CN112068564A - Intelligent ship control method based on economic navigation optimization - Google Patents
Intelligent ship control method based on economic navigation optimization Download PDFInfo
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Abstract
The invention discloses an intelligent ship control method based on economic navigation optimization, which relates to the technical field of ships and can determine the real-time host power and the real average speed of a ship during the navigation of the ship. The method takes the still water navigational speed as a third spatial dimension to realize the synchronous integrated optimization of the navigational speed and the airline, and particularly can determine the most economical navigational method on the basis of the shortest route according to different environmental conditions under the conditions of severe weather and complex geographic environment.
Description
Technical Field
The invention relates to the technical field of ships, in particular to an intelligent ship control method based on economic navigation optimization.
Background
In the operation process of an ocean vessel, the theoretical shortest path is a great circle route of the earth, however, the marine environment is complex and changeable, and how to make a decision in time according to the geography of the ocean and the meteorological environment in the sailing process so as to minimize the energy consumption and the time consumption of the sailing process of the vessel is an important problem in the sailing control of the vessel.
Compare in still water condition, the abominable stormy wave environment in the ocean can make boats and ships navigation resistance increase, and the navigational speed descends under the same power, in order to reach established navigational speed, needs to increase extra output to guarantee that boats and ships accurately reach the destination. However, in actual operation, there is a trade-off problem: if another route is selected to bypass the area with severe marine environment, the increase of oil consumption and the time cost caused by the increase of the route can be smaller than the condition of passing through severe sea conditions, thereby realizing the purpose of reducing the operating energy consumption by reasonably selecting a navigation route. Therefore, the economic sailing optimization problem of the ship can be defined as that on the basis of medium-long term weather forecast, the optimal sailing route and the optimal control mode are selected by evaluating the energy consumption level, the safety and the sailing time of possible sailing routes, taking the minimum voyage energy consumption as a target, restricting safe sailing behaviors and taking the sailing time as an auxiliary decision.
The rapid and reliable evaluation of the energy consumption level of the ship in the actual marine environment is the basis for developing economic sailing optimization, and the conventional evaluation method of the comprehensive energy consumption of the ship mainly comprises theoretical calculation and actual measurement analysis. The ship navigation performance under any working condition and environmental condition can be forecasted by theoretical calculation, but the time is consumed and uncertain factors are more; and the navigation data that actual measurement analysis can be collected is comparatively fixed, and acquires the degree of difficulty great, and the repetition data is more. Therefore, the existing method for evaluating the comprehensive energy consumption of the ship is difficult to meet the actual requirement.
Disclosure of Invention
The invention provides an intelligent ship control method based on economic sailing optimization aiming at the problems and the technical requirements, and the technical scheme of the invention is as follows:
an intelligent ship control method based on economic sailing optimization comprises the following steps:
carrying out gridding processing on a ship navigation area to obtain a plurality of regular grid unit areas and determining the coordinates of each grid unit area, wherein the navigation starting position and the navigation end position of the ship are respectively in the corresponding grid unit areas;
determining ship navigation state data during the ship navigation, and determining real-time sea condition data based on the ship navigation state data, wherein the ship navigation state data at least comprises real-time host power;
determining the real average navigational speed of the ship under the still water navigational speed corresponding to the power of the host according to the navigational state data and the real-time sea condition data of the ship;
when the ship is positioned in any nth grid unit area, calculating to obtain a navigation cost estimation value of an adjacent grid unit area according to the real-time host power and the real average navigation speed of the ship in the nth grid unit area, wherein the navigation cost estimation value is related to the coordinates of the adjacent grid unit area and the still water navigation speed of the ship;
and performing intelligent control planning according to the navigation cost estimated value of each adjacent grid unit area by using a three-dimensional A-x algorithm to obtain a result, and controlling the ship to adjust a navigation path and navigate to the (n +1) th grid unit area until the (n +1) th grid unit area is a navigation terminal position according to the planning result.
