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

Abstract

The invention discloses a method, device and equipment for predicting the navigation density of key water areas of hub river reach, and the method comprises the steps: predicting the navigation prediction trajectory of a target ship through employing a trajectory similarity algorithm based on historical ship navigation trajectory data, and integrating the influence factors related to the navigation time and the weights of the influence factors. Calculating and determining the navigation time of the target ship reaching the key water area, and further determining the number of ships reaching the key water area within the preset time and the navigation density of the key water area within the preset time. According to the method for predicting the navigation density of the key water area of the hub river reach, on one hand, the navigation track of the target ship can be predicted, the safety of the ship is guaranteed to a certain extent, and the ship which may collide or encounter other dangers is warned in advance; and on the other hand, the change of the navigation density of the key water area is monitored and analyzed, so that the potential safety risk and congestion condition can be identified, a shipping management department can make reasonable decisions and arrangements, and visual and comprehensive information can be provided.

Description

Technical Field

[0001] The present invention relates to the technical field of ship management, and in particular to a method, a device and equipment for predicting the navigation density of key waters in a hub river section. Background Art

[0002] Due to the water storage and power generation of the Three Gorges Dam, the flow of water in the channel between the two dams has returned to a stable state, and the safety of ship navigation is mainly limited by the basically solidified natural dimensions of the channel; in the flood season, a large amount of water needs to be released for flood control upstream, and the water storage capacity between the two dams is limited, so basically as much water as comes in is released. In addition, the two dams are mainly mountain rivers, and the "pipeline effect" causes the water flow conditions between the two dams to deteriorate sharply during the flood season, and dangerous accidents occur frequently, which greatly affects the safety of ship navigation and the passing capacity of the channel, brings great difficulties to the ship's driving and piloting operations, and poses a safety hazard to ship navigation, forming the famous "four beaches, one bend and one pass". Therefore, it is also regarded as a key water area and an area that is monitored by the management department.

[0003] Previously, in order to improve the navigable flow conditions of the river section and weaken the obstruction flow state, the Liantuo section waterway regulation project between the two dams was carried out. The key waters were regulated by means of reef blasting, reef clearing and abandonment, and certain results were achieved, but there are risk factors that affect navigation safety. In the field of ships, the navigation density reflects the busyness and danger of ship traffic in the waters to a certain extent. According to calculations by relevant maritime agencies, the occurrence of ship collision accidents is proportional to the square of the ship navigation density, and the navigation conditions in key waters are poor. Exploring the navigation density is of great significance to maintaining navigation safety in key waters.

[0004] In order to strengthen the safety supervision of key waters during the flood season, the current method of manually monitoring CCTV screens is adopted, which has high labor input costs, has monitoring blind spots, and the amount of CCTV monitoring data is huge, which requires effective data processing and analysis. In addition, due to the particularity of the location of the waterway, the traffic behavior of ships has obvious differences. For example, the speed of ships will be different due to the influence of the ship's own factors, and the passage time will be different due to the influence of the dam crossing rules. This makes it possible that the method relying on manual measurement may not accurately reflect the navigation density of key waters, nor can it form an effective prediction of the navigation density of key waters in the future time period. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the purpose of the present invention is to provide a method, device, equipment and storage medium for predicting the navigation density of key waters in hub river sections. By predicting the navigation trajectory of the ship, considering the influence of human factors, environmental factors, ship factors and rule factors on the passage of the ship, the sailing time of the ship to reach the key waters is determined, thereby effectively predicting the navigation density of the key waters within the preset time, which is helpful for the shipping management department to make reasonable decisions and arrangements and provide intuitive and comprehensive information for decision-making.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for predicting navigation density in key waters of a hub river section of the present invention comprises the following steps:

[0008] S100, predicting the predicted navigation trajectory of the target ship using a trajectory similarity algorithm based on the AIS data of the target ship and the historical ship navigation trajectory data, firstly obtaining the historical data of the ship automatic identification system, extracting the historical ship navigation trajectory data from the historical data, judging the similarity between the trajectories by setting a time threshold and a distance threshold, and calculating the distance between unequal time series by copying certain points, thereby reflecting the similarity between the two series, and evaluating the accuracy of the trajectory prediction algorithm by the similarity score;

[0009] S200, collecting basic data of historical ships arriving at key waters, extracting key features using data mining technology, and determining influencing factors related to navigation time, wherein the influencing factors include human factors, environmental factors, ship factors, and rule factors, and determining the weight of each influencing factor on navigation time through hierarchical analysis method;

[0010] S300, determining the influence of each of the influencing factors on the speed of the target ship according to the hierarchical analysis method, obtaining the speed of the target ship, and determining the sailing time of the target ship to reach the key waters according to the speed and the predicted sailing trajectory;

[0011] S400, push the target ships whose sailing time is greater than or equal to the preset time to the database, determine the number of ships arriving at the key waters within the preset time by polling calculation, and then determine the navigation density of the key waters within the preset time.

