Rapid route generation method and system for complex maritime environment

Abstract

The invention discloses a rapid route generation method and system for a complex maritime environment, and relates to the technical field of maritime traffic management, and the method comprises the steps: S1, collecting and integrating real-time marine environment data and ship dynamic information, S2, carrying out the congestion analysis and prediction based on the traffic flow data of a water area, and S3, according to the analysis result, carrying out the calculation of the traffic flow data of the ship. S4, dynamically adjusting the route planning to cope with the maritime environment of the emergency situation; according to the rapid ship route generation method and system for the complex maritime environment, a ship can avoid a high-density or congested area in a complex water area, so that the overall utilization efficiency of channel resources is improved, and the problem that the overall efficiency is reduced due to local dredging is solved; the method can quickly respond to complex environmental changes such as tidal changes, meteorological condition sudden changes and flow surge, and solves the problem of how to analyze and schedule traffic flow in a complex water area to solve channel congestion in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of maritime traffic management, and in particular to a method and system for quickly generating routes in a complex maritime environment. Background Art

[0002] In a complex maritime environment, efficient use of waterway resources is crucial to ensure the safety and smoothness of water transportation. Especially around ports, estuaries and other areas with high ship density, waterway congestion is frequent, which directly affects shipping efficiency and ship safety.

[0003] Existing technologies mainly analyze and dispatch traffic flow in the following ways: radar and automatic identification systems obtain information such as ship position, speed, and heading. Through data visualization technology, a maritime traffic situation map is generated to provide basic data for traffic flow analysis. However, this method has data redundancy problems when traffic flow is too dense, and it is difficult to quickly identify abnormal situations; regularized waterway design, traditional waterway design relies on fixed routes or regularized routes to ensure that ships sail along established routes. Although this method avoids the risk of collision between ships to a certain extent, it lacks dynamic scheduling capabilities in complex waters and cannot cope with emergencies or traffic surges; traffic dispatchers conduct manual intervention by observing the traffic conditions of the waterway in real time and combining the scheduled navigation plan. This method relies too much on the experience and judgment of the dispatcher, has low processing efficiency, and is prone to errors when facing complex or large-scale traffic management. Although the above-mentioned technologies can monitor and manage waterway traffic flow to a certain extent, they still have the following limitations when dealing with high-density traffic in complex maritime environments: existing methods are usually based on static rules or manual experience, and lack the ability to adapt to dynamic changes in real time, especially in scenarios such as tidal changes and sudden changes in meteorological conditions; data processing and optimization decisions for large-scale traffic flows rely on manual intervention, with low levels of automation and intelligence, making it difficult to respond quickly to waterway congestion problems; existing technologies focus more on local traffic flow management, and lack the ability to optimize and dispatch multiple routes and nodes in complex waters as a whole, which can easily lead to the problem of local waterway dredging and reduced overall traffic efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for quickly generating routes in complex maritime environments, so as to solve the problem of how to analyze and schedule traffic flow in complex waters to solve waterway congestion in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for quickly generating routes in a complex maritime environment, the method comprising: S1. Collect and integrate real-time marine environment data and ship dynamic information; S2. Congestion analysis and prediction based on water area traffic flow data, including modeling traffic flow and calculating traffic pressure in each water area. The specific formula is: × ; Among them, V x represents the traffic flow load value of the water area, V represents the total global traffic load value, R x Indicates the degree of obstruction to ship traffic flow in the water area, R1, R2, ..., R n represents the flow resistance value of each water area, n represents the number of water areas, and x represents the index of different water areas; S3. Generate an optimized route planning scheme based on the analysis results, including dividing the route planning waters into multiple sections, setting the navigation speed for each section, calculating the heading angle of the path in different waters according to the starting and ending points of the target path, optimizing the route, and ensuring the minimum total path cost. The specific formula is: ; Among them, θ1 represents the angle between the initial heading and the boundary normal when the route enters from one area to another, θ2 represents the angle between the adjusted heading and the boundary normal after entering the new area, v1 represents the navigation speed of the ship in the current area, and v2 represents the navigation speed of the ship when entering the new area; S4. Dynamically adjust route planning to cope with unexpected maritime environments.

