CN110608738B - Unmanned ship global meteorological air route dynamic planning method and system - Google Patents

Unmanned ship global meteorological air route dynamic planning method and system Download PDF

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CN110608738B
CN110608738B CN201910750052.3A CN201910750052A CN110608738B CN 110608738 B CN110608738 B CN 110608738B CN 201910750052 A CN201910750052 A CN 201910750052A CN 110608738 B CN110608738 B CN 110608738B
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王晓原
赵新越
夏媛媛
朱慎超
姜雨函
冯凯
张露露
孙懿飞
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Qingdao University of Science and Technology
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Abstract

The embodiment of the invention relates to a method and a system for dynamically planning a global meteorological air route of an unmanned ship, wherein the method comprises the following steps: acquiring an initial route, and performing discretization processing on the initial route to obtain a plurality of initial waypoints, wherein the plurality of initial waypoints comprise a starting point, an end point and a plurality of intermediate waypoints positioned between the starting point and the end point; collecting real-time meteorological data in the running process of a ship; for the current waypoint on which the ship runs, performing multi-level decision by using the position decision of the initial waypoint and combining real-time meteorological data from the initial waypoint to a plurality of waypoints of the current waypoint to obtain a plurality of corresponding optimal waypoints; and dynamically planning the non-navigation route behind the current waypoint according to the optimal waypoints and the terminal point. The invention adopts a segmented multi-level decision mode to dynamically adjust and plan the position of the waypoint on the initial route in real time, thereby realizing the dynamic optimization of the route during the running of the ship.

Description

Unmanned ship global meteorological air route dynamic planning method and system
Technical Field
The invention relates to the technical field of ships, in particular to a method and a system for dynamically planning a global meteorological air route of an unmanned ship.
Background
In recent years, with the rapid development of shipping industry, the design of global weather routes becomes an important research content for guaranteeing the safe and efficient navigation of intelligent unmanned ships. The safety and the economical efficiency of the ship route are two important indexes for measuring the quality of the ship route.
The existing research mostly adopts static meteorological information as a meteorological course design basis, the consideration of course decision under the condition of meteorological information change is lacked, and because of the change of marine meteorological information, a course obtained through one-time planning is difficult to ensure that the total course of navigation always keeps the optimal state, so the existing meteorological course static design method is difficult to meet the actual navigation requirement of a ship. Secondly, the existing research lacks consideration on energy consumption and navigation time, the existing research mostly realizes the single-target optimum of the route, and the actual navigation requirements of the ship can not be met only by realizing the shortest navigation time or the lowest energy consumption of the route. In addition, the existing research mostly adopts methods such as an isochronal method, a variational method, a grid model building and the like to design a meteorological flight path. The isochronal method is a recursive algorithm, and when the meteorological data are more, the consumption of storage space and the increase of complexity are easily caused, so the method can only be used for designing a route with a shorter flight path and is difficult to realize by using a computer program. The variation rule is a function for constructing a navigation time, and an Euler equation is adopted for extreme value solution. Because the equation is constructed with more constraint conditions, the solving difficulty is high, and when a second-order differential is needed, the equation solving can generate inaccurate conditions. The method for establishing the grid model to solve the meteorological flight line is to convert the ship flight line optimization problem into the network path problem, and when the flight line with a long flight distance is calculated, the method needs to read and process a large amount of data, so that the operation efficiency of the algorithm is low.
Therefore, the method in the prior art is lack of dynamic planning of the ship route according to real-time meteorological data during navigation.
The above drawbacks are expected to be overcome by those skilled in the art.
Disclosure of Invention
Technical problem to be solved
In order to solve the problems in the prior art, the invention provides a method and a system for dynamically planning an unmanned ship global meteorological flight line, and solves the problem that the prior art lacks dynamic planning on a ship flight line according to real-time meteorological data during navigation.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
an embodiment of the present invention provides a method for dynamically planning an unmanned ship global meteorological route, which includes:
obtaining an initial route, and performing discretization processing on the initial route to obtain a plurality of initial waypoints, wherein the plurality of initial waypoints comprise a starting point, an end point and a plurality of intermediate waypoints positioned between the starting point and the end point;
collecting real-time meteorological data in the running process of a ship;
for the current waypoint where the ship runs, performing multistage decision by using the position decision of the initial waypoint and combining real-time meteorological data from the initial waypoint to a plurality of waypoints of the current waypoint to obtain a plurality of corresponding optimal waypoints;
and dynamically planning the non-navigation route behind the current waypoint according to the optimal waypoints and the terminal.
In an embodiment of the present invention, the obtained initial route is a great circle route, and the great circle route is a route with the shortest route from the starting point to the end point.
In one embodiment of the present invention, the real-time weather data comprises: wind direction angle, hull windward angle, wave height and ship stall.
