Water intelligent navigation-aiding and navigation information service system for inland ship

Abstract

The invention, which relates to the edge computing field, discloses an inland ship overwater intelligent navigation aid and navigation information service system comprising: a system integration multi-source sensor for fusing and processing hydro meteorology and obstacle data under low visibility; the side cloud collaboration module processes real-time and global information in a labor division manner, analyzes a rule and optimizes a route; the channel modeling module constructs a real-time three-dimensional channel, and plans and dynamically adjusts an optimal path; the virtual navigation mark module dynamically deploys and manages navigation marks through AIS / Beidou; the information pushing module provides dynamic and static navigation data as required; the risk early warning module realizes graded warning and emergency linkage; the ship-shore cooperation module performs remote monitoring scheduling and channel analysis; the inland river adaptation module ensures that system parameters and algorithms are automatically matched with different river environments and rules. The method has the advantages that the multi-class perception and side cloud cooperation technology is fused, accurate navigation aiding, dynamic risk management and control and ship-shore efficient cooperation of the inland waterway are achieved, and intelligent upgrading of inland navigation is enabled in an all-around mode.

Description

Technical Field

[0001] This invention relates to the field of edge computing, and in particular to an intelligent navigation aid and navigation information service system for inland waterway vessels. Background Technology

[0002] As inland waterway transportation plays an increasingly prominent role in the comprehensive transportation system, vessel traffic continues to grow, and the navigation environment on waterways is becoming increasingly complex, posing a severe challenge to traditional navigation methods. Inland waterways are characterized by numerous bends, shallow waters, and dense bridges and culverts, with rapid changes in meteorological and hydrological conditions. In addition, some vessels have low levels of information technology and crew members rely on experience for navigation, leading to a high risk of accidents such as collisions and groundings, limiting traffic efficiency and hindering the improvement of water transport efficiency.

[0003] Current inland waterway vessel navigation aids and information services struggle to achieve end-to-end intelligent service capabilities. At the perception level, they largely rely on single devices, lacking multi-source data fusion mechanisms. This makes them susceptible to blind spots caused by low visibility and underwater hazards. Furthermore, traditional physical navigation aids are greatly affected by weather and hydrology, are easily damaged, have high maintenance costs, and lack flexibility. Data processing often fails to achieve deep edge-cloud collaboration, resulting in either delayed real-time responses at the edge or a lack of in-depth analysis capabilities across the entire watershed in the cloud, with weak communication coverage in some areas. Navigation and control largely depend on static waterway charts, leading to poor adaptability for route optimization. Virtual navigation aid applications are limited and poorly integrated with traditional aids, and risk warnings are often one-dimensional with insufficient tiered responses. Simultaneously, vessel-shore collaboration is inefficient, business processes are fragmented, and scenario adaptability and system scalability are weak, failing to meet the intelligent navigation and control needs of complex inland waterway scenarios. Summary of the Invention

[0004] To improve the existing system, a smart navigation aid and navigation information service system for inland waterway vessels is provided. This method integrates eight modules through coordinated linkage, multiple types of perception and edge-cloud collaborative technologies, to achieve precise navigation aid, dynamic risk management and efficient ship-shore collaboration in inland waterways. It also has strong scenario adaptability and comprehensively empowers the intelligent upgrading of inland waterway transportation.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A smart navigation aid and navigation information service system for inland waterway vessels includes:

[0007] Heterogeneous data fusion sensing module: integrates Beidou positioning, AIS, infrared thermal imaging, millimeter-wave radar, sonar detection and environmental sensing units, breaks through the low visibility perception blind zone, collects hydrological, meteorological and obstacle data, removes redundancy through fusion algorithm, and outputs accurate dataset;

[0008] Edge-cloud collaborative processing module: Adopting an edge-cloud collaborative architecture, the edge processes high real-time data, while the cloud aggregates and deeply analyzes data from the entire watershed to obtain navigation patterns and optimize routes;

[0009] Channel modeling and navigation aid module: Based on the processed data, a real-time 3D channel model is constructed, and the optimal route is intelligently planned by integrating static and dynamic information. Through dynamic adjustment of parameters in complex scenarios, the route is corrected in real time and deviation warning is provided.

[0010] Virtual navigation mark broadcasting module: Based on AIS and Beidou high-precision positioning technology, a virtual navigation mark broadcasting system is constructed. The virtual navigation mark position, quantity and broadcasting frequency are dynamically adjusted according to the waterway conditions. The virtual navigation mark is monitored in real time through navigation mark status self-check.

[0011] Navigation information push module: Pushes static service information based on user profiles, and pushes dynamic control information in a triggered manner based on the real-time location and navigation status of the vessel;

[0012] Risk warning and emergency response module: Construct a four-in-one early warning system, providing level-based warnings for collision, hydrological, and compliance risks, and triggering corresponding emergency plans;

[0013] Ship-shore collaborative management module: Establishes a two-way interactive channel between ships and shore to remotely monitor and precisely schedule ship navigation, and has a built-in data statistical analysis unit to provide data on waterway traffic efficiency, ship flow, and risk distribution;

[0014] Inland waterway scenario adaptation module: Automatically adjusts perception parameters, route algorithms and early warning thresholds for different inland waterway scenarios, and adapts to new scenarios and rules through firmware upgrades.

