CN112699497A - Method and system for establishing multi-target joint optimization model of air route speed - Google Patents
Method and system for establishing multi-target joint optimization model of air route speed Download PDFInfo
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Abstract
The invention provides a method and a system for establishing a multi-target joint optimization model of a course speed, which comprises the following steps: establishing a ship database; establishing a ship control model based on a ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control instruction; establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and a ship control model to obtain a target data set; wherein, one ship type corresponds to a plurality of route data; and taking the target data set as a training set, and training a preset intelligent forecasting model by using a machine learning method to obtain a multi-target combined optimization model of the air route speed. The invention solves the technical problems of low optimization accuracy and poor optimization effect of the ship navigation task planning in the prior art.
Description
Technical Field
The invention relates to the technical field of ship navigation task planning, in particular to a method and a system for establishing a multi-target joint optimization model of a course speed.
Background
The course and speed of the ship are the main factors influencing the energy consumption of the ship, and the course and speed are highly coupled. However, in the prior art, the ship navigation task planning is often performed separately, and the influences of the marine environment, the terrain, the ship maneuvering capacity and the like cannot be fully considered, so that the technical problems of low optimization accuracy and poor optimization effect of the ship navigation task planning are caused.
Disclosure of Invention
In view of the above, the present invention aims to provide a method and a system for establishing a multi-objective combined optimization model of a ship route speed, so as to alleviate the technical problems of low optimization accuracy and poor optimization effect on ship navigation task planning in the prior art.
In a first aspect, an embodiment of the present invention provides a method for establishing a multi-objective joint optimization model of a course speed, including: establishing a ship database; the ship database comprises ship type data, a cruise draft-resistance table, a wave resistance-increasing transfer function table, a wind resistance coefficient table, a propeller thrust torque coefficient table, a turbine steering engine performance parameter table and a diving resistance-increasing coefficient table under still water; establishing a ship control model based on the ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control instruction; establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and the ship control model to obtain a target data set; wherein, a ship type corresponds a plurality of airline data, and every airline data includes: the method comprises the following steps of (1) course, navigational speed, water depth, ocean environment parameters, ship control instructions, total navigation energy consumption and total navigation time; and taking the target data set as a training set, and training a preset intelligent forecast model by using a machine learning method to obtain a multi-target joint optimization model of the air route speed.
Further, based on the vessel database, building a vessel maneuvering model, comprising: establishing an initial relation model between thrust and torque required by ship navigation and the rotating speed and rudder angle of a ship propeller based on the ship database; and establishing the ship control model based on the initial relation model and the relation between the ship propeller rotating speed, the rudder angle and the ship control command.
Further, the initial relationship model includes: t isp＝ρn2D2KT(V，n)，NR＝-(1+αH)xRFNcos δ; wherein the content of the first and second substances,
Tpthrust required for sailing a ship, NRThe torque required by ship navigation, n is the rotation speed of ship propeller, delta is rudder angle of ship, KT(V, n) is the thrust coefficient of the ship propeller, V is the ship speed, rho is the density of water, D is the diameter of the propeller, alphaHIs the hydrodynamic proportionality coefficient of the rudder, λ is the spanwise ratio of the rudder, xRDistance of steering gear to center of gravity of ship, FNAs an intermediate parameter, ARTo the inflow area, αRIs the inflow angle.
Further, based on a preset ship AIS database and the ship control model, establishing data sets of different ship types corresponding to different ship route data to obtain a target data set, wherein the data sets comprise: extracting routes corresponding to different ship types from the preset ship AIS database; one ship type corresponds to a plurality of routes; carrying out segmentation processing on each air route corresponding to different ship types, and setting initial air route data for each air route section; optimizing the initial route data based on the ship control model and a preset optimization threshold value to obtain route data of each route corresponding to different ship types; and taking the route data of each route corresponding to different ship types as the target data set.
