environmental information and static chart data, the generated adaptive chart can dynamically adapt to the navigation risk, and realize dynamic risk warning for ship navigation; through the generation of multi-target path information of adaptive chart based on ship characteristic data and navigation plan information, the matching of route planning and actual ship characteristics is realized, and diversified multi-target paths are provided to the ship end and the base end; through the bidirectional data synchronization mechanism of the ship end and the base end and the feedback optimization mechanism based on the two ends, the dynamic refreshing of navigation information and the collaborative optimization of ship navigation resources are realized;

[0059] 2. The application realizes the fusion of static and dynamic navigation data by hierarchical processing and combination of adaptive chart, builds an adaptive chart system with environmental perception capability, provides reliable environmental perception support for navigation decision, and has the effect of improving the accuracy and timeliness of maritime situation awareness;

[0060] 3. The application realizes the generation and decision of multi-dimensional navigation scheme by building a path planning method of ship characteristic and navigation demand collaborative optimization, which not only meets the requirements of ship maneuvering characteristics, but also balances the safety and economy of navigation, provides scientific and reliable scheme support for ship navigation decision, and has the effect of improving the scientific nature of navigation decision, enhancing the safety margin of navigation and optimizing the energy efficiency management of ship;

[0061] 4. The application realizes the decision and optimization selection of navigation path by establishing a path solving mechanism of multi-target optimization and constraint collaboration, which not only guarantees the feasibility of path scheme, but also provides diversified navigation strategy selection, so that the ship can flexibly adjust the navigation plan according to the actual navigation environment and task demand, and provides scientific decision basis for intelligent navigation of ship. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 is a flowchart of an embodiment of a multi-modal dynamic regional chart generation and adaptive navigation method of the application;

[0063] Figure 2 is an implementation flowchart of step S20 in an embodiment of a multi-modal dynamic regional chart generation and adaptive navigation method of the application;

[0064] Figure 3 is an implementation flowchart of step S23 in an embodiment of a multi-modal dynamic regional chart generation and adaptive navigation method of the application;

[0065] Figure 4This is a flowchart for implementing step S234 in an embodiment of a multi-modal dynamic regional nautical chart generation, loading, and adaptive navigation method of the present application;

[0066] Figure 5 This is a flowchart for implementing step S30 in an embodiment of a multi-modal dynamic regional nautical chart generation, loading, and adaptive navigation method of the present application;

[0067] Figure 6 This is a flowchart for implementing step S35 in an embodiment of a multi-modal dynamic area chart generation, loading and adaptive navigation method of the present application. DETAILED DESCRIPTION

[0068] The following is combined with Figure 1-6 This application is described in further detail.

[0069] In one embodiment, if Figure 1 As shown, the present application discloses a multi-modal dynamic regional chart generation, loading and adaptive navigation method, which specifically includes the following steps:

[0070] S10: Acquiring multimodal data information, wherein the multimodal data information includes static chart data, dynamic environment information, ship characteristic data, and navigation plan information;

[0071] In this embodiment, multimodal data information is a general term for various types of data sources integrated in the marine navigation system, including static chart data, dynamic environmental information, ship characteristic data and navigation plan information, among which static chart data is basic chart data, including official electronic charts, port channel charts, etc.; dynamic environmental information involves real-time changing environmental information, including real-time tidal data from coastal monitoring stations, meteorological data from meteorological satellites, and ship AIS signals; ship characteristic data is the inherent parameters of the ship, including ship draft, main engine performance curve, etc.; navigation plan information includes task elements such as scheduled routes and time nodes.

[0072] Specifically, as described in step S10 above, multi-source data such as static chart data, dynamic environmental information, ship characteristic data and navigation plan information are collected in real time through the data interface. Static chart data usually comes from the official electronic chart database to ensure the accuracy of basic geographic information; dynamic environmental information is obtained through IoT terminals such as meteorological sensors and AIS receiving equipment to reflect the real-time navigation environment status; ship characteristic data is extracted from the shipboard monitoring system, including real-time parameters such as main engine operating conditions and draft changes; navigation plan information comes from the ship dispatch system or manual input by the crew; the synchronous collection of multimodal data establishes a complete data foundation for subsequent chart generation and path planning.