[0006] Preferably, the S1 comprises: Assign initial weights to each type of data, set the importance factor of the data, calculate the final weight of the data using the time update frequency, and fuse multi-source data to generate a unified environmental data input. The specific formula for calculating the final weight of the data is: Q=m×c×Δt; Among them, Q represents the importance weight of the data, m represents the credibility value of the data collection source, c represents the importance factor value of the data, Δt represents the update time interval of data collection, and t represents time.

[0007] Preferably, S4 includes: Establish a route adaptability model, adjust the response speed and environmental carrying capacity according to environmental changes, dynamically update route parameters, generate new route planning to adapt to environmental changes, and dynamically update route parameters. The specific formula is: P t+1 =P t +r×P t × (1-P t / K); Among them, P t+1 Indicates the route adaptability value at the next moment, P tIt represents the route adaptability value at the current moment, r represents the reaction speed of route adaptability to changes in the external environment, K represents the maximum route adaptability value allowed by the current maritime environment, and t represents time.

[0008] Preferably, the obstruction value R of the ship traffic flow in the water area in S2 is x The calculation formula is: R x =D / (W×S); Among them, R x It represents the degree of obstruction to the flow of ship traffic in the water area, D represents the number of ships per unit area, W represents the width of the channel available for navigation in the area, and S represents the average sailing speed of ships.

[0009] Preferably, the calculation formula of the navigation speed in S3 is: ; Among them, v represents the navigation speed of the area, F represents the comprehensive driving force of the regional environment, and h represents the unit mass of the ship.

[0010] Preferably, the calculation formula of the credibility value m of the data collection source in S1 is: m = A / B; Among them, m represents the credibility value of the data collection source, A represents the accuracy of the collection equipment, and B represents the failure rate of the equipment.

[0011] Preferably, the calculation formula of the importance factor value c of the data in S1 is: c=G1 / G; Among them, c represents the importance factor value of the data, G1 represents the data impact weight, and G represents the total weight.

[0012] Preferably, the calculation formula of the reaction speed r of the route adaptability to the external environment change in S4 is: r = ΔP 环境 / ΔT; Among them, r represents the response speed of route adaptability to changes in the external environment, ΔP 环境 It indicates the impact of environmental changes on route adaptability, and ΔT indicates the time interval of environmental changes.

[0013] Preferably, the calculation formula of the maximum route adaptability value K allowed by the current maritime environment in S4 is: K = C 航道 ×C 流量 / C 风险 ; Among them, K represents the maximum route adaptability value allowed by the current maritime environment, C 航道 represents the waterway capacity, C 流量 Indicates the maximum flow rate that can be accommodated by ships in the current channel, C 风险 Represents environmental risk factors.