In an embodiment of the present invention, the performing a multi-stage decision by using the position decision of the initial waypoint and the real-time meteorological data from the initial waypoint to the plurality of waypoints of the current waypoint comprises:
taking N route points including an initial point and a terminal point in the initial route as N decisions;
dividing the initial route into a navigated route and an unvarnished route by taking the current waypoint as a boundary;
and determining the position of the waypoint in the non-sailing route according to the state of the middle waypoint in the sailing route and the real-time meteorological data.
In an embodiment of the present invention, the dynamic equation of the decision process is:
x(k+1)＝f[x(k),u(k),k]
where x (k +1) is the position decision of the (k +1) th waypoint of the N waypoints, u (k) is the real-time meteorological data of the kth waypoint, x (k) is the position decision of the selected waypoint, k is the k-segment self-course before the selected waypoint, and k ranges from k to 0, 1.
In an embodiment of the present invention, the evaluation function of the multi-stage decision is:
Figure BDA0002166879010000031
wherein m is the number of the current waypoint, h [ x (k), m is the evaluation function of waypoint x (k).
In an embodiment of the present invention, the evaluation function h [ x (k), m ] is calculated by the following formula:
Figure BDA0002166879010000032
Figure BDA0002166879010000033
wherein h [ x (k), m]As a dual objective function value, omega1And ω2Weight factors of energy consumption and time of flight, respectively, and omega1+ω2＝1，γ1And gamma2Is a conversion factor used for converting energy consumption and time into economic indexes of the evaluation route, N is the number of the constant direction lines on the initial route, v is the number of the constant direction lines on the initial routeiActual speed, v, of the ship being the i-th section of the constant direction linemaxCritical speed of the ship, SiIs the length of the ith segment of the constant direction line,Teis the main engine thrust of the ship;
critical speed v of shipmaxThe calculation formula of (2) is as follows:
vmax＝e0.13[1.4×10-4q23+12.0-h]1.6+4.0×10-4q23+7.0
and h is the wave height, and q is the relative wave direction and is used for representing the included angle between the driving direction of the ship and the wave direction.
In one embodiment of the invention, a multi-stage dynamic reverse-pushing method is adopted to carry out multi-stage decision making.
In an embodiment of the present invention, the obtaining the corresponding plurality of optimal waypoints includes:
calculating an evaluation function of the route points determined by multi-stage decision through an ideal point algorithm;
and selecting the waypoint with the minimum evaluation function value as the optimal waypoint.
Another embodiment of the present invention further provides a system for dynamically planning a global meteorological route of an unmanned ship, including:
the system comprises a discrete processing module, a route calculation module and a route calculation module, wherein the discrete processing module is used for acquiring an initial route and performing discrete processing on the initial route to obtain a plurality of initial waypoints, and the initial waypoints comprise a starting point, an end point and a plurality of intermediate waypoints positioned between the starting point and the end point;
the meteorological data acquisition module is used for acquiring real-time meteorological data in the running process of the ship;
the multi-stage decision module is used for carrying out multi-stage decision on the current waypoint running by utilizing the position decision of the initial waypoint and combining real-time meteorological data from the initial waypoint to a plurality of waypoints of the current waypoint to obtain a plurality of corresponding optimal waypoints;
and the dynamic planning module is used for dynamically planning the non-navigation route behind the current waypoint according to the optimal waypoints and the terminal point.
(III) advantageous effects
The invention has the beneficial effects that: according to the unmanned ship global meteorological air route dynamic planning method and system provided by the embodiment of the invention, on the basis of an initial air route, the influence of energy consumption and air time on the air route is comprehensively considered by combining the collected real-time meteorological data, and the position of an air route point on the initial air route is dynamically adjusted and planned in real time by adopting a segmented multi-stage decision mode, so that the dynamic optimization of the air route in the running process of a ship is realized.
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FIG. 1 is a flowchart of a method for dynamically planning a global meteorological route of an unmanned ship according to an embodiment of the present invention;
FIG. 2 is a global wind velocity profile for a historical period according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating wind direction calculation according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating step S130 of FIG. 1 according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an N-level decision process according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of waypoint selection using a multi-stage back-stepping method in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of a waypoint optimization procedure in an embodiment of the invention;
FIG. 8 is a schematic diagram of an ideal point method according to an embodiment of the present invention;
FIG. 9 is a flowchart of a process for dynamic route planning in accordance with an embodiment of the present invention;
fig. 10 is a schematic diagram of a dual target meteorological route optimization system of an unmanned ship according to another embodiment of the invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The following embodiments of the present invention provide a method for dynamically planning an unmanned ship global meteorological flight path, fig. 1 is a flowchart of the method for dynamically planning the unmanned ship global meteorological flight path according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
as shown in fig. 1, in step S110, an initial route is obtained, and discretization is performed on the initial route to obtain a plurality of initial waypoints, where the plurality of initial waypoints include a start point, an end point, and a plurality of intermediate waypoints located between the start point and the end point;
as shown in fig. 1, in step S120, real-time weather data is collected during the running of the ship;
as shown in fig. 1, in step S130, for a current waypoint on which the ship travels, a multi-stage decision is performed by using a position decision of an initial waypoint and combining real-time meteorological data when the initial waypoint reaches a plurality of waypoints of the current waypoint, so as to obtain a plurality of corresponding optimal waypoints;
as shown in fig. 1, in step S140, an unvyawed route after the current waypoint is dynamically planned according to the optimal waypoints and the end point.