[0073] S20: Generate adaptive charts based on static chart data and dynamic environment information;

[0074] In this embodiment, the adaptive nautical chart is an electronic nautical chart system that dynamically integrates static geographic information and real-time environmental data. While maintaining the accuracy of static elements such as basic waterways and water depths, it forms a visual nautical chart that reflects the comprehensive status of the current navigation environment by superimposing dynamic information such as real-time updated weather warnings, tidal changes, and ship traffic conditions.

[0075] Specifically, as described in step S20 above, the static nautical chart data is integrated with the dynamic environmental information to form an adaptive nautical chart with environmental perception capabilities, which not only retains the standardization of traditional nautical charts but also enhances the intuitive display of dynamic risks.

[0076] S30: Generate multi-target path information associated with an adaptive chart based on the ship characteristic data and the voyage plan information, and send it to the ship end and the base shore end;

[0077] In this embodiment, the multi-target path information is a set of diversified route plans generated based on the ship's navigation needs. These route path plans are generated through a multi-target optimization algorithm to provide differentiated choices for different navigation scenarios. The ship end and the base shore end are a two-way communication architecture that constitutes the ship navigation system. Among them, the ship end is the navigation terminal equipment on the target ship, which is responsible for real-time display of nautical charts, path information and collection of ship operation data, etc. The base shore end is the shore-based monitoring center corresponding to the target ship, which can perform multi-ship collaborative scheduling and complex environment analysis. The two achieve data synchronization through satellite communication or mobile network to form a closed-loop optimized navigation system.

[0078] Specifically, as described in the above step S30, based on the ship characteristic data and the navigation plan information, the generated multi-target path information associated with the adaptive nautical chart is constructed, and the multi-target path plan is sent to the ship-side navigation terminal and the base shore monitoring platform respectively to ensure two-way synchronization of the navigation strategy.

[0079] S40: upon receiving target path information fed back by the ship and the shore terminal, generating navigation information based on the target path information and sending the navigation information to the ship and the shore terminal;

[0080] In this embodiment, navigation information is a comprehensive set of instructions generated to guide the navigation of the ship, which may include information such as heading adjustment points, recommended speeds, risk warning areas, etc. Navigation information not only provides basic route guidance, but also integrates real-time environmental data and ship status to form a dynamically optimized navigation strategy. Navigation information is usually presented in the form of visual charts and machine-readable data packets, which is convenient for crew members to intuitively understand and directly execute by the automated system.

[0081] Specifically, as described in the above step S40, the path selection feedback determined by the ship end and the shore end is received through the communication interface. The feedback from the ship end is usually the final navigation path confirmed by the crew, and the feedback from the shore end may also include path adjustment suggestions based on the global situation. A detailed navigation instruction sequence is generated according to the feedback information, including heading adjustment points, recommended speeds, risk warning areas and other elements, and the integrity of the instructions is ensured through a data verification mechanism. The generated navigation information is pushed to the ship end display terminal and the shore end decision system in the form of visual charts and machine-readable data packets, respectively, to form a closed-loop navigation instruction execution system.

[0082] S50: receiving feedback information from the ship end and the shore end based on a preset receiving frequency, updating navigation information based on the feedback information, and sending the navigation information to the ship end and the shore end.

[0083] In this embodiment, the preset receiving frequency is a fixed time interval for obtaining feedback information from the ship and the shore-based end, for example, once every 15 minutes or dynamically adjusted according to the navigation environment. The setting of this receiving frequency needs to balance the real-time nature of the data and the system load to ensure timely response to environmental changes without wasting resources due to excessively high communication frequency. In complex waters or severe weather conditions, the receiving frequency can be adaptively increased to enhance situational awareness capabilities, while in open waters or stable navigation conditions, the frequency can be reduced to optimize communication efficiency. The feedback information is real-time data and adjustment suggestions fed back by the ship and the shore-based end, which are used to dynamically correct navigation decisions. The ship-side feedback typically includes actual track deviation, engine operating parameters, sensor monitoring data, etc., reflecting the current status of the ship and changes in the navigation environment. The shore-based end feedback may involve macro information such as global traffic situation updates, newly discovered risk areas, or regulatory adjustments. The feedback information is compared with the existing navigation strategy through a difference analysis algorithm to trigger incremental updates or global path replanning, forming a closed-loop optimized adaptive navigation system.