[0014] A route rapid generation system for complex maritime environments, used to implement the steps of the route rapid generation method for complex maritime environments, the system comprising: Data acquisition module, used to collect and integrate real-time marine environment data and ship dynamic information; A congestion analysis module connected to the data acquisition module, for performing congestion analysis and prediction based on the traffic flow data provided by the data acquisition module; A route planning module connected to the congestion analysis module is used to generate an optimized route planning solution based on the results of congestion analysis and prediction; The dynamic adjustment module connected to the data acquisition module and the route planning module is used to receive the real-time update data of the data acquisition module and the initial planning scheme of the route planning module, and dynamically adjust the route planning to cope with the maritime environment of emergencies.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The method and system for rapid route generation for complex maritime environments collects and integrates real-time marine environment data and ship dynamic information, performs congestion analysis and prediction based on water traffic flow data, generates optimized route planning schemes according to the analysis results, dynamically adjusts route planning to cope with unexpected maritime environments, and enables ships to avoid high-density or congested areas in complex waters, thereby improving the overall utilization efficiency of waterway resources and avoiding the problem of local dredging and overall efficiency decline. The method and system can quickly respond to complex environmental changes such as tidal changes, sudden changes in meteorological conditions, and surges in traffic, effectively avoid the limitations of static rules or manual experience, significantly reduce the risk of ship collisions, improve navigation safety and smoothness in high-density areas, effectively reduce dependence on manual experience and scheduling, and reduce the risk of human misjudgment. At the same time, the method improves traffic flow processing efficiency, significantly improves the overall shipping efficiency in complex waters, avoids the problem of low processing efficiency caused by data redundancy, quickly identifies abnormal situations, and helps make accurate decisions. The method can provide real-time optimized route planning schemes according to actual conditions, and solves the problem of how the existing technology analyzes and schedules traffic flow in complex waters to solve waterway congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a method for quickly generating routes in a complex maritime environment, the method comprising: S1. Collect and integrate real-time marine environment data and ship dynamic information; S2. Congestion analysis and prediction based on water area traffic flow data, including modeling traffic flow and calculating traffic pressure in each water area. The specific formula is: × ; Among them, V x represents the traffic flow load value of the water area, V represents the total global traffic load value, R x Indicates the degree of obstruction to ship traffic flow in the water area, R1, R2, ..., R n represents the flow resistance value of each water area, n represents the number of water areas, and x represents the index of different water areas; S3. Generate an optimized route planning scheme based on the analysis results, including dividing the route planning waters into multiple sections, setting the navigation speed for each section, calculating the heading angle of the path in different waters according to the starting and ending points of the target path, optimizing the route, and ensuring the minimum total path cost. The specific formula is: ; Among them, θ1 represents the angle between the initial heading and the boundary normal when the route enters from one area to another, θ2 represents the angle between the adjusted heading and the boundary normal after entering the new area, v1 represents the navigation speed of the ship in the current area, and v2 represents the navigation speed of the ship when entering the new area; S4. Dynamically adjust route planning to cope with unexpected maritime environments.

[0019] The method obtains marine environmental data and ship dynamic information in real time, including islands, obstacles, restricted areas, wind, current, waves, surges, etc., and combines traffic flow analysis and prediction to determine the traffic pressure distribution in each water area and the optimal sailing speed of the ship. Through the resistance modeling formula and optimization algorithm, the route planning with the minimum total path cost is generated. At the same time, according to the heading adjustment formula, the connection of cross-regional routes is optimized to ensure the smooth transition of speed and heading. The dynamic adjustment mechanism further enhances the adaptability of the method to maritime emergencies. Compared with the existing technology, this method can significantly improve the speed and accuracy of route planning. Through the integration and analysis of real-time data, the traffic pressure distribution in a complex maritime environment can be quickly evaluated to provide ships with optimized route plans. The use of the optimization formula reduces the path cost and ensures efficient and safe passage of ships. At the same time, the dynamic adjustment mechanism ensures the flexibility and adaptability of the method, and it can maintain efficient operation even in emergencies.

[0020] S1 includes assigning initial weights to each type of data, setting the importance factor of the data, calculating the final weight of the data using the time update frequency, and fusing multi-source data to generate a unified environmental data input. The specific formula for calculating the final weight of the data is: Q=m×c×Δt; Among them, Q represents the importance weight of the data, m represents the credibility value of the data collection source, c represents the importance factor value of the data, Δt represents the update time interval of data collection, and t represents time.