Further, training a preset intelligent forecasting model by using a machine learning method to obtain a multi-target combined optimization model of the air route speed, comprising the following steps: training a preset intelligent forecasting model by using a machine learning method to obtain the trained intelligent forecasting model; the method of machine learning includes any one of: deep neural network algorithm, reinforcement learning algorithm; the independent variables of the preset intelligent forecasting model comprise: ship type, starting point of course, marine environmental parameters; the preset dependent variable of the intelligent forecasting model comprises the following steps: air route, speed, distance, time, command and energy consumption; setting a preset optimization algorithm on the trained intelligent forecasting model to obtain a multi-target combined optimization model of the air route and the air speed; the multi-target combined optimization model of the route speed is a model for determining the optimized route speed based on a target optimizing target; the target optimization objective comprises at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable.
Further, after obtaining the multi-objective joint optimization model of the route speed, the method further comprises the following steps: acquiring initial parameters of a target ship in a navigation process; the initial parameters include: the ship type of the target ship, the starting point of a route and marine environment parameters of the target ship in the navigation process; acquiring a target optimizing target of the target ship in the navigation process; the target optimization objective comprises at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable; and optimizing the navigational speed course of the target ship by utilizing the course navigational speed multi-target combined optimization model based on the initial parameters and the target optimizing target to obtain the optimized navigational speed course.
In a second aspect, an embodiment of the present invention further provides a system for establishing a multi-objective joint optimization model of a course speed, including: the system comprises a first establishing module, a second establishing module, a third establishing module and a training module, wherein the first establishing module is used for establishing a ship database; the ship database comprises ship type data, a cruise draft-resistance table, a wave resistance-increasing transfer function table, a wind resistance coefficient table, a propeller thrust torque coefficient table, a turbine steering engine performance parameter table and a diving resistance-increasing coefficient table under still water; the second establishing module is used for establishing a ship control model based on the ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control instruction; the third establishing module is used for establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and the ship control model to obtain a target data set; wherein, a ship type corresponds a plurality of airline data, and every airline data includes: the method comprises the following steps of (1) course, navigational speed, water depth, ocean environment parameters, ship control instructions, total navigation energy consumption and total navigation time; and the training module is used for training a preset intelligent forecasting model by using the target data set as a training set by using a machine learning method to obtain a multi-target combined optimization model of the air route and the speed.
Further, the system further comprises: an optimization module to: acquiring initial parameters of a target ship in a navigation process; the initial parameters include: the ship type of the target ship, the starting point of a route and marine environment parameters of the target ship in the navigation process; acquiring a target optimizing target of the target ship in the navigation process; the target optimization objective comprises at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable; and optimizing the navigational speed course of the target ship by utilizing the course navigational speed multi-target combined optimization model based on the initial parameters and the target optimizing target to obtain the optimized navigational speed course.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, the present invention further provides a computer-readable medium having non-volatile program code executable by a processor, where the program code causes the processor to execute the method according to the first aspect.
The embodiment of the invention provides a method and a system for establishing a multi-target combined optimization model of a ship speed, which are used for realizing the overall planning of various factors and the combined optimization of the ship speed, improving the accuracy of optimization, improving the optimization effect and relieving the technical problems of low optimization accuracy and poor optimization effect of planning of ship navigation tasks in the prior art by establishing a ship database and a ship control model, then establishing data sets of different ship types corresponding to different ship data and finally training a preset intelligent forecast model by using the data sets to obtain the multi-target combined optimization model of the ship speed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a method for establishing a multi-objective joint optimization model of a course speed according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a multi-objective joint optimization model building system for the speed of a flight path according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another system for establishing a multi-objective joint optimization model of the flight speed of a flight route according to the embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
FIG. 1 is a flowchart of a method for establishing a multi-objective joint optimization model of a course speed according to an embodiment of the invention. As shown in fig. 1, the method specifically includes the following steps:
step S102, establishing a ship database; the ship database comprises ship type data, a cruise draft-resistance table, a wave resistance-increasing transfer function table, a wind resistance coefficient table, a propeller thrust torque coefficient table, a turbine steering engine performance parameter table and a diving resistance-increasing coefficient table under still water. Specifically, each ship in the ship database contains all the data, parameters of different ship types are different, and the performance parameters of the same ship are different along with the lapse of the use time of the ship, so that the measured data on the ship needs to be analyzed periodically, and then the parameters in the ship database are updated.