The method further comprises the following steps of calculating and obtaining a navigation cost estimated value of an adjacent grid unit area according to the real-time host power and the real average speed of the ship in the nth grid unit area, wherein the calculation comprises the following steps of:
determining the actual travel consumption between the adjacent grid cell areas and the navigation starting point position according to the actual travel consumption of the nth grid cell area, the real-time host power and the real average speed of the ship in the nth grid cell area and the navigation direction vector corresponding to the adjacent grid cell areas, wherein the navigation direction vector corresponding to the adjacent grid cell areas is the vector from the nth grid cell area to the adjacent grid cell areas;
determining the predicted travel consumption between the adjacent grid cell areas and the navigation terminal position according to the linear distance between the adjacent grid cell areas and the great circle route of the navigation terminal position, the predicted host power and the predicted average speed in each passing grid cell area;
the sum of the actual trip cost and the predicted trip cost for each adjacent grid cell area is determined as a voyage cost estimate for the adjacent grid cell area.
The further technical scheme is that the actual travel consumption between the adjacent grid cell areas and the navigation starting point position is determined, and the method comprises the following steps:
g(n+1)＝g(n)+Δg；
wherein the content of the first and second substances,
and is
Representing the navigation direction vector corresponding to the adjacent grid cell area, g (n +1) is the actual travel cost between the adjacent grid cell area and the navigation starting point position, g (n) is the actual travel cost between the nth grid cell area and the navigation starting point position, (x)n,yn) Is the coordinate of the nth grid cell area, (x)n+1,yn+1) Is the coordinates of the adjacent grid cell area, P (n) is the real-time host power of the ship in the nth grid cell area, VS(n) is the true mean speed of the vessel when in the nth grid cell area.
The further technical scheme is that the method for determining the predicted travel consumption between the adjacent grid cell areas and the navigation terminal position comprises the following steps:
where h (n +1) is the expected travel cost between the adjacent grid cell area and the navigation terminal location, (x)n+1,yn+1) Is the coordinate of the adjacent grid cell area, (x)d,yd) Is the coordinates of the grid cell area where the navigation end position is located,
p (i) is the predicted host power V in the ith grid cell area on the great circle route from the adjacent grid cell area to the navigation terminal positionSw(i) The estimated average speed is from the adjacent grid cell area to the ith grid cell area on the great circle route at the navigation terminal position, and the Nth grid cell area is the grid cell area at the navigation terminal position.
The further technical scheme is that the method for determining the real average navigational speed of the ship under the still water navigational speed corresponding to the power of the host machine according to the navigational state data and the real-time sea state data of the ship comprises the following steps:
determining the corresponding actual wave speed of the ship under the influence of the real-time sea condition under the still water speed corresponding to the host power according to the host power and the real-time sea condition data in the ship navigation state data;
and obtaining the corresponding real average navigational speed of the ship under the influence of the control motion in the current grid unit area based on the actual navigational speed of the wind waves.
The further technical scheme is that the method for obtaining the corresponding real average navigational speed of the ship when the ship is influenced by the manipulation motion in the current grid unit area based on the actual navigational speed of the wind waves comprises the following steps:
determining a rotation diameter corresponding to the actual navigational speed of the storm and a stable navigational speed of the maneuvering motion in a predetermined corresponding relationship;
according to formula VS＝wVS+(1-w)VSwDetermining a true average speed;
wherein, VSIs the true average speed, VSThe stable navigational speed of the controlled movement corresponding to the actual navigational speed of the wind waves, VSwIs the actual speed of the wind wave,
and d isThe turning diameter corresponding to the actual navigational speed of the storm is L, and the linear distance between the driving-in position and the driving-out position of the ship in the grid unit area where the ship is located at present is L.
The further technical scheme is that the corresponding actual wave speed of the ship under the influence of the real-time sea condition under the still water speed corresponding to the host power is determined according to the host power and the real-time sea condition data in the ship navigation state data, and the method comprises the following steps:
calculating in real time according to the host power at the current moment and the real-time sea condition data to obtain wave resistance increase and determine a corresponding stall coefficient;
and calculating the actual speed of the storm under the still water speed corresponding to the power of the main engine according to the stall coefficient.
The further technical scheme is that the method for determining the actual speed of the wind wave when the ship is influenced by the real-time sea condition under the still water speed corresponding to the host power comprises the following steps of:
inputting the host power and the real-time sea condition data in the ship navigation state data into an offline forecasting model to obtain the actual speed of the storm;
the method further comprises:
acquiring sample navigation data of a ship in a ship navigation area, wherein the sample navigation data comprises sampled host power sampling data and sampled sea state sampling data;
calculating by using the sample navigation data to obtain wave resistance increase and determine a corresponding stall coefficient, and determining the actual wave speed corresponding to the sample navigation data based on the determined stall coefficient;
and obtaining an offline forecasting model by utilizing the sample navigation data and the corresponding actual wind wave navigation speed training through a neural network fitting method.