In the technical scheme provided by the embodiment of the invention shown in fig. 1, on the basis of an initial route, the influence of energy consumption and time on the route is comprehensively considered by combining collected real-time meteorological data, and the position of a route point on the initial route is dynamically adjusted and planned in real time by adopting a segmented multi-stage decision mode, so that the dynamic optimization of the route in the running process of a ship is realized.
The specific implementation of the steps of the embodiment shown in fig. 1 is described in detail below:
in step S110, an initial route is obtained, and discretization is performed on the initial route to obtain a plurality of initial waypoints, where the plurality of initial waypoints include a start point, an end point, and a plurality of intermediate waypoints located between the start point and the end point.
In an embodiment of the present invention, the obtained initial route is a great circle route, and since the great circle route is the shortest route between two points on the earth, the great circle route set for the ship in this embodiment is the route with the shortest route between the starting point and the end point. The meteorological flight line design is carried out based on the great circle flight line, the flight line optimization can be realized on the basis that the total flight line is not changed greatly, the flight time and the energy consumption are reduced, the flight line is relatively short, when the meteorological conditions are changed, the great circle flight line is used as a reference, the initial flight line can be optimized, and a new flight line can be generated more quickly.
The weather air route dynamic optimization means that the unmanned ship navigates in the ocean, and in order to reduce the influence of adverse weather factors on navigation, the air route is dynamically adjusted according to real-time weather information, so that the ship always travels along the optimal air route. The dynamic optimization method of the air route fully considers the influence of the oceanographic information on the air route, dynamically optimizes the energy consumption and the navigation time of the ship, saves the ship cost and meets the actual navigation requirement. Therefore, the method for deeply researching the dynamic optimization of the meteorological air route of the unmanned ship has strong practical significance, can provide decision basis for navigation of the unmanned ship, and improves the navigation economy of the ship.
In step S120, real-time weather data is collected during the travel of the ship.
In one embodiment of the present invention, the real-time meteorological data collected in this step includes: wind direction angle, hull windward angle, wave height and boats and ships stall specifically as follows:
1) wind farm data processing
FIG. 2 is a global wind velocity profile of a historical time period according to an embodiment of the present invention, as shown in FIG. 2, wherein the horizontal axis is latitude, the vertical axis is longitude, the gray areas represent land, the lower color bar is a color bar with the number representing wind velocity in m · s-1In fig. 2, the wind speed in the ocean area is corresponding to the wind speed in the horizontal bar, and the wind field distribution in the whole ocean environment can be seen from fig. 2.
In the present embodiment, the direction of increasing longitude is taken as the positive direction of the horizontal axis, the direction of increasing latitude is taken as the positive direction of the vertical axis, the wind direction at a certain grid point in the wind field data is calculated by the wind direction data on the longitude and latitude components, and the calculation formula of the wind direction angle is:
Figure BDA0002166879010000071
wherein alpha is the wind direction angle, Lon is the wind direction value of the transverse wind, Lat is the wind direction value of the latitudinal wind, and the angle range of the wind direction angle is [ -pi, pi ] obtained according to the formula (1).
2) Calculation of the angle of attack of the hull
The wind direction measurement standard is the positive direction of the x axis, the measurement standard of the course C is the positive direction of the y axis, and the angle ranges of the wind direction measurement standard and the measurement standard are both [0, pi ]. Therefore, a uniform measurement reference and manner for the wind direction and the heading are needed.
First, the metric basis is changed:
Figure BDA0002166879010000072
whereby the reference of the wind direction becomes due north and the measurement range becomes due north
Figure BDA0002166879010000073
Second, the metric range is changed: α ═ rem (α + pi, pi), where the rem function is a trend, the function α ═ rem (α, pi) function acts on the remainder of α divided by pi in order for the angles to be no less than zero and no greater than 180 °, α ═ rem (α, pi) is:
Figure BDA0002166879010000074
the ship body wind angle β is an included angle between the ship course C and the wind direction α, a rectangular coordinate system is established, fig. 3 is a schematic diagram of wind direction calculation in an embodiment of the invention, as shown in fig. 3, the positive y-axis direction is set to be the positive north direction, and according to fig. 3, the calculation formula of the wind angle β is as follows:
β ═ 180 ° - | C- α | | | formula (3)
Wherein alpha is the wind direction angle, C is the ship course, and the angle range of the ship body windward angle is [0, pi ] obtained according to the formula (3).
