Method, device and equipment for predicting navigation density of key water area of pivot reach

Abstract

The application discloses a method, device and equipment for predicting navigation density of key water area of hub river section, which comprises the following steps: predicting the navigation prediction track of the target ship based on the historical ship navigation track data and using the track similarity algorithm; comprehensively considering the influence factors related to the navigation time and the weight thereof; calculating and determining the navigation time of the target ship to the key water area; and then determining the number of ships arriving at the key water area within the preset time and the navigation density of the key water area within the preset time. The method for predicting the navigation density of the key water area of the hub river section can predict the navigation track of the target ship, guarantee the safety of the ship to a certain extent, give an early warning to the ships that may collide or encounter other dangers, monitor and analyze the change of the navigation density of the key water area, help identify potential safety risks and congestion conditions, help the shipping management department make reasonable decisions and arrangements, and provide intuitive and comprehensive information.

Description

Technical Field

[0001] This invention relates to the field of ship management technology, and in particular to a method, apparatus and equipment for predicting navigation density in key waterways of a key river section. Background Technology

[0002] Due to the impoundment and power generation of the Three Gorges Dam, the water flow in the channel between the two dams has become relatively stable, and the safety of ship navigation is mainly limited by the natural dimensions of the channel, which are essentially fixed. However, during the flood season, due to the large amount of water released for flood control upstream, and the limited water storage capacity between the two dams, the water is basically released as much as it comes in. In addition, the river between the two dams is mainly a mountainous river, and the "pipeline effect" leads to a sharp deterioration in the water flow conditions between the two dams during the flood season, resulting in frequent accidents and incidents. This greatly affects the safety of ship navigation and the passage capacity of the channel, bringing great difficulties to ship handling and creating safety hazards for ship navigation. This has formed the famous "four shoals, one bend, and one pass," and therefore it is also regarded as a key waterway and a key area monitored by management departments.

[0003] Previously, the Liantuo section of the river channel between the two dams was improved to enhance navigation conditions and reduce obstructive flow patterns. This involved methods such as blasting reefs and clearing debris to improve key waterways, achieving some success. However, risks to navigation safety remain. In the maritime sector, navigation density reflects, to a certain extent, the level of traffic activity and danger in a waterway. Calculations by maritime authorities show that the occurrence of ship collisions is directly proportional to the square of navigation density. Given the poor navigation conditions in key waterways, exploring navigation density is crucial for maintaining navigation safety in these areas.

[0004] During the flood season, to strengthen safety supervision of key waterways, the current method relies on manual CCTV monitoring. This method is costly, has blind spots, and generates massive amounts of data, making effective data processing and analysis extremely difficult. Furthermore, due to the unique location of waterways, vessel traffic behavior varies significantly. For example, vessel speeds differ due to individual vessel factors, and passage times differ due to dam crossing rules. This means that manual measurement methods may not accurately reflect the traffic density of key waterways, nor can they effectively predict traffic density in key waterways for future periods. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the purpose of this invention is to provide a method, device, equipment and storage medium for predicting the navigation density of key waters in a key river section. By predicting the navigation trajectory of ships and considering the influence of human factors, environmental factors, ship factors and rule factors on ship passage, the navigation time of ships to reach key waters is determined, thereby effectively predicting the navigation density of key waters within a preset time, which helps shipping management departments to make reasonable decisions and arrangements, and provides intuitive and comprehensive information for decision-making.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] The present invention provides a method for predicting navigation density in key waterways of a key river section, comprising the following steps:

[0008] S100. Based on the AIS data of the target vessel and historical vessel navigation trajectory data, a trajectory similarity algorithm is used to predict the navigation trajectory of the target vessel. First, historical data of the Automatic Identification System is acquired, and historical vessel navigation trajectory data is extracted from the historical data. The similarity between trajectories is judged by setting time thresholds and distance thresholds. By copying certain points, the distance between time sequences of unequal durations is calculated, thereby reflecting the degree of similarity between the two sequences. The accuracy of the trajectory similarity algorithm is evaluated by the similarity score.

[0009] S200. Collect historical basic data of ships arriving in key waters, extract key features using data mining techniques, and determine the influencing factors related to sailing time. These influencing factors include human factors, environmental factors, ship factors, and rule factors. The weight of each of these influencing factors on sailing time is determined using the analytic hierarchy process.