The further technical scheme is that the method for determining the real-time sea condition data based on the ship navigation state data comprises the following steps:
acquiring marine weather forecast information of a ship navigation area, wherein the marine weather forecast information at least comprises wave height, wave direction, wave period, wind speed and wind direction;
and interpolating the acquired oceanographic forecast information to obtain real-time sea condition data at the current moment, wherein the real-time sea condition data at least comprises wave height, wave period, wind speed and a wave direction angle and a wind direction angle which are determined according to the acquired wind direction and wave direction and the course and rudder angle in the ship navigation state data and are relative to the ship.
The beneficial technical effects of the invention are as follows:
the application discloses an intelligent ship control method based on economic sailing optimization, which provides a comprehensive sailing energy consumption assessment idea combining theoretical calculation and actual measurement analysis, can improve the confidence coefficient of rapid forecast by fusing actual measurement data and simulation calculation data, realizes the energy consumption assessment of comprehensive sailing by wave resistance-increasing calculation and control motion simulation, expands an A algorithm to a three-dimensional space, realizes the synchronous integrated optimization of the speed and the route by taking the hydrostatic speed as a third spatial dimension, and can determine the most economic sailing method on the basis of the shortest route according to different environmental conditions, especially under the conditions of severe weather and complex geographic environments. In addition, the method provides an off-line calculation mode of wave resistance increase, and trains an off-line forecasting model by using a neural network fitting method, so that the problems of long time consumption and slow response of real-time calculation are solved.
Drawings
Fig. 1 is a flowchart of a method for calculating a true average speed of a ship in the intelligent ship control method according to the present application.
Fig. 2 is a data flow diagram of the navigation cost estimation value of each adjacent grid cell region obtained in the ship intelligent control method of the present application.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
The application discloses an intelligent ship control method based on economic navigation optimization, which comprises the following steps:
firstly, preamble preparation and gridding processing.
And gridding the ship navigation area to obtain a plurality of regular grid unit areas, determining the coordinates of each grid unit area, and respectively arranging the navigation starting position and the navigation ending position of the ship in the corresponding grid unit areas. In the present application, the coordinates of the center position of each grid cell region are taken as the coordinates of the grid cell region.
And secondly, acquiring ship navigation state data and real-time sea condition data.
Vessel voyage status data is determined during vessel voyage and real-time sea state data is determined based on the vessel voyage status data. The ship navigation state data at least comprise real-time host power, and the real-time host power of the ship is a function of the still water navigation speed and is related to the still water navigation speed and can be obtained through navigation speed and power curve interpolation. The vessel navigation state data in the application also comprises course, rudder angle, navigation position, navigation time and the like.
The specific method for acquiring the real-time sea condition data comprises the following steps: the ship acquires the marine weather forecast information of the ship navigation area, the marine weather forecast information at least comprises wave height, wave direction, wave period, wind speed and wind direction, and the marine weather forecast information is acquired in two modes: from a weather forecasting centre or from an onboard local database. After the oceanographic forecast information is obtained, interpolation is carried out in combination with the current moment, and the wind direction angle and the wave direction angle of wind and wave relative to the ship are determined according to the obtained wind direction and wave direction and the course and rudder angle in the ship navigation state data, so that the real-time sea condition data of the current moment can be obtained, wherein the real-time sea condition data at least comprise wave height, wave period, wind speed and the determined wave direction angle and wind direction angle. The application does not describe the specific calculation mode of calculating the relative wind direction angle from the wind direction and calculating the relative wave direction angle from the wave direction.
In this step, ship type information is actually acquired, the ship type information includes ship geometry and model test results, the model test results include open water test results and resistance test results, and the ship type data are predetermined at the time of ship design.
And thirdly, calculating the true average speed of the ship, wherein the calculation flow of the true average speed is shown in figure 1.