3) Wave data processing
In the actual marine meteorological environment, wind and wave data can be respectively obtained, but the wind and wave data at the same moment can not be accurately obtained due to different data timeliness, and the wave height h is calculated by adopting the following formula in order to ensure the accuracy of the wave data:
Figure BDA0002166879010000081
wherein g is gravity acceleration, 9.8m/s is taken, F is wind zone length, the wind zone length F is sea area range with approximately same wind action state, q is relative wave direction, D is ship displacement, a1、a2、a3And a4 is a undetermined coefficient.
4) Ship stall data processing
The unmanned ship is influenced by meteorological and hydrological factors during the process of sailing, and further generates a stalling phenomenon, wherein the influence of wind and waves is particularly serious among various factors. The ship is influenced by wind and waves during navigation, the navigation resistance of the ship is far greater than the resistance of the ship in still water, and the phenomenon is called natural stall of the ship. Calculating the stall of the ship by adopting the following formula:
v＝v0-(1.08h-0.126qh+2.77vwindcosβ)(1-2.33Dv0) Formula (5)
Where v is the actual speed of the vessel, v0For hydrostatic navigational speed, v, of shipswindThe wind speed is h, the wave height is h, the windward angle of the ship body is beta, the displacement of the ship is D, and the relative wave direction is q. In order to simplify the calculation difficulty, the wave direction is assumed to be consistent with the wind direction, so that the relative wave direction q is consistent with the windward angle beta of the ship body.
In step S130, for the current waypoint on which the ship is traveling, a multi-stage decision is performed by using a position decision of the initial waypoint in combination with real-time weather data from the initial waypoint to a plurality of waypoints of the current waypoint, so as to obtain a plurality of corresponding optimal waypoints.
In an embodiment of the present invention, fig. 4 is a flowchart of performing a multi-stage decision by using a position decision of an initial waypoint and combining real-time weather data when the initial waypoint and a plurality of waypoints of the current waypoint are combined in step S130 in an embodiment of the present invention, as shown in fig. 4, which specifically includes the following steps:
in step S401, taking N waypoints including an initial point and an end point in the initial route as N decisions;
in step S402, dividing the initial route into a navigated route and an unvented route by taking the current waypoint as a boundary;
in step S403, the position of the waypoint in the non-voyage route is decided according to the state of the intermediate waypoint in the voyage route and the real-time weather data.
The process of dynamically planning the flight path in the embodiment is essentially a course of selecting waypoints, N flight path points are arranged between the starting point and the end point of one flight path, the course selection needs to be carried out for N times in the course of designing the flight path, namely N times of decision making, and the global flight path is regarded as two parts of a front k-segment sub flight path and a back N-k-segment sub flight path at the k +1 th section.
FIG. 5 is a schematic diagram of the N-level decision process in one embodiment of the present invention, as shown in FIG. 5, for the back sub-routes, x (k) can be regarded as x0And the first k initial decisions u0，u1，....， uk-1The initial state formed. The optimal strategy for multi-segment decision-making of routes therefore has the following properties: regardless of the initial state and the decision, the back-end decision must be the optimal strategy for the state formed by the initial decision.
The dynamic equation of the decision process in the above steps is:
x (k +1) ═ f [ x (k), u (k), k ] equation (6)
Where x (k +1) is the position decision of the (k +1) th waypoint of the N waypoints, u (k) is the real-time meteorological data of the kth waypoint, x (k) is the position decision of the selected waypoint, k is the k-segment self-course before the selected waypoint, and k ranges from k to 0, 1.
The evaluation function of the multi-stage decision in the above steps is:
Figure BDA0002166879010000091
wherein m is the number of the current waypoint, h [ x (k), m is the evaluation function of waypoint x (k).
In addition, the evaluation function h [ x (k), m ] in the formula (7) is calculated by the following formula:
Figure BDA0002166879010000092
wherein h [ x (k), m]As a dual objective function value, omega1And ω2Weight factors of energy consumption and time of flight, respectively, and omega1+ω2＝1，γ1And gamma2Is a conversion factor used for converting energy consumption and time into economic indexes of the evaluation route, N is the number of the constant direction lines on the initial route, v is the number of the constant direction lines on the initial routeiActual speed, v, of the ship being the i-th section of the constant direction linemaxCritical speed of the ship, SiIs the length of the i-th section of the constant direction line, TeIs the main engine thrust of the ship;
critical speed v of ship in equation (8)maxThe calculation formula of (2) is as follows:
vmax＝e0.13[1.4×10-4q23+12.0-h]1.6+4.0×10-4q23+7.0 equation (9)
And h is the wave height, and q is the relative wave direction and is used for representing the included angle between the driving direction of the ship and the wave direction.