[0084] Specifically, as described in the above step S50, information fed back from the ship side and the shore side is obtained at set time intervals. The feedback data from the ship side includes real-time status such as actual track deviation and main engine operating parameters. The feedback from the shore side may involve newly discovered risk areas or traffic control adjustments. The key change factors are identified through the difference analysis algorithm, and the current navigation information is incrementally updated: in case of slight deviations, heading fine-tuning suggestions can be automatically generated, and in case of major changes, path re-planning is triggered.

[0085] In one embodiment, if Figure 2 As shown, step S20 includes the steps of:

[0086] S21: Generate basic base map based on static chart data;

[0087] S22: performing coordinate conversion and scale optimization processing on the basic base map using dynamic environment information and navigation plan information to obtain a target base map;

[0088] S23: Generate dynamic risk layers based on dynamic environmental information, where the risk layers include a tidal risk layer, a meteorological risk layer, and a traffic risk layer;

[0089] S24: Generate adaptive nautical charts based on dynamic risk layers and target basemaps.

[0090] In this embodiment, the base map is a standardized electronic chart framework generated based on static nautical chart data, which includes basic geographic information elements such as waterways, water depths, and obstructions. The base map follows the standards of the International Hydrographic Organization (IHO) to ensure the accuracy and authority of geographic data and provide a stable spatial reference basis for subsequent dynamic information overlay. The base map has a layered display feature and can selectively display geographic elements at different levels according to navigation needs; coordinate conversion and scale optimization are spatial adaptability processing of the base map according to the characteristics of the navigation area. Coordinate conversion ensures that the spatial reference systems of different data sources are unified to the WGS84 standard coordinate system, and scale optimization is based on the ship's The chart display range is dynamically adjusted based on the ship's current position and navigation plan, so that the key navigation area is always at the best visualization scale; the target base map is an enhanced basic base map that has undergone coordinate conversion and scale optimization. While retaining the accuracy of the original geographic information, the coordinate system and scale are dynamically adjusted to make the chart display range highly consistent with the current navigation mission; the dynamic risk layer is an information overlay layer generated by real-time environmental data, which can be selected to reflect the distribution of navigation risks through color coding and symbolic annotation. Among them, the tidal risk layer displays areas with abnormal water depth changes, the meteorological risk layer identifies areas affected by severe weather such as storms and fog, and the traffic risk layer marks hot spots that ships may encounter.

[0091] Specifically, as described in the above steps S21-S24, first, based on the static nautical chart data, basic geographical elements such as channel boundaries, water depth points, and navigation aids are extracted to generate a basic base map that retains the topological relationship and attribute information of the original nautical chart. Then, combined with dynamic environmental information and navigation plan information, the basic base map is spatially adaptively processed. Coordinate conversion ensures that the spatial reference systems of different data sources are unified to the WGS84 standard coordinate system. Scale optimization dynamically adjusts the chart display range based on the current position of the ship and the navigation plan, so that the key navigation area is always at the optimal visualization scale to generate a target base map. Dynamic environmental information such as meteorological, tidal and AIS data streams are used to generate a dynamic risk layer. Among them, the tidal risk layer compares the deviation between the measured water depth and the predicted value to mark the area with the risk of grounding. The meteorological risk layer integrates multi-dimensional data such as wind speed and visibility to generate a heat map of the storm impact range. The traffic risk layer uses a clustering algorithm to identify hot spots that ships may encounter. Finally, the static target base map and the dynamic risk layer are fused and processed using methods such as overlay algorithms to generate an adaptive nautical chart.

[0092] In one embodiment, the dynamic environment information includes tidal data, weather data, and AIS ship information, such as Figure 3 As shown, step S23 includes the steps of:

[0093] S231: Generate a tidal risk layer based on real-time tidal data;

[0094] S232: Generate a meteorological risk layer based on meteorological data;

[0095] S233: Generate traffic risk layer based on AIS ship information;

[0096] S234: Perform spatial overlay analysis on the tidal risk layer, meteorological risk layer, and traffic risk layer to generate a dynamic risk layer.

