CN111928855A - Automatic shortest route planning method based on AIS data - Google Patents
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Abstract
A shortest route automatic planning method based on AIS data comprises the following steps: A. dividing the global longitude and latitude into a two-bit grid matrix; judging whether AIS data appear in each grid or not, if the AIS data appear, determining the grid as a navigable grid, and determining the distance between all adjacent navigable grids; B. converting the latitude and longitude of a given departure point and a given arrival point into corresponding grid position coordinates in a two-position grid matrix; C. and performing circular search in the two-position grid matrix through an ant colony algorithm to output the shortest route from the starting point to the arrival point. The advantages are that: screening the collected AIS data under the condition of not using a large number of electronic sea charts, thereby constructing a course meeting the actual application requirements; the AIS data updated in real time is utilized to fully research the ship navigation condition under complex navigation factors, so that the target ship can safely navigate; and obtaining the shortest route which avoids falling into local optimum by utilizing an ant colony algorithm.
Description
Technical Field
The invention relates to the technical field of ship route speed optimization, in particular to an automatic shortest route planning method based on AIS data.
Background
With the rapid development of shipping, a crew needs to perform more navigation work before navigating to cope with the changeable navigation environment at sea, and the automatic planning of the airline according to the given starting point and the given terminal point can reduce the workload of the crew and improve the working efficiency before navigating. In addition, the route planning algorithm aiming at the shortest route can help the crew to find the shortest route, thereby reducing the fuel consumption cost in navigation and improving the operation efficiency of the shipping company. In recent years, with the rapid development of computer networks and control theories, more and more shipping companies begin to pay attention to the hot spot problems of unmanned ships and the like, and the realization of the automatic air route planning technology can improve the intelligent operation level of ships, and plays a vital role in the development of intelligent ships and even unmanned ships.
Many scholars both at home and abroad have conducted intensive research on airline planning. Most scholars utilize the grid chart in combination with the modified maze algorithm to accurately analyze and determine the safest or shortest route. Some scholars develop a navigation network method based on a known waypoint library, and the method mainly refers to the thought of road traffic network so as to obtain the shortest route, but the method needs a comprehensive waypoint library, and the comprehensive and reasonable waypoint library is difficult to obtain in the practical engineering application. The scholars such as the Han dynasty design a meteorological alignment method under the influence of hydrological weather, and adopt technical means such as isochrones calculation and the like to obtain an optimal route. For most scholars, they developed an automatic generation algorithm for the flight path based on a vector electronic chart, which is divided into adjacent grids, and optimized for the flight path using Dijkstra's algorithm.
However, the above method has the following disadvantages:
1. existing automatic airline planning algorithms rely on a large amount of chart data, which is costly in terms of economy and time to dynamically update;
2. the navigation limiting factors needing to be considered by the route planning algorithm are more, and the complex navigation environment needs to be fully researched;
3. furthermore, most course planning algorithms can only calculate locally optimal courses.
The invention is provided for overcoming the defects in the prior art.
Disclosure of Invention
Aiming at the structural defects of the prior art, the method for automatically planning the shortest route based on the AIS data is provided, and the method is utilized to pass through a starting point A (longitude)
Latitude α a), reach point B (longitude)
Latitude α B) and ship draft (D), the shortest route that meets the crew's route design habit can be planned.
In order to achieve the purpose, the invention provides an automatic shortest route planning method based on AIS data, which is realized by the following technical scheme:
a shortest route automatic planning method based on AIS data is characterized by comprising the following steps:
A. dividing the global longitude and latitude into a two-bit grid matrix; judging whether AIS data appear in each grid or not, if the AIS data appear, determining the grid as a navigable grid, and determining the distance between all adjacent navigable grids;
B. will give a starting point A (longitude)
Latitude α a) and arrival point B (longitude)
Latitude α B) into a starting point a '(XA, YA) and an arrival point B' (XB, YB) of the corresponding grid position in the two-dimensional grid matrix;
C. initializing initial parameters of an ant colony algorithm, and performing circular search in the two-position grid matrix through the ant colony algorithm to output the shortest path from the starting point A 'to the reaching point B' and convert the shortest path into the shortest route from the starting point A to the reaching point B.
The automatic planning method for the shortest route further comprises a route secondary design step, and comprises the following steps:
splitting the shortest route obtained in the step C into a plurality of sections of sub routes which are divided by turning points with changed course;
carrying out segmentation division on all sub-routes with the distance exceeding L by taking the arc length d as a unit reference, and calculating the longitude and latitude of each intermediate point based on the course and the course angle of the sub-routes;
and outputting a secondary designed shortest route formed by a sub route with the distance lower than L and a segmented route with the arc length of d.