Step S104, building a ship control model based on a ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control command.
Step S106, establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and a ship control model to obtain a target data set; wherein, a ship type corresponds a plurality of airline data, and every airline data includes: the method comprises the following steps of route, speed, water depth, ocean environment parameters, ship control instructions, total navigation energy consumption and total navigation time.
And S108, taking the target data set as a training set, and training a preset intelligent forecasting model by using a machine learning method to obtain a multi-target combined optimization model of the air route speed.
The embodiment of the invention provides a method for establishing a multi-target combined optimization model of a ship route speed, which is characterized in that a ship database is established, a ship control model is established, data sets of different ship types corresponding to different ship route data are established, and a preset intelligent forecast model is trained by using the data sets to obtain the multi-target combined optimization model of the ship route speed, so that various factors are comprehensively established, the combined optimization of the ship route speed is realized, the accuracy of the optimization is improved, the optimization effect is improved, and the technical problems of low optimization accuracy and poor optimization effect of the planning of ship navigation tasks in the prior art are solved.
Specifically, the ship type data includes: the ship length, the ship width, the square and diamond coefficients under different draft directions, the water displacement, the wet surface area, the waterline surface length and the like under different draft directions can be inquired from a ship loading manual, and generally do not need to be updated.
The cruise draft-resistance meter under the still water can be obtained by pool experiments or Computational Fluid Dynamics (CFD) calculation, and when the cruise and draft are determined, the hydrostatic resistance value of the ship can be obtained by looking up a table.
Wave drag-increasing transfer function in wave drag-increasing transfer function table
(H is the wave height and F (T, theta, V, dr) is the wave force) can be understood as the wave force generated per wave height, which is a function related to the wave period T, the angle of attack theta, the speed V, and the draught dr. The wave resistance value is F when the speed and the draught are determined by using model test, numerical calculation and other methodswave＝∫∫∫f(T，θ，V0，dr0)dHdTdθ。
Wind resistance coefficient C in wind resistance coefficient tableA(psi) is a function of the wind direction angle psi, and can be obtained by wind tunnel experiment or CFD calculation, and wind resistance is calculated
Wherein VwindIs the wind speed and A is the wind area.
Thrust coefficient K in propeller thrust torque coefficient tableT(V, n) and the torque coefficient KQ(V，n) is a function of the ship speed V and the propeller speed n, and can be obtained by using a pool test or CFD.
Each performance parameter in the turbine steering engine performance parameter table is the inherent attribute of the equipment and is obtained from the parameter table provided by the equipment manufacturer.
Diving drag coefficient K (V, h, C) in shallow water drag coefficient tablebDr) is a function of navigational speed V, water depth h, square coefficient Cb and draft dr, and can be obtained through a pool experiment or CFD calculation.
Specifically, step S104 further includes the following steps:
and step S1041, establishing an initial relation model among thrust and torque required by ship navigation, the rotating speed of a ship propeller and a rudder angle based on a ship database.
And step S1042, establishing a ship control model based on the initial relation model and the relation between the ship propeller rotating speed, the rudder angle and the ship control command.
Optionally, the vessel maneuvering model further comprises:
and determining the sailing power of the ship and the total sailing energy consumption of the ship based on the rotating speed of the propeller of the ship.
Specifically, when the speed and the draft of the ship are determined and the conditions of wind, waves, water depth and the like are known, the thrust and the torque required by the ship for sailing can be calculated by using the data in the ship database and the initial relationship model, and the thrust and the torque are respectively provided by a propeller and a rudder of the ship. Specifically, the initial relationship model includes:
Tp＝ρn2D2KT(V，n)，NR＝-(1+αH)xRFNcosδ；
wherein the content of the first and second substances,
Tpthrust required for sailing a ship, NRThe torque required by ship navigation, n is the rotation speed of ship propeller, delta is rudder angle of ship, KT(V, n) is the thrust coefficient of the ship propeller, V is the ship speed, rho is the density of water, D is the diameter of the propeller, alphaHIs the hydrodynamic proportionality coefficient of the rudder, λ is the spanwise ratio of the rudder, xRDistance of steering gear to center of gravity of ship, FNAs an intermediate parameter, ARTo the inflow area, αRIs the inflow angle. The parameters can be obtained by looking up a steering engine performance parameter table or installing the parameters in a manual.