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

[0013] Furthermore, in step S200, the influencing factors of human factors include the proficiency 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; the influencing factors of rule factors include the priority of ships passing the dam and key priority ships passing the dam.

[0014] Furthermore, in the step S200, the relative importance of each influencing factor is determined by comparing the influencing factors in pairs, 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 according to the weight and the normalized value to obtain a comprehensive influencing factor score.

[0015] Furthermore, in the step S300, the comprehensive influencing factor score is mapped by an exponential function, and then the sailing time of the target ship to reach the key waters is calculated according to the real-time position of the target ship and the predicted navigation trajectory.

[0016] Furthermore, the method also includes the following steps: visually displaying the predicted results of the navigation density of the key waters.

[0017] The present invention also provides a device for predicting the navigation density of key waters in hub river sections, comprising a navigation trajectory prediction module, a navigation time calculation module and a navigation density prediction module, wherein the navigation trajectory prediction module is used to predict the predicted navigation trajectory of the target ship based on the AIS data of the target ship and the historical ship navigation trajectory data; the navigation time calculation module is used to extract influencing factors related to the navigation time, determine the weight of each of the influencing factors on the navigation time, and determine the navigation time of the target ship to the key waters based on the AIS data of the target ship and the predicted navigation trajectory in combination with the weight of each of the influencing factors; the navigation density prediction module is used to push target ships whose navigation time is greater than or equal to the preset time into a database, determine the number of ships arriving at the key waters within the preset time through polling calculation, and further determine the navigation density of the key waters within the preset time.

[0018] The present invention also provides an electronic device, wherein the processor and the memory are connected to each other, 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 waters in a hub river section when calling the computer program.

[0019] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for predicting the navigation density of key waters in a hub river section.

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

[0021] The method, device, equipment and storage medium for predicting the navigation density of key waters in hub river sections of the present invention predict the navigation trajectory of the target ship based on historical ship navigation trajectory data using a trajectory similarity algorithm. On the one hand, the prediction of the navigation trajectory of the target ship can ensure the safety of the ship to a certain extent, and can provide advance warning for ships that may collide or encounter other dangers. On the other hand, it also provides a reliable basis for predicting the navigation time.

[0022] The present invention utilizes data mining technology to extract influencing factors related to sailing time, determines the weight of each influencing factor on sailing time through hierarchical analysis method, and then applies it to the prediction of sailing time. The present invention integrates multiple factors such as ship traffic rules, environmental factors and ship's own factors to accurately predict ship sailing time, and then determines the number of ships and navigation density in key waters within a preset time according to the sailing time of each target ship in the waters to reach the key waters. By monitoring and analyzing the changes in the navigation density of key waters, it helps to identify potential safety risks and congestion conditions. The shipping management department can take corresponding safety management measures according to the early warning information and evaluation results to ensure the safety and sustainability of shipping activities in key waters. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0025] Figure 2 This is a schematic diagram of a device for predicting navigation density in key waters of a hub river section according to Example 2 of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0027] In the description of the present invention, unless otherwise clearly specified and limited, the terms "connected", "connected", and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0028] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the 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] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as in the embodiments of the present invention.

[0030] Example 1

[0031] The method for predicting the navigation density of key waters in a hub river section of this embodiment 1 has a flow chart as follows: Figure 1 As shown, steps S100-S400 are included.

[0032] S100, predicting the predicted navigation trajectory of the target ship using a trajectory similarity algorithm according to the AIS data of the target ship and the historical ship navigation trajectory data.

[0033] AIS system, or Automatic Identification System, is a ship navigation aid with the functions of automatic ship identification, communication and navigation. It can realize ship-to-ship communication and ship-to-shore communication. Through the AIS system, important data related to ship navigation can be obtained, such as ship identification information, location information, navigation status, ship status, etc., and the monitoring and identification of ships in a certain area can be realized.

