CN113505836A - Ship route automatic generation method based on big data - Google Patents

Ship route automatic generation method based on big data Download PDF

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CN113505836A
CN113505836A CN202110794625.XA CN202110794625A CN113505836A CN 113505836 A CN113505836 A CN 113505836A CN 202110794625 A CN202110794625 A CN 202110794625A CN 113505836 A CN113505836 A CN 113505836A
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唐皇
吴海忠
谭家万
罗春
唐倩
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Abstract

本发明公开了一种基于大数据的船舶航线自动生成方法，包括获取船舶航行轨迹的原始数据，建立船舶航行轨迹原始集合；提取船舶航行轨迹原始集合中航行轨迹的起始点和终止点，对航行轨迹进行分类，得到船舶航行轨迹聚类集合；给定航行计划及船舶信息，搜索相似的航行轨迹，确定转移概率最大的可航路径作为该航次的规划航线；重复执行步骤S3，建立船舶航线映射库；给定当前船舶航行计划的约束条件自动生成当前船舶的规划航线；本发明综合考虑了船舶航行的季节以及船舶信息等多个因素，建立船舶航线映射库，在后续的航线设计过程中，可直接根据约束条件在船舶航线映射库中进行动态查询，自动生成当前的规划航线，极大缩短了航线设计的时间。

Figure 202110794625

The invention discloses a method for automatically generating a ship's route based on big data, which includes acquiring the original data of the ship's navigation track, establishing an original set of the ship's navigation track; The trajectories are classified to obtain a cluster set of ship navigation trajectories; given the navigation plan and ship information, search for similar navigation trajectories, and determine the navigable path with the largest transition probability as the planned route of the voyage; repeat step S3 to establish the ship route map database; given the constraints of the current ship sailing plan, the planned route of the current ship is automatically generated; the present invention comprehensively considers the season of the ship and the ship information and other factors, and establishes a ship route mapping library, and in the subsequent route design process, Dynamic query can be performed in the ship route mapping library directly according to the constraints, and the current planned route can be automatically generated, which greatly shortens the route design time.

Figure 202110794625

Description

Ship route automatic generation method based on big data
Technical Field
The invention relates to the technical field of automatic generation of ship routes, in particular to a ship route automatic generation method based on big data.
Background
Shipping undertakes most of trade transportation tasks in the world, and ship navigation accidents happen occasionally, which causes casualties and major property loss, so how to ensure safe navigation of ships has important significance for improving production efficiency and ensuring life and property safety. The ship route planning can enable a ship to run in a safe sea area and be far away from a dangerous area; meanwhile, the optimized air route can shorten the navigation time of the ship, improve the navigation efficiency and play a role in energy conservation and emission reduction. At present, ship route planning is mainly drawn manually by a driver, the quality of a route depends excessively on the navigation experience and professional background of the driver, the planned route cannot provide a scientific route for the driver for reference, the productivity is low, and the ship navigation safety cannot be well guaranteed.
Existing automatic route generation techniques include dynamic route planning and static route design. The dynamic route planning mainly aims at that a moving target changes along with time in the process of sailing of a ship, and therefore the route planning is carried out by combining an avoidance algorithm. The existing dynamic route planning technology is mainly designed based on algorithms such as A-x dynamic search, heuristic genetic search, planning grid search and the like, and requires real-time acquisition of the motion state of a moving target. Different from dynamic route planning, the static route design is a ship navigation route which needs to be designed before a ship is launched, the ship navigates according to the route in the navigation process, and actively returns to the navigation route after passing through a maneuvering obstacle avoidance in the process. The prior art of static route design is mainly realized by optimizing steering points and setting an objective function such as the shortest path, most of the static route design adopts a graph theory method to search the shortest path, does not consider the seaworthiness of a ship in a sea area, and cannot adapt to the inherent navigation rule of the ship in the sea.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for automatically generating a ship route based on big data, so as to solve the problems in the prior art that route planning excessively depends on navigation experience of a driver and professional background, so that the referential is not high, the productivity is low, the navigation safety cannot be well guaranteed, and the inherent navigation law of a ship cannot be adapted.