[0010] S300. Determine the influence of each of the aforementioned influencing factors on the target vessel's speed using the Analytic Hierarchy Process (AHP) to obtain the target vessel's speed. Based on this speed and the predicted navigation trajectory, determine the sailing time for the target vessel to reach the key waterway.

[0011] S400: Push the target ships whose sailing time is less than or equal to the preset time to the database, poll and calculate to determine the number of ships that will arrive at the key water area within the preset time, and then determine the navigation density of the key water area within the preset time.

[0012] Further, in step S100, the real-time position of the target vessel is used as the prediction starting point and the destination of the target vessel is used as the prediction ending point. The trajectory similarity algorithm is used to match the historical vessel navigation trajectory data, and the historical navigation trajectory with the closest trajectory and the same ship type as the target vessel is used as the navigation prediction trajectory of the target vessel.

[0013] Furthermore, in step S200, the influencing factors of human factors include the skill level of the driver; the influencing factors of environmental factors include meteorological conditions, water flow conditions, waterway width and depth, and navigation control; the influencing factors of ship factors include ship size, ship loading status, ship loading type, and ship main engine power; and the influencing factors of rule factors include the priority order of ships passing through the dam and priority ships passing through the dam.

[0014] Further, in step S200, the relative importance of each influencing factor is determined by comparing each influencing factor pairwise, and then the weight of each influencing factor is determined; the historical ship basic data corresponding to each influencing factor is normalized to determine the normalized value of each influencing factor; and a weighted sum is performed based on the weight and the normalized value to obtain the comprehensive influencing factor score.

[0015] Furthermore, in step S300, the scores of the comprehensive influencing factors are mapped by an exponential function to obtain the speed of the target vessel under the influence of each influencing factor. Then, based on the real-time position of the target vessel and the predicted navigation trajectory, the sailing time of the target vessel to reach the key waters is calculated.

[0016] Furthermore, it also includes the following steps: visualizing the predicted navigation density of the key waterways.

[0017] This invention also provides an apparatus for predicting navigation density in key waterways of a key river section, comprising a navigation trajectory prediction module, a navigation time calculation module, and a navigation density prediction module. The navigation trajectory prediction module is used to predict the navigation trajectory of the target vessel based on its AIS data and historical vessel navigation trajectory data. The navigation time calculation module is used to extract influencing factors related to navigation time, determine the weight of each influencing factor on the navigation time, and, based on the weights of each influencing factor and the target vessel's AIS data and the predicted navigation trajectory, determine the navigation time for the target vessel to reach the key waterway. The navigation density prediction module is used to push target vessels with navigation times less than or equal to a preset time to a database, poll and calculate the number of vessels arriving at the key waterway within the preset time, and thus determine the navigation density of the key waterway within the preset time.

[0018] The present invention also provides an electronic device, wherein the processor and the memory are interconnected, the memory is used to store a computer program, and the processor is configured to execute the method for predicting the navigation density of key waterways in the key section of the hub river when the computer program is invoked.

[0019] The present invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the method for predicting navigation density in key waterways of a key river section.

[0020] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0021] The present invention provides a method, apparatus, equipment, and storage medium for predicting navigation density in key waterways of a key river section. Based on historical ship navigation trajectory data, it uses a trajectory similarity algorithm to predict the navigation trajectory of target ships. On the one hand, the ability to predict the navigation trajectory of target ships can, to a certain extent, ensure the safety of ships and provide early warnings for ships that may collide or encounter other dangers. On the other hand, it also provides a reliable basis for predicting navigation time.

[0022] This invention utilizes data mining techniques to extract influencing factors related to sailing time, and uses the analytic hierarchy process (AHP) to determine the weight of each influencing factor on sailing time. These factors are then applied to sailing time prediction. This invention integrates multiple factors, including ship traffic rules, environmental factors, and ship-specific factors, to accurately predict ship sailing times. Furthermore, based on the sailing times of target ships arriving at key waterways, it determines the number of ships and traffic density in key waterways within a preset timeframe. By monitoring and analyzing changes in traffic density in key waterways, it helps identify potential safety risks and congestion. Shipping management departments can then take corresponding safety management measures based on early warning information and assessment results to ensure the safety and sustainability of shipping activities in key waterways. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the method for predicting navigation density in key waterways of a key river section according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of the device for predicting navigation density in key water areas of a key river section according to Embodiment 2 of the present invention. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0027] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0028] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the word “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0029] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as in the embodiments of this invention.

