CN115146836A - Dynamic meteorological route optimization method based on A-star algorithm - Google Patents
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Abstract
The invention discloses a dynamic meteorological route optimization method based on an A-star algorithm, which comprises the following steps: the method comprises the following steps: acquiring a recommended climate air route of the current voyage, and respectively translating the recommended climate air route to two sides by a first distance threshold value to acquire a recommended air route band; acquiring meteorological data grid nodes; generating navigation grid nodes with an obstacle and wind, wave and flow with the attribute of the obstacle in the recommended route zone; acquiring a meteorological air route from a departure port position to a destination port position based on an A star algorithm; determining the optimal meteorological route of the current voyage; calculating the position of the ship after the weather update time threshold from the departure port; and acquiring all ship positions in the optimal meteorological flight path, thereby acquiring the whole optimal meteorological flight path. The invention continuously updates the navigation environment by using the forecasted meteorological data, continuously optimizes the rest navigation sections, and finally obtains the optimal route, thereby realizing the minimum total cost of ship operation by using the meteorological conditions to the maximum extent and promoting the healthy development of intelligent navigation.
Description
Technical Field
The invention relates to the field of optimization of ocean routes of intelligent ships, in particular to a dynamic optimization method of a meteorological route based on an A-star algorithm.
Background
Autonomous navigation of ships is a remarkable feature of intelligent ships, and is increasingly concerned by industrial and marine enterprises. In addition, fuel costs and carbon emission policies present a number of challenges to the intellectualization of ships. It is known that the consumption of fuel is a main cost for the operation of ships and a main source of emission of pollution gas, and the research and application of ship route optimization are effective ways for reducing the fuel consumption of ships.
The optimization of routes has different definitions according to different optimization objectives. Simonsen et al define course optimization as a process of finding an optimal course for parameters such as Estimated Time of Arrival (ETA), turning points, and speeds of each leg of a certain voyage, according to meteorological data and ship performance. In one sense, route optimization is the selection of the best route for a given voyage (given origin and destination ports) taking into account expected weather and sea conditions. The optimization of a flight path depends on optimization objectives such as minimizing travel distance, travel time, or fuel consumption.
Aiming at the problem of route optimization of intelligent ships, a large number of theoretical achievements have been accumulated at home and abroad. The main methods for solving the problem of route optimization include an isochrone method, a dynamic programming method, a graph search algorithm, an intelligent algorithm, artificial intelligence, a machine learning method and the like. The research methods have unique advantages in processing optimization problems, but the following four problems still exist to prevent the progress of conversion of theoretical results into practical application. Meteorological conditions are not considered adequately and some studies do not consider the effects of ocean currents or waves. The meteorological conditions are not considered sufficiently, accumulated errors of predicted navigational speed, ship position and predicted arrival time are caused, and the practicability of the optimized route is reduced; dynamic optimization performance is insufficient. The time of a ship sailing across the ocean often exceeds the time range of weather forecast, so that weather data needs to be continuously updated during sailing to perform dynamic optimization on an air route.
Disclosure of Invention
The invention provides a dynamic meteorological route optimization method based on an A star algorithm, which aims to overcome the technical problems.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a star algorithm based dynamic optimization method for meteorological routes comprises the following steps:
s1: acquiring a recommended climate route of the current voyage, and translating the recommended climate route to two sides respectively by a first distance threshold value to acquire a recommended route band;
s2: acquiring a meteorological data grid containing the whole voyage number within a meteorological updating time threshold value time according to the predicted meteorological data and the latitude and longitude grids so as to acquire meteorological data grid nodes; the forecast meteorological data comprise wind direction, wind speed, wave height, surge direction, surge height, ocean current flow direction and ocean current flow speed;
s3: generating navigation grid nodes with an obstacle and wind, wave and flow with the attribute of the obstacle in the recommended route zone;
s4: acquiring a ship position P from a departure port in the recommended route zone based on an A star algorithm according to the navigation grid nodes and the meteorological data grid nodes 0 To the destination port position P N A meteorological route therebetween; the meteorological flight lines comprise a lowest oil consumption flight line, a shortest time flight line and a shortest distance flight line;
s5: determining the probability of selecting the lowest oil consumption route, the probability of selecting the shortest time route and the probability of selecting the shortest distance route according to the fuel cost and the navigation time; to determine the optimal meteorological route of the current voyage;
s6: calculating the ship position P from the departure port when the weather update time threshold value passes according to the optimal weather route 1 ；
S7: repeatedly executing S2-S3, and acquiring the next weather update time threshold in the recommended route band based on the A star algorithm according to the optimal weather route of the current voyage numberTime-worth slave berth P 1 To the destination port position P N Updated optimal meteorological routes therebetween; to obtain the ship position P when the next weather update time threshold value passes 2 (ii) a By analogy, the ship position P in the optimal meteorological flight line is obtained i I ∈ 1, \8230; \ N-1, thereby obtaining the entire optimal weather route.