In order to achieve the purpose, the invention provides the following technical scheme:
a ship route automatic generation method based on big data comprises the following steps
S1: acquiring original data of a ship navigation track, and sorting and cleaning the original data to establish an original set of the ship navigation track;
s2: extracting starting points and ending points of all navigation tracks in the original set of the ship navigation tracks, and classifying the ship navigation tracks through a clustering algorithm to obtain a ship navigation track clustering set;
s3: giving a navigation plan and ship information of a ship, searching similar navigation tracks in a ship navigation track cluster set, obtaining a navigable path set based on the similar navigation tracks, calculating a transition probability corresponding to each navigable path in the navigable path set, and determining the navigable path with the maximum transition probability as a planned route of the voyage;
s4: repeatedly executing the step S3 for multiple times, calculating and storing a plurality of planned routes corresponding to the navigation plan of the given ship, setting constraint conditions based on the planned routes and the corresponding ship information, and establishing a ship route mapping library;
s5: and giving the constraint condition of the current ship navigation plan, carrying out route mapping in the ship route mapping library, and automatically generating the planned route of the current ship.
Further, the specific method of step S1 is as follows:
acquiring navigation tracks of all voyages of each ship in a preset time and a preset sea area to form original data, sorting and cleaning the original data, eliminating the navigation track with abnormal data in the original data, obtaining the normal navigation track of each voyage of each ship, and establishing an original set of the ship navigation tracks.
Further, the step S2 includes the following steps:
s201: extracting the starting point and the end point of each navigation track in the original set of the navigation tracks of the ship to form a starting point set and an end point set, and marking the navigation tracks according to corresponding ship information;
s202: clustering the initial points and the end points extracted in the step S201 respectively by adopting a clustering algorithm to obtain corresponding clustering results;
s203: traversing all navigation tracks in the original set of the ship navigation tracks, and classifying the navigation tracks according to the clustering results of the starting points and the end points in the step S202 to form a ship navigation track clustering set.
Further, the step S202 adopts a density-based clustering method and a DBSCAN algorithm to cluster the start point and the end point, respectively, and the specific steps are as follows:
s2021: setting a neighborhood radius of the DBSCAN algorithm and a preset number of starting points/end points in the neighborhood radius;
s2022: randomly selecting unprocessed first starting points/first ending points in the starting point set/the ending point set, and checking the number of the starting points/the ending points contained in the adjacent radius of the first starting points/the first ending points;
s2023: judging whether the number of the start points/the end points included in the first start point/first end point neighborhood radius in the step S2022 is greater than or equal to a preset number, if so, continuing to execute the step S2024, otherwise, repeating the step S2022;
s2024: establishing a corresponding first clustering set by using a first initial point/a first end point, establishing a candidate set, and classifying initial points/end points which are not classified into any clustering set or marked as noise and are contained in the neighborhood radius of the first initial point/the first end point into the candidate set;
s2025: selecting unprocessed second starting points/second end points in the candidate set, and checking the number of the starting points/end points contained in the adjacent radius of the second starting points/second end points;
s2026: judging whether the number of the start points/end points included in the neighborhood radius of the second start point/second end point in the step S2025 is greater than or equal to the preset number, if so, adding the second start point/second end point and the start point/end point which is not classified into any cluster set or not marked as noise in the neighborhood radius to the corresponding first cluster set, otherwise, only classifying the second start point/second end point into the corresponding first cluster set;
s2027: repeating steps S2025-S2026 until the candidate set is empty, and continuing to execute step S2028;
s2028: and repeating the steps S2022-S2027 until all the starting points/the end points in the starting point set/the end point set are classified into a certain cluster set or marked as noise, and obtaining a cluster set of the starting points/the end points of the ship navigation track.
Further, when determining the starting point/ending point included in the neighborhood radius of the starting point/ending point, calculating the distance between the two starting points/ending points, and if the distance between the two starting points/ending points is smaller than the neighborhood radius, the starting point/ending point is included in the neighborhood radius of the corresponding starting point/ending point.
Further, the distance between the two starting points/the ending points is calculated by using an Euclidean distance formula, and the calculation formula is as follows:
Figure BDA0003162419710000031
wherein: (x)1,x2) And (y)1,y2) Two-dimensional coordinates corresponding to the two start points/end points respectively.
Further, the step S3 includes the following steps:
s301: giving a ship navigation plan, classifying the navigation plan into a category represented by a corresponding ship navigation track cluster set according to an initial point and an end point of the navigation plan, dynamically searching similar ship navigation tracks in the similar ship navigation track cluster set according to ship information of a ship, and establishing an approximate track set;
s302: establishing a transition probability directed graph aiming at the approximate track set in the step S301;
s303: searching the navigable paths of the ship in the transition probability directed graph, establishing a navigable path set, calculating the transition probability of each navigable path in the navigable path set, and taking the navigable path with the maximum transition probability as a planned route from a starting point to an ending point.