[0015] Preferably, the heterogeneous data fusion sensing module specifically includes:

[0016] Beidou Positioning Unit: Integrates Beidou ground-based augmentation module to perform real-time ship positioning and synchronously collect ship heading and speed data;

[0017] AIS Automatic Identification Unit: Receives information on the identity, size, and navigation status of surrounding vessels, and performs dynamic interaction between vessels to avoid information blind spots when vessels converge in dense inland waterways.

[0018] Infrared thermal imaging and millimeter-wave radar unit: The infrared thermal imaging unit breaks through the limitations of low visibility at night and in rain and fog, capturing the outlines of surrounding ships and shorelines, while the millimeter-wave radar calculates the target distance and relative speed, enhancing the detection of nearby obstacles;

[0019] Sonar detection unit: It performs underwater scanning detection to address potential hazards such as shallow waters, reefs, and underwater debris in inland rivers, thus compensating for the shortcomings in above-water perception.

[0020] Environmental sensing unit: collects hydrological and meteorological data such as water level, flow velocity, wind force, and visibility, and provides real-time feedback on the dynamics of the waterway environment;

[0021] Data fusion processing unit: Standardizes the data from each unit, removes interference information through redundancy removal algorithms, and merges and outputs a complete and accurate dataset of waterway environment and ship dynamics.

[0022] Preferably, the edge-cloud collaborative processing module specifically includes:

[0023] Edge-cloud collaborative control unit: responsible for computing power allocation and data flow scheduling, prioritizing the allocation of data with high real-time requirements to the edge, and uploading non-real-time data to the cloud for in-depth analysis;

[0024] Edge processing unit: used to quickly process real-time data related to positioning and obstacle detection, avoiding the risk of latency in inland waterway networks;

[0025] Cloud-based data aggregation and analysis unit: Aggregates data on ships, waterways, and management across the entire basin; uses big data analysis to uncover navigation patterns and optimize route models; and performs in-depth verification and supplementary processing on edge data.

[0026] Dual-mode communication adapter unit: Supports dual-mode communication of 5G-A and Beidou short message. In areas with good network, 5G-A high-speed transmission is given priority, while in areas with weak communication, Beidou short message is given priority.

[0027] Preferably, the waterway modeling and navigation aid module specifically includes:

[0028] Channel model construction unit: Integrates electronic channel charts, channel dimensions, static data on bridge clearance, and dynamic data on water level changes, water flow velocity, and temporary construction areas to construct a real-time updated 3D model;

[0029] Route planning unit: Combines ship size, draft and preset navigation plan to generate multiple optimal route schemes, and optimizes route parameters and turning suggestions for complex scenarios such as inland waterway bends, bridge areas and locks;

[0030] Real-time route correction unit: It dynamically tracks the ship's navigation trajectory through a synergistic algorithm, compares the model data with the ship's actual position, obtains navigation deviations, and issues warnings.

[0031] Preferably, the virtual navigation mark broadcasting module specifically includes:

[0032] Virtual navigation mark broadcasting unit: Based on AIS and BeiDou positioning technology, it generates standardized virtual navigation mark signals and broadcasts them externally, including navigation mark location, type, and warning information;

[0033] Adaptive control unit: Receives real-time dynamic data of the waterway, adjusts the layout parameters of virtual navigation marks, adds more and more navigation marks for dangerous waters, and optimizes the number and broadcasting frequency of navigation marks for open waterways;

[0034] Status self-check unit: Real-time monitoring of virtual navigation beacon signal strength, coverage and operating status, troubleshooting signal anomalies and positioning deviations, and generating self-check reports.

[0035] The new and old system are compatible units, which are compatible with the traditional physical navigation mark signal reception and parsing functions, and realize the integrated display of virtual and physical navigation mark information. This avoids the connection gap caused by the replacement of the navigation system and ensures that ships smoothly transition to the virtual navigation mark navigation mode.

[0036] Preferably, the navigation information push module specifically includes:

[0037] User profiling and demand matching unit: Based on ship type, navigation purpose, and crew operation preferences, a unique user profile is constructed, and the priority of static service and dynamic control information demand is classified and labeled.

[0038] Information classification unit: The received full amount of information is broken down into static service information and dynamic control information. Static information includes lock scheduling rules, port berth resources, and refueling and maintenance points. Dynamic information includes traffic control instructions, temporary changes to waterways, and sudden weather warnings.

[0039] Scene-triggered push unit: Based on ship positioning and navigation status data, multiple push trigger scenarios are preset. When a ship approaches a lock, enters a controlled area, or encounters severe weather, the corresponding information is pushed.

[0040] Preferably, the risk warning and emergency response module specifically includes:

[0041] Risk identification unit: Covering collision, hydrology, equipment, and compliance risk types, it captures real-time data on vessel trajectory, hydrological and meteorological conditions, equipment operation, and waterway rules to identify risk sources;

[0042] Risk level determination unit: The system has a preset dynamic threshold adaptation mechanism, which adjusts the determination criteria based on the density of inland waterway vessels and the complexity of waterways. The risk is divided into three levels: general, emergency and special, and the scope of risk impact and development trend are marked.

[0043] Emergency response plan generation unit: Based on risk level and scenario characteristics, it retrieves a pre-set emergency database, outputs differentiated response plans, provides avoidance suggestions for general risks, plans the optimal evacuation route for emergency risks, and generates a search and rescue coordination plan by linking ship and shore resources for extremely high risks.

