CN111401668A - Unmanned ship route planning method based on big data - Google Patents
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Abstract
The invention relates to the field of big data processing and unmanned air route planning, in particular to an unmanned ship air route planning method based on big data. The method comprises the following steps: the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; and after data processing is carried out on the data in the database, the data are compressed and stored. Step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm. The method and the device aim at the problems that the air route track has large data volume, the data is compressed, and the problems that the data processing is slow, the data occupies large memory and the processing efficiency is low are solved.
Description
Technical Field
The invention relates to the field of big data processing and unmanned air route planning, in particular to an unmanned ship air route planning method based on big data.
Background
With the continuous progress of human science and technology, the technological development level of ships is continuously improved, and the ships are developed towards large-scale, specialization, high-speed and unmanned driving. Especially, the rapid development of high and new technologies such as artificial intelligence, internet of things, big data and the like and the combination of the technologies with ships and ocean engineering are highly emphasized by various shipbuilding shipping countries in the world in recent years, and the adoption of unmanned ships is becoming the development trend of the ship industry. Unmanned ships have wide application markets in the military field and the civil field, and the improvement of the automation level of the ships becomes urgent. The design of the unmanned ship relates to planning and navigation, and particularly, the automatic generation of the route of the unmanned ship becomes a factor restricting the rapid development of the unmanned ship industry. Therefore, an unmanned ship route planning method based on big data is needed.
Disclosure of Invention
1. The technical problem to be solved is as follows:
aiming at the technical problems, the invention provides a big data based unmanned ship route planning method, which comprises the steps of firstly establishing a track database by using a big data technology, and then obtaining the optimal route of a preset starting point and a preset end point by using a clustering algorithm.
2. The technical scheme is as follows:
an unmanned ship route planning method based on big data is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; and after data processing is carried out on the data in the database, the data are compressed and stored.
Step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm.
Further, the processing of the data in the database in the first step specifically includes cleaning abnormal data, converting the data from longitude and latitude coordinates into mercator coordinates, setting a compression threshold, and compressing the data according to the compression threshold.
Further, the trajectory data of the vessel voyage of the sea area of the predetermined range is obtained by acquiring data of the vessel AIS device and decoding the AIS data into a plain text.
Further, identifying track data between a predetermined starting point and a predetermined ending point of the unmanned ship to be sailed in a track database, specifically comprising the following steps:
step 21: and decompressing track data between the preset starting point and the preset end point, and extracting a turning point in the track.
Step 22: and obtaining the route turning points in the route based on clustering analysis on the turning points in the track.
Step 23: and (4) identifying the connectivity of the route turning points generated in the step (22), finding out communication paths among the route turning points to form a directed graph, and forming a route network.
Further, the step 21 specifically includes obtaining a heading of a constant direction line formed by a connection line of every two adjacent feature points in the trajectory data, obtaining an angle of an included angle formed by every 3 adjacent feature points in the trajectory, and if the angle of the included angle is greater than a preset threshold angle, determining that a point among the three feature points is a turning point in the trajectory.
Further, step 22 specifically includes: and identifying all similar turning points in the track data, and clustering the turning points based on a hierarchical DBSCAN algorithm to obtain all route turning points.
The method specifically comprises the steps of searching all route turning points by which the route turning points are clustered, extracting all route turning points containing a specified route by adopting an SQ L statement, storing the route turning points in a list or an array container, finding out another route turning point which is spatially closest to the route turning points from any one route turning point, and obtaining a directed graph G = (TN, <tttranslation = L & "" gTt/gTt) formed by the route turning points and the connectivity thereof after the connectivity identification between the route turning points is completed, wherein TN represents an isolated route turning point, and L represents the connectivity between the route turning points.
Further, in the second step, searching for the optimal route from the set starting point to the set end point based on the ant colony optimization algorithm specifically includes the following steps:
step 24: and combining the track data sets of the preset starting point and the preset end point into the to-be-selected air route track, and acquiring the initial cost value of each edge of the to-be-selected air route track.
Step 25: initializing the visibility of adjacent route turning points and the intensity of bioinformatics hormones according to the initial cost value of each edge of the to-be-selected route track, and acquiring the node navigation probability of the to-be-selected route track, thereby obtaining the optimal route from a set starting point to a set terminal point.