Then, when the thrust and the torque are known, the relationship in the initial relationship model can be used to obtain the propeller rotation speed and the rudder angle, and thus obtain the steering command.
Alternatively, after obtaining the propeller speed n, the following relation is used: p2 pi nQP＝2πρn3D5KQ(V, n), the power of ship navigation can be obtained, and then the power integral: and E ═ Pdt can calculate the total energy consumption of the ship navigation.
Optionally, step S106 further includes the following steps:
step S1061, extracting routes corresponding to different ship types from a preset ship AIS database; a ship type corresponds to a plurality of routes.
And step S1062, performing segmentation processing on each route corresponding to different ship types, and setting initial route data for each route segment.
And S1063, optimizing the initial air route data based on the ship control model and a preset optimization threshold value to obtain air route data of each air route corresponding to different ship types.
And S1064, taking the air route data of each air route corresponding to different ship types as a target data set.
In the embodiment of the invention, various ship sailing air routes are extracted from the existing preset ship AIS database, and ships of different ship types sail different air routes due to different transported goods and the like. The same type of ship has different routes for each ship due to the habits of the crew and the requirements of the shipping company, but the routes are not very different. These routes are determined by the crew from years of experience.
Specifically, the routes are classified according to different ship types, and all routes of the same ship type are divided together. The maximum range of the variation of each route of the same ship type is taken as the optimizing range of the route optimizing in the route speed multi-target combined optimizing model provided by the embodiment of the invention.
Then, aiming at a specific ship type, a starting point is randomly set in the optimization range of the flight path to randomly generate a flight path, after the flight path of the ship is determined, the water depth on the flight path is also determined, and the ship resistance increase caused by the corresponding shallow water effect is also determined. According to the magnitude of the calculated force of the computer, the flight path is divided into N sections, and the greater the calculated force is, the greater N is. And randomly setting the ship speed V on the N sections of routes, and randomly designing marine environment parameters (wind, wave and flow). It should be noted that the influence of wind and waves on the ship is to increase the resistance, and the influence of flow on the ship is to influence the speed of the ship: v ═ V + Vc. Where V is the actual marine speed, and is the randomly assigned speed over N legs, and V is the actual marine speedcIs the component of the flow velocity in the direction of flight. After determining the course, the water depth, the speed and the marine environment parameters, the control instruction, the total navigation energy consumption and the total navigation time of the ship can be calculated by using the relational expression in the ship control model. If any one of the maximum change amplitude, the maximum change speed and the like of the propeller or the rudder angle in the calculated steering command exceeds the value allowed by the equipment (namely, the preset optimization threshold), the piece of data is deleted. The above processes are repeated for M times, the magnitude of M is set according to the calculated force, and the larger the calculated force is, the larger M is. Repeating the above process for different vessel types creates a data set for each vessel type.
Specifically, step S108 further includes the following steps:
step S1081, training a preset intelligent forecasting model by using a machine learning method to obtain the trained intelligent forecasting model; the method of machine learning includes any one of: deep neural network algorithm, reinforcement learning algorithm; the preset independent variables of the intelligent forecasting model comprise: ship type, starting point of course, marine environmental parameters; presetting dependent variables of the intelligent forecasting model comprises the following steps: course, speed, distance, time, command and energy consumption.
Step S1082, a preset optimization algorithm is set on the intelligent forecasting model after training, and a multi-target combined optimization model of the air route speed is obtained; the route speed multi-target combined optimization model is a model for determining the optimized route speed based on the target optimization target; the target optimization objective includes at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable.