[0044] Preferably, the ship-shore collaborative management module specifically includes:

[0045] Ship-to-shore two-way communication unit: Establishes an encrypted two-way data channel, compatible with 5G-A and Beidou short message dual-mode communication, and automatically strengthens signal priority in areas with weak communication;

[0046] Shore-based control unit: Real-time aggregation of vessel, waterway, and early warning data across the entire basin; remote monitoring, precise scheduling, and violation warnings of vessels by investigating abnormal navigation behavior and potential waterway hazards.

[0047] Business collaboration processing unit: It connects the business links of lock application, government affairs processing and emergency search and rescue, integrates cross-departmental data resources, and enables one-stop lock passage reservation and cross-regional government affairs processing;

[0048] Data statistics unit: Collects data on vessel traffic flow, waterway traffic efficiency, and risk distribution in real time, and provides decision support data on waterway traffic efficiency, vessel traffic flow, and risk distribution.

[0049] Preferably, the inland waterway scene adaptation module specifically includes:

[0050] Scene recognition unit: Real-time collection of waterway grade, terrain features, water type and navigation environment data, and identification of typical scenes such as inland waterways, lakes, canals, meandering river sections and bridge areas;

[0051] Parameter adaptation unit: Links scene recognition results to adjust system perception parameters, route algorithms and warning thresholds, optimizes obstacle detection accuracy for shallow and narrow waterways, and adjusts navigation mark broadcasting density for open waters;

[0052] Special area optimization unit: For complex lock areas, optimize the ship queuing scheduling and entry / exit guidance navigation logic; for winding river sections and bridge areas, enhance the accuracy of route turning prediction and collision warning.

[0053] Extended upgrade unit: Supports online firmware upgrades to adapt to new inland waterway scenarios and updated shipping rules.

[0054] Compared with the prior art, the advantages of the present invention are:

[0055] Leveraging heterogeneous data fusion sensing technology, this system integrates multiple detection devices to overcome low visibility and underwater sensing blind spots. Through an edge-cloud collaborative architecture, it achieves real-time data processing and in-depth analysis across the entire watershed, balancing response efficiency with scientific decision-making. The combination of 3D waterway modeling, dynamic route optimization, and adaptive virtual navigation mark control replaces traditional reliance on external systems, resolving navigation connectivity issues in complex inland waterway scenarios. A tiered risk warning and ship-shore collaboration mechanism is established, linking emergency response and one-stop business processing, significantly improving navigation safety and management efficiency. With strong scenario adaptability and scalability, it can dynamically adjust parameters to adapt to different inland waterway environments, is compatible with both old and new navigation mark systems, and provides inland waterway vessels with precise navigation aids, efficient information services, and full-process risk management, contributing to the intelligent upgrading of shipping. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the system proposed in this invention;

[0057] Figure 2 This is a diagram of the heterogeneous data fusion sensing module proposed in this invention;

[0058] Figure 3 This is a diagram of the edge-cloud collaborative processing module proposed in this invention;

[0059] Figure 4 This is a diagram of the waterway modeling and navigation aid module proposed in this invention;

[0060] Figure 5 This is a diagram of the virtual navigation mark broadcasting module proposed in this invention;

[0061] Figure 6 This is a diagram of the navigation information push module proposed in this invention;

[0062] Figure 7 This is a diagram of the risk warning and emergency response module proposed in this invention;

[0063] Figure 8 This is a diagram of the ship-shore collaborative management module proposed in this invention;

[0064] Figure 9 This is a diagram of the inland river scenario adaptation module proposed in this invention. Detailed Implementation

[0065] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0066] See Figure 1 As shown, an intelligent navigation aid and navigation information service system for inland waterway vessels includes:

[0067] Heterogeneous data fusion sensing module: integrates Beidou positioning, AIS, infrared thermal imaging, millimeter-wave radar, sonar detection and environmental sensing units, breaks through the low visibility perception blind zone, collects hydrological, meteorological and obstacle data, removes redundancy through fusion algorithm, and outputs accurate dataset;

[0068] Edge-cloud collaborative processing module: Adopting an edge-cloud collaborative architecture, the edge processes high real-time data, while the cloud aggregates and deeply analyzes data from the entire watershed to obtain navigation patterns and optimize routes;

[0069] Channel modeling and navigation aid module: Based on the processed data, a real-time 3D channel model is constructed, and the optimal route is intelligently planned by integrating static and dynamic information. Through dynamic adjustment of parameters in complex scenarios, the route is corrected in real time and deviation warning is provided.

[0070] Virtual navigation mark broadcasting module: Based on AIS and Beidou high-precision positioning technology, a virtual navigation mark broadcasting system is constructed. The virtual navigation mark position, quantity and broadcasting frequency are dynamically adjusted according to the waterway conditions. The virtual navigation mark is monitored in real time through navigation mark status self-check.

[0071] Navigation information push module: Pushes static service information based on user profiles, and pushes dynamic control information in a triggered manner based on the real-time location and navigation status of the vessel;

[0072] Risk warning and emergency response module: Construct a four-in-one early warning system, providing level-based warnings for collision, hydrological, and compliance risks, and triggering corresponding emergency plans;

[0073] Ship-shore collaborative management module: Establishes a two-way interactive channel between ships and shore to remotely monitor and precisely schedule ship navigation, and has a built-in data statistical analysis unit to provide data on waterway traffic efficiency, ship flow, and risk distribution;

[0074] Inland waterway scenario adaptation module: Automatically adjusts perception parameters, route algorithms and early warning thresholds for different inland waterway scenarios, and adapts to new scenarios and rules through firmware upgrades.