3. Has the advantages that:
(1) the method and the device aim at the problems that the air route track has large data volume, the data is compressed, and the problems that the data processing is slow, the data occupies large memory and the processing efficiency is low are solved.
(2) The method and the device perform cluster analysis on the steering points in the identified track characteristic points to identify the airway steering points, have strong anti-noise capability and high processing efficiency, intelligently search the optimal air route in the directed graph formed by the airway steering points and the connectivity by utilizing the ant colony optimization algorithm, and can ensure the correctness of the depicted data.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart according to the ant colony optimization algorithm of the present invention.
Detailed Description
As shown in the attached figure 1, the unmanned ship route planning method based on big data is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; and after data processing is carried out on the data in the database, the data are compressed and stored.
Step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm.
Further, the processing of the data in the database in the first step specifically includes cleaning abnormal data, converting the data from longitude and latitude coordinates into mercator coordinates, setting a compression threshold, and compressing the data according to the compression threshold.
Further, the trajectory data of the vessel voyage of the sea area of the predetermined range is obtained by acquiring data of the vessel AIS device and decoding the AIS data into a plain text.
Further, identifying track data between a predetermined starting point and a predetermined ending point of the unmanned ship to be sailed in a track database, specifically comprising the following steps:
step 21: and decompressing track data between the preset starting point and the preset end point, and extracting a turning point in the track.
Step 22: and obtaining the route turning points in the route based on clustering analysis on the turning points in the track.
Step 23: and (4) identifying the connectivity of the route turning points generated in the step (22), finding out communication paths among the route turning points to form a directed graph, and forming a route network.
Further, the step 21 specifically includes obtaining a heading of a constant direction line formed by a connecting line of every two adjacent track points in the track data, obtaining an angle of a composition included angle of every 3 adjacent feature points in the track, and if the angle is larger than a preset threshold angle, determining that a point among the three feature points is a turning point in the track.
Further, step 22 specifically includes: and identifying all similar turning points in the track data, and clustering the turning points based on a hierarchical DBSCAN algorithm to obtain all route turning points.
The method specifically comprises the steps of searching all route turning points by which the route turning points are clustered, extracting all route turning points containing a specified route by adopting an SQ L statement, storing the route turning points in a list or an array container, finding out another route turning point which is spatially closest to the route turning points from any one route turning point, and obtaining a directed graph G = (TN, <tttranslation = L & "" gTt/gTt) formed by the route turning points and the connectivity thereof after the connectivity identification between the route turning points is completed, wherein TN represents an isolated route turning point, and L represents the connectivity between the route turning points.
Further, in the second step, searching for the optimal route from the set starting point to the set end point based on the ant colony optimization algorithm specifically includes the following steps:
step 24: and combining the track data sets of the preset starting point and the preset end point into the to-be-selected air route track, and acquiring the initial cost value of each edge of the to-be-selected air route track. The calculation formula is as follows:
C(r,s)=kCa+(1-k)Cb
wherein: c (r, s) represents the initial cost of the navigation of the ship from the turning point r to the turning point s along the route track; ca represents the cost value of the nearby flight in the flight path to the flight path; cb represents the length of the flight path; k represents a weight value, 0< k <1.
Step 25: initializing the visibility of adjacent route turning points and the intensity of bioinformatics hormones according to the initial cost value of each edge of the to-be-selected route track, and acquiring the node navigation probability of the to-be-selected route track, thereby obtaining the optimal route from a set starting point to a set terminal point. The flow chart is shown in fig. 2, and specifically includes assuming that m ships navigate by using different course routes according to an ant colony optimization algorithm to obtain the actual navigation cost of each specific course route, and repeating for multiple times until the shortest cost route is found, from a preset starting point to a preset terminal point. Where n represents the number of cycles.
The set rule is as follows: the probability of an artificial ant selecting a new feasible turning point is determined by the cost of two turning points and the strength of the bioinformatics hormone.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. An unmanned ship route planning method based on big data is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring track data of ship navigation in a preset range of sea area within a preset time period, and establishing a track database; after data processing is carried out on the data in the database, the data are compressed and stored;
step two: and identifying track data of a preset starting point and a preset ending point of the unmanned ship to be navigated in a track database, and searching for an optimal route from the set starting point to the set ending point based on an ant colony optimization algorithm.
