CN113033073A - Unmanned ship energy efficiency digital twinning method and system based on data driving - Google Patents
Unmanned ship energy efficiency digital twinning method and system based on data driving Download PDFInfo
- Publication number
- CN113033073A CN113033073A CN202110198914.3A CN202110198914A CN113033073A CN 113033073 A CN113033073 A CN 113033073A CN 202110198914 A CN202110198914 A CN 202110198914A CN 113033073 A CN113033073 A CN 113033073A
- Authority
- CN
- China
- Prior art keywords
- ship
- energy efficiency
- data
- speed
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
- G06T13/20—3D [Three Dimensional] animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Computer Graphics (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a data-driven unmanned ship energy efficiency digital twin method and a system, comprising an entity basic layer, a data information interaction layer, a software and hardware configuration layer and an interface design layer, wherein the entity basic layer is used for data information interaction, software and hardware configuration and interface design; the data layer is used for carrying out data cleaning and data processing on the acquired data and constructing a database; the model layer is used for establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model to realize intelligent optimization decision of the unmanned ship energy efficiency; the dynamic visualization layer is used for constructing a three-dimensional model of a ship body, a host, a propeller and a navigation environment, mapping data in real time and dynamically displaying the three-dimensional model; and the functional application layer is used for energy efficiency remote monitoring, remote intelligent optimization management and energy efficiency management virtual experiments. The unmanned ship energy efficiency digital twin system can realize dynamic visualization of a navigation environment scene, ship speed, host rotating speed and energy efficiency level which are matched with a real ship, and can be used for energy efficiency remote optimization management and virtual verification experiments.
Description
Technical Field
The invention relates to the field of unmanned ship energy efficiency intelligent optimization management and remote control, in particular to an unmanned ship energy efficiency digital twinning method and system based on data driving.
Background
In recent years, the shipping industry is rapidly developed and simultaneously faces the problems of energy consumption, pollutant emission and the like, so that research on ship energy efficiency optimization technology is imperative. The intelligent ship standard issued by the Chinese classification society takes intelligent energy efficiency management as an important ring for intelligent ship development, and has important significance for improving the energy efficiency and the greening level of an intelligent ship.
With the rapid development and application of artificial intelligence, big data and other technologies, the concepts of informatization and digitization are gradually integrated into various industries, and meanwhile, the digital twin technology is widely researched and applied at home and abroad. The digital twin technology is characterized in that a virtual model of a physical entity is constructed in a digital mode, functions of equipment management and control, target optimization, fault diagnosis, behavior prediction and the like are completed by utilizing dynamic interactive simulation of the physical model and the digital twin body and combining methods of data fusion analysis, decision optimization and the like, and the digital twin technology has the characteristics of multi-dimension, multi-scale, high reliability and the like.
At present, the research on the intelligent energy efficiency of ships is mostly based on historical test data, the special consideration of real ship navigation environment and the analysis of dynamic influence factors on an energy consumption model are lacked, the provided intelligent energy efficiency optimization management strategy needs to be further verified and analyzed, and in addition, because an unmanned ship is not actually launched for operation, the real ship test is difficult to perform. The development and application of the digital twin technology can assist intelligent management and virtual experiment verification analysis of unmanned ship energy efficiency, but the technology is not widely applied in the field of ship energy efficiency, lacks a ship energy efficiency digital twin system, and does not realize three-dimensional dynamic visual display of ship navigation information, host rotating speed, ship navigation speed and energy efficiency level and virtual experiment verification analysis of unmanned ship energy efficiency intelligent optimization decision.
The unmanned ship energy efficiency digital twin method and system based on data driving can realize real-time remote online monitoring of unmanned ship energy efficiency and facilitate shipping companies to realize unmanned ship energy efficiency intelligent decision and remote management; meanwhile, the method can be applied to an unmanned ship energy efficiency intelligent optimization control virtual experiment, experiment tests and verification which are difficult to perform on an actual ship are realized, and the navigation condition of the actual ship is reflected by a data-driven high-precision virtual model, so that the effectiveness of an intelligent optimization decision algorithm and the energy efficiency optimization effect of the intelligent optimization decision algorithm can be verified.