The sub-course range is obtained by calculation of a spherical triangle cosine formula, and the course angle is obtained by calculation of a spherical triangle calculation formula; the longitude and latitude of the intermediate point are calculated in the following mode:
the coordinates of the intermediate points are expressed as Di (psi i, λ i), and Si (Si ═ i × d) is the arc length from any intermediate point Di to the starting point a of the subpath, where i ═ 1, 2, 3, …, n-1;
according to the spherical triangle formula
Psi ═ arcsin (sin psi 1cos (Si/(60 × 57.3)) + cos psi 1sin (Si/(60 × 57.3)) cos (c)) yields the latitude psi i of Di; wherein C is the course angle of the sub-route;
according to the formula
The longitude difference Ai between the starting point A and the intermediate point Di is obtained, and the longitude lambada i of the intermediate point Di is lambada 1+ Ai.
Before the step A, the step of cleaning and screening the original AIS data is further included, and the method comprises the following steps:
cleaning the AIS data which are collected wrongly, and reserving normal points of latitude, navigational speed, draft and course field data in the AIS data; and keeping AIS data meeting the navigation conditions; and removing discrete points of the common navigation area in the AIS data by using a DBSCAn density clustering algorithm.
The longitude normal data is between-180 degrees and 180 degrees, the latitude normal data is between-90 degrees and 90 degrees, the navigational speed normal data is between 0 and 50 knots, the draft normal data is between 0 and 50 meters, and the heading normal data is between 0 and 360 degrees;
the standard meeting the navigation condition is that the draught is more than 5 meters and the navigation speed is more than 5 knots;
the L is 300 nautical miles, and the d is 150 nautical miles.
The concrete steps of the step C are as follows:
each ant population is initialized to a predetermined number of times,
selecting a next adjacent navigable grid by using a roulette method, wherein the adjacent navigable grid and the adjacent navigable grid distance are derived from a calculation result of a global navigable grid division model;
each ant finds a path according to the pheromone until reaching a point B;
recording the navigation route and the total navigation distance of each ant;
updating pheromones left by ants;
judging whether the maximum population number is reached;
and outputting the solution with the shortest route.
The distance between the adjacent navigable grids is calculated by a adjacency matrix algorithm, starting point geographic coordinates a (ψ 1, λ 1) and ending point geographic coordinates B (ψ 2, λ 2), and the distance between two points S is calculated as follows:
flatten＝(ra-rb)/ra (1)
pA＝atan(rb*tan(ψ1)/ra) (2)
pB＝atan(rb*tan(ψ2)/ra) (3)
α＝arccos(sin(ψ1)sin(ψ2)+cos(λ1)cos(λ2)cos(λ2-λ1)) (4)
C1＝(sin(α)-α)*(sin(pA)+sin(pB))^2/cos(α/2)^2 (5)
C2＝(sin(α)+α)*(sin(pA)-sin(pB))^2/sin(α/2)^2 (6)
Dr＝flatten*(C1-C2)/8 (7)
S＝ra*(α+Dr) (8)
in the above equations 1 to 8, ra is the equatorial radius and rb is the polar radius.
Compared with the prior art, the invention has the beneficial effects that:
1. screening the collected AIS data under the condition of not using a large number of electronic sea charts, thereby constructing a course meeting the actual application requirements;
2. the AIS data updated in real time is utilized to fully research the ship navigation condition under complex navigation factors, so that the target ship can safely navigate;
3. and obtaining the shortest route which avoids falling into local optimum by utilizing an ant colony algorithm.
Drawings
The above features and advantages of the present invention will become more apparent and readily appreciated from the following description of the exemplary embodiments thereof taken in conjunction with the accompanying drawings.