2. The big data based unmanned ship route planning method according to claim 1, wherein: the data processing in the database in the first step specifically comprises cleaning abnormal data, converting the cleaned data into the coordinates of the mercator system from the longitude and latitude coordinates, setting a compression threshold value, and compressing the data according to the compression threshold value.
3. The big data based unmanned ship route planning method according to claim 1, wherein: the trajectory data of the vessel's voyage in the sea area of the predetermined range is obtained by acquiring the data of the vessel AIS device and decoding the AIS data into plain text.
4. The big data based unmanned ship route planning method according to claim 1, wherein: identifying track data between a preset starting point and a preset terminal point of an unmanned ship to be sailed in a track database, and specifically comprising the following steps:
step 21: decompressing track data between a preset starting point and a preset end point, and extracting a turning point in the track;
step 22: obtaining an airway turning point in the airway based on clustering analysis on the turning point in the track;
step 23: and (4) identifying the connectivity of the route turning points generated in the step (22), finding out communication paths among the route turning points to form a directed graph, and forming a route network.
5. The big data based unmanned ship route planning method according to claim 4, wherein: step 21 specifically includes obtaining a heading of a constant direction line formed by a connecting line of every two adjacent feature points in the trajectory data, obtaining an angle of an included angle formed by every 3 adjacent feature points in the trajectory, and if the angle of the included angle is larger than a preset threshold angle, determining that a point among the three feature points is a turning point in the trajectory.
6. The big data based unmanned ship route planning method according to claim 4, wherein: step 22 specifically includes: and identifying all similar turning points in the track data, and clustering the turning points based on a hierarchical DBSCAN algorithm to obtain all route turning points.
7. The big data based unmanned ship route planning method as claimed in claim 4, wherein the step 23 comprises searching all the route turning points by which the route turning points are clustered, using SQ L sentence, extracting all the route turning points including a specific route and storing them in a list or array container, then starting from any one of the route turning points, finding out another route turning point which is spatially nearest to the route turning point to recognize connectivity, and after completing the connectivity recognition between the route turning points, obtaining a directed graph G = (TN, <tttransition = L &/lTt/gTt), where TN represents isolated route turning points and L represents connectivity between the route turning points.
8. The big data based unmanned ship route planning method according to claim 4, wherein: in the second step, searching the optimal route from the set starting point to the set end point based on the ant colony optimization algorithm specifically comprises the following steps:
step 24: the track data set of the preset starting point and the preset end point is a to-be-selected route track, and the initial cost value of each side of the to-be-selected route track is obtained;
step 25: initializing the visibility of adjacent route turning points and the intensity of bioinformatics hormones according to the initial cost value of each edge of the to-be-selected route track, and acquiring the node navigation probability of the to-be-selected route track, thereby obtaining the optimal route from a set starting point to a set terminal point.
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Cited By (10)
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CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
CN111861045A (en) * | 2020-08-06 | 2020-10-30 | 中国科学院地理科学与资源研究所 | Method for rapidly generating marine shortest route oriented to massive digital water depth model data body |
CN112164247A (en) * | 2020-09-03 | 2021-01-01 | 重庆大学 | Ship route prediction method based on ship track clustering |
CN112836851A (en) * | 2020-12-30 | 2021-05-25 | 成都方位导向科技开发有限公司 | Hierarchical database building method for airlines |
CN112862156A (en) * | 2021-01-08 | 2021-05-28 | 北京工业大学 | Ship path planning method based on ship track and ant colony algorithm |
CN113435639A (en) * | 2021-06-23 | 2021-09-24 | 交通运输部规划研究院 | Port water area planning method and system integrating AIS data mining |
CN113505836A (en) * | 2021-07-14 | 2021-10-15 | 金睛兽数字科技(重庆)有限公司 | Ship route automatic generation method based on big data |
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CN114088097A (en) * | 2021-11-12 | 2022-02-25 | 天津大学 | Method for extracting marine vessel navigable manifold frame based on AIS big data |
CN114879656A (en) * | 2022-03-30 | 2022-08-09 | 深圳拓邦股份有限公司 | Method and device for adjusting operation direction of intelligent mower, electronic equipment and storage medium |
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