[0034] Ship navigation trajectory prediction refers to predicting the future navigation dynamics of a ship based on the known current trajectory of the ship. In this embodiment 1, based on AIS historical data, the route trajectory prediction is achieved through machine learning and other technical means.

[0035] Specifically, firstly, the historical data of the ship automatic identification system is obtained, and the historical ship navigation track data is extracted from the historical data. Assume that the set of historical ship navigation track data is:

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

[0037] Among them, S is the collection of historical ship navigation trajectory data, s n is the nth historical ship navigation track.

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

[0039] The Longest Common Subsequences algorithm, or LCSS algorithm, calculates the similarity of two trajectories by simply setting the distance threshold and time threshold between two points. Assume that two trajectories are given: the target trajectory and the candidate trajectory, and the similarity between the trajectories is judged by setting the time threshold and the distance threshold.

[0040] The Dynamic TimeWraping algorithm, also known as the DTW algorithm, can calculate the distance between time series of unequal lengths by copying certain points, thereby reflecting the similarity between two sequences.

[0041] In this embodiment 1, in the trajectory prediction algorithm, LCSS, DTW and other algorithms are used to calculate the trajectory similarity, and the accuracy of the trajectory prediction algorithm is evaluated by the similarity score. For the prediction of the trajectory of the known destination, 1 point is given if the similarity is above 95%, and 0 point is given if it is not satisfied; for the prediction of the trajectory of the location destination, 1 point is given if the similarity is above 80%, and 0 point is given if it is not satisfied; the ratio of the total score to the total number of trajectories is taken as the accuracy.

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

[0043] S200, extracting influencing factors related to the sailing time by using data mining technology, and determining the weight of each influencing factor on the sailing time by using the hierarchical analysis method.

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

[0045] In this embodiment 1, the basic data of historical ships arriving at key waters in the Three Gorges hub river section are collected through big data, including the ship's speed, heading, actual draft of the ship, main engine power of the ship, ship's sailing destination, sailing port and other information, and the key features are extracted by using data mining technology, and the changing trend and influencing factors of the ship's passing time are calculated and analyzed, that is, the influencing factors related to the sailing time are determined.

[0046] Among them, the influencing factors include human factors, environmental factors, ship factors and regulatory factors.

[0047] Human factors: Drivers with rich navigation knowledge and skilled ship handling techniques can usually more accurately judge the safe speed of the ship, and know how to reasonably control the speed of the ship to obtain the best fuel economy and achieve the best performance of the ship. Therefore, during navigation, the proficiency of the driver usually affects the speed. A highly skilled driver can avoid excessively high or low speeds and reduce the instability of the ship caused by speed.

[0048] Environmental factors: (1) Meteorological conditions: The speed of a ship is affected by the wind speed and direction in the waters. Strong winds and headwinds will affect the passage of ships. Strong winds may cause ships to slow down or suspend navigation. Rain, snow and fog mainly affect visibility. Reduced visibility will also lead to a reduction in speed. (2) Water flow conditions: The speed of a ship sailing downstream will be significantly greater than the speed against the current, so the speed of downstream traffic will be greater than the speed of overhead traffic. (3) Waterway width and depth: If the width of the waterway is narrow or the water depth is insufficient, the space for ships to pass will be limited. (4) Navigation control: Navigation control measures will restrict and regulate the passage of ships, such as temporary bans on navigation in a certain area of ​​the waterway, which will affect the navigation route of ships.

[0049] Ship factors: (1) Ship size: Ships of different sizes occupy different spaces and have different maneuverability. As the size of the ship increases, the ship's inertia will gradually increase, the rudder effect will gradually decrease, and the ship will be significantly affected by wind, current and water depth, which will affect the speed. (2) Ship loading status: When the ship is unloaded, the displacement of the ship is small, the part of the hull floating on the water is large, the draft of the ship is shallow, the ship's sailing speed is usually relatively fast, and the navigation density is increased; when the ship is fully loaded, the displacement of the ship is large, the draft of the ship is deep, and in this state, the ship's sailing speed is usually relatively slow. (3) Ship loading type: For example, dangerous goods ships usually adopt a lower speed to ensure navigation safety and to be able to respond in time in an emergency to avoid harming the water environment; while fresh goods and grain ships usually adopt a higher speed to ensure the freshness and quality of the goods and meet the transportation time limit. The Three Gorges Dam crossing plan will also give priority to fresh goods and grain ships. (4) Ship main engine power: The ship's main engine power directly determines the size of the propulsion force. The larger the main engine power, the faster the ship's speed is usually.