[0030] Example 1

[0031] The flowchart of the method for predicting navigation density in key waterways of the key river section in this embodiment 1 is as follows: Figure 1 As shown, it includes steps S100-S400.

[0032] S100. Based on the AIS data of the target vessel and historical vessel navigation trajectory data, predict the navigation trajectory of the target vessel using a trajectory similarity algorithm.

[0033] AIS, or Automatic Identification System, is a ship navigation aid with automatic identification, communication, and navigation functions. It enables ship-to-ship and ship-to-shore communication. Through AIS, important data related to ship navigation can be acquired, such as ship identification information, location information, navigation status, and ship condition, and the system can monitor and identify ships in a specific area.

[0034] Ship trajectory prediction refers to the prediction of a ship's future navigation dynamics based on its current trajectory. In this embodiment 1, the prediction of the route trajectory is achieved using historical AIS data and machine learning and other technologies.

[0035] Specifically, the first step is to acquire historical data from the Automatic Identification System (AIS) and then extract historical vessel navigation trajectory data from this data. Let the set of historical vessel navigation trajectory data be:

[0036] S = {s1, s2, ..., s} n ...};

[0037] Where S is the set of historical ship navigation trajectory data, s n This is the navigation trajectory of the nth historical ship.

[0038] Let the current trajectory of the target vessel be T. i The problem is to match the most suitable current trajectory T of the target ship from the set S of historical ship navigation trajectory data. i The trajectory.

[0039] The Longest Common Subsequences (LCSS) algorithm calculates the similarity between two trajectories by simply setting distance and time thresholds between two points. Given two trajectories: a target trajectory and a candidate trajectory, the similarity between them is determined by setting time and distance thresholds.

[0040] The Dynamic Time Warping (DTW) algorithm calculates the distance between time sequences of unequal durations by copying certain points, thus reflecting the similarity between the two sequences.

[0041] In this embodiment 1, the trajectory prediction algorithm uses algorithms such as LCSS and DTW to calculate trajectory similarity. The accuracy of the trajectory similarity algorithm is evaluated by the similarity score. Specifically, for trajectory prediction of a known destination, a similarity of 95% or higher earns 1 point, and a similarity of 0% or lower earns 0 points. For trajectory prediction of a location destination, a similarity of 80% or higher earns 1 point, and a similarity of 0% or lower earns 0 points. The ratio of the total score to the total number of trajectories is used as the accuracy.

[0042] Furthermore, using the real-time position of the target vessel as the prediction starting point and the destination of the target vessel as the prediction ending point, a trajectory similarity algorithm is used to match historical vessel navigation trajectory data, and the historical navigation trajectory with the closest trajectory and the same vessel type as the target vessel is taken as the predicted navigation trajectory of the target vessel.

[0043] S200. Use data mining techniques to extract influencing factors related to sailing time, and use the analytic hierarchy process to determine the weight of each influencing factor on sailing time.

[0044] In actual navigation, ship navigation involves three aspects: the ship itself, the quality and skills of the navigator, and the navigation environment. In addition, various navigation rules must be followed during navigation. Any change in any of these aspects will affect the ship's speed and navigation status, and thus affect the ship's sailing time.

[0045] In this embodiment 1, historical basic data of ships arriving at key waters in the Three Gorges Project section are collected through big data, including information such as ship speed, course, actual draft, main engine power, destination, and port of origin. Key features are extracted using data mining technology, and the changing trends and influencing factors of ship transit time are calculated and analyzed, that is, the influencing factors related to transit time are determined.

[0046] The influencing factors include human factors, environmental factors, ship factors, and regulatory factors.

[0047] Human factors: Navigators with extensive navigation knowledge and skilled ship handling techniques are generally better able to judge the safe speed of a vessel and understand how to rationally control its speed to achieve optimal fuel economy and bring the vessel to its best performance state. Therefore, during navigation, the skill level of the navigator usually affects the speed; highly skilled navigators can avoid excessively high or low speeds, reducing instability caused by speed.