Disclosure of Invention
The invention provides a data-driven unmanned ship energy efficiency digital twinning method and system, which aim to overcome the technical problems.
A data-driven unmanned ship energy efficiency digital twin system comprises an entity base layer, a data layer, a model layer, a dynamic visualization layer and a functional application layer,
an entity basic layer used for data information interaction, software and hardware configuration and interface design,
the data layer is used for data cleaning, data processing and database construction,
the model layer is used for establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model and carrying out energy efficiency intelligent optimization decision,
a dynamic visualization layer used for three-dimensional model construction, data real-time mapping and three-dimensional model dynamic display,
and the functional application layer is used for energy efficiency remote monitoring, remote intelligent optimization management and virtual experiments.
Preferably, the interface design refers to designing the interface layout of a navigational speed information window, a navigational route information window, a host rotational speed information window, a navigational speed optimization decision window and a twin window,
the navigation speed information window is used for displaying real-time dynamic navigation speed information of the ship;
the course information window is used for displaying real-time dynamic course information of the ship;
the main engine rotating speed information window is used for displaying real-time dynamic rotating speed information of the ship main engine;
the navigation speed optimization decision window is used for displaying navigation speed dynamic optimization result information;
the twin window is used for displaying three-dimensional ship model and virtual navigation environment information, and can be used for displaying three-dimensional multi-view angles.
Preferably, the three-dimensional model construction means that a three-dimensional ship body, a host and a propeller of the virtual ship are modeled through Solidworks, and then 3DMax and Blender software are used for three-dimensional rendering of the three-dimensional ship body, the host, the propeller and a navigation environment.
Preferably, the data real-time mapping is to map data in the database and output data of the unmanned ship energy consumption model and the unmanned ship energy efficiency dynamic optimization model into visual elements, wherein the visual elements comprise visual space scenes, visual marks and visual animations;
the visual space scene is visually developed by adopting a mode of combining three dimensions and two dimensions;
the visual mark is matched into the established virtual space scene after coordinate conversion based on longitude and latitude coordinates and steering point coordinates of the real ship route;
the visual animation is real-time mapping from the ship speed, the main engine rotating speed, the propeller rotating speed and the data value of the air route to the three-dimensional model display.
Preferably, the three-dimensional multi-view display is performed by adjusting a three-dimensional view and partially enlarging and reducing, so that the viewing view adjustment and the specific detail information display of the three-dimensional animation are realized.
A data-driven unmanned ship energy efficiency digital twinning method is characterized by comprising the following steps:
the method comprises the following steps: acquiring data including weather, sea conditions, flight paths, operation and energy consumption data through a European middle-term weather forecast center and ship data acquisition equipment based on a target course and a target ship;
step two: performing data cleaning and data analysis on the acquired data, and constructing an unmanned ship energy efficiency digital twin system database;
step three: based on an unmanned ship energy efficiency digital twin system database, carrying out navigation segment division on a target air route, establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model, and carrying out optimization decision analysis according to optimal ship energy efficiency;
step four: the method comprises the steps that real-time mapping is carried out on output data and ship acquisition data based on an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model and a ship body, a host, a propeller and a navigation environment three-dimensional model, and three-dimensional dynamic visualization is achieved;
step five: the ship energy efficiency is remotely monitored based on a system design interface, ship navigation information, ship speed, host rotating speed, propeller rotating speed and ship energy efficiency level are displayed in real time through a three-dimensional model and a chart, decision analysis results are transmitted to a ship speed control system, and the decision analysis results are verified through virtual experiments.
Preferably, the establishing of the unmanned ship energy consumption model means that the unmanned ship energy consumption model is obtained by taking multivariable such as wind speed, wind direction, wave height, rotating speed and shaft power as input characteristics and the unmanned ship host machine oil consumption as output characteristics through repeated iterative training and optimization of a machine learning algorithm.