[0050] Rule factors: The "Three Gorges-Gezhouba Water Conservancy Project Navigation Dispatch Regulations" clearly defines the priority order of ships passing through the dam and the priority of ships passing through the dam, indicating that ships of the same type can pass through the lock in priority or in sequence.

[0051] From the above analysis of influencing factors, the influencing factors of each influencing factor are extracted. Among them, the influencing factors of human factors include the proficiency of the drivers. 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. The influencing factors of regulatory factors include the priority of ships passing the dam and the priority of ships passing the dam.

[0052] Furthermore, the weight of each influencing factor on the sailing time is determined by the hierarchical analysis method. This includes collecting the original data of each influencing factor, normalizing the original data of each influencing factor, that is, normalizing the historical ship basic data corresponding to each influencing factor, and determining the normalized value of each influencing factor. The relative importance of each influencing factor is determined by comparing the influencing factors one by one, and then the weight of each influencing factor is determined. The weight of each influencing factor quantitatively reflects the importance of the influencing factor in affecting the sailing time of the ship.

[0053] In this embodiment 1, the weights and normalized values ​​of the above-mentioned influencing factors are formed through analysis of a large amount of sample data, see Table 1.

[0054]

[0055] Table 1

[0056] Furthermore, after determining the weight and normalized value of each influencing factor, a weighted sum is performed according to the weight and the normalized value to obtain a comprehensive influencing factor score.

[0057] In this embodiment 1, the calculation formula of the comprehensive influencing factor score is:

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

[0059] Among them, S is the comprehensive influencing factor score, w1, w2, w3...w n is the weight of each influencing factor, F1, F2, F3...F n is the normalized value of each impact factor.

[0060] S300: Determine the sailing time of the target ship to reach the key waters according to the AIS data and the predicted navigation track of the target ship in combination with the weights of various influencing factors.

[0061] In this embodiment 1, the influence of various influencing factors (and influencing factors) on the speed of the target ship is determined according to the hierarchical analysis method to obtain the speed of the target ship, and then the sailing time is estimated according to the speed and the predicted navigation trajectory.

[0062] Specifically, the comprehensive influencing factor scores are mapped through an exponential function to obtain the speed of the target ship under the influence of each influencing factor. Then, based on the real-time position and predicted navigation trajectory of the target ship, the sailing time for the target ship to reach the key waters is estimated.

[0063] S400, push the target ships whose sailing time is greater than or equal to the preset time to the database, determine the number of ships arriving at the key waters within the preset time through polling calculation, and then determine the navigation density of the key waters within the preset time.

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

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

[0066] Furthermore, the method for predicting the navigation density of key waters in hub river sections in this embodiment 1 also includes visually displaying the predicted results of the navigation density of key waters through a heat map. The navigation density and flow direction data are mapped to the geographic space, and the prediction results are visualized in the form of a heat map. The density of ships is represented in the form of a color gradient in the heat map, thereby intuitively showing the busyness of navigation in different areas of the key waters, and intuitively displaying the density and flow direction of ships in the key waters in the next 5 hours, helping users to quickly understand and analyze future ship traffic flows, and helping the shipping management department to make reasonable decisions and arrangements to cope with future navigation needs 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 a hub river section within a preset time period (such as within 5 hours). By analyzing and calculating key features, the prediction of navigation time is made more accurate, and a relatively accurate navigation density prediction result is obtained. Early warning is given based on this prediction result, which can effectively avoid navigation congestion, reduce the risk of collision between ships, improve navigation safety in navigable waters, and assist in manual optimization of ship scheduling.

[0068] In addition, the method for predicting the navigation density of key waters in hub river sections in this embodiment 1 can also be used to evaluate the ship traffic capacity of key waters, and the shipping management department can also formulate corresponding measures and strategies based on the evaluation results to improve the navigation efficiency and safety of the waterway.

[0069] Example 2

[0070] The schematic diagram of the device for predicting the navigation density of key waters in a hub river section of this embodiment 2 is as follows: Figure 2 As shown, it includes a navigation trajectory prediction module, a navigation time calculation module and a navigation density prediction module.

[0071] Among them, the navigation trajectory prediction module is used to predict the navigation trajectory of the target ship based on the AIS data of the target ship and the historical ship navigation trajectory data using the trajectory similarity algorithm.