Further, the specific method for establishing the transition probability directed graph in step S302 is as follows:
marking a plurality of nodes on each navigation track in the approximate track set at equal distance, calculating the transition probability from each node position to the adjacent node position, and establishing a transition probability directed graph.
Further, in step S303, a calculation formula of the transition probability of the navigable path is as follows:
Figure BDA0003162419710000041
wherein: p (Tr)j) The transition probability of the jth navigable path in the navigable path set is obtained;
Figure BDA0003162419710000042
for the ith node in the jth navigable path, i is 1,2, …, m is the number of nodes in any navigable path;
Figure BDA0003162419710000043
the transition probability of the ith node in the jth navigable path is obtained;
the planned route based on the navigable path with the highest transition probability as the starting point to the ending point may be represented as:
path＝argmaxP(Trj)；
wherein: and the path is a planned route generated by the voyage route based on the navigable path with the highest patent probability.
Further, the ship information includes ship type, ship size, loading state, draft and sailing season.
According to the scheme, the historical navigation tracks of the ship are classified by adopting a density-based clustering method, so that the universality and the accuracy of ship navigation track clustering are improved, and a data base is laid for real-time route generation; in addition, when a ship route mapping library is established, a plurality of constraint conditions such as the current navigation season, ship information and the like are combined, and big data search based on the ship navigation track can improve the accuracy of real-time route generation of a ship and ensure the scientificity of route generation; finally, dynamic inquiry of the ship route is carried out in the ship route mapping library, so that the time required by route generation can be greatly shortened, the calculation cost is greatly saved, and the real-time performance of route generation is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
FIG. 1 is a flow chart of a ship route automatic generation method based on big data.
Fig. 2 is a flowchart of step S2 in fig. 1.
Fig. 3 is a flowchart of step 202 in fig. 1.
Fig. 4 is a flowchart of step S3 in fig. 1.
Detailed Description
The following is further detailed by way of specific embodiments:
examples
Fig. 1 is a flow chart of a big data-based ship route automatic generation method according to the present invention. The ship route automatic generation method based on big data of the embodiment specifically comprises the following steps:
s1: and establishing an original set of ship navigation tracks.
Specifically, acquiring navigation tracks of all voyages of each ship in a preset time and a preset sea area, and taking all the navigation tracks as original data; and sorting and cleaning the original data, and eliminating the navigation track with abnormal data (such as abnormal navigation speed, abnormal navigation track and the like) in the original data to obtain the normal navigation track of each ship in each voyage, so as to establish an original set of ship navigation tracks.
S2: and classifying the ship navigation tracks and establishing a ship navigation track clustering set.
Extracting the starting points and the end points of all navigation tracks in the original set of the navigation tracks of the ship, clustering all the starting points and the end points through a clustering algorithm, and classifying the navigation tracks of the ship according to the clustering results of the starting points and the end points to obtain a ship navigation track clustering set.
As shown in fig. 2, the step S2 includes the following steps:
s201: and extracting a starting point and an end point and marking ship information.
Firstly, extracting a starting point and an end point of each navigation track in an original set of navigation tracks of a ship to form a starting point set and an end point set, wherein the starting point set D isSAnd set of end points DERespectively expressed as:
Figure BDA0003162419710000051
Figure BDA0003162419710000052
wherein:
Figure BDA0003162419710000053
as a starting point, the position of the probe,
Figure BDA0003162419710000054
is an end pointAnd n is the number of starting points or the number of end points.
Because the ship has an inherent navigation rule when sailing on the sea, in order to increase the seaworthiness of the ship in the sea and integrate the navigation rules of historical ships, each navigation track needs to be marked according to corresponding ship information. In the present embodiment, the ship information includes, but is not limited to, ship type, ship size, ship state, draught, and sailing season.
S202: and clustering the starting points and the end points.
In order to improve the universality and accuracy of ship track clustering, a clustering algorithm is adopted to cluster the initial points and the end points extracted in the step S201 respectively to obtain corresponding clustering results. In this embodiment, the density-based clustering method uses a DBSCAN algorithm to cluster the start points and the end points, respectively.