Further, the shortest distance route has the shortest total range from departure port to destination port, i.e. the shortest distance route has
Wherein L represents the total voyage from the departure port to the destination port; l i Representing the voyage from the ith-1 meteorological data grid node to the ith meteorological data grid node; n represents the number of meteorological data grid nodes; i represents a meteorological data grid node number;
the cost function of the A star algorithm is improved into a function representing the spherical distance, namely:
in the formula, g i The spherical distance from the current meteorological data grid node to the next meteorological data grid node;
the latitude values of the grid nodes of the current meteorological data are obtained;
the latitude value of the next meteorological data grid node; lambda [ alpha ] i The longitude value of the current meteorological data grid node is obtained; lambda [ alpha ] i+1 The longitude value of the next meteorological data grid node;
in the formula, h i The distance from the next weather data grid node to the destination node;
a latitude value as a destination node;
the latitude value of the next meteorological data grid node; lambda [ alpha ] goal Is the longitude value of the destination node; lambda [ alpha ] i+1 Is the longitude value of the next weather data grid node.
Further, the lowest fuel consumption route minimizes fuel consumption from departure port to destination port, i.e.
In the formula: fuel (Fuel) total Total fuel consumption from departure port to destination port; fuel (fossil fuel oil) i Fuel consumption from the ith-1 meteorological data grid node to the ith meteorological data grid node;
the cost function with the lowest fuel consumption as the optimization goal is:
g fuel ＝v i ·S(v) (6)
in the formula, g fuel The oil consumption between the ith meteorological data grid node and the (i + 1) th meteorological data grid node is calculated; s (v) is a function of fuel consumption to ship speed v, and the ship speed v is an independent variable; v. of i The ship speed between the ith meteorological data grid node and the (i + 1) th meteorological data grid node is obtained;
the heuristic function with the lowest fuel consumption as the optimization objective is then:
h fuel ＝v mean ·S(v)·t rest (7)
in the formula: h is a total of fuel A heuristic function with minimum fuel consumption as an optimization goal; v. of mean Is the average speed from the (i + 1) th node to the destination node; t is t rest Is from the first toProjected time of flight from i +1 nodes to the destination node; wherein:
in the formula, s rest A voyage from the (i + 1) th meteorological data grid node to a destination point;
in the formula, t total Estimated total time for the entire voyage; t is t i The required voyage time for the ith voyage segment; t is t i+1 The required voyage time from the current meteorological data grid node to the next meteorological data grid node.
Further, the shortest time flight path minimizes the time from departure port to destination port, i.e.
In the formula: t is the total navigation time from the departure port to the destination port; s is i For the course of the ith flight, v i The speed of the ith voyage;
using the stall function as the cost function of the shortest time route, namely:
V lost ＝V w +V current (11)
in the formula, V lost Speed loss for a vessel sailing from a current meteorological data grid node to a next meteorological data grid node; v w The speed loss caused by wind and wave in the current meteorological data grid node and the next meteorological data grid node voyage is solved; v current The speed loss caused by ocean current in the current meteorological data grid node and the next meteorological data grid node voyage; wherein:
in the formula, b 0 Bj is the ship stall equation coefficient; m is the number of stall factors; x is a radical of a fluorine atom j Is a stall factor; j is the number of the stall factor;
in the formula, V oc Is the flow velocity of the ocean current,
is a flow angle, namely an included angle between the ocean current flow direction and the ship heading;
the heuristic function for the shortest time flight path is as follows:
in the formula, g t Is a heuristic value in units of time; t is t total The predicted total time for the entire voyage; t is t i The required voyage time for the ith voyage segment; t is t i+1 The required voyage time from the current meteorological data grid node to the next meteorological data grid node.