FIG. 1 is a simplified flow chart of the AIS data based method for automatically planning a shortest route according to the present invention;
FIG. 2 is a flow chart of AIS data cleaning and screening of a method for automatically planning a shortest route based on AIS data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of AIS discrete points according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a grid matrix according to an embodiment of the present invention;
FIG. 5 is a flowchart of a shortest route planning for the ant colony algorithm according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures to facilitate understanding by those skilled in the art:
referring to fig. 1, an embodiment of the present invention provides an automatic shortest route planning method based on AIS data, and aims to determine a global navigable point based on AIS data, perform grid division and matrixing on a global chart, determine a matrix value of a global grid network according to the navigable point in the AIS data, and define a navigable grid. And (4) calculating the distance of each navigable grid by utilizing the adjacency matrix and combining a global longitude and latitude distance formula. And based on the ant colony algorithm and the calculated adjacency matrix, finding the optimal distance from the starting point to the end point, and outputting the planned route. Because the primarily planned route is obtained based on the grid network, the output route has more turning points, and a designed route which accords with the actual habit needs to be planned again according to the habit of planning the route by a crew. The following is a detailed description of the individual steps:
AIS data cleaning and screening
Referring to fig. 2, the step of cleaning and screening the raw AIS data comprises:
1.1 cleaning the AIS data, and keeping normal data points of latitude, navigational speed, draft and heading field in the AIS data, wherein the normal data of longitude is between-180 degrees and 180 degrees, the normal data of latitude is between-90 degrees and 90 degrees, the normal data of navigational speed is between 0 and 50 knots, the normal data of draft is between 0 and 50 meters, and the normal data of heading is between 0 and 360 degrees. The abnormal points of the field signals are defaulted to be errors of collected data, and the errors are prevented from interfering the division of the subsequent navigable grid network.
1.2 keeping AIS data meeting navigation conditions by taking draft of more than 5 meters and navigational speed of more than 5 knots as standards;
1.3, removing discrete points of a common navigation area in AIS data by using a DBSCAn density clustering algorithm, and removing the interference of the AIS data discrete points on route planning, wherein the specific discrete points are shown as a third drawing, and points circled in the third drawing are discrete points needing to be removed. The DBScan density clustering algorithm clusters AIS data-dense areas and can form spatial clusters conforming to main channels.
2. Global navigable grid meshing
And constructing a global navigable area based on the screened AIS data, and establishing a navigable grid according to a grid division method. As shown in fig. four, each cell is a grid.
2.1 dividing degrees by taking 0.025 degrees as a unit to divide global latitude and longitude, eliminating high latitude areas from south latitude 75 degrees to south pole and north latitude 75 degrees to north pole, and determining a global grid range;
2.2 dividing the global grid range according to the unit division degree determined in the step 2.1 to obtain 6000 x 14400 grids. And judging whether the AIS data appear in each grid or not according to the screened AIS data, and determining the grid as a navigable grid if the AIS data appear. Forming navigable grids into a navigable area by using a DBscan density clustering algorithm, thereby eliminating navigable discrete grids, wherein the navigable grids framed by dotted lines in the lower left corner are discrete grids needing to be eliminated as shown in the fourth drawing;
2.3, determining the distance between the adjacent navigable grids by using an adjacency matrix algorithm, converting the specific grids into longitude and latitude, and calculating the distance between the two adjacent navigable grids according to a calculation formula between two points on the earth, wherein the formula is as follows.
Starting point geographic coordinates a (ψ 1, λ 1), ending point geographic coordinates B (ψ 2, λ 2), and the distance between two points S is calculated as follows:
flatten＝(ra-rb)/ra (1)
wherein ra is the equatorial radius, in km;
rb-polar radius, in km.
pA＝atan(rb*tan(ψ1)/ra) (2)
pB＝atan(rb*tan(ψ2)/ra) (3)
α＝arccos(sin(ψ1)sin(ψ2)+cos(λ1)cos(λ2)cos(λ2-λ1)) (4)
C1＝(sin(α)-α)*(sin(pA)+sin(pB))^2/cos(α/2)^2 (5)
C2＝(sin(α)+α)*(sin(pA)-sin(pB))^2/sin(α/2)^2 (6)
Dr＝flatten*(C1-C2)/8 (7)
S＝ra*(α+Dr) (8)
3. Ant colony algorithm based shortest route planning
Initializing initial parameters of an ant colony algorithm, performing cyclic search in the two-position grid matrix through the ant colony algorithm to output a shortest path from a starting point A 'to a reaching point B', and converting the shortest path into a shortest route from the starting point A to the reaching point B, wherein the specific steps are as follows:
will give a starting point A (longitude)
Latitude α a) and arrival point B (longitude)
Latitude α B) into the starting point a' (XA, YA) of the corresponding grid position in the two-dimensional grid matrix and to the arrivalPoint B' (XB, YB);
initializing initial parameters of the ant colony algorithm, and initializing each ant colony;
thirdly, selecting the next adjacent navigable grid by using a roulette method, wherein the distances between the adjacent navigable grids and the adjacent navigable grids are obtained from the result obtained in the step 2;
finding a path for each ant according to the pheromone until reaching a point B;
recording the navigation route and the total navigation distance of each ant;
sixthly, updating pheromones left by the ants;
seventhly, judging whether the maximum population number is reached;
and outputting the solution with the shortest route.