[0097] In this embodiment, tidal data is observation and prediction data reflecting the periodic changes in the water level in the sea area, including parameters such as tide height, tide time, and tidal velocity. Tidal data can be obtained through various means such as coastal tide stations, satellite altimeters, and numerical models, and can predict the changes in water depth at a specific location in the future period; meteorological data covers various atmospheric environmental factors that affect maritime navigation, including wind speed and direction, visibility, wave height, precipitation probability, and other parameters. Meteorological data can be collected through multiple sources such as meteorological buoys, shore-based radars, satellite remote sensing, and numerical weather forecast models, and after quality control and time-space interpolation processing, the meteorological data can be used to obtain the data. After processing, a gridded data set covering the route is formed; AIS ship information is real-time dynamic data broadcast by the ship's automatic identification system, including navigation status parameters such as ship position, speed and heading, and draft. AIS ship information can be transmitted via VHF radio and received by coastal base stations and other ships to form a ship traffic situation map in the area. AIS ship information not only reflects the movement status of a single ship, but also can identify traffic flow patterns and potential risk areas through aggregate analysis; the tidal risk layer is a risk visualization layer generated based on tidal data, which can be displayed in different color levels and contour lines. The abnormal water depth risk level of the area, the tidal risk layer comprehensively considers factors such as tidal forecast accuracy, ship draft requirements and seabed topography changes, and quantifies the difference between theoretical water depth and actual navigable water depth as a risk value. Among them, the update frequency of the tidal risk layer can be adapted to synchronize with the tidal change cycle, and the refresh rate is automatically increased during the high and low tide transition period; the meteorological risk layer is a comprehensive assessment result generated by integrating multi-source meteorological data. The degree of impact of different meteorological factors on navigation safety can be marked in the form of a heat map. The meteorological risk layer not only shows the current meteorological conditions, but also predicts the risk evolution in the next few hours through trend analysis. Changes, among which high-risk areas can be highlighted with eye-catching marks and associated with specific risk type descriptions, such as high wind warning areas and low visibility warning areas; the traffic risk layer is a traffic situation visualization result generated by spatial analysis and density calculation of AIS ship information. The traffic risk layer identifies hot spots, abnormal navigation behaviors and high-density traffic areas that ships may encounter, and can choose to mark risk levels with different colors and symbols. Among them, the data of the traffic risk layer can be updated at a high frequency, such as every few minutes, dynamically reflecting changes in ship traffic patterns in the area and providing real-time reference for collision avoidance decisions.

[0098] Specifically, as described in the above steps S231-S234, first, the real-time tidal data is processed, and the grounding risk probability of each location point is calculated by comparing the relationship between the predicted tide height and the ship's draft, and a tidal risk layer is generated; the meteorological observation and forecast data are integrated, and the influence of factors such as wind speed and visibility on navigation operations is analyzed to generate a meteorological risk layer; for the received AIS ship information, the hot spots and traffic conflict areas where the ship will encounter are identified through spatial density analysis and motion trajectory prediction, and a traffic risk layer is formed; finally, the above three types of risk layers are spatially superimposed and weighted fusion processed to generate a dynamic risk layer that comprehensively reflects the tidal, meteorological and traffic risk situations.

[0099] In one embodiment, if Figure 4 As shown, step S234 includes the steps of:

[0100] S2341: Unify the tidal risk layer, meteorological risk layer, and traffic risk layer into the same spatial coordinate system and construct a risk assessment matrix;

[0101] S2342: Calculate the comprehensive risk value of each spatial unit in the risk assessment matrix using a preset superposition algorithm;

[0102] S2343: Set the risk weight coefficient of each spatial unit in the risk assessment matrix based on AIS ship information and generate a dynamic risk layer.