It should be noted that, in this embodiment, a multi-stage dynamic reverse-pushing method is adopted to perform multi-stage decision making. Fig. 6 is a schematic diagram illustrating waypoint selection by using a multistage backward thrust method in an embodiment of the present invention, where each waypoint on which a course passes is a dynamic planning point as shown in fig. 6, and when a ship passes through the dynamic planning point, real-time weather information is acquired, and the course is re-planned. The A point is the current position of the ship and is the pointFor dynamic planning of points, assuming that the ship still needs to pass through N-k waypoints when going to the terminal S, the calculation is started from the terminal S, and the optimal waypoint position F is selectedN-1Then from FN-1Starting to search the next waypoint until waypoint F close to point Ak+1And connecting all route points, namely the dynamic optimal route. Based on the above, the problem of the global meteorological route dynamic planning of the unmanned ship is solved by adopting a multi-stage dynamic reverse pushing method.
When the unmanned ship moves to the dynamically planned route point i and selects the next route point j, the position of the original route point on the route is adjusted according to the real-time weather data, and the optimal route point is selected for driving. Fig. 7 is a schematic diagram of an optimization process of waypoints in the embodiment of the present invention, and as shown in fig. 7, longitude values and latitude values of initial waypoints are randomly disturbed within a specified certain range, and a specific adjustment rule is as follows: generating a random number rand1 within the range of 0-1, and judging whether the rand1 is larger than 0.5, if so, increasing the longitude of the current waypoint by a random number within the range of 0.25-1.25, and if not, increasing the longitude of the current waypoint by a random number within the range of 0.25-1.25; and simultaneously, generating a random value rand2 within the range of 0-1, and judging whether the rand2 is larger than 0.5, if so, increasing the latitude of the current waypoint by a random value within the range of 0.25-1.25, and if not, reducing the latitude of the current waypoint by a random value within the range of 0.25-1.25. And then, obtaining a plurality of new waypoints according to the longitude and the latitude after disturbance.
Finally, the step of obtaining a plurality of corresponding optimal waypoints specifically comprises: firstly, calculating an evaluation function of a route point determined by multi-stage decision through an ideal point algorithm; then, the waypoint having the smallest evaluation function value is selected as the optimal waypoint.
Based on the above, n new waypoints j are generated after disturbancenAll possible waypoints to select for the unmanned ship. And connecting the waypoint i with the n new adjusted waypoints to obtain n constant directional lines, sequentially calculating the energy consumption and time required by the ship to pass through each section of constant directional line, calculating an evaluation function value h through an ideal point algorithm, and selecting the waypoint with the minimum h after comparison, wherein the waypoint is the optimal waypoint.
The ideal point algorithm calculates an objective function value h, and the calculation formula of the objective function value h is as follows:
Figure RE-GDA0002250550920000111
wherein f isaAnd fbFor the abscissa and ordinate, ω, within the feasible region determined according to the ideal point algorithm1And ω2Weight factors of energy consumption and time of flight, respectively, and omega1+ω2＝1，γ1And gamma2Is a conversion factor used for converting energy consumption and time of flight into economic indexes for evaluating the flight line,
Figure BDA0002166879010000112
as a single objective function ZaThe minimum value of (a) is determined,
Figure BDA0002166879010000113
single objective function ZbA minimum value of (a), and faAnd fbSatisfies the following conditions:
Figure BDA0002166879010000114
the ideal point method is to construct an ideal point according to the prior information of a decision maker and search the closest feasible solution of the ideal point under the specified condition. FIG. 8 is a schematic diagram of an ideal point method in an embodiment of the present invention, as shown in FIG. 8, which has the geometrical meaning that an ideal point Z is first determined in the target space*Then, a feasible point F is searched in the feasible field F*Let F stand for*And Z*Is as minimal as possible, at which time F*Is the optimal solution. In the embodiment, the output power and the speed of the ship during navigation are constantly changed, and for simplifying the calculation, the assumption is made according to the weight value in the evaluation function formula h. If omega1≥ω2Setting the ship speed to be unchanged; if omega1<ω2Then the output power of the ship is setAnd is not changed. Is provided with ZaAs a value of the energy consumption function, ZbFor a function of the time of flight
Figure BDA0002166879010000115
Wherein the minimum values are respectively
Figure BDA0002166879010000116
And
Figure BDA0002166879010000117
for the construction of the evaluation function h, see equation (10) above, where faAnd Za *A numerical value, and fbAnd Zb *The values are not equal, Za *And Zb *As an optimal solution in the single objective function, it approaches infinitely in the dual objective functions.
Based on the above, the optimal energy consumption route optimal solution
Figure BDA0002166879010000121
The solving method comprises the following steps:
setting N-1 route points between a starting point and a target point, namely the whole route is composed of N constant direction lines, and on the route, assuming that the ship speed is constant, the optimal energy consumption optimization objective function of the unmanned ship is as follows:
Figure BDA0002166879010000122
in the formula, N is the number of the constant direction lines on the route, SiIs the length of the i-th section of the constant direction line, TeIs the thrust of the main engine.
Optimal solution for shortest flight path
Figure BDA0002166879010000123
The solving method comprises the following steps:
assuming that the output power of the ship main engine is constant, the optimal objective function of the unmanned ship in the shortest time is as follows:
Figure BDA0002166879010000124
wherein t is the total voyage time of the voyage, SiIs the length of the i-th section of the constant direction line, viThe actual navigational speed of the ship is the constant direction line of the ith section.