[0021] This implementation method assigns initial weights to multi-source data, combines the importance factor of the data and the credibility of the collection source, and uses the update time interval to weight the data to calculate the final weight Q. This method effectively unifies the input format of multi-source data, making environmental data more representative and accurate in subsequent analysis, and ensuring that the basic data for route planning is reliable and applicable. By comprehensively considering the credibility and importance of the data source, the weights of various types of data are dynamically adjusted to avoid the interference of low-quality data on the overall accuracy of environmental data. The real-time updated weight calculation method enables this method to adapt to the rapidly changing maritime environment and provide efficient data support for route optimization. This implementation method unifies and integrates multi-source data, effectively reducing the computational complexity caused by the diversity of data sources and improving the overall operating efficiency of the system.

[0022] S4 includes establishing a route adaptability model, adjusting the response speed and environmental carrying capacity according to environmental changes, dynamically updating route parameters, and generating new route planning to adapt to environmental changes. The specific formula for dynamically updating route parameters is: P t+1 =P t +r×P t × (1-P t / K); Among them, Pt+1 Indicates the route adaptability value at the next moment, P t It represents the route adaptability value at the current moment, r represents the reaction speed of route adaptability to changes in the external environment, K represents the maximum route adaptability value allowed by the current maritime environment, and t represents time.

[0023] This implementation method dynamically adjusts the adaptability value of the route by constructing a route adaptability model and combining the self-growth logic in the formula. When the current environmental changes affect the adaptability of the route, the route adaptability value P is updated in real time according to the route's response speed r to the environment and the maximum carrying capacity K of the environment. t . Through nonlinear adjustment, the model makes the route adaptability gradually approach the optimal state, ensuring that the route planning is efficient and reliable in a dynamically changing maritime environment. By dynamically adjusting the route parameters, this method can quickly respond to changes in the external environment, generate more adaptive route planning, reduce navigation risks, and rationally utilize the maximum carrying capacity K of the maritime environment, avoiding waste of resources or potential environmental pressure caused by over-planning, and improving the operating efficiency and environmental friendliness of the route. The self-adjusting characteristics of the route adaptability model enhance the robustness of the system, enabling it to maintain efficient operation in an uncertain maritime environment.

[0024] The obstruction value R of the ship traffic flow in the water area S2 x The calculation formula is: x =D / (W×S); Among them, R x It represents the degree of obstruction to the flow of ship traffic in the water area, D represents the number of ships per unit area, W represents the width of the channel available for navigation in the area, and S represents the average sailing speed of ships.

[0025] In this embodiment, the traffic flow obstruction value R in the water area is calculated. x , providing basic parameter support for route planning. In the formula, ship density D represents the degree of traffic congestion in the water area, while channel width W and average ship speed S reflect the traffic capacity of the water area. By taking these factors into consideration, the formula can quantify the degree of obstruction in the water area, thus providing a key basis for subsequent traffic flow analysis and route optimization. The formula combines multi-dimensional factors such as water traffic density, channel width and ship speed, and can accurately reflect the degree of traffic obstruction in different waters, laying a data foundation for route optimization. By quickly calculating R x , can analyze the traffic flow in different waters in real time, speed up the route planning process, and adapt to the dynamically changing maritime environment. The quantitative assessment of the degree of obstruction helps to avoid high traffic obstruction areas, reduce navigation risks, and improve the overall safety and efficiency of navigation.

[0026] The calculation formula for the sailing speed in S3 is: ; Among them, v represents the navigation speed of the area, F represents the comprehensive driving force of the regional environment, and h represents the unit mass of the ship.

[0027] This implementation provides accurate parameter support for route optimization by calculating the navigation speed v of the ship in the area. The formula is based on the principle of dynamics and takes into account the influence of the comprehensive driving force F of the environment and the unit mass h of the ship on the navigation speed. The comprehensive driving force F includes environmental factors (such as tides, wind power) and the output power of the ship's propulsion system. Through the formula calculation, the optimal navigation speed of the ship in a specific area can be quickly determined, thereby achieving efficient planning of the route. By introducing the two key parameters of the comprehensive driving force of the environment and the unit mass of the ship, the optimal navigation speed of the ship in different areas can be scientifically calculated to improve navigation efficiency. The speed calculation formula fully considers the balance between the mass and driving force of the ship, avoids energy waste caused by too fast or too slow navigation, and helps to save energy and reduce emissions. By rationally planning the navigation speed, it can avoid ship loss of control or navigation deviation caused by improper speed, thereby improving the overall safety of navigation.