Further, in S5, the method for determining the optimal meteorological route of the current voyage is as follows:
in the formula: p (y = 0) representsProbability of the shortest distance route being selected; p (y = 1) represents the probability of the shortest time course being selected; p (y = 2) represents the probability that the lowest fuel consumption route is selected; y represents an event; x k Representing selection support property variables, i.e. X 1 、X 2 (ii) a Wherein, X 1 Representing the time of flight; beta is a 1 Is a parameter representing the time of flight; x 2 Represents the cost of fuel; beta is a 2 Is a parameter representing the cost of fuel; beta is a k The method comprises the steps of solving an optimal estimation value by adopting a maximum likelihood estimation method; k is the influence factor serial number representing the air route selection;
thus, the optimal meteorological route of the current voyage is obtained:
has the advantages that: the invention relates to a meteorological route dynamic optimization method based on an A-star algorithm, which comprises the steps of constructing a navigation environment model according to meteorological data of wind, wave, surge and ocean current; according to the method, a recommended weather air route is translated to two sides by a first distance threshold to obtain a recommended air route zone, so that the optimization range of the air route is limited, and the operation efficiency of an algorithm is improved; acquiring a meteorological air route comprising a lowest oil consumption air route, a shortest time air route and a shortest distance air route based on an A star algorithm, and determining the probability of selecting the lowest oil consumption air route, the probability of selecting the shortest time air route and the probability of selecting the shortest distance air route according to the fuel cost and the air time; to determine the optimal meteorological route of the current voyage; and the sailing environment is continuously updated by using the forecasted meteorological data, the rest sailing sections are continuously optimized, and finally the optimal sailing line is obtained, so that the minimum total cost of ship operation is realized by using the meteorological conditions to the maximum extent, and the healthy development of intelligent sailing is promoted.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating the effect of restricting recommended flight bands in an embodiment of the present invention;
FIG. 2 is a diagram illustrating meteorological data and latitude and longitude grids in a certain sea area of the Pacific ocean according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an optimized flight path with the shortest flight distance as the optimization objective according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an optimized route with minimum fuel consumption as an optimization target according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating weather conditions in local east sea area of Tokyo harbor at 1 st east of 5.5.2022 in accordance with an embodiment of the present invention;
FIG. 6 is a diagram of the meteorological conditions in the middle of the Pacific ocean at 5 months and 10 days 2022 in accordance with an embodiment of the present invention;
FIG. 7 is a flowchart of a dynamic meteorological route optimization method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problems in the prior art, the invention aims to design a dynamic optimization method of the meteorological flight line, which considers wind, sea waves, ocean currents, has strong real-time performance and can add constraint conditions.
In order to achieve the purpose, the technical scheme of the invention is as follows: the dynamic meteorological route optimization method based on the A star algorithm comprises the following steps as shown in the attached figure 7:
s1: acquiring a recommended climate air route of the current voyage from a navigation recommended air route database as an initial air route, and translating the recommended climate course to two sides respectively by a first distance threshold value to acquire a recommended air route band, limiting a search area and improving algorithm efficiency;
specifically, the first distance threshold in this embodiment is specified by the seaman according to experience, so that the ship can avoid a severe sea state; in actual operation, the sailing personnel can continuously input different first distance thresholds, and a plurality of routes can be generated for comparison.
S2: acquiring a meteorological data grid containing the whole voyage number within a meteorological updating time threshold value time according to the predicted meteorological data and the latitude and longitude grids; the predicted meteorological data comprises wind direction, wind speed, wave height, surge direction, surge height, flow direction and flow speed;
specifically, the meteorological data grid in this embodiment is a longitude and latitude grid combined with meteorological data, and the meteorological data grid is set as a method commonly used in the field, which is not described in detail here.
The predicted weather data can be obtained from the National Oceanic and Atmospheric Administration (NOAA) in the United states or the middle weather forecast center (ECWMF) in Europe, the downloaded weather data file is in a netCDF format, and is respectively analyzed by using an analysis program written in C + + or a function in a Mapping toolbox built in matlab and added to a recommended navigation band, namely a longitude and latitude grid of a search area. According to the accuracy of meteorological data, the grid size of longitude and latitude is 1 degree multiplied by 1 degree, 0.5 degree multiplied by 0.5 degree, 0.25 degree multiplied by 0.25 degree, and a large grid is specifically selected and selected by a ship operator;
s3: generating navigation grid nodes with an obstacle and wind, wave and flow with the attribute of the obstacle in the recommended route zone;
specifically, in the present embodiment, the wind, wave, and flow having the obstacle attribute are set according to a specific user. The standards of each ship user for the barrier properties of stormy waves and currents are different, for example, the ships with different tonnages can bear different wind speeds, wave heights, surge heights, flow directions and flow speeds which can ensure safe sailing. The meteorological data and the latitude and longitude grids are combined to generate a meteorological data grid of the whole voyage number, critical values of wind speed, wave height, flow speed and flow direction are set according to the wave resistance performance of the ship, and if the critical values are exceeded, the meteorological data grid is regarded as an obstacle, and the meteorological data grid can be automatically avoided during voyage.