4. Reconstruction model of designed route
And (4) carrying out secondary design on the shortest route obtained in the step (3), thereby ensuring that the designed route conforms to the sailing habit of the crew.
4.1 the shortest route obtained in the step 3 is split into a plurality of sections of sub routes which are divided by turning points with changed course, and the method specifically comprises the following steps:
4.1.1 extract the points where the course heading of the primary planned route changes.
4.1.2 converting the extracted steering points into longitude and latitude from the grid coordinates;
4.1.3 calculating the distance between two adjacent turning points according to a calculation formula between two points on the earth (see the formulas (1) - (8)). And distinguishing a great circle route and a constant direction route, wherein the great circle route is the great circle route when the distance between the great circle route and the constant direction route exceeds 300 nautical miles, and the constant direction route is the non-constant direction route.
4.2 in order to accord with sailing habits of the crew, the great circle route is divided in sections by taking the arc length d as a unit reference, the longitude and the latitude of each middle point are calculated based on the range and the course angle of the sub route, and the great circle route is designed at equal intervals in the following steps:
4.2.1 calculating the Total Range of the great circular course
The geographic coordinates A (psi 1, lambda 1) of the starting point and the geographic coordinates B (psi 2, lambda 2) of the ending point, and the distance from the starting point to the ending point can be obtained according to the spherical trigonometric cosine equation (10):
S＝arccos(sinψ1sinψ2+cosψ1cosψ2cos(λ2-λ1)) (9)
in the formula, S is the total range of the great circular arc, represented by 'DEG', and the spherical distance (N mile) can be obtained by multiplying S by 60, and the calculation method of the formula carries out calculation by substituting longitude and latitude according to the principles of 'north positive south negative, east positive west negative', such as 25 degrees N of north latitude, and the carry-in type is +25 degrees, and the carry-in type is-10 degrees in the 10 degrees W of west longitude.
4.2.2 calculating course angle of great circle route
According to the formula (11) of the spherical triangle calculation, the initial course angle C from the starting point a to the end point B can be obtained:
determining the angle of C according to the geographical rule condition, namely (12):
4.2.3 calculating the latitude and longitude of each branch point
According to the above assumption, the division is performed by segments with 150 nautical miles as a unit basis, the great circle route is divided into n segments according to unit distance, and the rounding is performed upwards as shown in formula (13):
n＝[S/d] (12)
n-1 intermediate points are obtained, and the coordinates of the intermediate points can be expressed as Di (ψ i, λ i), where i is 1, 2, 3, …, n-1, and Si (Si i d) is the arc length from the inserted intermediate point Di to the starting point a of the sub route, according to the spherical triangle formula (14):
ψi＝arcsin(sinψ1cos(Si/(60*57.3))+cosψ1sin(Si/(60*57.3))cos(C)) (14)
the latitude ψ i of Di can be obtained according to formula (15):
the longitude difference Ai between the starting point A and the intermediate point Di is obtained, so that the longitude of Di can be obtained as
λi＝λ1+Ai (16)
4.2.4 output the quadratic design shortest route composed of the sub-routes with the distance lower than 300 nautical miles and the 150 nautical branch routes.
Compared with the prior art, the invention has the beneficial effects that:
1. screening the collected AIS data under the condition of not using a large number of electronic sea charts, thereby constructing a course meeting the actual application requirements;
2. the AIS data updated in real time is utilized to fully research the ship navigation condition under complex navigation factors, so that the target ship can safely navigate;
3. and obtaining the shortest route which avoids falling into local optimum by utilizing an ant colony algorithm.
Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that the above embodiments are only one of the preferred embodiments of the present invention, and not all embodiments can be enumerated herein for the sake of brevity, and any embodiment that can embody the claims of the present invention is within the protection scope of the present invention.
Claims (7)
1.A shortest route automatic planning method based on AIS data is characterized by comprising the following steps:
A. dividing the global longitude and latitude into a two-bit grid matrix; judging whether AIS data appear in each grid or not, if the AIS data appear, determining the grid as a navigable grid, and determining the distance between all adjacent navigable grids;
B. will give a starting point A (longitude)
Latitude α a) and arrival point B (longitude)
Latitude α B) into corresponding grid positions in the two-dimensional grid matrix, starting points a' (XA, YA) andreach point B' (XB, YB);
C. initializing initial parameters of an ant colony algorithm, and performing circular search in the two-position grid matrix through the ant colony algorithm to output the shortest path from the starting point A 'to the reaching point B' and convert the shortest path into the shortest route from the starting point A to the reaching point B.