[0028] The calculation formula of the credibility value m of the data collection source in S1 is: m = A / B; Among them, m represents the credibility value of the data collection source, A represents the accuracy of the collection equipment, and B represents the failure rate of the equipment.

[0029] This implementation method calculates the credibility value m of the data collection source through the formula m=A / B. The accuracy A of the equipment reflects the accuracy of the data collected by the equipment, and the failure rate B indicates the probability that the equipment may produce errors or interruptions. By combining the two, the credibility value m reflects the comprehensive reliability of the equipment in the data collection process. The credibility value is used for weight allocation to ensure that data from high-credibility sources is given priority when multi-source data is fused. By quantifying the collection accuracy and failure rate of the equipment, the reliability of the data source is comprehensively evaluated, thereby effectively filtering low-credibility data and giving high-credibility data a higher weight, ensuring that the fused environmental data has higher accuracy and representativeness, providing high-quality data support for subsequent route planning, calculating the credibility value m in real time, and dynamically adjusting the data weight so that the system can adapt to changes in equipment status or environment and improve the stability of data processing.

[0030] The calculation formula for the importance factor value c of the data in S1 is: c = G1 / G; Among them, c represents the importance factor value of the data, G1 represents the data impact weight, and G represents the total weight.

[0031] This implementation method determines the importance factor value c of the data by calculating the formula c=G1 / G. Among them, the data impact weight G1 reflects the degree of influence of a certain type of data on route planning, such as the criticality of weather data to route selection, and the total weight G is the sum of the impact weights of all data. Through this calculation method, the importance of different data is quantified as a factor value c, and introduced in the process of data fusion and weight allocation to guide the processing and utilization of environmental data. By accurately calculating the importance factor value c of each type of data, a quantitative analysis of the role of different data in route planning is achieved, thereby optimizing the utilization efficiency of data, giving higher weights to key data, ensuring priority processing of important data in the route planning process, improving the rationality and accuracy of planning, and adjusting the impact weight G1 and the total weight G in real time to adapt to the changing needs of different maritime environments and ensure the flexibility of data processing.

[0032] The calculation formula for the reaction speed r of route adaptability to changes in the external environment in S4 is: r = ΔP 环境 / ΔT; Among them, r represents the response speed of route adaptability to changes in the external environment, ΔP 环境 It indicates the impact of environmental changes on route adaptability, and ΔT indicates the time interval of environmental changes.

[0033] This embodiment quantifies the dynamic impact of environmental changes on route adaptability by calculating the reaction speed r of route adaptability to external environmental changes. 环境 The factors including tide, wind speed, visibility and other factors affect the need for route adjustment, while the time interval ΔT reflects the frequency and rate of environmental changes. The formula combines the intensity and time span of environmental changes to calculate the response speed of route adaptability, thereby providing support for dynamic adjustment of route parameters. By quantitatively calculating the response speed r of route adaptability to environmental changes, a scientific basis is provided for rapid adjustment of route planning, improving the real-time and flexibility of planning, and using the numerical changes in the response speed r to quickly determine the urgency of the need for route adaptability adjustment, thereby improving the efficiency and effectiveness of route adjustment. By quickly responding to environmental changes, navigation risks caused by delayed adjustments can be avoided, thereby improving the adaptability and safety of routes in complex maritime environments.

[0034] The calculation formula of the maximum route adaptability value K allowed by the current maritime environment in S4 is: K = C 航道 ×C 流量 / C 风险 ; Among them, K represents the maximum route adaptability value allowed by the current maritime environment, C 航道 represents the waterway capacity, C 流量 Indicates the maximum flow rate that can be accommodated by ships in the current channel, C 风险Represents environmental risk factors.