Since the same method is used when clustering is performed on the start point and the end point, in this embodiment, the start point is taken as an example for description.
As shown in fig. 3, the step S202 includes the following steps:
s2021: initializing a DBSCAN algorithm, and setting a preset number of other starting points contained in a neighborhood radius e of the DBSCAN algorithm and a neighborhood radius e corresponding to a certain starting point, namely, a minimum number minPts of the starting points or the ending points contained in the neighborhood radius e of a processing object (starting point).
S2022: randomly selecting a set of starting points DSThe first starting point of the cluster that is not processed (i.e., not classified as a cluster set or marked as noise)
Figure BDA0003162419710000061
Is denoted as a first object p; checking the number Pts of starting points contained by the first object p within the set neighborhood radius e1。
When other starting points included in the neighborhood radius e of the first object p are determined, the distance rho from the first object p to other starting points in the neighborhood radius e is calculated by adopting an Euclidean distance formula:
Figure BDA0003162419710000062
wherein: (x)1,x2) And (y)1,y2) Two-dimensional coordinates corresponding to the two starting points respectively.
Judging whether the Euclidean distance rho between the two initial points calculated in the formula (3) is smaller than the neighborhood radius e, if so, indicating that the initial points are contained in the neighborhood radius e of the first object p, and thus obtaining the quantity Pts of the initial points contained in the neighborhood radius e of the first object p1。
S2023: it is determined whether the number Pts of the start points included in the neighborhood radius e of the first object p in step S2022 is greater than or equal to the preset number minPts.
If the number of starting points Pts contained within the neighborhood radius e of the first object p1If the number is greater than or equal to the preset number minPts, the process continues to step S2024.
If the number of starting points Pts contained within the neighborhood radius e of the first object p1If the number is less than the preset number minPts, step S2022 is repeatedly performed to continue to select other unprocessed first starting points for processing.
S2024: with the first object p (i.e. the first starting point selected in step S2021)
Figure BDA0003162419710000071
) Establishing a corresponding first cluster set C for core points1And establishing a candidate set N, and then classifying the starting points which are not classified into any cluster set or marked as noise and are contained in the neighborhood radius e of the first object p into the candidate set N, namely:
Figure BDA0003162419710000072
wherein:
Figure BDA0003162419710000073
for a start contained within a neighborhood radius e of the first object pAnd (4) point.
S2025: selecting a second starting point within the candidate set where N is not processed
Figure BDA0003162419710000074
Marking as a second object q, determining other starting points contained in the radius e in the neighborhood of the second object q by adopting the method in the step S2022, and obtaining the number Pts of the starting points contained in the radius e in the neighborhood of the second object q2。
S2026: it is determined whether the number of the start points included in the neighborhood radius e of the second object q in step S2025 is greater than or equal to the preset number minPts.
If the number of starting points Pts contained within the neighborhood radius e of the second object q2If the number is greater than or equal to the predetermined number minPts, the second object q (i.e. the second starting point) is set
Figure BDA0003162419710000075
) Adding the initial point which is not classified into any cluster set or marked as noise in the neighborhood radius e to the corresponding first cluster set C1In (1). The starting point within the neighborhood radius e of the second object q, which is not classified into any cluster set or marked as noise, can be represented as:
Figure BDA0003162419710000076
wherein: r is the number of starting points within the neighborhood radius e of the second object q that have been classified into any cluster set, which is marked as noise.
If the number of starting points Pts contained within the neighborhood radius e of the second object q2Less than a predetermined number minPts, then only the second object q (i.e., the second starting point) is selected
Figure BDA0003162419710000077
) Adding to the corresponding first cluster set C1And (4) performing neutralization.
S2027: steps S2025-S2026 are repeated until the candidate set N is empty (i.e., all the second starting points in the candidate set N
Figure BDA0003162419710000078
All classified as a cluster set or marked as noise), continue to execute step S2028;
s2028: repeating the steps S2022-S2027 until the starting point set DSAll starting points in
Figure BDA0003162419710000079
And all the cluster sets are classified as a certain cluster set or marked as noise, and the cluster set of the starting point of the ship navigation track is obtained. The set of starting points DSCan be expressed as:
DS＝C1+C2+…+CM+ε (6)
wherein: c1,…,CMRespectively clustering sets for starting points; m is the number of the starting point cluster sets, namely the starting point set DSAll starting points in
Figure BDA0003162419710000081
The number of categories to be divided; ε is the set of starting points marked as noise.