[0048] Environmental factors: (1) Meteorological conditions: Ship speed is affected by wind speed and direction in the waters. Strong winds and headwinds will affect ship passage. Strong winds may cause ships to slow down or stop sailing. Rain, snow and fog mainly affect visibility. Reduced visibility will also lead to a decrease in speed. (2) Current conditions: Ships sailing downstream will have a significantly higher speed than those sailing upstream. As a result, the speed of downstream traffic will be greater than that of upstream traffic. (3) Channel width and depth: If the channel is narrow or the water depth is insufficient, the space for ship passage will be restricted. (4) Navigation control: Navigation control measures will restrict and regulate ship passage. For example, temporary navigation restrictions in a certain area of ​​the channel will affect the ship's navigation route.

[0049] Ship factors: (1) Ship size: Ships of different sizes occupy different spaces and have different maneuverability. As the size of a ship increases, its inertia gradually increases, its rudder efficiency gradually decreases, and it is more affected by wind, current and water depth, thus affecting its speed. (2) Ship loading status: When the ship is unloaded, its displacement is smaller, more of the hull floats on the water, and its draft is shallower. The ship's speed is usually relatively faster, and the traffic density increases. When the ship is fully loaded, its displacement is larger and its draft is deeper. Under these conditions, the ship's speed is usually relatively slower. (3) Ship loading type: Dangerous goods ships usually adopt a lower speed to ensure navigation safety and timely response in emergency situations, and to avoid harming the aquatic environment. Fresh cargo and grain ships usually adopt a higher speed to ensure the freshness and quality of the cargo and to meet the transportation time limit. Fresh cargo and grain ships are also given priority in the Three Gorges Dam crossing plan. (4) Main engine power: The main engine power of a ship directly determines the size of the propulsion force. The greater the main engine power, the faster the ship's speed is usually.

[0050] Rule factors: The "Navigation and Scheduling Regulations of the Three Gorges-Gezhouba Hydropower Project" clearly stipulates the priority order of vessels passing through the dam and the priority of key vessels passing through the dam, indicating that vessels of the same type can pass through the lock in order or with priority.

[0051] The analysis of the influencing factors above extracts the sub-factors for each factor. Among these, human factors include the skill level of the driver; environmental factors include weather conditions, current conditions, waterway width and depth, and navigation regulations; ship factors include ship size, loading status, loading type, and main engine power; and regulatory factors include the priority order for ships passing through dams and priority for ships passing through dams.

[0052] Furthermore, the weights of each influencing factor on sailing time are determined using the analytic hierarchy process (AHP). This involves collecting raw data for each influencing factor, normalizing the raw data for each factor (i.e., normalizing the historical ship baseline data corresponding to each factor), and determining the normalized value for each factor. The relative importance of each influencing factor is determined through pairwise comparisons, thereby determining the weight of each factor. The weight of each influencing factor quantitatively reflects its importance in affecting ship sailing time.

[0053] In this embodiment 1, the weights and normalized values ​​of the above-mentioned influencing factors were obtained through analysis of a large amount of sample data, as shown in Table 1.

[0054]

[0055] Table 1

[0056] Furthermore, after determining the weights and normalized values ​​of each influencing factor, a weighted sum is performed based on the weights and normalized values ​​to obtain the comprehensive influencing factor score.

[0057] In this Example 1, the formula for calculating the comprehensive influencing factor score is as follows:

[0058] S=w1·F1+w2·F2+w3·F3+...+w n ·F n ;

[0059] Where S represents the comprehensive influencing factor score, and w1, w2, w3...w n The weights of each influencing factor, F1, F2, F3...F n These are the normalized values ​​for each influencing factor.

[0060] S300: Combining the weights of various influencing factors, and based on the AIS data and navigation prediction trajectory of the target vessel, determine the sailing time for the target vessel to reach the key waters.

[0061] In this embodiment 1, the influence of each influencing factor (and influencing factor) on the target ship's speed is determined by the analytic hierarchy process, and the target ship's speed is obtained. Then, the sailing time is estimated based on the speed and the predicted sailing trajectory.