S4: acquiring a ship position P from a departure port in the recommended route zone based on an A-star algorithm according to the navigation grid nodes and the meteorological data grid nodes 0 To the destination port position P N A meteorological route therebetween; the meteorological flight lines comprise a lowest oil consumption flight line, a shortest time flight line and a shortest distance flight line;
specifically, in this embodiment, set up obstacle areas such as island reef, big stormy waves district in the recommended course area, set up high latitude limit area, avoid optimizing the course and pass through the region of can not navigating. Longitude and latitude coordinates of the reef area can be derived from an electronic chart, an A star algorithm can determine the coordinates of the reef area as a barrier, sailing personnel can set a latitude critical value according to the latitude crossed by the voyage number to avoid that a ship drives to a severe sea condition area with high latitude, and finally a meteorological course comprising a lowest oil consumption course, a shortest time course and a shortest distance course is solved according to different optimization targets:
preferably, the shortest distance route in this embodiment is a cost function based on a navigation distance, and the shortest distance route is solved;
let L be the total voyage from departure port to destination port, L i For the voyage between two adjacent meteorological data grid nodes, the optimization goal of the shortest distance problem is to minimize the total voyage L of the whole voyage process, namely:
wherein L represents the total voyage from the departure port to the destination port; l i Representing the voyage from the ith-1 meteorological data grid node to the ith meteorological data grid node; n represents the number of meteorological data grid nodes; i represents a meteorological data grid node number;
specifically, when the traditional a-star algorithm searches for the shortest path, the cost represents the cost that has been paid for navigating to the current meteorological data grid node, that is, the distance, and the heuristic value represents how much cost needs to be paid from the current node to the destination node. The cost function and the heuristic function are used to calculate a cost value and a heuristic value. Conventional a-star algorithms often represent cost values and heuristic values in terms of manhattan distances, euclidean distances, and diagonal distances, namely:
f＝g+h (2)
wherein f is the total cost value; g is the cost paid by sailing to the current meteorological data grid node; h is the cost from the current meteorological data grid node to the target meteorological data grid node, and is also called a heuristic value. And when the ship sails to the current meteorological data grid node, comparing the f values of the next candidate meteorological data grid nodes, and finding out the meteorological data grid node with the minimum f value, namely the meteorological data grid node for the next sailing. Specifically, in this embodiment, the cost in the calculation of the shortest time route refers to time, the cost in the calculation of the shortest distance route refers to distance, and the cost in the calculation of the lowest oil consumption route refers to oil consumption.
The cost function is modified to be a function representing the spherical distance, i.e.:
in the formula, g i The spherical distance from the current meteorological data grid node to the next meteorological data grid node;
the latitude values of the grid nodes of the current meteorological data are obtained;
the latitude value of the next meteorological data grid node; lambda [ alpha ] i The longitude value of the current meteorological data grid node is obtained; lambda [ alpha ] i+1 The longitude value of the next meteorological data grid node;
the heuristic function is improved into the spherical distance, and the cost function is kept uniform, so that the calculation is convenient, namely:
in the formula, h i Is the distance to the destination node traveled from the next node;
a latitude value of the destination node;
is the latitude value of the next node; lambda [ alpha ] goal The longitude value of the current node is obtained; lambda [ alpha ] i+1 Is the longitude value of the next node.
Preferably, the lowest oil consumption route is solved with the minimum fuel consumption from the departure port to the destination port, namely based on the fuel consumption as a cost function;
the optimization target of the lowest oil consumption route is the total oil consumption Fuel of the whole sailing process total The minimum, namely:
in the formula: fuel (Fuel) total Total fuel consumption from departure port to destination port; fuel (fossil fuel oil) i Fuel consumption from the ith-1 meteorological data grid node to the ith meteorological data grid node;
the fuel consumption of the ship depends on the output power of the main engine and the voyage time, and the voyage time is determined by the voyage and the speed. In the actual sailing process of the ship, the ship is influenced by wind, sea waves and ocean currents in the area, and the sailing speed changes. The output power of the ship main engine can correspond to the corresponding navigational speed, the fuel consumption also has a corresponding relation with the output power of the ship main engine, and the corresponding relation is embodied in a navigational speed and fuel consumption table of the ship. The speed of the ship moving from the current node to the next node is constant, so that the fuel consumption of the ship in the current navigation section can be calculated, and an algorithm cost function taking the lowest fuel consumption as an optimization target can be designed as follows:
g fuel ＝v i ·S(v) (6)
in the formula, g fuel The oil consumption between the ith node and the (i + 1) th node is calculated; s (v) is a function of fuel consumption to ship speed v, the ship speed v is an independent variable, and the fuel consumption S (v) is a function value; v. of i The ship speed between the ith meteorological data grid node and the (i + 1) th meteorological data grid node is calculated;
for ease of calculation, the heuristic function is also modified to be a function expressed in terms of fuel consumption, namely:
h fuel ＝v mean ·S(v)·t rest (7)
in the formula: h is a total of fuel Is a heuristic function with minimum fuel consumption as an optimization objective; v. of mean The average speed from the (i + 1) th node to the destination node can be obtained by equation (8); t is t rest The estimated time to navigate from the (i + 1) th node to the destination node can be obtained from equation (9);
in the formula, s rest Representing the range from the (i + 1) th meteorological data grid node to the destination point by using a spherical distance;
in the formula, t total The predicted total time for the entire voyage; t is t i The required voyage time for the ith voyage section; t is t i+1 The required navigation time from the current meteorological data grid node to the next meteorological data grid node, namely the navigation time required by the (i + 1) th navigation section.