2. The AIS data-based automatic shortest route planning method according to claim 1, characterized in that the automatic shortest route planning method further comprises a route secondary design step, comprising the steps of:
splitting the shortest route obtained in the step C into a plurality of sections of sub routes which are divided by turning points with changed course;
carrying out segmentation division on all sub-routes with the distance exceeding L by taking the arc length d as a unit reference, and calculating the longitude and latitude of each intermediate point based on the course and the course angle of the sub-routes;
and outputting a secondary designed shortest route formed by a sub route with the distance lower than L and a segmented route with the arc length of d.
3. The AIS data-based automatic shortest route planning method according to claim 2, wherein the sub route course is calculated by a spherical trigonometric cosine formula, and the course angle is calculated according to a spherical trigonometric calculation formula; the longitude and latitude of the intermediate point are calculated in the following mode:
the coordinates of the intermediate points are expressed as Di (psi i, λ i), and Si (Si ═ i × d) is the arc length from any intermediate point Di to the starting point a of the subpath, where i ═ 1, 2, 3, …, n-1;
according to the spherical triangle formula
Psi ═ arcsin (sin psi 1cos (Si/(60 × 57.3)) + cos psi 1sin (Si/(60 × 57.3)) cos (c)) yields the latitude psi i of Di; wherein C is the course angle of the sub-route;
according to the formula
The longitude difference Ai between the starting point A and the intermediate point Di is obtained, and the longitude lambada i of the intermediate point Di is lambada 1+ Ai.
4. The method for automatically planning the shortest route based on the AIS data according to claim 2, characterized by further comprising a step of cleaning and screening the original AIS data before the step A, and comprising the following steps:
cleaning the AIS data which are collected wrongly, and reserving normal points of latitude, navigational speed, draft and course field data in the AIS data; and keeping AIS data meeting the navigation conditions; and removing discrete points of the common navigation area in the AIS data by using a DBSCAn density clustering algorithm.
5. The AIS data-based automatic shortest route planning method according to claim 4, wherein the AIS data-based automatic shortest route planning method comprises the following steps:
the longitude normal data is between-180 degrees and 180 degrees, the latitude normal data is between-90 degrees and 90 degrees, the navigational speed normal data is between 0 and 50 knots, the draft normal data is between 0 and 50 meters, and the heading normal data is between 0 and 360 degrees;
the standard meeting the navigation condition is that the draught is more than 5 meters and the navigation speed is more than 5 knots;
the L is 300 nautical miles, and the d is 150 nautical miles.
6. The AIS data-based automatic shortest route planning method according to claim 4, wherein the specific steps of step C are as follows:
each ant population is initialized to a predetermined number of times,
selecting a next adjacent navigable grid by using a roulette method, wherein the adjacent navigable grid and the adjacent navigable grid distance are derived from a calculation result of a global navigable grid division model;
each ant finds a path according to the pheromone until reaching a point B;
recording the navigation route and the total navigation distance of each ant;
updating pheromones left by ants;
judging whether the maximum population number is reached;
and outputting the solution with the shortest route.
7. The AIS data-based automatic shortest route planning method according to claim 6, characterized in that: the distance between the adjacent navigable grids is calculated by a adjacency matrix algorithm, starting point geographic coordinates a (ψ 1, λ 1) and ending point geographic coordinates B (ψ 2, λ 2), and the distance between two points S is calculated as follows:
flatten＝(ra-rb)/ra (1)
pA＝atan(rb*tan(ψ1)/ra) (2)
pB＝atan(rb*tan(ψ2)/ra) (3)
α＝arccos(sin(ψ1)sin(ψ2)+cos(λ1)cos(λ2)cos(λ2-λ1)) (4)
C1＝(sin(α)-α)*(sin(pA)+sin(pB))^2/cos(α/2)^2 (5)
C2＝(sin(α)+α)*(sin(pA)-sin(pB))^2/sin(α/2)^2 (6)
Dr＝flatten*(C1-C2)/8 (7)
S＝ra*(α+Dr) (8)
in the above equations 1 to 8, ra is the equatorial radius and rb is the polar radius.
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