S203: and classifying the navigation tracks according to the clustering result.
Traversing all navigation tracks in the original set of the ship navigation tracks, classifying the navigation tracks according to the clustering results of the starting points and the end points in the step S202, and classifying all the ship navigation tracks into a plurality of categories to form a ship navigation track clustering set.
S3: and calculating the transition probability and selecting a planning route.
Specifically, a navigation plan and corresponding ship information of a ship are given, a navigation track similar to the given ship navigation plan and the ship information is searched in a ship navigation track clustering set, a current navigable path set of the given ship is obtained based on the similar navigation track, a transition probability corresponding to each navigable path in the navigable path set is calculated, and the navigable path with the maximum transition probability is determined as a planned route of the current voyage number of the ship.
As shown in fig. 4, the step S3 includes the following steps:
s301: dynamically searching the ship navigation track and establishing an approximate track set.
The method comprises the steps of giving a navigation plan of a ship, classifying the navigation plan into categories represented by corresponding ship navigation track cluster sets according to a starting point and an ending point of the navigation plan, dynamically searching similar ship navigation tracks in the ship navigation track cluster sets of the same category according to ship information of the ship, comparing the ship information (the type, the size, the loading state, the draft, the navigation season and the like of the ship) of the given ship with the type, the size, the loading state, the draft and the navigation season of the ship marked by the navigation tracks in the ship navigation track cluster sets of the same category, searching the similar ship navigation tracks, and accordingly establishing an approximate track set of the given ship at the current navigation time.
S302: and establishing a transition probability directed graph.
For the approximate track set in step S301, a plurality of nodes are marked at equal intervals on each navigation track, the transition probability from each node position to the adjacent node position (the adjacent node position may be the node position on the same navigation track, or the node position on other navigation tracks in the approximate track set) is calculated, and a transition probability directed graph is established.
In this embodiment, the established transition probability directed graph is defined as G (V, E, W), where V represents a set of all nodes on each navigation track in the approximate track set; e represents a set of directed edges formed by connecting lines of the current node position and the next adjacent node position; w represents the transition probability of each directed edge, i.e., the probability of transitioning from the current node position to the next node position.
S303: and calculating the transition probability to obtain a planned route.
Searching the navigable path of the given ship at the current voyage number according to the transition probability directed graph established in the step S302, and establishing a navigable path set { Tr1,Tr2,…,TrjJ 1,2, …, k, k is a set of navigable paths { Tr }1,Tr2,…,TrjInNumber of navigable paths, i.e. TrjRepresenting a set of navigable paths { Tr1,Tr2,…,TrjJ-th navigable path in (j), and then separately compute a set of navigable paths { Tr }1,Tr2,…,TrjAnd (4) taking the navigable path with the maximum transition probability as a planning route from a starting point to an ending point.
For a set of navigable paths { Tr1,Tr2,…,TrjEach navigable path Tr in the } can be regarded as a chain with m nodes, denoted as:
Figure BDA0003162419710000091
for i-1, 2, …, m-1, the transition probability of the current node can be determined
Figure BDA0003162419710000092
Calculating the transition probability of the next node
Figure BDA0003162419710000093
The transition probability for each navigable path Tr can thus be calculated:
Figure BDA0003162419710000094
wherein: p (Tr)j) As a set of navigable paths { Tr1,Tr2,…,TrjThe transition probability of the jth navigable path in the } is determined;
Figure BDA0003162419710000095
is the ith node in the jth navigable path.
Thus, the planned route path based on the navigable path with the highest transition probability as the starting point to the ending point can be represented as:
path＝argmaxP(Trj) (8)
s4: and establishing a ship route mapping library.
A large number of ship navigation plans and corresponding ship information are given, step S3 is repeatedly executed, a planned route corresponding to the navigation plan of each given ship is calculated and stored, and a ship route mapping library is established based on each planned route (including a start point, an end point, etc.) and corresponding ship information (ship type, ship size, loading state, draft, navigation season, etc.) to set constraint conditions.
S5: and automatically planning a ship route.
Before the ship sails, a sailing plan and constraint conditions of the ship are given, route mapping is carried out in the ship route mapping library, and a planned route of the current ship is automatically generated.