Preferably, the shortest time route has the least time from departure port to destination port, that is, based on the time of flight as a cost function, the shortest time route can be solved; then the optimal objective of the shortest flight time problem, i.e. the solution, is the minimum total flight time of the whole flight process, i.e.:
in the formula: t is the total navigation time from the departure port to the destination port; s i Voyage of ith voyage, v i The speed of the ith flight segment;
as can be seen, course time is determined by range and speed, and the range s of the ship from the current node to the next node i Is deterministic, so the greater the speed the smaller the time to navigate. If the speed of the ship is made as large as possible, the stall caused by meteorological conditions must be minimized. The stall function can be used as a cost function for the shortest time flight path, namely:
V lost ＝V w +V current (11)
in the formula, V lost Speed loss for a vessel sailing from a current meteorological data grid node to a next meteorological data grid node; v w The speed loss caused by wind and wave in the current meteorological data grid node and the next meteorological data grid node voyage; obtained from formula (12); v current The speed loss caused by ocean current in the current meteorological data grid node and the next meteorological data grid node voyage; obtained from formula (13); the variables are positive and negative, the positive values increase the navigation speed, and the negative values reduce the navigation speed.
In the formula, b 0 Bj is the ship stall equation coefficient; m is the number of stall factors; x is the number of j Is a stall factor; j is the number of the stall factor; the stall factor takes wind direction, wind speed, wave height, surge direction, surge height, displacement, course and navigational speed into consideration;
in the formula V oc Is the flow rate of the ocean current,
is the flow side angle, i.e. the angle between the flow direction and the ship heading.
The heuristic function is also modified into a functional expression with time as a unit, and the cost function is kept uniform, namely:
in the formula, g t Is a heuristic value in units of time; t is t total Estimated total time for the entire voyage; t is t i The required voyage time for the ith voyage section; t is t i+1 The required navigation time from the current meteorological data grid node to the next meteorological data grid node, namely the navigation time required by the (i + 1) th navigation section.
S5: determining the probability of selecting the lowest oil consumption route, the probability of selecting the shortest time route and the probability of selecting the shortest distance route according to the fuel cost and the navigation time; determining the optimal meteorological route of the current voyage;
in S5, the method for determining the optimal weather route of the current voyage is as follows:
establishing a ternary logic model, and calculating the probability of selecting the lowest oil consumption route, the shortest time route and the shortest distance route:
in the formula: p (y = 0) represents the probability of the shortest distance route being selected; p (y = 1) represents the probability that the shortest time course is selected; p (y = 2) represents the probability of the lowest-oil-consumption route being selected; y represents an event, specifically a shortest distance event, a shortest time event and a lowest oil consumption test piece; x k Representing selection support property variables, i.e. X 1 、X 2 (ii) a The navigation speed, the navigation mileage and the fuel consumption of the lowest fuel consumption route, the shortest time route and the shortest distance route can be calculated; wherein X 1 Representing a time of flight; beta is a 1 Is a parameter representing the time of flight; x 2 Represents the fuel cost; beta is a beta 2 Is a parameter indicative of fuel cost; beta is a beta k The method comprises the steps of solving an optimal estimation value by adopting a maximum likelihood estimation method; k is an influence factor serial number representing the route selection, wherein the influence factors in the embodiment are fuel cost and navigation time;
thus, the optimal meteorological route of the current voyage is obtained:
specifically, in another embodiment of the invention, the optimal weather route determines the optimization strategy of the route according to the operation requirements of the ship operator. And according to actual operation requirements, the predicted arrival time is used as a constraint condition for optimizing the air route, so that the practicability of the optimized air route is improved.
S6: calculating the ship position P from the departure port when the weather update time threshold value passes according to the optimal weather route 1 ；
Specifically, in the embodiment, the ship position P when the weather update time threshold value is passed is calculated according to the optimal weather route 1 The method (b) is a method commonly used in the art and will not be described in detail herein.