[0103] In this embodiment, the spatial coordinate system is a reference framework for positioning and aligning geographic spatial data. In this solution, it refers to the process of unifying multi-source risk data into the WGS84 geodetic coordinate system. This conversion ensures that tidal, meteorological and ship data from different sources have a consistent spatial reference, eliminating position deviations caused by differences in projection methods or coordinate formats; the risk assessment matrix is ​​a structured data organization form, which divides the chart area into regular grid cells, each cell stores quantitative values ​​of multi-dimensional risk indicators such as tides, meteorology and traffic, and the risk assessment matrix corresponds to a specific geographic location through row and column indexes. Its cell values ​​reflect the preliminary assessment results of the comprehensive risk level of the location, providing a standardized data container for risk superposition calculations; the comprehensive risk value is a value that represents the navigation safety threat in a specific sea area. The quantitative indicator of the degree of threat is calculated by using a preset superposition algorithm to consider multiple factors such as tidal anomalies, meteorological threats and traffic density. The risk weight coefficient is an adjustment parameter used to adjust the impact of different types of risks on the final assessment results. The risk weight coefficient is dynamically calculated based on AIS ship dynamic data (such as ship density and speed distribution) to reflect the relative importance of various risks under the current traffic environment. The preset superposition algorithm is a calculation method for fusing multi-source risk data. By combining the values ​​of different risk layers according to preset rules, this superposition algorithm considers the synergistic effects between risk factors to avoid assessment distortion caused by simple addition. At the same time, spatial smoothing can be optionally introduced to eliminate data noise, ultimately generating a continuous and reasonable comprehensive risk distribution surface.

[0104] Specifically, as described in steps S2341-S2343 above, coordinate system unification is implemented for the tidal risk layer, meteorological risk layer, and traffic risk layer. All data are converted to the same spatial reference frame through resampling and projection transformation to ensure that the geographical locations of each risk factor are accurately matched. On this basis, the nautical chart area is divided into several standard grid cells, and a three-dimensional risk assessment matrix containing tidal risk values, meteorological threat levels, and traffic density indexes is constructed. A preset overlay calculation algorithm is used to perform multi-factor fusion operations on each spatial unit in the risk assessment matrix, and the interaction relationship between different risk types is comprehensively considered to generate a comprehensive risk score for each unit. In this process, the traffic situation characteristics reflected by AIS ship information are analyzed in real time, and the weight distribution of various risks is dynamically adjusted. For example, the contribution ratio of traffic risk is appropriately increased in areas with dense ships, and the assessment weight of meteorological factors is enhanced in areas affected by severe weather, and finally a dynamic risk layer is generated.

[0105] In one embodiment, if Figure 5 As shown, step S30 includes the steps of:

[0106] S31: Determine a number of flight segments based on the flight plan information;

[0107] S32: Calculate the minimum safe water depth of each section based on the ship characteristic data and generate water depth constraint conditions;

[0108] S33: Setting the ETA threshold based on the ship characteristic data and the voyage plan information;

[0109] S34: Establishing a speed-fuel consumption model based on ship characteristic data;

[0110] S35: Generate multi-objective path information based on the speed-fuel consumption model, the water depth constraint condition, and the ETA threshold, wherein the multi-objective path information includes the shortest path, the most economical path, and the safest path.

[0111] In this embodiment, the segment division is to divide the entire route into several sub-intervals with independent characteristics according to the turning points and key areas in the navigation plan. Each segment has clear starting and ending coordinates and is associated with specific navigation parameter requirements. Among them, the segment division needs to take into account the ship's maneuvering characteristics, the complexity of the channel and the navigation mission requirements, and the segment length is usually dynamically adjusted according to the characteristics of the sea area. A more fine-grained division strategy is adopted in complex waters; the minimum safe water depth is the minimum water depth requirement required for a ship to safely pass through a specific water area. It consists of three parts: the actual draft of the ship, the ship type correction coefficient and the dynamic environment margin. This parameter not only considers the static draft depth, but also includes the sinking caused by the movement of the ship, the tidal change margin and the safety buffer brought by the uncertainty of the seabed topography. The minimum safe water depth is a hard constraint condition that directly determines the spatial feasibility range of the route; the ETA threshold is the feasibility of the estimated arrival time The acceptable deviation range reflects the degree of time accuracy required by the voyage plan. The ETA threshold is determined comprehensively based on the ship type, cargo characteristics and port scheduling plan. It includes both the single-segment time window constraint and the full-process cumulative time error control. The speed-fuel consumption model is a mathematical model that describes the relationship between the fuel consumption rate of the ship's main engine and the speed. It is constructed based on the ship's main engine bench test data and actual navigation records contained in the ship's characteristic data. The speed-fuel consumption model takes into account factors such as changes in propulsion efficiency under different load conditions, the impact of hull fouling and main engine performance degradation, and can accurately predict the fuel consumption per unit distance at a specific speed. The multi-objective path information is a set of route plans generated by collaboratively optimizing multiple performance indicators. Among them, the shortest path focuses on minimizing sailing time, the most economical path aims to optimize fuel consumption, and the safest path prioritizes avoiding high-risk areas, providing diverse options for different navigation scenarios.