Preferably, the establishing of the unmanned ship energy efficiency dynamic optimization model comprises the following steps:
the method comprises the following steps: analyzing weather and sea condition data of a target route through a clustering algorithm, and dividing the target route into sections;
step two: calculating an average ship energy efficiency operation index of each navigation section through an unmanned ship energy consumption model, wherein the average ship energy efficiency operation index is obtained through an equation (1):
in the formula: EEOIaveMean ship energy efficiency operationCounting; j is a fuel type;
CO as fuel oil2A conversion factor;
is the oil consumption of the host machine in unit time;
the oil consumption of the auxiliary engine in unit time; v. ofsIs the ship speed to the ground; m is the ship cargo capacity; t is an integration period;
step three: dynamically optimizing and solving the ship speed by using a group intelligent optimization algorithm by taking the ship speed in each navigation section as an optimization variable, taking an average ship energy efficiency operation index as an optimization target and taking the navigation time and the rotating speed of a host as constraint conditions to obtain the optimal ship speed corresponding to the optimal objective function value;
wherein the constraint conditions are as follows:
nmin＜ne≤nmax (4)
in the formula, n represents the number of divided segments; l isiThe voyage is the voyage of the ith voyage section; l is0Is the total voyage of the airline; vsiOptimizing the speed for the ship in the ith voyage section; t is0Representing a voyage time limit; n isminIndicating a host minimum speed limit; n ismaxRepresenting a maximum speed limit of the main engine; n iseThe rotating speed of the main engine after optimization.
Preferably, the virtual experiment comprises:
the method comprises the following steps: selecting historical voyage times, and acquiring navigation information including requirements of a ship departure port, a destination port, departure time, arrival time, a course distance and voyage time, and main engine oil consumption, main engine rotating speed, propeller rotating speed, main engine shaft power, ship speed, ship course, longitude and latitude, weather and sea condition data of actual navigation of the ship;
step two: constructing a three-dimensional scene of an actual navigation environment of a ship, constructing two three-dimensional virtual ships with the same parameters under the same scene, and respectively corresponding to a virtual ship driven by historical actual navigation data and a virtual ship driven by energy efficiency intelligent optimization decision data;
step three: establishing real-time mapping of historical actual navigation data virtual ships and historical actual storage data, and displaying the running state and the energy efficiency of the virtual ships in a three-dimensional dynamic manner; the method comprises the steps of establishing real-time mapping of a virtual ship based on energy efficiency intelligent optimization decision data and energy efficiency intelligent optimization decision output result data, displaying the operation state and energy efficiency of the virtual ship in a three-dimensional dynamic mode, and comparing and analyzing the operation state and energy efficiency level of two virtual ships, so that the effectiveness of an energy efficiency management method is verified.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a schematic diagram of the method of the present invention;
FIG. 3 is a flow chart of an embodiment of the present invention;
FIG. 4 is a schematic view of a virtual navigation of the present invention with varying navigation environments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention is further explained below with reference to the drawings and the embodiments.
Fig. 1 is a system module architecture diagram of the present invention, and as shown in fig. 1, the present invention provides a data-driven unmanned ship energy efficiency digital twin system, which includes an entity base layer, a data layer, a model layer, a dynamic visualization layer, and a functional application layer.
And the entity basic layer is used for data information interaction, software and hardware configuration and interface design.
The data information interaction comprises weather and sea condition data of a ship route and data of a track, operation, energy consumption and the like of an actual ship, and information sharing between the actual ship data information and the data twin system is realized through communication networks such as satellites and 5G.
The meteorological data comprise wind speed and wind direction data, the sea condition data comprise wave height, water flow speed and water depth data, and the data are acquired by a European mid-term meteorological forecasting center.
The track data comprises longitude and latitude position information of the ship and is collected through a GPS; the operation data comprises a main engine rotating speed and main engine shaft power data, ship speed and course data and ship heeling and trim angles, the main engine rotating speed and the main engine shaft power data are collected through a shaft power meter, the ship speed and course data are collected through a log and a GPS instrument, and the ship heeling and trim angles are collected through a heeling and trim measuring instrument; the energy consumption data comprises host oil consumption data, and is acquired through a flowmeter arranged on a conveying pipeline between the host and the host daily oil tank.