[0035] This implementation method quantitatively evaluates the environmental carrying capacity of route planning by calculating the maximum route adaptability value K allowed by the current maritime environment. 航道 Indicates the traffic capacity of a specific water area. The current waterway can accommodate a maximum flow rate of C 流量 Reflecting the upper limit of actual traffic flow, the environmental risk factor C 风险 The impact of risks such as severe weather and maritime incidents on the route is comprehensively assessed. Through the calculation formula, the channel characteristics and environmental risks are combined to provide a reference for dynamically adjusting route planning. By introducing channel capacity, flow and risk factors, the impact of the maritime environment on the adaptability of the route is comprehensively assessed, providing accurate data support for route optimization. By adjusting the calculated value of K in real time, high-risk areas can be quickly identified to avoid route congestion or navigation accidents caused by excessive planning. K is dynamically calculated in combination with current environmental conditions, so that route planning can flexibly respond to changes in a complex maritime environment and improve the real-time and flexibility of planning.

[0036] A route rapid generation system for complex maritime environments is also provided, which is used to implement the steps of the route rapid generation method for complex maritime environments. The system comprises: Data acquisition module, used to collect and integrate real-time marine environment data and ship dynamic information; A congestion analysis module connected to the data acquisition module, for performing congestion analysis and prediction based on the traffic flow data provided by the data acquisition module; A route planning module connected to the congestion analysis module is used to generate an optimized route planning solution based on the results of congestion analysis and prediction; The dynamic adjustment module connected to the data acquisition module and the route planning module is used to receive the real-time update data of the data acquisition module and the initial planning scheme of the route planning module, and dynamically adjust the route planning to cope with the maritime environment of emergencies.

[0037] This system realizes the rapid generation of routes in complex maritime environments through the collaborative work of various modules. The data acquisition module provides high-quality environmental and ship dynamic data in real time. The congestion analysis module uses traffic flow data modeling to analyze the degree of obstruction in each area. The route planning module generates an initial route plan based on the analysis results. The dynamic adjustment module dynamically optimizes the initial plan by monitoring real-time changing data to ensure the adaptability and efficiency of the route plan to sudden environments. The system can quickly collect and process real-time data, provide high-quality basic data for route planning, and improve the accuracy of planning plans. Combined with traffic flow analysis and dynamic adjustment functions, the system can quickly respond to sudden changes in the maritime environment and ensure the efficiency and safety of routes. The functions of each module are clearly divided, and support expansion and upgrading. Functions can be adjusted or new modules can be added according to specific needs to adapt to different application scenarios.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for quickly generating routes in complex maritime environments, characterized in that: The method comprises: S1. Collect and integrate real-time marine environment data and ship dynamic information; S2. Congestion analysis and prediction based on water area traffic flow data, including modeling traffic flow and calculating traffic pressure in each water area. The specific formula is: × ； Among them, V x represents the traffic flow load value of the water area, V represents the total global traffic load value, R x Indicates the degree of obstruction to ship traffic flow in the water area, R1, R2, ..., R n represents the flow resistance value of each water area, n represents the number of water areas, and x represents the index of different water areas; S3. Generate an optimized route planning scheme based on the analysis results, including dividing the route planning waters into multiple sections, setting the navigation speed for each section, calculating the heading angle of the path in different waters according to the starting and ending points of the target path, optimizing the route, and ensuring the minimum total path cost. The specific formula is: ; Among them, θ1 represents the angle between the initial heading and the boundary normal when the route enters from one area to another, θ2 represents the angle between the adjusted heading and the boundary normal after entering the new area, v1 represents the navigation speed of the ship in the current area, and v2 represents the navigation speed of the ship when entering the new area; S4. Dynamically adjust route planning to cope with unexpected maritime environments.