[0112] Specifically, as described in steps S31-S35 above, the turning point and key area information in the navigation plan are parsed, and the entire route is divided into several continuous segments in combination with electronic nautical chart data. Basic attributes such as the starting point coordinates, end point coordinates, and planned transit time are recorded for each segment. The current draft is calculated based on the ship's load data, and the predicted hull sinkage and tidal variation margin are superimposed to generate a minimum safe water depth constraint for each segment, which serves as a spatial filtering condition for subsequent path search. At the same time, the time node requirements in the navigation plan are analyzed, and the ETA time window threshold is set for each segment in combination with the power characteristics of the ship's main engine, allowing for local speed optimization within the framework of the overall plan. Based on the ship's main engine performance curve and recent fuel consumption monitoring data, a speed-fuel consumption correspondence model reflecting the current ship condition is constructed. This model takes into account changes in propulsion efficiency at different speeds and environmental impact corrections. Finally, the aforementioned parameters are input into a multi-objective optimization engine. Under the premise of meeting the water depth safety constraint and time window requirements, a representative path solution optimized in terms of navigation time, fuel consumption, and risk avoidance is generated through parallel calculation, forming a complete decision support information package.

[0113] In one embodiment, if Figure 6 As shown, step S35 includes the steps of:

[0114] S351: Constructing a target vector including the total voyage time, total fuel consumption and cumulative risk value as the target function;

[0115] S352: Setting speed feasible region constraints based on the speed-fuel consumption model, and loading water depth constraints, ETA thresholds, and speed feasible region constraints to constrain the target vector;

[0116] S353: Solve the target vector using a preset solution algorithm to obtain a basic solution set, and extract the shortest path, the most economical path, and the safest path.

[0117] In this embodiment, the target vector is a collective representation of multiple performance indicators that need to be optimized simultaneously in the path optimization problem. The target vector includes three optimization objectives: total sailing time, total fuel consumption, and cumulative risk value. The speed feasible domain constraint is a speed range determined based on the ship's main engine performance and safe navigation requirements. The speed feasible domain constraint is derived from the effective definition domain of the speed-fuel consumption model, while taking into account the main engine load limit, ship stability requirements, and the upper and lower speed limits determined by actual sailing experience. The speed feasible domain may vary in different sections, and will automatically narrow in complex waters or severe weather conditions to ensure the rationality and safety of speed selection. The basic solution set is a set of candidate path solutions generated by the multi-objective optimization algorithm, which includes non-inferior solutions that cannot be fully surpassed by other solutions under the existing constraints. Each solution in the basic solution set achieves a local optimum on at least one optimization objective. The preset solution algorithm is a mathematical optimization method designed and adjusted for the ship path optimization problem. The solution algorithm takes into account the special constraints and target characteristics of the navigation problem, and optionally has a built-in processing mechanism for sparse matrices, nonlinear constraints, and mixed integer variables, which can effectively cope with the calculation of large-scale maritime path planning.