[0075] See Figure 2 As shown, the heterogeneous data fusion sensing module specifically includes:

[0076] Beidou Positioning Unit: Integrates Beidou ground-based augmentation module to perform real-time ship positioning and synchronously collect ship heading and speed data;

[0077] AIS Automatic Identification Unit: Receives information on the identity, size, and navigation status of surrounding vessels, and performs dynamic interaction between vessels to avoid information blind spots when vessels converge in dense inland waterways.

[0078] Infrared thermal imaging and millimeter-wave radar unit: The infrared thermal imaging unit breaks through the limitations of low visibility at night and in rain and fog, capturing the outlines of surrounding ships and shorelines, while the millimeter-wave radar calculates the target distance and relative speed, enhancing the detection of nearby obstacles;

[0079] Sonar detection unit: It performs underwater scanning detection to address potential hazards such as shallow waters, reefs, and underwater debris in inland rivers, thus compensating for the shortcomings in above-water perception.

[0080] Environmental sensing unit: collects hydrological and meteorological data such as water level, flow velocity, wind force, and visibility, and provides real-time feedback on the dynamics of the waterway environment;

[0081] Data fusion processing unit: Standardizes the data from each unit, removes interference information through redundancy removal algorithms, and merges and outputs a complete and accurate dataset of waterway environment and ship dynamics.

[0082] Specifically, a "same-source verification - heterogeneous-source complementarity" fusion strategy is adopted: at the same-source data level, BeiDou positioning data and AIS location data are compared, and the positioning error is calibrated through a deviation correction algorithm to improve the reliability of ship position data; at the heterogeneous data level, infrared thermal imaging and millimeter-wave radar data are fused, and the contour and distance information of the same target are associated through a feature matching algorithm to generate accurate target data; underwater obstacle data detected by sonar is linked with water level data from environmental sensors for analysis to determine the degree of obstacle exposure and navigation risk; finally, through a weighted fusion algorithm, differentiated weights are assigned to the data of each dimension, redundant information is eliminated, and a comprehensive dataset containing channel environment, ship dynamics, and obstacle distribution is formed.

[0083] See Figure 3 As shown, the edge-cloud collaborative processing module specifically includes:

[0084] Edge-cloud collaborative control unit: responsible for computing power allocation and data flow scheduling, prioritizing the allocation of data with high real-time requirements to the edge, and uploading non-real-time data to the cloud for in-depth analysis;

[0085] Edge processing unit: used to quickly process real-time data related to positioning and obstacle detection, avoiding the risk of latency in inland waterway networks;

[0086] Cloud-based data aggregation and analysis unit: Aggregates data on ships, waterways, and management across the entire basin; uses big data analysis to uncover navigation patterns and optimize route models; and performs in-depth verification and supplementary processing on edge data.

[0087] Dual-mode communication adapter unit: Supports dual-mode communication of 5G-A and Beidou short message. In areas with good network, 5G-A high-speed transmission is given priority, while in areas with weak communication, Beidou short message is given priority.

[0088] Specifically, edge computing nodes are deployed on the ship's local embedded terminals, prioritizing the retrieval of primary and secondary priority data for local processing. For primary data, lightweight processing algorithms are used to quickly extract core information such as the ship's real-time position, heading, and distance to obstacles, generating basic navigation aid data and initial risk prediction values, which are then directly pushed to the dynamic 3D waterway modeling and risk warning modules to meet the real-time requirements of navigation. For secondary data, format optimization and redundancy removal are performed simultaneously, retaining key hydrological and ship interaction information to form a streamlined dataset.

[0089] Data interaction is achieved by adopting a dual-mode communication architecture of 5G-A and Beidou short message: When the network signal is good, the simplified dataset processed at the edge, local cached data and device operation status information are synchronized to the cloud through the 5G-A link, and the synchronization frequency is dynamically adjusted according to the data priority; for communication-deficient areas such as inland river mountains and canyons, the system automatically switches to Beidou short message mode to transmit only core data such as ship position and emergency warning, ensuring uninterrupted data synchronization.

[0090] The cloud platform aggregates data uploaded from multiple vessels across the entire river basin via edge devices. Through big data analytics, it mines deeper information such as navigation trajectory patterns, waterway congestion points, and hydrological change trends. This optimizes route planning models, risk warning threshold algorithms, and virtual navigation mark layout strategies. The platform also performs full backup and archiving of cached data from the edge devices to build an inland waterway shipping database. The cloud regularly pushes optimized algorithm parameters and updated models to the edge devices, enabling iterative upgrades to edge device processing capabilities.

[0091] See Figure 4 As shown, the channel modeling and navigation aid module specifically includes:

[0092] Channel model construction unit: Integrates electronic channel charts, channel dimensions, static data on bridge clearance, and dynamic data on water level changes, water flow velocity, and temporary construction areas to construct a real-time updated 3D model;

[0093] Route planning unit: Combines ship size, draft and preset navigation plan to generate multiple optimal route schemes, and optimizes route parameters and turning suggestions for complex scenarios such as inland waterway bends, bridge areas and locks;

[0094] Real-time route correction unit: It dynamically tracks the ship's navigation trajectory through a synergistic algorithm, compares the model data with the ship's actual position, obtains navigation deviations, and issues warnings.