According to the scheme, multiple factors such as the navigation season of the ship, ship information and the like are comprehensively considered, dynamic search is carried out based on the ship track big data, a strong ship route mapping library is established, and dynamic query can be directly carried out in the ship route mapping library according to constraint conditions in the subsequent route design process, so that the current planned route is automatically generated, and the time for route design is greatly shortened.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the present invention.

Claims (10)

1.一种基于大数据的船舶航线自动生成方法，其特征在于，包括以下步骤：1. a method for automatically generating ship routes based on big data, is characterized in that, comprises the following steps: S1：获取船舶航行轨迹的原始数据，并对该原始数据进行整理、清洗，建立船舶航行轨迹原始集合；S1: Obtain the original data of the ship's navigation track, and organize and clean the original data to establish the original set of the ship's navigation track; S2：提取船舶航行轨迹原始集合中所有航行轨迹的起始点和终止点，通过聚类算法对船舶航行轨迹进行分类，得到船舶航行轨迹聚类集合；S2: Extract the start point and end point of all the voyage trajectories in the original set of ship voyage trajectories, classify the voyage trajectories of ships through a clustering algorithm, and obtain a cluster set of ship voyage trajectories; S3：给定船舶的航行计划及船舶信息，在船舶航行轨迹聚类集合中搜索相似的航行轨迹，基于相似的航行轨迹得到可航路径集合，并计算可航路径集合中每一可航路径对应的转移概率，确定转移概率最大的可航路径作为该航次的规划航线；S3: Given the ship's navigation plan and ship information, search for similar navigation trajectories in the ship's navigation trajectory cluster set, obtain a navigable path set based on the similar navigation trajectories, and calculate the corresponding navigable path in the navigable path set. The navigable path with the highest transfer probability is determined as the planned route of the voyage; S4：多次重复执行步骤S3，计算并保存若干与给定船舶的航行计划对应的规划航线，并基于规划航线及对应的船舶信息设定约束条件，建立船舶航线映射库；S4: Repeating step S3 for many times, calculating and saving a number of planned routes corresponding to the navigation plan of a given ship, and setting constraints based on the planned routes and corresponding ship information, and establishing a ship route mapping library; S5：给定当前船舶航行计划的约束条件，在所述船舶航线映射库中进行航线映射，自动生成当前船舶的规划航线。S5: Given the constraints of the current ship's navigation plan, perform route mapping in the ship's route mapping library, and automatically generate the planned route of the current ship. 2.根据权利要求1所述的基于大数据的船舶航线自动生成方法，其特征在于，所述步骤S1的具体方法为：2. the method for automatically generating ship routes based on big data according to claim 1, is characterized in that, the concrete method of described step S1 is: 获取预设时间、预设海域内每一船舶所有航次的航行轨迹形成原始数据，并对所述原始数据进行整理和清洗，剔除原始数据中的数据异常的航行轨迹，得到每艘船舶每一航次正常的航行轨迹，建立船舶航行轨迹原始集合。Obtain the preset time and the navigation trajectories of all voyages of each ship in the preset sea area to form raw data, organize and clean the raw data, eliminate the abnormal voyage trajectories in the original data, and obtain each voyage of each ship. The normal voyage track is used to establish the original set of ship voyage trajectories. 3.根据权利要求1所述的基于大数据的船舶航线自动生成方法，其特征在于，所述步骤S2包括以下步骤：3. The method for automatically generating ship routes based on big data according to claim 1, wherein the step S2 comprises the following steps: S201：提取船舶航行轨迹原始集合中每一航行轨迹的起始点和终止点，形成起始点集合和终止点集合，并对该航行轨迹按对应的船舶信息进行标记；S201: Extract the starting point and the ending point of each navigating track in the original set of ship voyage tracks, form a set of starting points and a set of ending points, and mark the voyage track according to the corresponding ship information; S202：采用聚类算法分别对步骤S201中提取的起始点和终止点进行聚类，得到对应的聚类结果；S202: Clustering the starting point and the ending point extracted in step S201 by using a clustering algorithm to obtain a corresponding clustering result; S203：遍历船舶航行轨迹原始集合中所有的航行轨迹，将航行轨迹按照步骤S202中起始点和终止点的聚类结果进行分类，形成船舶航行轨迹聚类集合。S203: Traverse all the navigation trajectories in the original set of ship navigation trajectories, and classify the navigation trajectories according to the clustering results of the start point and the end point in step S202 to form a cluster set of ship navigation trajectories. 