S7: repeatedly executing S2-S3, and acquiring a slave ship position P of the next weather update time threshold time in the recommended route zone based on the A star algorithm according to the optimal weather route of the current voyage 1 To the eyesHarbor berth P N Updated optimal weather patterns in between; to obtain the ship position P when the next weather update time threshold value passes 2 (ii) a By analogy, the ship position P in the optimal meteorological flight line is obtained i I ∈ 1, \8230; \ N-1, thereby obtaining the entire optimal weather route.
Specifically, the weather update time threshold in this embodiment is 12h, that is, the ship calculates the optimal weather route from the departure port, and calculates the ship position after 12 hours according to the optimal weather route at that time; and updating the meteorological data after 12h, calculating an updated optimal meteorological flight line from the current ship position to the destination port, acquiring the ship position 12 hours after the current ship position according to the updated optimal meteorological flight line, and repeating the steps to obtain the optimal meteorological flight line of the whole flight line.
The simulation experiment of the embodiment of the invention is based on an MATLAB simulation platform and an electronic chart and information display system (ECDIS). The simulation example in the figure is an ocean route with Tokyo harbor as the departure harbor and los Angeles harbor, USA, as the destination harbor. In the embodiment, only the transoceanic route from the anchor place to the anchor place is optimized, the starting coordinate and the target coordinate are respectively (35.5 degrees N,140 degrees E) and (33.5 degrees N and 118.5 degrees W), the departure time is 2022 years, 5 months and 1 day, and the ship parameters are shown in the table 1 in the embodiment.
TABLE 1 simulation experiment Ship parameters
1. Generating a navigation environment model containing a whole voyage number based on predicted meteorological data
(1) Obtaining a recommended climatic route of the current voyage, and respectively longitudinally expanding the recommended climatic route to south and north by a first distance threshold to form a recommended route band so as to limit a search area and improve the calculation efficiency, as shown in fig. 1, which is a schematic diagram of the effect of the limited search area with the climatic routes from tokyo port in japan to los angeles port in usa as initial routes;
(2) Acquiring predicted wind, wave and flow data, analyzing and adding the data into meteorological attributes of grid nodes in a search area, and obtaining a schematic diagram of meteorological data and longitude and latitude grids in a certain sea area of the pacific ocean as shown in FIG. 2;
(3) Navigation grid nodes with wind, wave, flow and obstacle attributes are generated within the restricted search area.
2. Searching the optimal route from the departure port to the destination port based on the A star algorithm,
(1) Based on the sailing distance as a cost function, the shortest distance route can be solved;
the schematic diagram of the optimized route with the shortest navigation distance as the optimization target is shown in FIG. 3. Because the cost function is the spherical distance, the searched air route of the optimal meteorological air route is basically consistent with the big circular air route under the condition of ensuring the navigation safety.
(2) Based on the fuel consumption as a cost function, a minimum fuel consumption route can be solved;
the optimized route with the lowest oil consumption as the optimization target is shown in figure 4. Fig. 5 shows the meteorological conditions of local east sea areas of tokyo port in tokyo 1 th 5 th month, and the comparison of the optimized routes shows that the ship avoids the top wind and wave area from the local east sea areas of tokyo port, and is fuel-saving and economical in sailing practice. The experimental data of the lowest oil consumption route are shown in a table 2. As is well known, the navigation oil consumption and the navigation speed are in a direct proportion relationship to the third power, so the experiment assumes that the relationship between the fuel consumption and the speed is as follows:
F＝a·v 3
in the formula, F is the fuel consumption in unit time, a is a proportionality coefficient, and v is the ship speed. The optimization algorithm of the invention also adopts the same method to calculate the oil consumption when compared with the initial recommended climate route, so the accuracy of the oil consumption calculation does not influence the optimization effect, and the subsequent consumption calculation can be approximately replaced by a formula.
TABLE 2 lowest fuel route experimental results
(3) Based on the time of flight as a cost function, the shortest time course can be solved.
The optimized route taking the shortest time as the optimization target is shown in figure 6, and because the middle part of the Pacific ocean has a great wave area, as shown in figure 2, if the great circle route is continuously walked, the safety of ships and goods is influenced, the algorithm avoids the area of the top wind and the top wave, and searches the node with the minimum stall, thereby generating the shortest time route. Experimental data for the shortest time optimized flight path are shown in table 3.