[0062] Specifically, by mapping the scores of comprehensive influencing factors through an exponential function, the speed of the target vessel under the influence of each influencing factor is obtained. Then, based on the real-time position and predicted navigation trajectory of the target vessel, the sailing time of the target vessel to the key waters is estimated.

[0063] S400: Push the target ships whose sailing time is less than or equal to the preset time to the database, poll and calculate to determine the number of ships that will arrive at the key waters within the preset time, and then determine the navigation density of the key waters within the preset time.

[0064] In this embodiment 1, by monitoring and analyzing changes in navigation density in key waterways, potential safety risks and congestion can be identified. Shipping management departments can take corresponding safety management measures based on early warning information and assessment results to ensure the safety and sustainability of shipping activities in key waterways.

[0065] In this embodiment 1, the preset time is set to 5 hours, that is, the navigation density of the destination waterway is predicted within 5 hours through the above steps.

[0066] Furthermore, the method for predicting navigation density in key waterways of the hub river section in Embodiment 1 also includes visualizing the prediction results of navigation density in key waterways using heat maps. Navigation density and flow direction data are mapped onto geographic space, and the prediction results are visualized using heat maps. The heat maps use color gradients to represent vessel density, thus intuitively showing the level of navigation activity in different areas of the key waterways. This provides a clear view of vessel density and flow direction in the key waterways over the next 5 hours, helping users quickly understand and analyze future vessel traffic flow. This assists shipping management in making reasonable decisions and arrangements to address future navigation demands and challenges, thereby providing intuitive and comprehensive information for decision-making.

[0067] This embodiment 1 can provide a prediction of the navigation density of key waters in the hub river section within a preset time period (e.g., within 5 hours). By analyzing and calculating key features, the prediction of navigation time becomes more accurate, thus obtaining a more accurate navigation density prediction result. Based on this prediction result, early warning can be issued, which can effectively avoid navigation congestion, reduce the risk of collisions between ships, improve navigation safety in navigable waters, and assist in the reasonable optimization of ship scheduling.

[0068] Furthermore, the method for predicting navigation density in key waterways of the hub section in Example 1 can also be used to assess the vessel traffic capacity of key waterways. Shipping management departments can also formulate corresponding measures and strategies based on the assessment results to improve the efficiency and safety of waterways.

[0069] Example 2

[0070] The device for predicting navigation density in key waterways of the key river section in this embodiment 2 is illustrated in the following diagram. Figure 2 As shown, it includes a navigation trajectory prediction module, a navigation time calculation module, and a traffic density prediction module.

[0071] The navigation trajectory prediction module is used to predict the navigation trajectory of the target vessel based on its AIS data and historical vessel navigation trajectory data, using a trajectory similarity algorithm.

[0072] The navigation time calculation module uses data mining techniques to extract influencing factors related to navigation time, determines the weight of each influencing factor on navigation time through the analytic hierarchy process, and combines the weights of each influencing factor with the target vessel's AIS data and navigation prediction trajectory to determine the navigation time for the target vessel to reach the key waters.

[0073] The navigation density prediction module is used to push target ships with a travel time less than or equal to a preset time to the database, poll and calculate to determine the number of ships arriving at the key waters within the preset time, and then determine the navigation density of the key waters within the preset time.

[0074] Example 3

[0075] The electronic device of this embodiment 3 includes a processor and a memory, which are interconnected. The memory is used to store computer programs, and the processor is configured to execute a method for predicting the navigation density of key waterways in the key section of the hub river when the computer program is invoked.

[0076] Example 4

[0077] The computer-readable storage medium of this embodiment 4 stores a computer program that is executed by a processor to implement a method for predicting navigation density in key waterways of a key river section.

[0078] The method, apparatus, equipment, and storage medium for predicting navigation density in key waterways of a key river section according to embodiments of the present invention predict the navigation trajectory of ships, consider the influence of human factors, environmental factors, ship factors, and rule factors on ship passage, determine the navigation time of ships to reach key waterways, thereby effectively predicting the navigation density of key waterways within a preset time, and providing intuitive and comprehensive information for shipping management departments to make reasonable decisions and arrangements.