2. The method for quickly generating routes in a complex maritime environment according to claim 1, characterized in that: The S1 includes: Assign initial weights to each type of data, set the importance factor of the data, calculate the final weight of the data using the time update frequency, and fuse multi-source data to generate a unified environmental data input. The specific formula for calculating the final weight of the data is: Q=m×c×Δt; Among them, Q represents the importance weight of the data, m represents the credibility value of the data collection source, c represents the importance factor value of the data, Δt represents the update time interval of data collection, and t represents time.

3. The method for quickly generating routes in a complex maritime environment according to claim 1, characterized in that: The S4 includes: Establish a route adaptability model, adjust the response speed and environmental carrying capacity according to environmental changes, dynamically update route parameters, generate new route planning to adapt to environmental changes, and dynamically update route parameters. The specific formula is: P t+1 =P t +r×P t × (1-P t / K); Among them, P t+1 Indicates the route adaptability value at the next moment, P t It represents the route adaptability value at the current moment, r represents the reaction speed of route adaptability to changes in the external environment, K represents the maximum route adaptability value allowed by the current maritime environment, and t represents time.

4. The method for quickly generating routes in a complex maritime environment according to claim 1, characterized in that: The obstruction value R of the ship traffic flow in the water area S2 x The calculation formula is: R x =D / (W×S); Among them, R x It represents the degree of obstruction to the flow of ship traffic in the water area, D represents the number of ships per unit area, W represents the width of the channel available for navigation in the area, and S represents the average sailing speed of ships.

5. The method for quickly generating routes in a complex maritime environment according to claim 1, characterized in that: The calculation formula of the navigation speed in S3 is: ; Among them, v represents the navigation speed of the area, F represents the comprehensive driving force of the regional environment, and h represents the unit mass of the ship.

6. The method for quickly generating routes in a complex maritime environment according to claim 2, characterized in that: The calculation formula of the credibility value m of the data collection source in S1 is: m = A / B; Among them, m represents the credibility value of the data collection source, A represents the accuracy of the collection equipment, and B represents the failure rate of the equipment.

7. The method for quickly generating routes in a complex maritime environment according to claim 2, characterized in that: The calculation formula of the importance factor value c of the data in S1 is: c=G1 / G; Among them, c represents the importance factor value of the data, G1 represents the data impact weight, and G represents the total weight.

8. The method for quickly generating routes in a complex maritime environment according to claim 3, characterized in that: The calculation formula of the reaction speed r of the route adaptability to the external environment change in S4 is: r = ΔP 环境 / ΔT; Among them, r represents the response speed of route adaptability to changes in the external environment, ΔP 环境 It indicates the impact of environmental changes on route adaptability, and ΔT indicates the time interval of environmental changes.

9. The method for quickly generating routes in a complex maritime environment according to claim 3, characterized in that: The calculation formula of the maximum route adaptability value K allowed by the current maritime environment in S4 is: K = C 航道 ×C 流量 / C 风险 ; Among them, K represents the maximum route adaptability value allowed by the current maritime environment, C 航道 represents the waterway capacity, C 流量 Indicates the maximum flow rate that can be accommodated by ships in the current channel, C 风险 Represents environmental risk factors.

10. A system for quickly generating routes in a complex maritime environment, used to implement the steps of the method for quickly generating routes in a complex maritime environment as described in any one of claims 1 to 9, characterized in that: The system comprises: Data acquisition module, used to collect and integrate real-time marine environment data and ship dynamic information; A congestion analysis module connected to the data acquisition module, for performing congestion analysis and prediction based on the traffic flow data provided by the data acquisition module; A route planning module connected to the congestion analysis module is used to generate an optimized route planning solution based on the results of congestion analysis and prediction; The dynamic adjustment module connected to the data acquisition module and the route planning module is used to receive the real-time update data of the data acquisition module and the initial planning scheme of the route planning module, and dynamically adjust the route planning to cope with the maritime environment of emergencies.

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