In one embodiment of the invention, the objective function Z is optimized for said energy consumptionaAnd said shortest time of flight objective function ZbAnd solving by adopting an intelligent water drop algorithm to obtain a minimum value.
The adjustment rule when the intelligent water drop algorithm optimizes the middle route point of the initial route is as follows:
assuming that the navigation time between two waypoints i and j is silt content soil (i, j) and the running speed of water drops is vel (i, j), determining an initial point and a target point, establishing a great circle route between the two points, and discretizing the great circle route to obtain N initial waypoints.
Fixing longitude value plon of initial route point, only disturbing its latitude value plat randomly within a certain range, starting from route point i, the next adjacent route point on the initial route is j, after disturbance generating n new route points, the water drop will tend to select the route with less silt content when selecting the route, expressing the probability that the water drop selects j as the next position at position i by p (i, j), which is in inverse proportion relation with silt content soil (i, j) of route (i, j), the node selection probability formula is:
Figure BDA0002166879010000125
Figure BDA0002166879010000131
wherein ε is a very small positive real number, the prevention denominator is 0, and
Figure BDA0002166879010000132
in the formula, k is the node of all possible choices of the water drop, and k is the [1, n ].
In step S140, an unlyawed route after the current waypoint is dynamically planned according to the optimal waypoints and the end point.
Based on the foregoing steps, in this step, dynamic planning is performed on each subsequent waypoint, fig. 9 is a flowchart of a process of performing dynamic planning on routes in an embodiment of the present invention, as shown in fig. 9, including:
in step S901, a large circular route is set between the starting and ending ports (i.e., the starting point and the ending point);
in step S902, acquiring initial lane data and real-time weather data;
in step S903, designing a first-aid route according to a route optimization criterion and an algorithm;
in step S904, after the ship sails, the first waypoint on the initial route becomes the dynamic planning point;
in step S905, when the dynamic planning point is reached, adjusting a strategy according to real-time meteorological data and a route;
in step S906, the position of the waypoint is adjusted by a dynamic optimization algorithm;
in step S907, a new course is obtained, when the ship reaches a dynamic planning point of the new course, the course is redesigned, and the like is performed until the terminal point is reached;
in step S908, a course is output, and a set of the constant direction lines that the ship passes after reaching the end point is a dynamically optimized global meteorological course.
The method of the present embodiment is used for dynamically planning routes by combining with historical meteorological data as follows:
the starting harbor and the target harbor established in this example were the Japanese lateral harbor (34 ° 40 'N, 140 ° E) and the United states Long beach harbor (34 ° 25' N, 120 ° W), respectively. And solving the dual-target route optimization model by using the wind field data of 3 months in 2010. And setting the number N of waypoints as 10 to obtain an initial waypoint table 1, and optimizing the dual-target route based on a multi-stage dynamic reverse-extrapolation method.
TABLE 1
Figure BDA0002166879010000141
The target simulated ship selected in the embodiment is a common container ship "long forest" wheel, and the main ship parameters of the target simulated ship are shown in table 2.
TABLE 2
Figure BDA0002166879010000142
According to the parameters in table 2, each parameter in the ship stall formula is solved by an iteration method, and the obtained stall formula of the ship is as follows:
v＝v0-(1.08h-0.126qh+2.77vwindcosβ)(1-2.33Dv0)
assuming that the energy consumption is equal to the weight value in the design of an aviation flight path during aviation, let omega1＝0.5，ω2＝0.5，γ1＝1， γ2＝1.725×108And then the unmanned ship meteorological course dual-target evaluation function formula h is as follows:
Figure BDA0002166879010000151
s.t.0<v≤vmax
N＞0
Si＞0
Te≥0
ω1+ω2＝1
suppose that a dual-target route model based on an ideal point method is adopted to solve in the embodiment, the longitude and latitude values of route points are shown in table 3, the route is used as an initial route for dynamic optimization, a route optimization model based on a multi-stage dynamic inverse method is adopted to solve the route, the longitude and latitude values of the route points are shown in table 4, and the operation result is shown in table 5.
TABLE 3
Figure BDA0002166879010000152
TABLE 4
Figure BDA0002166879010000153
TABLE 5
Figure BDA0002166879010000154
In order to verify the reliability of the established model, the operation result of the model needs to be compared and analyzed with the operation results of the single-target route model and the double-target route model, so that whether the model meets the design requirement of the dynamic optimization route is judged. In the embodiment, the operation results of the lowest energy consumption route model, the shortest time route model and the dual-target route model are respectively compared with the dynamic optimization route model.