In the embodiment of the invention, for the intelligent forecasting model after training, under the same ship type, starting point and marine environment parameters, forecasting results of a plurality of groups of air routes and navigation speeds can be obtained, and correspondingly, a plurality of groups of navigation routes, navigation time, control instructions and energy consumption results can also be obtained; therefore, on the basis of the intelligent forecasting model after training, a preset optimizing algorithm is set, and according to different optimizing targets, such as: the speed is fastest, the distance is shortest, the energy consumption is lowest, the operation is most stable and the like, and the combination of the corresponding air route and the air speed is determined as the final optimizing result.
Optionally, after the model for multi-objective combined optimization of the speed of the flight path is established, the method provided by the embodiment of the invention further includes: and deploying the multi-target combined optimization model of the ship route speed, periodically executing the optimization process of the ship route speed in the ship navigation process, and pushing the latest ship route speed result to a crew on the ship. Meanwhile, the multi-target combined optimization model of the route speed is updated regularly according to big data acquired when the ship sails. Specifically, the method comprises the following steps:
acquiring initial parameters of a target ship in a navigation process; the initial parameters include: the ship type of the target ship, the starting point of the air route and marine environment parameters of the target ship in the navigation process; acquiring a target optimizing target of a target ship in a navigation process; the target optimization objective includes at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable; and optimizing the navigational speed course of the target ship by using a course navigational speed multi-target combined optimization model based on the initial parameters and the target optimization target to obtain the optimized navigational speed course.
The method comprises the steps of establishing data sets of different ship types corresponding to different ship data by comprehensively utilizing a ship database and a ship control model, training a preset intelligent forecast model by utilizing the data sets to obtain a ship route and ship speed multi-target joint optimization model, optimizing the ship route and ship speed of a ship by utilizing the model finally obtained by the method provided by the embodiment of the invention, and fully considering the influences of marine environment, terrain, ship control capability and the like in the optimization process, so that the optimization result can be more accurate and the optimization effect is more obvious.
Example two:
FIG. 2 is a schematic diagram of a multi-objective joint optimization model building system for the speed of a flight path according to an embodiment of the invention. As shown in fig. 2, the system includes: a first building module 10, a second building module 20, a third building module 30 and a training module 40.
Specifically, the first establishing module 10 is configured to establish a ship database; the ship database comprises ship type data, a cruise draft-resistance table, a wave resistance-increasing transfer function table, a wind resistance coefficient table, a propeller thrust torque coefficient table, a turbine steering engine performance parameter table and a diving resistance-increasing coefficient table under still water.
A second establishing module 20, configured to establish a ship maneuvering model based on a ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control command.
The third establishing module 30 is used for establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and a ship control model to obtain a target data set; wherein, a ship type corresponds a plurality of airline data, and every airline data includes: the method comprises the following steps of route, speed, water depth, ocean environment parameters, ship control instructions, total navigation energy consumption and total navigation time.
And the training module 40 is used for training the preset intelligent forecasting model by using a machine learning method by taking the target data set as a training set to obtain the multi-target combined optimization model of the air route speed.
The embodiment of the invention provides a system for establishing a multi-target combined optimization model of the ship speed of a ship, which is characterized in that a ship database is established, a ship control model is established, data sets of different ship types corresponding to different ship data are established, and a preset intelligent forecast model is trained by using the data sets to obtain the multi-target combined optimization model of the ship speed of the ship, so that various factors are comprehensively established, the combined optimization of the ship speed of the ship is realized, the accuracy of the optimization is improved, the optimization effect is improved, and the technical problems of low optimization accuracy and poor optimization effect of the planning of the ship navigation task in the prior art are solved.
Specifically, the second establishing module 20 is further configured to: establishing an initial relation model between thrust and torque required by ship navigation and the rotating speed and rudder angle of a ship propeller based on a ship database; establishing a ship control model based on the initial relation model and the relation between the ship propeller rotating speed, the rudder angle and the ship control instruction; and determining the sailing power of the ship and the total sailing energy consumption of the ship based on the rotating speed of the propeller of the ship. Specifically, the initial relationship model includes:
Tp＝ρn2D2KT(V，n)，NR＝-(1+αH)xRFNcosδ；
wherein the content of the first and second substances,
Tpthrust required for sailing a ship, NRThe torque required by ship navigation, n is the rotation speed of ship propeller, delta is rudder angle of ship, KT(V, n) is the thrust coefficient of the ship propeller, V is the ship speed, rho is the density of water, D is the diameter of the propeller, alphaHIs the hydrodynamic proportionality coefficient of the rudder, λ is the spanwise ratio of the rudder, xRDistance of steering gear to center of gravity of ship, FNAs an intermediate parameter, ARTo the inflow area, αRIs the inflow angle. The parameters can be obtained by looking up a steering engine performance parameter table or installing the parameters in a manual.