[0095] Specifically, the model is built using a "static framework + dynamic fusion" approach: First, a basic 3D framework is constructed based on static data. Terrain elevation rendering technology is used to restore the bottom topography and coastal landforms of the waterway, accurately modeling fixed facilities such as bridges, locks, and navigation marks, and labeling the key parameters of each facility. Then, dynamic data is superimposed onto the basic framework in real time. The water level changes are adjusted synchronously to adjust the waterway depth model, the water flow velocity is labeled with visual streamlines, and temporary construction areas and underwater obstacles are highlighted with their range, duration, and risk level.

[0096] Based on a 3D model, the system plans routes by combining vessel size, draft, load capacity, and preset navigation start and end points. Using a scenario adaptation algorithm, it generates 3-5 alternative routes and comprehensively evaluates them from dimensions such as voyage length, travel time, risk factor, and navigation efficiency. For curved waterways, it optimizes turning radius and speed recommendations. For bridge areas, it plans passage routes based on bridge clearance and water flow direction. For lock areas, it integrates with the ship-shore integrated module to obtain scheduling information, plans optimal queuing and lock entry / exit routes, and outputs priority-ranked route options for crew selection.

[0097] After the crew selects a route, the module activates the accompanying navigation aid function, marking the ship's position, route trajectory, and next operation instructions in the 3D scene in real time. Through voice and visual prompts, it monitors the deviation between the ship's actual trajectory and the planned route in real time, sets a 5-meter deviation threshold, and immediately analyzes the cause of the deviation when it exceeds the threshold. It then generates correction instructions based on the 3D model data and adjusts the heading and speed suggestions. If a sudden obstacle is encountered, a temporary avoidance route is quickly generated.

[0098] See Figure 5 As shown, the virtual beacon broadcasting module specifically includes:

[0099] Virtual navigation mark broadcasting unit: Based on AIS and BeiDou positioning technology, it generates standardized virtual navigation mark signals and broadcasts them externally, including navigation mark location, type, and warning information;

[0100] Adaptive control unit: Receives real-time dynamic data of the waterway, adjusts the layout parameters of virtual navigation marks, adds more and more navigation marks for dangerous waters, and optimizes the number and broadcasting frequency of navigation marks for open waterways;

[0101] Status self-check unit: Real-time monitoring of virtual navigation beacon signal strength, coverage and operating status, troubleshooting signal anomalies and positioning deviations, and generating self-check reports.

[0102] The new and old system are compatible units, which are compatible with the traditional physical navigation mark signal reception and parsing functions, and realize the integrated display of virtual and physical navigation mark information. This avoids the connection gap caused by the replacement of the navigation system and ensures that ships smoothly transition to the virtual navigation mark navigation mode.

[0103] Specifically, the module receives static and dynamic channel data after edge-cloud collaborative processing. The static data includes fixed information such as channel direction, distribution of shoals and reefs, and location of bifurcations, while the dynamic data covers water level changes, temporary construction areas, vessel traffic flow, and the status of physical navigation marks. Through scenario analysis algorithms, it classifies four typical scenarios: dangerous waters, open channels, bridge areas, and channel bifurcations, and labels the risk level, navigation requirements, and vessel traffic density of each scenario. At the same time, it verifies the timeliness and accuracy of the data and removes invalid data.

[0104] Based on the scenario analysis results, the core parameters of the virtual navigation marks are dynamically set: in terms of location, they are densely deployed in dangerous waters at intervals of 50-80 meters to accurately cover the edges of shoals and reefs; at the junctions of main and secondary navigation channels, directional navigation marks are deployed; and in open channels, they are optimized at intervals of 200-300 meters. The attributes of the navigation marks are defined, and core information such as type, warning level, and navigation direction are marked to generate a standardized configuration scheme.

[0105] The formula for dynamically adjusting the broadcast frequency of virtual navigation marks is:

[0106]

[0107] in, This refers to the actual broadcast frequency of the virtual navigation beacon. Based on the basic broadcast frequency, To monitor the real-time number of ships in the area, For ship density threshold, For scene correction factors, dangerous water areas =3; Channel bifurcation =2; Open waters =1.

[0108] See Figure 6 As shown, the navigation information push module specifically includes:

[0109] User profiling and demand matching unit: Based on ship type, navigation purpose, and crew operation preferences, a unique user profile is constructed, and the priority of static service and dynamic control information demand is classified and labeled.

[0110] Information classification unit: The received full amount of information is broken down into static service information and dynamic control information. Static information includes lock scheduling rules, port berth resources, and refueling and maintenance points. Dynamic information includes traffic control instructions, temporary changes to waterways, and sudden weather warnings.

[0111] Scene-triggered push unit: Based on ship positioning and navigation status data, multiple push trigger scenarios are preset. When a ship approaches a lock, enters a controlled area, or encounters severe weather, the corresponding information is pushed.

[0112] Specifically, personalized user profiles are built based on vessel type, voyage purpose, crew operating preferences, and historical reception records; customized information push lists are created for vessels with different profiles: cargo ships are prioritized for information such as port berths, loading and unloading efficiency, and freight scheduling; passenger ships are prioritized for information such as stops along the route, weather warnings, and tourist service facilities; and engineering vessels are focused on information such as construction area control and temporary waterway occupation permits.