The software and hardware configuration comprises the construction of hardware computer facilities and workstations, and the selection of professional 3D modeling software such as Solidworks, 3DsMax, Blender and the like.
The interface design comprises the interface layout of a navigational speed information window, a course information window, a host rotational speed information window, a navigational speed optimization decision window and a twin body window, wherein the navigational speed information window is used for displaying real-time dynamic navigational speed information of a ship, the course information window is used for displaying the real-time dynamic course information of the ship, the host rotational speed information window is used for displaying the real-time dynamic rotational speed information of the host of the ship, the navigational speed optimization decision window is used for displaying navigational speed dynamic optimization result information, the twin body window is used for displaying three-dimensional ship model and virtual navigational environment information, the navigational state information of the ship is reflected through the three-dimensional ship model and the virtual navigational environment, and the navigational state information of the ship comprises navigational environment information, the navigational speed of the ship, the rotational speed of the host and.
The data layer is used for carrying out data cleaning, data processing and database construction on the acquired data.
The data cleaning comprises the steps of deleting abnormal values, missing values and noise points and carrying out interpolation processing by using a Python language; the data processing comprises the operations of principal component analysis, data dimension reduction, correlation analysis and the like, and the acquired data are analyzed and simplified; the construction of the database comprises the step of constructing an unmanned ship energy efficiency digital twin system database which is used for storing data such as the rotating speed, the shaft power, the heeling, the pitching, the wind speed, the wind direction, the navigational speed, the course, the longitude and latitude, the wave height, the oil consumption and the like of the host.
The model layer is used for establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model, and intelligent optimization decision of the unmanned ship energy efficiency is achieved.
The unmanned ship energy consumption model is established based on a machine learning algorithm, the input characteristics of the model are multivariable such as wind speed, wave height, rotating speed, shaft power and the like, the output characteristics of the model are the oil consumption of the unmanned ship host, and the unmanned ship energy consumption model is finally obtained through repeated iterative training and optimization of the machine learning algorithm;
the establishing process of the unmanned ship energy efficiency dynamic optimization model comprises the following steps:
the method comprises the following steps: analyzing weather and sea condition data of a ship route in an unmanned ship energy efficiency digital twin system database through a clustering algorithm, and dividing the ship route into sections;
step two: characterizing a vessel energy efficiency level (EEOI) by an unmanned vessel energy consumption model using a vessel Energy Efficiency Operation Index (EEOI)CO representing the turnover number of a ship per unit transportation2Emission amount), solving the average EEOI value of each flight segment, and obtaining the energy efficiency level change condition of each flight segment, wherein the EEOI value can be obtained by the following formula:
in the formula: i is the fuel type; FCiThe total amount of fuel consumed by the vessel in the voyage; ccarbonCO as fuel2A conversion factor; m iscargonIs the load of the vessel; dis is the voyage mileage of the ship voyage.
Step three: and taking the ship speed in each navigation section as an optimization variable, taking the average EEOI value as an optimization target, taking the navigation time and the host rotating speed as constraint conditions, and utilizing a group intelligent optimization algorithm to dynamically optimize and solve the ship speed to obtain the optimal ship speed corresponding to the optimal objective function value, thereby realizing the intelligent optimization decision of the unmanned ship energy efficiency.
A dynamic visualization layer used for three-dimensional model construction, data real-time mapping and three-dimensional model dynamic display,
the three-dimensional model construction refers to the establishment of a three-dimensional model of a ship body, a host, a propeller and a navigation environment, SolidWorks is selected to realize initial modeling based on a design drawing of a real ship, on the basis, 3D rendering work is further carried out by using 3DMax and Blender software, so that three-dimensional virtual display of the ship body, the host and the propeller model is realized, three-dimensional virtual scene rendering of the unmanned ship navigation environment is carried out through the 3DMax, the Blender and other software, and a three-dimensional virtual navigation environment scene which is highly consistent with the real navigation environment is constructed.
The real-time data mapping is to map the processed data in the energy efficiency database and the output data of the unmanned ship energy consumption model and the unmanned ship energy efficiency dynamic optimization model into visual elements including visual space scenes, visual marks and visual animations.