[0072] The sailing time calculation module is used to extract the influencing factors related to the sailing time by using data mining technology, determine the weight of each influencing factor on the sailing time through the hierarchical analysis method, and combine the weights of each influencing factor to determine the sailing time of the target ship to reach the key waters according to the AIS data and navigation prediction trajectory of the target ship.

[0073] The navigation density prediction module is used to push target ships whose sailing time is greater than or equal to the preset time to the database, and to determine the number of ships arriving at the key waters within the preset time through polling calculation, thereby determining 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, the processor and the memory are connected to each other, the memory is used to store a computer program, and the processor is configured to execute a method for predicting the navigation density of key waters in a hub river section when calling the computer program.

[0076] Example 4

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

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

[0079] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0080] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for predicting navigation density in key waters of a hub river section, characterized in that: The following steps are involved: S100, predicting the predicted navigation trajectory of the target ship using a trajectory similarity algorithm based on the AIS data of the target ship and the historical ship navigation trajectory data, firstly obtaining the historical data of the ship automatic identification system, extracting the historical ship navigation trajectory data from the historical data, judging the similarity between the trajectories by setting a time threshold and a distance threshold, and calculating the distance between unequal time series by copying certain points, thereby reflecting the similarity between the two series, and evaluating the accuracy of the trajectory prediction algorithm by the similarity score; S200, collecting basic data of historical ships arriving at key waters, extracting key features using data mining technology, and determining influencing factors related to navigation time, wherein the influencing factors include human factors, environmental factors, ship factors, and rule factors, and determining the weight of each influencing factor on navigation time through hierarchical analysis method; S300, determining the influence of each of the influencing factors on the speed of the target ship according to the hierarchical analysis method, obtaining the speed of the target ship, and determining the sailing time of the target ship to reach the key waters according to the speed and the predicted sailing trajectory; S400, push the target ships whose sailing time is greater than or equal to the preset time to the database, determine the number of ships arriving at the key waters within the preset time by polling calculation, and then determine the navigation density of the key waters within the preset time.

2. The method for predicting navigation density in key waters of a hub river section according to claim 1, characterized in that: In step S100, the real-time position of the target ship is used as the prediction starting point, the destination of the target ship is used as the prediction end point, and a track similarity algorithm is used to match the historical ship navigation track data, and the historical navigation track with the closest trajectory and the same ship type as the target ship is used as the predicted navigation track of the target ship.

3. The method for predicting navigation density in key waters of a hub river section according to claim 1, characterized in that: In step S200, the influencing factors of human factors include the proficiency 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; the influencing factors of rule factors include the priority order of ships passing the dam and key priority ships passing the dam.

4. The method for predicting navigation density in key waters of a hub river section according to claim 1, characterized in that: In step S200, the relative importance of each influencing factor is determined by comparing the influencing factors in pairs, 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 according to the weight and the normalized value to obtain a comprehensive influencing factor score.

5. The method for predicting navigation density in key waters of a hub river section according to claim 4 is characterized in that: In the step S300, the comprehensive influencing factor score is mapped by an exponential function, and then the sailing time of the target ship to reach the key waters is calculated according to the real-time position of the target ship and the predicted navigation trajectory.

6. The method for predicting navigation density in key waters of a hub river section according to any one of claims 1 to 5, characterized in that: The following steps are also included: The predicted results of the navigation density in the key waters are displayed visually.

7. A device for predicting navigation density in key waters of a hub river section, characterized in that: It includes a navigation track prediction module, a navigation time calculation module and a navigation density prediction module. The navigation track prediction module is used to predict the navigation prediction track of the target ship according to the AIS data of the target ship and the historical ship navigation track data; the navigation time calculation module is used to extract the influencing factors related to the navigation time, determine the weight of each influencing factor on the navigation time, and combine the weights of each influencing factor, according to the AIS data of the target ship and the navigation prediction track, determine the navigation time of the target ship to reach the key waters; The navigation density prediction module is used to push target ships whose sailing time is greater than or equal to the preset time to the database, and to determine the number of ships arriving at the key waters within the preset time through polling calculation, thereby determining the navigation density of the key waters within the preset time.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor and the memory are connected to each other, 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 waters in a hub river section as described in any one of claims 1 to 6 when calling the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for predicting the navigation density of key waters in a hub river section as described in any one of claims 1-6.

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