The minimum energy consumption model and the shortest time model are solved by adopting an intelligent water drop algorithm, and parameters selected by the algorithm are shown in table 6. The longitude and latitude values of the waypoints obtained by the operation of the lowest energy consumption model and the shortest time model are respectively shown in tables 7 and 8, and the simulation results of the total voyage, the energy consumption and the voyage are respectively shown in table 9.
TABLE 6
Figure BDA0002166879010000161
TABLE 7
Figure BDA0002166879010000162
TABLE 8
Figure BDA0002166879010000163
TABLE 9
Figure BDA0002166879010000164
As can be seen from the results in table 9:
the total range of the great circle route is shortest, but the energy consumption value and the navigation time are the highest compared with other three routes;
the model of the lowest energy consumption flight path optimizes the energy consumption value most obviously and is 4.8 multiplied by 10 lower than that of a great circle flight path7kJ, but 3.14h higher in voyage;
the shortest route model has the best optimization on the navigation time, and the larger round route is 9.36h lower;
the optimization effect of the binocular standard route on energy consumption and time of flight is remarkable, and the two routes are respectively 3.53 multiplied by 10 lower than that of a larger round route7kJ and 5.78 h;
the dynamic optimization route adopts a dual-target route model, and the change of real-time meteorological information is considered, so that the energy consumption and the time value are respectively reduced by 1.082 multiplied by 10 compared with the dual-target route8kJ and 7.33 h.
Based on the above, it is shown that the model established by the embodiment has higher reliability and applicability.
In summary, the method provided by the embodiment of the present invention converts the dynamic optimization problem of the ship route into a multi-stage decision problem, establishes a recursive equation for dynamic planning of the ship route to make a sectional decision on the ship route, and dynamically plans a relevant model by using a discrete system to realize dynamic optimization of the ship route. The method comprises the steps of setting a dynamic planning point of a course, comprehensively considering the influence of energy consumption and flight time on the course according to real-time marine meteorological information and combining a course optimization criterion, carrying out real-time adjustment on the position of a waypoint on the original course, properly winding and changing the course, reducing the influence of adverse meteorological hydrological factors on the navigation of a ship, saving the energy consumption of the ship and the flight time, and designing the unmanned ship meteorological course.
Fig. 10 is a schematic diagram of a system for dynamically planning a global meteorological route of an unmanned ship according to another embodiment of the present invention, as shown in fig. 10, the system 1000 includes: a discrete processing module 1010, a meteorological data acquisition module 1020, a multi-level decision module 1030, and a dynamic planning module 1040.
The discrete processing module 1010 is configured to obtain an initial route, and perform discrete processing on the initial route to obtain a plurality of initial waypoints, where the plurality of initial waypoints include a start point, an end point, and a plurality of intermediate waypoints located between the start point and the end point; the meteorological data acquisition module 1020 is used for acquiring real-time meteorological data in the running process of the ship; the multi-stage decision module 1030 is configured to perform multi-stage decision on a current waypoint on which the ship runs by using a position decision of an initial waypoint and combining real-time meteorological data from the initial waypoint to a plurality of waypoints of the current waypoint to obtain a plurality of corresponding optimal waypoints; the dynamic planning module 1040 is configured to dynamically plan the non-navigation route after the current waypoint according to the plurality of optimal waypoints and the end point.
Since each functional module of the unmanned ship global meteorological flight line dynamic planning system of the exemplary embodiment of the present disclosure corresponds to the steps of the above-described exemplary embodiment of the unmanned ship global meteorological flight line dynamic planning method shown in fig. 1, for details that are not disclosed in the embodiment of the apparatus of the present disclosure, please refer to the above-described embodiment of the unmanned ship global meteorological flight line dynamic planning method of the present disclosure.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is only limited by the appended claims.

Claims (7)

1. A global meteorological route dynamic planning method for an unmanned ship is characterized by comprising the following steps:
obtaining an initial route, and performing discretization processing on the initial route to obtain a plurality of initial waypoints, wherein the plurality of initial waypoints comprise a starting point, an end point and a plurality of intermediate waypoints positioned between the starting point and the end point;
collecting real-time meteorological data in the running process of a ship;
for the current waypoint where the ship runs, performing multi-stage decision by using the position decision of the initial waypoint and combining real-time meteorological data from the initial waypoint to a plurality of waypoints of the current waypoint to obtain a plurality of corresponding optimal waypoints; wherein the performing of the multi-stage decision by using the position decision of the initial waypoint in combination with the real-time meteorological data when the initial waypoint reaches the plurality of waypoints of the current waypoint comprises:
taking N route points including an initial point and a terminal point in the initial route as N decisions;
dividing the initial route into a navigated route and an unvarnished route by taking the current waypoint as a boundary;
the position of the waypoint in the non-sailing route is decided according to the state of the middle waypoint in the sailing route and the real-time meteorological data;
the dynamic equation of the decision process is as follows:
x(k+1)＝f[x(k)，u(k)，k]
wherein x (k +1) is a position decision of a k +1 th waypoint of the N waypoints, u (k) is real-time meteorological data of the k-th waypoint, x (k) is a position decision of the selected waypoint, k is a k-segment self-course before the selected waypoint, and k is in a range of 0, 1.