[0118] Specifically, as described in the above steps S351-S353, a multidimensional target vector including the total navigation time, the total fuel consumption and the cumulative risk value is constructed, wherein the total navigation time is calculated by the integral relationship between the segment length and the speed, the total fuel consumption is derived based on the speed-power curve of the speed-fuel consumption model, and the cumulative risk value integrates the spatiotemporal distribution characteristics of environmental threats and traffic conflicts; the feasible domain of the speed of each segment is determined according to the effective range of the speed-fuel consumption model, the calculated minimum safe water depth constraint is converted into a spatial no-entry zone mark, and the ETA threshold is decomposed into the time window limit of each segment. The above constraints together constitute the boundary of the feasible solution space of the optimization problem; the preset solution algorithm is called to process the target vector of the multi-objective constraint optimization, and the candidate solutions with advantages in each target direction are retained through non-dominated sorting. After multiple generations of iteration, a basic solution set covering the Pareto front is formed, and finally three typical solutions are extracted from the basic solution set: the shortest path, the most economical path and the safest path solution, which together constitute the final multi-objective path information output.

[0119] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] In one embodiment, a multimodal dynamic regional nautical chart generation, loading, and adaptive navigation system is provided. The multimodal dynamic regional nautical chart generation, loading, and adaptive navigation system corresponds one-to-one to the multimodal dynamic regional nautical chart generation, loading, and adaptive navigation method described in the above embodiment. The multimodal dynamic regional nautical chart generation, loading, and adaptive navigation system includes:

[0121] A data acquisition module, configured to acquire multimodal data information, wherein the multimodal data information includes static chart data, dynamic environment information, ship characteristic data, and voyage plan information;

[0122] A chart generation module for generating adaptive charts based on static chart data and dynamic environmental information;

[0123] A path generation module is used to generate multi-target path information associated with an adaptive chart based on the ship's characteristic data and the voyage plan information, and send it to the ship and the shore terminal;

[0124] The navigation generation module is used to generate navigation information based on the target path information fed back by the ship end and the base shore end, and send it to the ship end and the base shore end;

[0125] A navigation update module is used to receive feedback information from the ship end and the base shore end based on a preset receiving frequency, update the navigation information based on the feedback information, and send it to the ship end and the base shore end;

[0126] Optionally, the chart generation module includes:

[0127] Base map generation submodule, used to generate basic base maps based on static chart data;

[0128] The base map optimization submodule is used to perform coordinate conversion and scale optimization on the basic base map through dynamic environment information and navigation plan information to obtain the target base map;

[0129] A layer generation submodule is used to generate dynamic risk layers based on dynamic environmental information, wherein the risk layers include a tidal risk layer, a meteorological risk layer, and a traffic risk layer;

[0130] The chart synthesis submodule is used to generate adaptive charts based on dynamic risk layers and target base maps.

[0131] Regarding the specific definition of a multimodal dynamic regional chart generation, loading, and adaptive navigation system, please refer to the definition of a multimodal dynamic regional chart generation, loading, and adaptive navigation method above, which will not be repeated here. The various modules in the above-mentioned multimodal dynamic regional chart generation, loading, and adaptive navigation system can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0132] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A multi-modal dynamic regional chart generation, loading, and adaptive navigation method, characterized by: Including steps: Acquiring multimodal data information, the multimodal data information including static nautical chart data, dynamic environmental information, ship characteristic data, and voyage plan information; Generate adaptive charts based on static chart data and dynamic environmental information; Generates multi-target path information associated with adaptive charts based on ship characteristic data and voyage plan information, and sends it to the ship and shore terminals; When receiving the target path information fed back by the ship side and the shore side, the navigation information is generated based on the target path information and sent to the ship side and the shore side; Feedback information is received from the ship end and the shore end based on a preset receiving frequency, and navigation information is updated based on the feedback information and sent to the ship end and the shore end.

2. The method for generating, loading and adaptively navigating multimodal dynamic regional nautical charts according to claim 1, characterized in that: The step of generating an adaptive nautical chart based on static nautical chart data and dynamic environmental information comprises the following steps: Generate basic base maps based on static chart data; Through dynamic environment information and navigation plan information, coordinate conversion and scale optimization processing are performed on the basic base map to obtain the target base map; Generate dynamic risk layers based on dynamic environmental information, including tidal risk layers, meteorological risk layers, and traffic risk layers; Generate adaptive charts based on dynamic risk layers and target basemaps.