TABLE 3 shortest time course test results
3. Dynamic optimization of air route based on dynamic planning idea
(1) Setting the updating time of meteorological data to be 12h, taking the first optimized route as a reference, predicting the ship position 12h after the departure port, and setting the ship position as P 1 ；
(2) With P 1 Updating meteorological data for the initial position, and optimizing the air route again;
(3) Repeating the step (1) and the step (2) until P N-1 Number ship position, so as to obtain whole course (P) 0 ，P 1 ， P 2 ，…，P N-1 ，P N )。
The embodiment fully considers meteorological condition factors, solves the accumulated errors of predicted navigational speed, ship position and predicted arrival time caused by the meteorological condition factors, and improves the practicability of the optimal meteorological route; at the same time, the constraint conditions in navigation practice are considered, for example, in a crowded port, a ship operator often hopes that a ship arrives at a specific time instead of arriving too early or too late, so that the constraint condition of predicted arrival time is applied to the optimization of a flight line; in addition, because the time of a voyage of a ship across the ocean often exceeds the time range of weather forecast, weather data needs to be continuously updated to perform dynamic optimization on the air route in the sailing process, the change of the weather forecast time is fully considered, and the dynamic optimization performance is good.
The embodiment has the following beneficial effects:
1. in order to improve the optimization performance of the meteorological route, the influence of wind, wave and ocean current on ship navigation is considered, a meteorological data grid is established, the optimal meteorological route is obtained based on an A-star algorithm, the accuracy of the navigational speed prediction, the ship dead reckoning and the estimated arrival time is improved, and the navigation safety is guaranteed;
2. according to the method and the device, the search direction and the search area of the A star algorithm are limited through the established recommended route band, and the time efficiency of the operation of the algorithm is improved. Firstly, the searching direction of the algorithm is limited according to the relative positions of a departure port and a destination port of a voyage, the number of candidate nodes is reduced, and the operation efficiency of the algorithm is greatly improved; secondly, according to the recommended route band of the initial recommended climate route limit search area, the search area is reasonably limited, and the operation efficiency and the optimization effect of the algorithm are improved.
3. In order to apply the A star algorithm to the navigation practice, the invention designs a route optimization strategy based on three optimization targets of shortest time, shortest distance and lowest oil consumption by combining the operation requirements of ship operators. And according to actual operation requirements, the predicted arrival time is used as a constraint condition for optimizing the air route, so that the practicability of the optimized air route is improved.
4. The invention provides a ternary logic model for evaluating the total navigation cost of the shortest distance route, the shortest time route and the lowest oil consumption route, and an optimized route with the lowest total cost can be selected under the condition of no human intervention, thereby laying a foundation for the application of the algorithm in an intelligent ship navigation system.
5. The embodiment is based on the idea of dynamic planning, the meteorological data is updated in real time by using the dead reckoning ship position and the time of arriving at the dead reckoning ship position, and the dead reckoning ship position is used as a new starting point to optimize the air route again. Repeating the steps to obtain the dynamic optimal meteorological route.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A star algorithm based dynamic optimization method of meteorological routes is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring a recommended climate air route of the current voyage, and respectively translating the recommended climate air route to two sides by a first distance threshold value to acquire a recommended air route band;
s2: acquiring a meteorological data grid containing the whole voyage number within a meteorological updating time threshold value time according to the predicted meteorological data and the latitude and longitude grids so as to acquire meteorological data grid nodes; the predicted meteorological data comprise wind direction, wind speed, wave height, surge direction, surge height, ocean current flow direction and ocean current flow speed;
s3: generating navigation grid nodes with an obstacle and wind, wave and flow with the attribute of the obstacle in the recommended route zone;
s4: acquiring a ship position P from a departure port in the recommended route zone based on an A star algorithm according to the navigation grid nodes and the meteorological data grid nodes 0 To the destination port position P N A meteorological route therebetween; the meteorological flight lines comprise a lowest oil consumption flight line, a shortest time flight line and a shortest distance flight line;
s5: determining the probability of selecting the lowest oil consumption route, the probability of selecting the shortest time route and the probability of selecting the shortest distance route according to the fuel cost and the navigation time; to determine the optimal meteorological route of the current voyage;
s6: calculating the ship position P from the departure port when the weather update time threshold value passes according to the optimal weather route 1 ；
S7: repeatedly executing S2-S3, and acquiring a slave ship position P of the next weather update time threshold time in the recommended route zone based on the A star algorithm according to the optimal weather route of the current voyage 1 To the destination port position P N Updated optimal meteorological routes therebetween; to obtain the ship position P when the next weather update time threshold value passes 2 (ii) a In this kind ofPushing and obtaining the ship position P in the optimal meteorological flight path i I ∈ 1, \8230; \ N-1, thereby obtaining the entire optimal weather route.