[0113] Built-in scene recognition algorithms combine real-time ship location, navigation status, and waterway environment to determine push notification trigger scenarios, covering six core scenarios: lock passage scenario, triggered when a ship enters the lock control area; bridge area navigation scenario, triggered when approaching a bridge area; waterway branching / merging scenario, triggered when reaching a waterway branching node; severe weather scenario, triggered when receiving weather warning information that affects navigation safety; dense ship scenario, triggered when the number of surrounding ships exceeds a threshold; and service replenishment scenario, triggered when a ship's endurance is insufficient or it needs to dock for rest, ensuring that information pushes are aligned with actual navigation needs.

[0114] The system prioritizes information based on its urgency, using a three-tiered system: "Top Priority" addresses warnings that threaten navigation safety, delivered simultaneously via a combination of audio-visual alerts, system pop-ups, and BeiDou short messages; "Urgent Priority" addresses information affecting navigation plans, delivered through voice broadcasts and terminal notifications; and "Regular Priority" addresses service-related information, delivered via text pop-ups. Adapting to complex inland waterway scenarios, the system prioritizes 5G-A links when communication is good, automatically switching to BeiDou short messages in areas with weak communication. It also supports multi-terminal synchronization, enabling information exchange between ship terminals, crew mobile devices, and shore-based management terminals.

[0115] See Figure 7 As shown, the risk warning and emergency response module specifically includes:

[0116] Risk identification unit: Covering collision, hydrology, equipment, and compliance risk types, it captures real-time data on vessel trajectory, hydrological and meteorological conditions, equipment operation, and waterway rules to identify risk sources;

[0117] Risk level determination unit: The system has a preset dynamic threshold adaptation mechanism, which adjusts the determination criteria based on the density of inland waterway vessels and the complexity of waterways. The risk is divided into three levels: general, emergency and special, and the scope of risk impact and development trend are marked.

[0118] Emergency response plan generation unit: Based on risk level and scenario characteristics, it retrieves a pre-set emergency database, outputs differentiated response plans, provides avoidance suggestions for general risks, plans the optimal evacuation route for emergency risks, and generates a search and rescue coordination plan by linking ship and shore resources for extremely high risks.

[0119] Specifically, precise assessments are conducted for four major risk dimensions, employing a "threshold determination + scenario adaptation" algorithm to optimize the assessment logic: Collision risk is assessed by calculating the encounter distance, relative speed, and trajectory intersections between the vessel and surrounding vessels, dynamically adjusting the warning threshold to adapt to complex scenarios such as dense convergence, overtaking, and U-turns of inland waterway vessels; Hydrological risk is assessed by combining historical hydrological data of the waterway to predict water level fluctuations, the impact of water flow on vessel handling, and the duration of severe weather; Equipment risk is assessed by comparing standard operating parameters of equipment to identify potential faults such as parameter deviations and signal interruptions; Compliance risk is assessed in real time to verify the matching degree between the vessel's position and navigation rules, investigating issues such as illegal entry into prohibited navigation areas and violations of navigation order, and classifying risks into three levels: general, emergency, and special, based on their impact.

[0120] The corresponding early warning mechanism is activated according to the risk level: for extremely high risks, an audible and visual warning is immediately triggered and pushed simultaneously through three channels: system pop-up, voice broadcast, and Beidou short message, indicating the risk location, scope of impact, and emergency evacuation guidance; for emergency risks, an audible and visual prompt and a terminal pop-up are triggered, pushing risk details and response suggestions; for general risks, only a text pop-up is used for prompting.

[0121] See Figure 8 As shown, the ship-shore collaborative management module specifically includes:

[0122] Ship-to-shore two-way communication unit: Establishes an encrypted two-way data channel, compatible with 5G-A and Beidou short message dual-mode communication, and automatically strengthens signal priority in areas with weak communication;

[0123] Shore-based control unit: Real-time aggregation of vessel, waterway, and early warning data across the entire basin; remote monitoring, precise scheduling, and violation warnings of vessels by investigating abnormal navigation behavior and potential waterway hazards.

[0124] Business collaboration processing unit: It connects the business links of lock application, government affairs processing and emergency search and rescue, integrates cross-departmental data resources, and enables one-stop lock passage reservation and cross-regional government affairs processing;

[0125] Data statistics unit: Collects data on vessel traffic flow, waterway traffic efficiency, and risk distribution in real time, and provides decision support data on waterway traffic efficiency, vessel traffic flow, and risk distribution.

[0126] Specifically, the shore-based platform aggregates data uploaded by multiple vessels across the entire basin, presenting real-time vessel distribution, waterway status, risk points, and business processing progress to form a comprehensive shipping situation map. By investigating vessel violations, waterway congestion, and abnormal risk warnings, it generates optimized scheduling plans for densely converging vessel areas and lock control sections, rationally planning vessel passage sequences. It also links with government systems to enable online processing of lock applications, navigation permits, and violation handling, breaking down data barriers and improving government efficiency. Simultaneously, it compiles data on waterway traffic efficiency, vessel flow, and risk distribution.

[0127] After receiving instructions and scheduling information from the shore-based platform, the ship terminal automatically matches the corresponding business scenario and breaks down the execution tasks: the lock scheduling instructions are synchronized to the dynamic 3D waterway modeling module to optimize the entry and exit routes and berthing positions; the traffic control instructions are pushed to the full-scenario information push module to remind the crew to adjust the navigation plan; the crew can provide feedback on the execution progress and emergencies through the terminal, and the shore-based platform adjusts the scheduling plan in real time based on the feedback, forming a two-way collaborative closed loop of "shore-based instructions - ship execution - status feedback".