3. The method for generating, loading and adaptively navigating multimodal dynamic regional nautical charts according to claim 2, characterized in that: The dynamic environmental information includes tidal data, meteorological data, and AIS ship information. The step of generating a dynamic risk layer based on the dynamic environmental information, wherein the risk layer includes a tidal risk layer, a meteorological risk layer, and a traffic risk layer, comprises the following steps: Generate tidal risk layers based on real-time tidal data; Generate meteorological risk layers based on meteorological data; Generate traffic risk layers based on AIS ship information; The tidal risk layer, meteorological risk layer and traffic risk layer are spatially overlaid and analyzed to generate a dynamic risk layer.

4. The method for generating, loading and adaptively navigating multimodal dynamic regional nautical charts according to claim 3, characterized in that: The step of performing spatial overlay analysis on the tidal risk layer, the meteorological risk layer, and the traffic risk layer to generate a dynamic risk layer comprises the following steps: Unify the tidal risk layer, meteorological risk layer, and traffic risk layer into the same spatial coordinate system and construct a risk assessment matrix; Calculate the comprehensive risk value of each spatial unit in the risk assessment matrix through a preset superposition algorithm; The risk weight coefficient of each spatial unit in the risk assessment matrix is ​​set based on AIS ship information, and a dynamic risk layer is generated.

5. The method for generating, loading and adaptively navigating multimodal dynamic regional nautical charts according to claim 1, characterized in that: The step of generating multi-target path information associated with an adaptive nautical chart based on ship characteristic data and voyage plan information and sending the information to the ship end and the base shore end comprises the following steps: Determine a number of flight segments based on the voyage plan information; Calculate the minimum safe water depth for each section based on the ship's characteristic data and generate water depth constraints; Set ETA thresholds based on ship characteristics data and voyage plan information; Establish a speed-fuel consumption model based on ship characteristic data; Multi-objective path information is generated based on a speed-fuel consumption model, a water depth constraint, and an ETA threshold. The multi-objective path information includes the shortest path, the most economical path, and the safest path.

6. The method for generating, loading and adaptively navigating multimodal dynamic regional nautical charts according to claim 5, characterized in that: The step of generating multi-objective path information based on a speed-fuel consumption model, a water depth constraint condition, and an ETA threshold, wherein the multi-objective path information includes the shortest path, the most economical path, and the safest path, comprises the following steps: Construct a target vector including the total voyage time, total fuel consumption and cumulative risk value as the objective function; Set speed feasible region constraints based on the speed-fuel consumption model, and load water depth constraints, ETA thresholds, and speed feasible region constraints to constrain the target vector; The target vector is solved by a preset solution algorithm to obtain a basic solution set, and the shortest path, the most economical path, and the safest path are extracted.

7. A multi-modal dynamic regional chart generation, loading and adaptive navigation system, characterized by: include: A data acquisition module, configured to acquire multimodal data information, wherein the multimodal data information includes static chart data, dynamic environment information, ship characteristic data, and voyage plan information; A chart generation module for generating adaptive charts based on static chart data and dynamic environmental information; A path generation module is used to generate multi-target path information associated with an adaptive chart based on the ship's characteristic data and the voyage plan information, and send it to the ship and the shore terminal; A navigation generation module is used to generate navigation information based on the target path information fed back by the ship end and the shore end, and send the navigation information to the ship end and the shore end; The navigation update module is used to receive feedback information from the ship end and the base shore end based on a preset receiving frequency, update the navigation information based on the feedback information and send it to the ship end and the base shore end.

8. The multi-modal dynamic regional chart generation, loading and adaptive navigation system according to claim 7, characterized in that: The nautical chart generation module includes: Base map generation submodule, used to generate basic base maps based on static chart data; The base map optimization submodule is used to perform coordinate conversion and scale optimization on the basic base map through dynamic environment information and navigation plan information to obtain the target base map; A layer generation submodule is used to generate dynamic risk layers based on dynamic environmental information, wherein the risk layers include a tidal risk layer, a meteorological risk layer, and a traffic risk layer; The chart synthesis submodule is used to generate adaptive charts based on dynamic risk layers and target base maps.

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