Determining the true average navigational speed V of the ship under the still water navigational speed corresponding to the host power according to the ship navigational state data and the real-time sea condition dataSTrue mean speed V in the present applicationSHydrostatic navigational speed V corresponding to the power of the main machine0The corresponding navigational speed under the influence of real-time sea conditions and maneuvering motions, so the method of the application is as follows:
1. determining the static water navigational speed V corresponding to the host power of the ship according to the host power and the real-time sea condition data in the ship navigation state data0The corresponding actual wave speed V under the influence of real-time sea conditionsSwThis step provides two implementations:
(1) a real-time online computing mode:
according to the host power at the current moment and the real-time sea condition data, wave resistance increase is obtained through real-time calculation, a corresponding stall coefficient is determined, and then the actual wave speed under the still water speed corresponding to the host power is obtained through calculation according to the stall coefficient. The wave drag and stall coefficient comprises drag generated by ship motion in waves and drag caused by ship reflected waves, different conditions of the ship moving in regular waves and irregular waves are considered, ship type data can be used in the calculation process, a specific calculation mode is the prior art, and detailed description is omitted in the application.
(2) An off-line calculation mode:
inputting the host power and the real-time sea condition data in the ship navigation state data into an offline forecasting model to obtain the actual wind wave speed VSw。
In this case, training of the off-line forecasting model is first required, specifically: acquiring sample navigation data of a ship in a ship navigation area, wherein the sample navigation data comprises sampled host power sampling data and sampled sea state sampling data; calculating by using the sample navigation data to obtain wave resistance increasing and determine a corresponding stall coefficient, determining the actual wave navigation speed corresponding to the sample navigation data based on the determined stall coefficient, and training by using the sample navigation data and the actual wave navigation speed corresponding to the sample navigation data by using a neural network fitting method to obtain an offline forecasting model.
In the model training process, the acquisition of the sample navigation data of the ship in the ship navigation area is as follows: the method comprises the steps of determining upper and lower limits of sampling parameters according to statistic analysis of weather and meteorological data of a ship navigation area, obtaining first sampling data through Latin hypercube sampling, wherein the first sampling data comprise wave height, wave period, wave direction angle and host power, obtaining second sampling data through mapping the first sampling data through independent and distributed random mapping, the second sampling data comprise wind speed and wind direction angle, and the first sampling data and the second sampling data form sample navigation data, so that the sample navigation data actually comprise host power and sea state data.
2. Actual navigational speed V based on wind wavesSwObtaining the corresponding real average navigational speed V when the ship is influenced by the control motion in the grid unit area where the ship is locatedSSpecifically, the method comprises the following steps:
in the present application, the navigation process within each grid cell area is specified as follows: the course of the ship entering the grid unit area is the ship course of the area, the course of the ship leaving the area is the course of the next grid unit area, and all course adjustments of the ship are completed in one grid unit area.
The influence of the control movement is mainly reflected on the reduction of the navigational speed, so that the energy consumption of the navigation process is increased, and the actual navigational speed V relative to the storm is determined in the preset corresponding relation according to the regulation on the navigation processSwCorresponding diameter of revolution dAnd steering the motion-stabilized navigational speed VSThe predetermined relationship is obtained by fitting in advance by methods such as sampling and interpolation. Then according to the formula
And calculating the proportion of the control motion in the whole regional course L, wherein the regional course L is the linear distance between the entering position and the exiting position of the ship in the current grid unit region. Further according toFormula VS＝wVS+(1-w)VSwDetermining a true average speed V affected by an operationS。
Fourthly, comprehensive navigation energy consumption evaluation and integrated optimization of the navigation speed and the path are performed based on the obtained data, and please refer to fig. 2 for an information flow schematic diagram:
the method comprises the following steps that A, an algorithm and a Dijkstra algorithm are the most commonly used grid-based path optimization algorithm, the Dijkstra algorithm is the most classical graph theory-based shortest path planning algorithm, the A algorithm can be regarded as the development of Dijikstra algorithm, the efficiency of shortest path search is improved by introducing greedy attributes, the method is realized on the basis of the A algorithm, the conventional A algorithm calculates the priority of each node through f (n) (g (n)) + h (n), and accordingly value evaluation criteria of different paths are determined, wherein f (n) is the comprehensive priority of the nth grid unit area and is called an evaluation function; g (n) is the actual travel cost from the navigation starting point position to the nth grid unit area, and is called a cost function; h (n) is the predicted travel cost from the nth grid cell area to the navigation terminal position, and is called a heuristic function. However, the application improves the conventional a-algorithm, the a-algorithm is expanded from the conventional two-dimensional plane to the three-dimensional space to form the three-dimensional a-algorithm, the hydrostatic navigational speed is taken as the third spatial dimension except the two-dimensional plane corresponding to the coordinates, and the synchronous optimization of the navigational speed and the path is realized by assuming that the ship can move freely in the three-dimensional space, and the method comprises the following steps:
defining a direction which can be searched on each grid unit area, determining the number of the road-seeking directions according to the size of grid division, and when the ship is positioned in any nth grid unit area, for each adjacent grid unit area adjacent to the nth grid unit area in the road-seeking direction, according to the real-time host power P (n) of the ship in the nth grid unit area and the real average speed VS(n) calculating to obtain a navigation cost estimation value f (n +1) of the adjacent grid unit area, and the navigation cost estimation value f (n +1) and the coordinate (x) of the adjacent grid unit arean+1,yn+1) And the still water speed of the vessel. The concrete description is as follows:
(1) according to the actual travel cost g (n) of the nth grid unit area, the real-time host power P (n) of the ship in the nth grid unit area and the real average speed VS(n) and navigation direction vectors corresponding to adjacent grid cell regions
Determining the actual travel cost g (n +1) between the adjacent grid cell area and the navigation starting point position, and determining the navigation direction vector corresponding to the adjacent grid cell area
Is the vector from the nth grid cell region to the adjacent grid cell region. Namely, it is determined that:
g(n+1)＝g(n)+Δg；
wherein the content of the first and second substances,
Hw(n) is the true sea state influence coefficient of the nth grid cell area,
(xn,yn) Is the coordinate of the nth grid cell area, (x)n+1,yn+1) Is the coordinates of the adjacent grid cell area.
(2) Determining the predicted travel consumption h (n +1) between the adjacent grid cell area and the navigation terminal position according to the linear distance between the adjacent grid cell area and the great circle route of the navigation terminal position, the predicted host power and the predicted average speed in each passing grid cell area, namely determining:
wherein (x)n+1,yn+1) Is the coordinate of the adjacent grid cell area, (x)d,yd) Is the coordinates of the grid cell area where the navigation end position is located,
namely the straight line distance from the adjacent grid cell area to the great circle route at the navigation terminal position.
(3) After the actual trip cost g (n +1) and the predicted trip cost h (n +1) of each adjacent grid cell area are obtained, the sum of the two is the estimated navigation cost f (n +1) ═ g (n +1) + h (n +1) of the adjacent grid cell area.
According to the calculation process, the navigation cost estimation value of each adjacent grid unit area can be calculated, then intelligent control planning is carried out according to the navigation cost estimation value of each adjacent grid unit area by utilizing a three-dimensional A-x algorithm to obtain a planning result, and the ship is controlled to adjust the navigation path and the hydrostatic navigation speed according to the planning result to navigate to the (n +1) th grid unit area until the (n +1) th grid unit area is the navigation end point. In the iterative process, each iteration step selects an adjacent grid unit region with the minimum f (n +1) from the searched adjacent grid unit regions as an n + 1-th grid unit region, and the selected adjacent grid unit region is used as a starting point of the next search, that is, the operation planning process is repeatedly executed under the condition that n is equal to n +1 until the navigation end point position is reached, then the actual stroke consumption g (n +1) of the adjacent grid unit region is the actual stroke consumption of the n + 1-th grid unit region in the next cycle, and the actual stroke consumption of the first grid unit region, that is, the navigation starting point position, can be recorded as 0.
In the above process, it can be seen that the real sea state influence coefficient H is the same as the hydrostatic navigational speed, the main engine rotational speed and the main engine power changew(n) and average sea-state influence coefficient
The influence of the specified hydrostatic navigational speed is considered in the sea condition influence coefficient, and is further considered in the navigational cost estimation value, so that the synchronous integrated optimization of navigational speed and path is realized, and the navigational speed optimization, namely the optimization of the specified hydrostatic navigational speed, is equivalent to the optimization of the rotational speed and the power of the main engine.
Compared with the existing gridding navigation optimization method, the method has the advantages that: the method can realize rapid forecast with high confidence level, and realize energy consumption evaluation of comprehensive navigation through wave resistance-increasing calculation and control motion simulation. The existing gridding navigation optimization method firstly carries out discretization on marine geographic environment to form a discrete numerical chart, then generates a navigation line by connecting the central point of each grid, separately solves two problems of path optimization and navigation speed optimization, has certain expansibility by coupling in an iteration mode, and increases the calculation amount of the optimization problem. Meanwhile, the conventional energy consumption evaluation modeling and navigation optimization method mostly adopts a single forecasting model and separately considers the path optimization and the navigation speed optimization, so that the conventional method cannot realize the rapid forecasting of high confidence level and cannot take the influence of the change of the navigation speed on the path optimization into consideration. In addition, the method and the device realize synchronous optimization of the air route and the air speed on the basis of gridding navigation optimization, and particularly can determine the most economical navigation method on the basis of the shortest path according to different environmental conditions under the conditions of severe weather and complex geographic environment.