[0128] See Figure 9 As shown, the inland waterway scene adaptation module specifically includes:

[0129] Scene recognition unit: Real-time collection of waterway grade, terrain features, water type and navigation environment data, and identification of typical scenes such as inland waterways, lakes, canals, meandering river sections and bridge areas;

[0130] Parameter adaptation unit: Links scene recognition results to adjust system perception parameters, route algorithms and warning thresholds, optimizes obstacle detection accuracy for shallow and narrow waterways, and adjusts navigation mark broadcasting density for open waters;

[0131] Special area optimization unit: For complex lock areas, optimize the ship queuing scheduling and entry / exit guidance navigation logic; for winding river sections and bridge areas, enhance the accuracy of route turning prediction and collision warning.

[0132] Extended upgrade unit: Supports online firmware upgrades to adapt to new inland waterway scenarios and updated shipping rules.

[0133] Specifically, based on the identified scene type, the system automatically optimizes core parameters: at the perception parameter level, sonar detection frequency is enhanced in shallow and densely populated river sections, and the sensitivity of infrared and millimeter-wave radar collaboration is improved in low-visibility scenes; at the algorithm parameter level, the turning radius algorithm for winding river sections is optimized, and the queuing scheduling and navigation assistance logic for lock areas are adjusted; at the early warning parameter level, the collision warning threshold is lowered in densely populated waterways, and the water level and flow risk assessment standards are refined in hydrologically complex areas; at the communication parameter level, BeiDou short message service is prioritized in areas with weak communication to improve signal transmission priority and stability, achieving precise matching between parameters and scenes.

[0134] The system monitors the ship's navigation trajectory in real time, predicts scene switching points, and initiates parameter pre-adjustment 500 meters in advance to generate a scene transition adaptation plan. During scene switching, a gradual parameter adjustment strategy is adopted to avoid system fluctuations caused by sudden parameter changes. The scene adaptation logic of modules such as 3D modeling, navigation aids, and early warning is updated simultaneously to ensure that the system service remains continuous and uninterrupted during scene switching, and the adaptation process is smooth and stable.

[0135] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0136] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart navigation aid and navigation information service system for inland waterway vessels, characterized in that, include: Heterogeneous data fusion sensing module: integrates Beidou positioning, AIS, infrared thermal imaging, millimeter-wave radar, sonar detection and environmental sensing units, breaks through the low visibility perception blind zone, collects hydrological, meteorological and obstacle data, removes redundancy through fusion algorithm, and outputs accurate dataset; Edge-cloud collaborative processing module: Adopting an edge-cloud collaborative architecture, the edge processes high real-time data, while the cloud aggregates and deeply analyzes data from the entire watershed to obtain navigation patterns and optimize routes; Channel modeling and navigation aid module: Based on the processed data, a real-time 3D channel model is constructed, and the optimal route is intelligently planned by integrating static and dynamic information. Through dynamic adjustment of parameters in complex scenarios, the route is corrected in real time and deviation warning is provided. Virtual navigation mark broadcasting module: Based on AIS and Beidou high-precision positioning technology, a virtual navigation mark broadcasting system is constructed. The virtual navigation mark position, quantity and broadcasting frequency are dynamically adjusted according to the waterway conditions. The virtual navigation mark is monitored in real time through navigation mark status self-check. Navigation information push module: Pushes static service information based on user profiles, and pushes dynamic control information in a triggered manner based on the real-time location and navigation status of the vessel; Risk warning and emergency response module: Construct a four-in-one early warning system, providing level-based warnings for collision, hydrological, and compliance risks, and triggering corresponding emergency plans; Ship-shore collaborative management module: Establishes a two-way interactive channel between ships and shore to remotely monitor and precisely schedule ship navigation, and has a built-in data statistical analysis unit to provide data on waterway traffic efficiency, ship flow, and risk distribution; Inland waterway scenario adaptation module: Automatically adjusts perception parameters, route algorithms and early warning thresholds for different inland waterway scenarios, and adapts to new scenarios and rules through firmware upgrades.

2. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The heterogeneous data fusion sensing module specifically includes: Beidou Positioning Unit: Integrates Beidou ground-based augmentation module to perform real-time ship positioning and synchronously collect ship heading and speed data; AIS Automatic Identification Unit: Receives information on the identity, size, and navigation status of surrounding vessels, and performs dynamic interaction between vessels to avoid information blind spots when vessels converge in dense inland waterways. Infrared thermal imaging and millimeter-wave radar unit: The infrared thermal imaging unit breaks through the limitations of low visibility at night and in rain and fog, capturing the outlines of surrounding ships and shorelines, while the millimeter-wave radar calculates the target distance and relative speed, enhancing the detection of nearby obstacles; Sonar detection unit: It performs underwater scanning detection to address potential hazards such as shallow waters, reefs, and underwater debris in inland rivers, thus compensating for the shortcomings in above-water perception. Environmental sensing unit: collects hydrological and meteorological data such as water level, flow velocity, wind force, and visibility, and provides real-time feedback on the dynamics of the waterway environment; Data fusion processing unit: Standardizes the data from each unit, removes interference information through redundancy removal algorithms, and merges and outputs a complete and accurate dataset of waterway environment and ship dynamics.

3. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The edge-cloud collaborative processing module specifically includes: Edge-cloud collaborative control unit: responsible for computing power allocation and data flow scheduling, prioritizing the allocation of data with high real-time requirements to the edge, and uploading non-real-time data to the cloud for in-depth analysis; Edge processing unit: used to quickly process real-time data related to positioning and obstacle detection, avoiding the risk of latency in inland waterway networks; Cloud-based data aggregation and analysis unit: Aggregates data on ships, waterways, and management across the entire basin; uses big data analysis to uncover navigation patterns and optimize route models; and performs in-depth verification and supplementary processing on edge data. Dual-mode communication adapter unit: Supports dual-mode communication of 5G-A and Beidou short message. In areas with good network, 5G-A high-speed transmission is given priority, while in areas with weak communication, Beidou short message is given priority.

4. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The waterway modeling and navigation aid module specifically includes: Channel model construction unit: Integrates electronic channel charts, channel dimensions, static data on bridge clearance, and dynamic data on water level changes, water flow velocity, and temporary construction areas to construct a real-time updated 3D model; Route planning unit: Combines ship size, draft and preset navigation plan to generate multiple optimal route schemes, and optimizes route parameters and turning suggestions for complex scenarios such as inland waterway bends, bridge areas and locks; Real-time route correction unit: It dynamically tracks the ship's navigation trajectory through a synergistic algorithm, compares the model data with the ship's actual position, obtains navigation deviations, and issues warnings.

5. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The virtual navigation mark broadcasting module specifically includes: Virtual navigation mark broadcasting unit: Based on AIS and BeiDou positioning technology, it generates standardized virtual navigation mark signals and broadcasts them externally, including navigation mark location, type, and warning information; Adaptive control unit: Receives real-time dynamic data of the waterway, adjusts the layout parameters of virtual navigation marks, adds more and more navigation marks for dangerous waters, and optimizes the number and broadcasting frequency of navigation marks for open waterways; Status self-check unit: Real-time monitoring of virtual navigation beacon signal strength, coverage and operating status, troubleshooting signal anomalies and positioning deviations, and generating self-check reports. The new and old system are compatible units, which are compatible with the traditional physical navigation mark signal reception and parsing functions, and realize the integrated display of virtual and physical navigation mark information. This avoids the connection gap caused by the replacement of the navigation system and ensures that ships smoothly transition to the virtual navigation mark navigation mode.

6. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The navigation information push module specifically includes: User profiling and demand matching unit: Based on ship type, navigation purpose, and crew operation preferences, a unique user profile is constructed, and the priority of static service and dynamic control information demand is classified and labeled. Information classification unit: The received full amount of information is broken down into static service information and dynamic control information. Static information includes lock scheduling rules, port berth resources, and refueling and maintenance points. Dynamic information includes traffic control instructions, temporary changes to waterways, and sudden weather warnings. Scene-triggered push unit: Based on ship positioning and navigation status data, multiple push trigger scenarios are preset. When a ship approaches a lock, enters a controlled area, or encounters severe weather, the corresponding information is pushed.

7. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The risk warning and emergency response module specifically includes: Risk identification unit: Covering collision, hydrology, equipment, and compliance risk types, it captures real-time data on vessel trajectory, hydrological and meteorological conditions, equipment operation, and waterway rules to identify risk sources; Risk level determination unit: The system has a preset dynamic threshold adaptation mechanism, which adjusts the determination criteria based on the density of inland waterway vessels and the complexity of waterways. The risk is divided into three levels: general, emergency and special, and the scope of risk impact and development trend are marked. Emergency response plan generation unit: Based on risk level and scenario characteristics, it retrieves a pre-set emergency database, outputs differentiated response plans, provides avoidance suggestions for general risks, plans the optimal evacuation route for emergency risks, and generates a search and rescue coordination plan by linking ship and shore resources for extremely high risks.

8. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The ship-shore collaborative management module specifically includes: Ship-to-shore two-way communication unit: Establishes an encrypted two-way data channel, compatible with 5G-A and Beidou short message dual-mode communication, and automatically strengthens signal priority in areas with weak communication; Shore-based control unit: Real-time aggregation of vessel, waterway, and early warning data across the entire basin; remote monitoring, precise scheduling, and violation warnings of vessels by investigating abnormal navigation behavior and potential waterway hazards. Business collaboration processing unit: It connects the business links of lock application, government affairs processing and emergency search and rescue, integrates cross-departmental data resources, and enables one-stop lock passage reservation and cross-regional government affairs processing; Data statistics unit: Collects data on vessel traffic flow, waterway traffic efficiency, and risk distribution in real time, and provides decision support data on waterway traffic efficiency, vessel traffic flow, and risk distribution.

9. The intelligent navigation aid and navigation information service system for inland waterway vessels according to claim 1, characterized in that, The inland waterway scene adaptation module specifically includes: Scene recognition unit: Real-time collection of waterway grade, terrain features, water type and navigation environment data, and identification of typical scenes such as inland waterways, lakes, canals, meandering river sections and bridge areas; Parameter adaptation unit: Links scene recognition results to adjust system perception parameters, route algorithms and warning thresholds, optimizes obstacle detection accuracy for shallow and narrow waterways, and adjusts navigation mark broadcasting density for open waters; Special area optimization unit: For complex lock areas, optimize the ship queuing scheduling and entry / exit guidance navigation logic; for winding river sections and bridge areas, enhance the accuracy of route turning prediction and collision warning. Extended upgrade unit: Supports online firmware upgrades to adapt to new inland waterway scenarios and updated shipping rules.

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