Optionally, the third establishing module 30 is further configured to: extracting routes corresponding to different ship types from a preset ship AIS database; one ship type corresponds to a plurality of routes; carrying out segmentation processing on each air route corresponding to different ship types, and setting initial air route data for each air route section; optimizing the initial route data based on the ship control model and a preset optimization threshold value to obtain route data of each route corresponding to different ship types; and taking the air route data of each air route corresponding to different ship types as a target data set.
Optionally, fig. 3 is a schematic diagram of another multi-objective joint optimization model building system for flight speed of a flight route provided according to an embodiment of the present invention, and as shown in fig. 3, the training module 40 further includes: a training unit 41 and a setting unit 42.
Specifically, the training unit 41 is configured to: training a preset intelligent forecasting model by using a machine learning method to obtain the trained intelligent forecasting model; the method of machine learning includes any one of: deep neural network algorithm, reinforcement learning algorithm; the preset independent variables of the intelligent forecasting model comprise: ship type, starting point of course, marine environmental parameters; presetting dependent variables of the intelligent forecasting model comprises the following steps: course, speed, distance, time, command and energy consumption.
A setting unit 42 for: setting a preset optimization algorithm on the trained intelligent forecasting model to obtain a multi-target combined optimization model of the air route and the air speed; the route speed multi-target combined optimization model is a model for determining the optimized route speed based on the target optimization target; the target optimization objective includes at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable.
Optionally, as shown in fig. 3, the system further includes: an optimization module 50 for: acquiring initial parameters of a target ship in a navigation process; the initial parameters include: the ship type of the target ship, the starting point of the air route and marine environment parameters of the target ship in the navigation process; acquiring a target optimizing target of a target ship in a navigation process; the target optimization objective includes at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable; and optimizing the navigational speed course of the target ship by using a course navigational speed multi-target combined optimization model based on the initial parameters and the target optimization target to obtain the optimized navigational speed course.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the method in the first embodiment are implemented.
The embodiment of the invention also provides a computer readable medium with a non-volatile program code executable by a processor, wherein the program code causes the processor to execute the method in the first embodiment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for establishing a multi-objective joint optimization model of a route speed is characterized by comprising the following steps:
establishing a ship database; the ship database comprises ship type data, a cruise draft-resistance table, a wave resistance-increasing transfer function table, a wind resistance coefficient table, a propeller thrust torque coefficient table, a turbine steering engine performance parameter table and a diving resistance-increasing coefficient table under still water;
establishing a ship control model based on the ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control instruction;
establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and the ship control model to obtain a target data set; wherein, a ship type corresponds a plurality of airline data, and every airline data includes: the method comprises the following steps of (1) course, navigational speed, water depth, ocean environment parameters, ship control instructions, total navigation energy consumption and total navigation time;
and taking the target data set as a training set, and training a preset intelligent forecast model by using a machine learning method to obtain a multi-target joint optimization model of the air route speed.
2. The method of claim 1, wherein building a vessel maneuvering model based on the vessel database comprises:
establishing an initial relation model between thrust and torque required by ship navigation and the rotating speed and rudder angle of a ship propeller based on the ship database;
and establishing the ship control model based on the initial relation model and the relation between the ship propeller rotating speed, the rudder angle and the ship control command.