In addition, the method provided by the application has certain expandability, the algorithm can be combined with a shipborne energy consumption monitoring hardware system, and meanwhile, a real-time meteorological environment data input interface is added, so that real-time economic navigation optimization guidance is realized. In addition, the invention can also be combined with energy-saving equipment such as a wind power boosting rotor, a wind sail and the like depending on the marine meteorological environment, so that the optimal route selection and equipment regulation strategy is provided.
Taking the problem of optimizing the navigation of a certain ultra-large oil tanker on a pacific sea route as an example, compared with the prior art, the maximum 10% comprehensive energy consumption reduction can be obtained by adopting the technology of the invention, the economic benefit of the ship navigation is obviously improved, the comparison data is shown in the following table, the route 0 is data under the non-optimized condition, the route 1 is data under the optimized condition of the prior art, and the route 2 is data under the optimized condition by adopting the method, so that the intelligent control method of the ship can effectively reduce the navigation energy consumption:
| air route | Voyage energy consumption | Time of flight | Number of flights | Environmental impact | Speed of flight optimization |
| Route 0 | 100％ | 31.02 days | 185 | Irrespective of whether | |
| Route | |||||
| 1 | 98.25％ | 30.9 days | 196 | Irrespective of whether | Constant |
| Route 2 | 89.28％ | 33.58 | 186 | Consider that | Variations in |
What has been described above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiment. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.
Claims (9)
1. An intelligent ship control method based on economic sailing optimization is characterized by comprising the following steps:
carrying out gridding processing on a ship navigation area to obtain a plurality of regular grid unit areas and determining the coordinates of each grid unit area, wherein the navigation starting position and the navigation end position of the ship are respectively in the corresponding grid unit areas;
determining ship navigation state data during ship navigation, and determining real-time sea state data based on the ship navigation state data, wherein the ship navigation state data at least comprises real-time host power;
determining the real average navigational speed of the ship under the still water navigational speed corresponding to the host power according to the ship navigational state data and the real-time sea condition data;
when the ship is positioned in any nth grid unit area, calculating to obtain a navigation cost estimation value of an adjacent grid unit area according to the real-time host power and the real average speed of the ship in the nth grid unit area, wherein the navigation cost estimation value is related to the coordinates of the adjacent grid unit area and the still water speed of the ship;
and performing intelligent control planning according to the navigation cost estimated value of each adjacent grid unit area by using a three-dimensional A-x algorithm to obtain a result, and controlling the ship to adjust the navigation path and the still water navigation speed to navigate according to the planning result until the (n +1) th grid unit area is the navigation terminal position.
2. The intelligent ship control method according to claim 1, wherein the calculating of the estimated navigation cost values of the adjacent grid cell areas according to the real-time host power and the real average speed of the ship in the nth grid cell area comprises, for each adjacent grid cell area:
determining the actual travel cost between the adjacent grid cell area and the navigation starting point position according to the actual travel cost of the nth grid cell area, the real-time host power and the real average speed of the ship in the nth grid cell area, and the navigation direction vector corresponding to the adjacent grid cell area, wherein the navigation direction vector corresponding to the adjacent grid cell area is the vector from the nth grid cell area to the adjacent grid cell area;
determining the predicted travel consumption between the adjacent grid cell areas and the navigation terminal position according to the linear distance from the adjacent grid cell areas to the great circle route of the navigation terminal position, the predicted host power and the predicted average speed in each passing grid cell area;
determining the sum of the actual travel cost and the predicted travel cost of each adjacent grid cell area as the navigation cost estimation value of the adjacent grid cell area.