[0079] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0080] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for predicting navigation density in key waterways of a key river section, characterized in that, Includes the following steps: S100. Based on the AIS data of the target vessel and historical vessel navigation trajectory data, a trajectory similarity algorithm is used to predict the navigation trajectory of the target vessel. First, historical data of the Automatic Identification System (AIS) is acquired, and historical vessel navigation trajectory data is extracted from the historical data. The similarity between trajectories is judged by setting time and distance thresholds. By copying certain points, the distance between time sequences of unequal durations is calculated, thereby reflecting the degree of similarity between the two sequences. The accuracy of the trajectory similarity algorithm is evaluated by the similarity score. In step S100, the real-time position of the target vessel is used as the prediction starting point, and the destination of the target vessel is used as the prediction ending point. The trajectory similarity algorithm is used to match the historical vessel navigation trajectory data, and the historical navigation trajectory with the closest trajectory and the same vessel type as the target vessel is taken as the predicted navigation trajectory of the target vessel. S200. Collect historical basic data of ships arriving in key waters, extract key features using data mining techniques, and determine the influencing factors related to sailing time. These influencing factors include human factors, environmental factors, ship factors, and rule factors. The weight of each of these influencing factors on sailing time is determined using the analytic hierarchy process. S300. Determine the influence of each of the aforementioned influencing factors on the target vessel's speed using the analytic hierarchy process (AHP), obtain the target vessel's speed, and determine the sailing time for the target vessel to reach the key waterway based on the speed and the predicted sailing trajectory. S400: Push the target ships whose sailing time is less than or equal to the preset time to the database, poll and calculate to determine the number of ships that will arrive at the key water area within the preset time, and then determine the navigation density of the key water area within the preset time.

2. The method for predicting navigation density in key waterways of a key river section according to claim 1, characterized in that, In step S200, the influencing factors of human factors include the skill level of the driver; the influencing factors of environmental factors include meteorological conditions, water flow conditions, waterway width and depth, and navigation control; the influencing factors of ship factors include ship size, ship loading status, ship loading type, and ship main engine power; and the influencing factors of rule factors include the priority order of ships passing through the dam and priority ships passing through the dam.

3. The method for predicting navigation density in key waterways of a key river section according to claim 1, characterized in that, In step S200, the relative importance of each influencing factor is determined by comparing each influencing factor pairwise, and then the weight of each influencing factor is determined. The historical ship baseline data corresponding to each influencing factor is normalized to determine the normalized value of each influencing factor; the weighted sum is then calculated based on the weight and the normalized value to obtain the comprehensive influencing factor score.

4. The method for predicting navigation density in key waterways of a key river section according to claim 3, characterized in that, In step S300, the scores of the comprehensive influencing factors are mapped by an exponential function to obtain the speed of the target vessel under the influence of each influencing factor. Then, based on the real-time position of the target vessel and the predicted navigation trajectory, the sailing time of the target vessel to reach the key waters is calculated.

5. The method for predicting navigation density in key waterways of a key river section according to any one of claims 1-4, characterized in that, It also includes the following steps: The predicted navigation density of the key waterways is visualized.

6. A device for predicting navigation density in key waterways of a key river section, characterized in that, The system includes a navigation trajectory prediction module, a navigation time calculation module, and a traffic density prediction module. The navigation trajectory prediction module is used to predict the navigation trajectory of the target vessel based on the target vessel's AIS data and historical vessel navigation trajectory data. The navigation time calculation module is used to extract influencing factors related to navigation time, determine the weight of each influencing factor on the navigation time, and combine the weights of each influencing factor with the target vessel's AIS data and the navigation trajectory to determine the navigation time for the target vessel to reach the key waterway. The navigation density prediction module is used to push target ships with a travel time less than or equal to a preset time to the database, poll and calculate to determine the number of ships arriving at the key water area within the preset time, and then determine the navigation density of the key water area within the preset time.

7. An electronic device, characterized in that, It includes a processor and a memory, the processor and the memory being interconnected, the memory being used to store a computer program, and the processor being configured to, when invoked, execute a method for predicting navigation density in key waterways of a key river section as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method for predicting navigation density in key waterways of a key river section as described in any one of claims 1-5.

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