3. The method of claim 2, wherein the initial relationship model comprises:
Tp＝ρn2D2KT(V，n)，NR＝-(1+αH)xRFNcosδ；
wherein the content of the first and second substances,
Tpthrust required for sailing a ship, NRThe torque required by ship navigation, n is the rotation speed of ship propeller, delta is rudder angle of ship, KT(V, n) is the thrust coefficient of the ship propeller, V is the ship speed, rho is the density of water, D is the diameter of the propeller, alphaHIs the hydrodynamic proportionality coefficient of the rudder, λ is the spanwise ratio of the rudder, xRDistance of steering gear to center of gravity of ship, FNAs an intermediate parameter, ARTo the inflow area, αRIs the inflow angle.
4. The method of claim 1, wherein the establishing of the data sets corresponding to different ship types and different ship route data based on a preset ship AIS database and the ship maneuvering model to obtain a target data set comprises:
extracting routes corresponding to different ship types from the preset ship AIS database; one ship type corresponds to a plurality of routes;
carrying out segmentation processing on each air route corresponding to different ship types, and setting initial air route data for each air route section;
optimizing the initial route data based on the ship control model and a preset optimization threshold value to obtain route data of each route corresponding to different ship types;
and taking the route data of each route corresponding to different ship types as the target data set.
5. The method as claimed in claim 1, wherein the training of the preset intelligent forecasting model by using a machine learning method to obtain the multi-objective joint optimization model of the air route and the speed comprises:
training a preset intelligent forecasting model by using a machine learning method to obtain the trained intelligent forecasting model; the method of machine learning includes any one of: deep neural network algorithm, reinforcement learning algorithm; the independent variables of the preset intelligent forecasting model comprise: ship type, starting point of course, marine environmental parameters; the preset dependent variable of the intelligent forecasting model comprises the following steps: air route, speed, distance, time, command and energy consumption;
setting a preset optimization algorithm on the trained intelligent forecasting model to obtain a multi-target combined optimization model of the air route and the air speed; the multi-target combined optimization model of the route speed is a model for determining the optimized route speed based on a target optimizing target; the target optimization objective comprises at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable.
6. The method of claim 1, wherein after obtaining the multi-objective joint optimization model for the speed of the flight path, the method further comprises:
acquiring initial parameters of a target ship in a navigation process; the initial parameters include: the ship type of the target ship, the starting point of a route and marine environment parameters of the target ship in the navigation process;
acquiring a target optimizing target of the target ship in the navigation process; the target optimization objective comprises at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable;
and optimizing the navigational speed course of the target ship by utilizing the course navigational speed multi-target combined optimization model based on the initial parameters and the target optimizing target to obtain the optimized navigational speed course.
7. A multi-objective combined optimization model building system for the speed of a flight path is characterized by comprising the following steps: a first building module, a second building module, a third building module and a training module, wherein,
the first establishing module is used for establishing a ship database; the ship database comprises ship type data, a cruise draft-resistance table, a wave resistance-increasing transfer function table, a wind resistance coefficient table, a propeller thrust torque coefficient table, a turbine steering engine performance parameter table and a diving resistance-increasing coefficient table under still water;
the second establishing module is used for establishing a ship control model based on the ship database; the ship control model is a relation model between thrust and torque required by ship navigation and a ship control instruction;
the third establishing module is used for establishing data sets of different ship types corresponding to different air route data based on a preset ship AIS database and the ship control model to obtain a target data set; wherein, a ship type corresponds a plurality of airline data, and every airline data includes: the method comprises the following steps of (1) course, navigational speed, water depth, ocean environment parameters, ship control instructions, total navigation energy consumption and total navigation time;
and the training module is used for training a preset intelligent forecasting model by using the target data set as a training set by using a machine learning method to obtain a multi-target combined optimization model of the air route and the speed.
8. The system of claim 7, further comprising: an optimization module to:
acquiring initial parameters of a target ship in a navigation process; the initial parameters include: the ship type of the target ship, the starting point of a route and marine environment parameters of the target ship in the navigation process;
acquiring a target optimizing target of the target ship in the navigation process; the target optimization objective comprises at least one of: the speed is fastest, the distance is shortest, the energy consumption is lowest, and the operation is most stable;
and optimizing the navigational speed course of the target ship by utilizing the course navigational speed multi-target combined optimization model based on the initial parameters and the target optimizing target to obtain the optimized navigational speed course.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1-6.
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