3. The intelligent vessel maneuvering method according to claim 2, wherein the determining an actual trip cost between the adjacent grid cell area and the sailing start location comprises determining:
g(n+1)＝g(n)+Δg；
wherein the content of the first and second substances,
and is
Representing a navigation direction vector corresponding to the adjacent grid cell region, g (n +1) being an actual trip cost between the adjacent grid cell region and the navigation start point position, g (n) being an actual trip cost between the nth grid cell region and the navigation start point position, (x)n,yn) Is the coordinates of the nth grid cell area, (x)n+1,yn+1) Is the coordinates of said adjacent grid cell area, P (n) is the real-time host power of the vessel while in said nth grid cell area, VS(n) is the true mean speed of the vessel when within the nth grid cell area.
4. The intelligent vessel maneuvering method of claim 2, wherein the determining an expected trip cost between the adjacent grid cell area and the voyage terminal location comprises determining:
wherein h (n +1) is the projected travel cost between the adjacent grid cell area and the voyage terminal location, (x)n+1,yn+1) Is the adjacent grid cell areaCoordinates of the Domain, (x)d,yd) Is the coordinates of the grid cell area where the navigation end position is located,
p (i) is the predicted host power, V, in the ith grid cell area on the great circle route from the adjacent grid cell area to the navigation terminal positionSw(i) The estimated average speed is from the adjacent grid cell area to the ith grid cell area on the great circle route of the navigation terminal position, and the Nth grid cell area is the grid cell area where the navigation terminal position is located.
5. The intelligent vessel control method according to any one of claims 1 to 4, wherein the determining the true average speed of the vessel at the still water speed corresponding to the host power according to the vessel voyage state data and the real-time sea state data comprises:
determining the actual wave speed of the ship under the influence of the real-time sea condition under the still water speed corresponding to the host power according to the host power and the real-time sea condition data in the ship navigation state data;
and obtaining the corresponding real average navigational speed of the ship under the influence of the control motion in the current grid unit area based on the actual navigational speed of the wind waves.
6. The intelligent control method for the ship according to claim 5, wherein the obtaining the true average speed of the ship when the ship is affected by the maneuvering motion in the grid cell area at present based on the actual speed of the storm comprises:
determining a slewing diameter corresponding to the actual navigational speed of the storm and a stable navigational speed of the maneuvering motion in a predetermined corresponding relationship;
according to formula VS＝wVS+(1-w)VSwDetermining the true average speed;
wherein, VSIs the true average speed of the ship，VSThe stable navigational speed, V, of the control movement corresponding to the actual navigational speed of the wind wavesSwIs the actual speed of the wave,
and d isAnd L is the linear distance between the driving-in position and the driving-out position of the ship in the grid unit area where the ship is currently located.
7. The intelligent ship control method according to claim 5, wherein the determining, according to the host power and the real-time sea state data in the ship navigation state data, the actual speed of the ship due to the wind waves when the ship is affected by the real-time sea state at the still water speed corresponding to the host power comprises:
calculating in real time according to the host power at the current moment and the real-time sea condition data to obtain wave resistance increase and determine a corresponding stall coefficient;
and calculating the actual sailing speed of the storm at the still water sailing speed corresponding to the host power according to the stall coefficient.
8. The intelligent ship control method according to claim 5, wherein the determining, according to the host power and the real-time sea state data in the ship navigation state data, the actual speed of the ship due to the wind waves when the ship is affected by the real-time sea state at the still water speed corresponding to the host power comprises:
inputting the host power and the real-time sea condition data in the ship navigation state data into an off-line forecasting model to obtain the actual speed of the wind waves;
the method further comprises:
acquiring sample navigation data of a ship in the ship navigation area, wherein the sample navigation data comprises sampled host power sampling data and sampled sea state sampling data;
calculating by using the sample navigation data to obtain wave resistance increase and determine a corresponding stall coefficient, and determining the actual wave speed corresponding to the sample navigation data based on the determined stall coefficient;
and training by utilizing the sample navigation data and the corresponding actual wind wave navigation speed through a neural network fitting method to obtain the offline forecasting model.
9. The intelligent vessel maneuvering method according to claim 1, wherein the determining real-time sea state data based on the vessel voyage state data comprises:
acquiring marine weather forecast information of a ship navigation area, wherein the marine weather forecast information at least comprises wave height, wave direction, wave period, wind speed and wind direction;
and interpolating the acquired oceanographic forecast information to obtain the real-time sea condition data at the current moment, wherein the real-time sea condition data at least comprises wave height, wave period, wind speed and a wave direction angle and a wind direction angle which are determined according to the acquired wind direction and wave direction and the course and rudder angle in the ship navigation state data and are relative to the ship.
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