The evaluation function of the multi-stage decision is as follows:
Figure FDA0003022586920000011
wherein m is the number of the current waypoint, h [ x (k), and m is the evaluation function of the waypoint x (k);
the calculation formula of the evaluation function h [ x (k), m ] is as follows:
Figure FDA0003022586920000021
Figure FDA0003022586920000022
wherein h [ x (k), m]As a dual objective function value, omega1And ω2Weight factors of energy consumption and time of flight, respectively, and omega1+ω2＝1，γ1And gamma2Is a conversion factor used for converting energy consumption and time of flight into economic indexes for evaluating the flight line,n is the number of the constant direction lines on the initial route, viActual speed, v, of the ship being the i-th section of the constant direction linemaxCritical speed of the ship, SiIs the length of the i-th section of the constant direction line, TeIs the main engine thrust of the ship;
and dynamically planning the non-navigation route behind the current waypoint according to the optimal waypoints and the terminal.
2. The method for dynamically planning global meteorological flight path of an unmanned ship according to claim 1, wherein the initial flight path is a great circle flight path, and the great circle flight path is the flight path with the shortest flight path from the starting point to the end point.
3. The method for dynamically planning a global meteorological envelope for an unmanned ship according to claim 1, wherein the real-time meteorological data comprises: wind direction angle, hull windward angle, wave height and ship stall.
4. The method for dynamically planning global meteorological flight path of an unmanned ship according to claim 1, wherein the critical speed v of the shipmaxThe calculation formula of (2) is as follows:
vmax＝e0.13[1.4×10-4q23+12.0-h]1.6+4.0×10-4q23+7.0
wherein h is the wave height, and q is the relative wave direction, and is used for showing the included angle between the ship driving direction and the wave direction.
5. The unmanned ship global meteorological flight path dynamic programming method of claim 4, wherein a multi-stage dynamic reverse thrust method is used for multi-stage decision making.
6. The method for dynamically planning a global meteorological envelope for an unmanned ship according to claim 5, wherein said obtaining a corresponding plurality of optimal waypoints comprises:
calculating an evaluation function of the route points determined by multi-stage decision through an ideal point algorithm;
and selecting the waypoint with the minimum evaluation function value as the optimal waypoint.
7. A global meteorological flight path dynamic planning system for unmanned ship is characterized by comprising:
the system comprises a discrete processing module, a route calculation module and a route calculation module, wherein the discrete processing module is used for acquiring an initial route and performing discrete processing on the initial route to obtain a plurality of initial waypoints, and the initial waypoints comprise a starting point, an end point and a plurality of intermediate waypoints positioned between the starting point and the end point;
the meteorological data acquisition module is used for acquiring real-time meteorological data in the running process of the ship;
the multi-stage decision module is used for carrying out multi-stage decision on the current waypoint running by utilizing the position decision of the initial waypoint and combining real-time meteorological data from the initial waypoint to a plurality of waypoints of the current waypoint to obtain a plurality of corresponding optimal waypoints; wherein the performing of the multi-stage decision by using the position decision of the initial waypoint in combination with the real-time meteorological data when the initial waypoint reaches the plurality of waypoints of the current waypoint comprises:
taking N route points including an initial point and a terminal point in the initial route as N decisions;
dividing the initial route into a navigated route and an unvarnished route by taking the current waypoint as a boundary;
the position of the waypoint in the non-sailing route is decided according to the state of the middle waypoint in the sailing route and the real-time meteorological data;
the dynamic equation of the decision process is as follows:
x(k+1)＝f[x(k)，u(k)，k]
wherein x (k +1) is a position decision of a k +1 th waypoint of the N waypoints, u (k) is real-time meteorological data of the k-th waypoint, x (k) is a position decision of the selected waypoint, k is a k-segment self-course before the selected waypoint, and k is in a range of 0, 1.
The evaluation function of the multi-stage decision is as follows:
Figure FDA0003022586920000031
wherein m is the number of the current waypoint, h [ x (k), and m is the evaluation function of the waypoint x (k);
the calculation formula of the evaluation function h [ x (k), m ] is as follows:
Figure FDA0003022586920000041
Figure FDA0003022586920000042
wherein h [ x (k), m]As a dual objective function value, omega1And ω2Weight factors of energy consumption and time of flight, respectively, and omega1+ω2＝1，γ1And gamma2Is a conversion factor used for converting energy consumption and time into economic indexes of the evaluation route, N is the number of the constant direction lines on the initial route, v is the number of the constant direction lines on the initial routeiActual speed, v, of the ship being the i-th section of the constant direction linemaxCritical speed of the ship, SiIs the length of the i-th section of the constant direction line, TeIs the main engine thrust of the ship;
and the dynamic planning module is used for dynamically planning the non-navigation route behind the current waypoint according to the optimal waypoints and the terminal.
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