4.根据权利要求3所述的基于大数据的船舶航线自动生成方法，其特征在于，所述步骤S202采用基于密度的聚类方法采用DBSCAN算法分别对起始点和终止点进行聚类，其具体步骤为：4. the method for automatically generating ship routes based on big data according to claim 3, is characterized in that, described step S202 adopts the clustering method based on density to adopt DBSCAN algorithm to carry out clustering to starting point and ending point respectively, its concrete The steps are: S2021：设置DBSCAN算法的邻域半径以及邻域半径内起始点/终止点的预设数量；S2021: Set the neighborhood radius of the DBSCAN algorithm and the preset number of start points/end points within the neighborhood radius; S2022：随机选择起始点集合/终止点集合中未被处理的第一起始点/第一终止点，检查第一起始点/第一终止点邻域半径内所包含的起始点/终止点数量；S2022: Randomly select the unprocessed first starting point/first ending point in the starting point set/ending point set, and check the number of starting points/terminating points included in the neighborhood radius of the first starting point/first ending point; S2023：判断步骤S2022中第一起始点/第一终止点邻域半径内所包含的起始点/终止点数量是否大于等于预设数量，若是，则继续执行步骤S2024，否则重复执行步骤S2022；S2023: Determine whether the number of starting points/terminating points included in the neighborhood radius of the first starting point/first ending point in step S2022 is greater than or equal to the preset number, if so, continue to perform step S2024, otherwise repeat step S2022; S2024：以第一起始点/第一终止点建立对应的第一聚类集合，并建立候选集合，将第一起始点/第一终止点邻域半径内所包含的未归入任一聚类集合或未标记为噪声的起始点/终止点归入候选集合内；S2024: Establish a corresponding first cluster set with the first starting point/first ending point, and establish a candidate set, and include the non-inclusive clusters included in the neighborhood radius of the first starting point/first ending point into any cluster set or The start/end points not marked as noise are included in the candidate set; S2025：选择候选集合内未被处理的第二起始点/第二终止点，检查第二起始点/第二终止点邻域半径内所包含的起始点/终止点数量；S2025: Select the unprocessed second starting point/second ending point in the candidate set, and check the number of starting points/terminating points included in the neighborhood radius of the second starting point/second ending point; S2026：判断步骤S2025中第二起始点/第二终止点邻域半径内所包含的起始点/终止点数量是否大于等于预设数量，若是，则将该第二起始点/第二终止点以及其邻域半径内未归入任一聚类集合或未被标记为噪声的起始点/终止点添加至对应的第一聚类集合中，否则仅将第二起始点/第二终止点归入对应第一聚类集合；S2026: Determine whether the number of starting points/terminating points included in the neighborhood radius of the second starting point/second ending point in step S2025 is greater than or equal to a preset number, and if so, then the second starting point/second ending point and The starting point/end point that is not classified into any cluster set or not marked as noise within its neighborhood radius is added to the corresponding first cluster set, otherwise only the second starting point/second ending point is included in the corresponding to the first cluster set; S2027：重复执行步骤S2025-S2026，直至候选集合为空时，继续执行步骤S2028；S2027: Repeat steps S2025-S2026 until the candidate set is empty, continue to execute step S2028; S2028：重复执行步骤S2022-S2027，直至起始点集合/终止点集合中所有起始点/终止点均归为某一聚类集合或被标记为噪声为止，得到船舶航行轨迹起始点/终止点的聚类集合。S2028: Repeat steps S2022-S2027 until all the starting points/ending points in the starting point set/ending point set are classified into a certain cluster set or marked as noise, and the cluster of starting points/ending points of the ship's navigation track is obtained. class collection. 5.根据权利要求4所示的基于大数据的船舶航线自动生成方法，其特征在于，确定所述起始点/终止点邻域半径内所包含的起始点/终止点时，计算两个起始点/终止点之间的距离，若两个起始点/终止点之间的距离小于邻域半径，则该起始点/终止点为包含在对应起始点/终止点的邻域半径内。5. The method for automatically generating a ship route based on big data according to claim 4, wherein when determining the starting point/terminating point contained in the neighborhood radius of the starting point/terminating point, two starting points are calculated. /The distance between the end points, if the distance between the two start points/end points is less than the neighborhood radius, the start point/end point is included in the neighborhood radius of the corresponding start point/end point. 6.根据权利要求5所示的基于大数据的船舶航线自动生成方法，其特征在于，两个起始点/终止点之间的距离采用欧式距离公式计算得到，其计算公式为：6. according to the ship route automatic generation method based on big data shown in claim 5, it is characterized in that, the distance between two starting points/termination points adopts Euclidean distance formula to calculate, and its calculation formula is:
Figure FDA0003162419700000021
Figure FDA0003162419700000021
 其中：(x1,x2)和(y1,y2)分别为两个起始点/终止点对应的二维坐标。Among them: (x 1 , x 2 ) and (y 1 , y 2 ) are the two-dimensional coordinates corresponding to the two starting points/end points, respectively. 7.根据权利要求4所述的基于大数据的船舶航线自动生成方法，其特征在于，所述步骤S3包括以下步骤：7. The method for automatically generating ship routes based on big data according to claim 4, wherein the step S3 comprises the following steps: S301：给定船舶航行计划，按照该航行计划的起始点和终止点将该航行计划归为对应的船舶航行轨迹聚类集合所代表的类别，并根据船舶的船舶信息在同类船舶航行轨迹聚类集合中动态搜索相似的船舶航行轨迹，建立近似轨迹集合；S301: Given a ship sailing plan, classify the sailing plan into the category represented by the corresponding ship sailing trajectory clustering set according to the starting point and the ending point of the sailing plan, and cluster the sailing trajectory of similar ships according to the ship information of the ship. Dynamically search for similar ship navigation trajectories in the set, and establish an approximate trajectory set; S302：针对步骤S301中的近似轨迹集合建立转移概率有向图；S302: Create a directed graph of transition probability for the approximate trajectory set in step S301; S303：在转移概率有向图中搜索船舶的可航路径，建立可航路径集合，计算可航路径集合中每一可航路径的转移概率，并将转移概率最大的可航路径作为起始点到终止点的规划航线。S303: Search the navigable path of the ship in the transition probability directed graph, establish a navigable path set, calculate the transition probability of each navigable path in the navigable path set, and use the navigable path with the largest transition probability as the starting point to The planned route to the end point. 8.根据权利要求7所述的基于大数据的船舶航线自动生成方法，其特征在于，所述步骤S302建立转移概率有向图的具体方法为：8. The method for automatically generating ship routes based on big data according to claim 7, wherein the specific method for establishing a directed graph of transition probability in the step S302 is: 在近似轨迹集合中每一航行轨迹上等距标记若干节点，计算每一节点位置处至相邻的节点位置之间的转移概率，建立转移概率有向图。A number of nodes are marked equidistantly on each navigation track in the approximate track set, and the transition probability between the position of each node and the adjacent node positions is calculated, and a directed graph of the transition probability is established. 9.根据权利要求7所述的基于大数据的船舶航线自动生成方法，其特征在于，所述步骤S303中，可航路径的转移概率的计算公式为：9. The method for automatically generating ship routes based on big data according to claim 7, wherein in the step S303, the calculation formula of the transition probability of the navigable route is:
Figure FDA0003162419700000031
Figure FDA0003162419700000031
 其中：P(Trj)为可航路径集合中第j条可航路径的转移概率；
Figure FDA0003162419700000032
为第j条可航路径中第i个节点，i＝1,2,…,m，m为任一可航路径中节点的数量；
Figure FDA0003162419700000033
为第j条可航路径中第i个节点的转移概率；Where: P(Tr j ) is the transition probability of the jth navigable path in the set of navigable paths;
Figure FDA0003162419700000032
is the ith node in the jth navigable path, i=1,2,...,m, where m is the number of nodes in any navigable path;
Figure FDA0003162419700000033
is the transition probability of the i-th node in the j-th navigable path; 基于转移概率最大的可航路径作为起始点到终止点的规划航线可表示为：The planned route based on the navigable path with the highest transition probability as the starting point to the ending point can be expressed as: path＝arg max P(Trj)；path=arg max P(Tr j ); 其中：path为该航次基于专利概率最大的可航路径生成的规划航线。Where: path is the planned route generated by the voyage based on the navigable path with the highest patent probability. 10.根据权利要求1所述的基于大数据的船舶航线自动生成方法，其特征在于，所述船舶信息包括船舶种类、船型尺寸、载态、吃水以及航行季节。10 . The method for automatically generating ship routes based on big data according to claim 1 , wherein the ship information includes ship type, ship size, load state, draft and sailing season. 11 .
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