2. The dynamic meteorological route optimization method based on the A-star algorithm according to claim 1, wherein: the shortest distance route is shortest in terms of total range from departure port to destination port, i.e.
Wherein L represents the total voyage from the departure port to the destination port; l i Representing the voyage from the ith-1 meteorological data grid node to the ith meteorological data grid node; n represents the number of meteorological data grid nodes; i represents a meteorological data grid node number;
the cost function of the A star algorithm is improved into a function representing the spherical distance, namely:
in the formula, g i The spherical distance from the current meteorological data grid node to the next meteorological data grid node is navigated;
the latitude values of the grid nodes of the current meteorological data are obtained;
the latitude value of the next meteorological data grid node; lambda i The longitude value of the current meteorological data grid node is obtained; lambda [ alpha ] i+1 The longitude value of the next meteorological data grid node;
in the formula, h i The distance from the next weather data grid node to the destination node;
a latitude value of the destination node;
the latitude value of the next weather data grid node; lambda [ alpha ] goal Is the longitude value of the destination node; lambda i+1 Is the longitude value of the next weather data grid node.
3. The dynamic meteorological route optimization method based on the A-star algorithm, as claimed in claim 1, wherein: the lowest fuel consumption route has the least fuel consumption from departure port to destination port, i.e.
In the formula: fuel (Fuel) total Total fuel consumption from departure port to destination port; fuel i Fuel consumption from the ith-1 meteorological data grid node to the ith meteorological data grid node;
the cost function with the lowest fuel consumption as the optimization goal is:
g fuel ＝v i ·S(v) (6)
in the formula, g fuel The oil consumption between the ith meteorological data grid node and the (i + 1) th meteorological data grid node is calculated; s (v) is a function of fuel consumption to ship speed v, and the ship speed v is an independent variable; v. of i The ship speed between the ith meteorological data grid node and the (i + 1) th meteorological data grid node is obtained;
the heuristic function with the lowest fuel consumption as the optimization objective is then:
h fuel ＝v mean ·S(v)·t rest (7)
in the formula: h is fuel To be the most excellentLow fuel consumption is a heuristic function of an optimization objective; v. of mean Is the average speed from the (i + 1) th node to the destination node; t is t rest Is the predicted time of flight from the i +1 th node to the destination node; wherein:
in the formula, s rest A voyage from the (i + 1) th meteorological data grid node to a destination point;
in the formula, t total The predicted total time for the entire voyage; t is t i The required voyage time for the ith voyage section; t is t i+1 The required voyage time from the current weather data grid node to the next weather data grid node.
4. The dynamic meteorological route optimization method based on the A-star algorithm according to claim 1, wherein: the shortest time route has the least time from departure port to destination port, i.e.
In the formula: t is the total navigation time from the departure port to the destination port; s i For the course of the ith flight, v i The speed of the ith voyage;
using the stall function as the cost function of the shortest time route, namely:
V lost ＝V w +V current (11)
in the formula, V lost Speed loss for a vessel sailing from a current meteorological data grid node to a next meteorological data grid node; v w For the grid nodes and the next weather in the current weather dataSpeed loss caused by wind and wave in a data grid node voyage; v current The speed loss caused by ocean current in the current meteorological data grid node and the next meteorological data grid node voyage; wherein:
in the formula, b 0 Bj is the ship stall equation coefficient; m is the number of stall factors; x is the number of j Is a stall factor; j is the number of the stall factor;
in the formula, V oc Is the flow velocity of the ocean current,
is a flow angle, namely an included angle between the ocean current flow direction and the ship heading;
the heuristic function for the shortest time route is as follows:
in the formula, g t Is a heuristic value in units of time; t is t total The predicted total time for the entire voyage; t is t i The required voyage time for the ith voyage section; t is t i+1 The required voyage time from the current weather data grid node to the next weather data grid node.
5. The dynamic meteorological route optimization method based on the A-star algorithm according to claim 1, wherein:
in S5, the method for determining the optimal weather route of the current voyage is as follows:
in the formula: p (y = 0) represents the probability that the shortest distance route is selected; p (y = 1) represents the probability that the shortest time course is selected; p (y = 2) represents the probability that the lowest fuel consumption route is selected; y represents an event; x k Representing selection support property variables, i.e. X 1 、X 2 (ii) a Wherein, X 1 Representing a time of flight; beta is a 1 Is a parameter representing the time of flight; x 2 Represents the cost of fuel; beta is a 2 Is a parameter representing the cost of fuel; beta is a k The method comprises the steps of solving an optimal estimation value by adopting a maximum likelihood estimation method; k is the influence factor serial number representing the air route selection;
thus, the optimal meteorological route of the current voyage is obtained:
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