The visual space scene is a general overview space of the unmanned ship energy efficiency digital twin system, is developed visually in a mode of combining three dimensions and two dimensions, and comprises a ship body, a host, a propeller model, a navigation environment virtual scene of actual navigation and a ship operation data chart;
the visual mark is mapping from real ship data to virtual space coordinates, and is matched into the established virtual space scene after coordinate conversion based on longitude and latitude coordinates and steering point coordinates of a real ship route, so that dynamic visual display of navigation of a ship model on a set route is realized, and a three-dimensional virtual dynamic navigation environment corresponding to the ship model is matched according to navigation environment information of different navigation sections when the real ship runs;
the invention controls the movement speed of a hull model, the rotating speed of a host model and the rotating speed of a propeller model in a Blender through Python script language, so that the dynamic visualization of navigation of a virtual ship on a route after the coordinate conversion of the ship is realized, and the dynamic visualization comprises the dynamic visualization of the ship speed, the rotating speed of the host and the rotating speed of the propeller, thereby realizing the real-time dynamic visualization of the ship navigation state, the host and the propeller running state based on data driving.
And the dynamic display of the three-dimensional model is to dynamically display the three-dimensional model and information on an unmanned ship energy efficiency digital twin system interface in real time by combining the data mapping result comprising the ship speed, the host rotating speed, the propeller rotating speed and the ship energy efficiency level with the three-dimensional models of the ship body, the host and the propeller.
And the functional application layer is used for energy efficiency remote monitoring, remote intelligent optimization management and virtual experiments.
The energy efficiency remote monitoring mainly realizes real-time display and statistical analysis of meteorological data, sea condition data, flight path data, operation data and energy consumption data.
The remote intelligent optimization management is to carry out intelligent decision on ship energy efficiency optimization through an unmanned ship energy consumption model and a ship energy efficiency dynamic optimization model, and send and act a decision result on a ship speed control system to realize the optimal control of the ship speed.
The virtual experiment can realize verification analysis of intelligent ship energy efficiency optimization decisions based on a digital twin system.
As shown in fig. 2, the unmanned ship energy efficiency digital twin method based on data driving is as follows:
the method comprises the following steps: acquiring data including meteorological data, sea condition data, track data, operation data and energy consumption data through a European middle-term weather forecast center and real ship data acquisition equipment based on a target airline and a target ship;
step two: carrying out data cleaning and data mining analysis on the acquired data, and constructing an unmanned ship energy efficiency digital twin system database;
step three: based on an unmanned ship energy efficiency digital twin system database, carrying out navigation segment division on a target air route, establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model, and carrying out optimization decision analysis, wherein the aim is to optimize the ship energy efficiency;
step four: real-time mapping is carried out on the output data and real ship acquisition data based on the unmanned ship energy consumption model and the unmanned ship energy efficiency dynamic optimization model and the ship body, the host, the propeller and the navigation environment three-dimensional model, so that three-dimensional dynamic visualization is realized;
step five: the ship energy efficiency is remotely monitored based on a system design interface, ship navigation information, ship speed, host rotating speed, propeller rotating speed and ship energy efficiency level are displayed in real time through a three-dimensional model and a chart, decision analysis results are transmitted to a ship speed control system, and the decision analysis results are verified through virtual experiments.
Referring to fig. 1 and 2, an embodiment flowchart is shown in fig. 3, and the operation process of the unmanned ship energy efficiency digital twin system based on data driving is as follows:
selecting the number of times of the ship, and determining the departure port, the destination port, the departure time, the arrival time, the route distance and the navigation time requirement information of the ship.
Selecting a specific unmanned ship, and acquiring data through sensors and monitoring equipment installed on the ship, wherein the data comprises the following steps: the system comprises host machine oil consumption data, host machine rotating speed and host machine shaft power data, ship speed and course data and longitude and latitude position information.
Acquiring meteorological information and sea condition information from a European middle-term meteorological forecast center according to the selected route, wherein the meteorological information comprises wind speed and wind direction data; the sea state information includes wave height, water velocity and water depth data.
The acquired data are subjected to data preprocessing, missing values and repeated data are deleted, abnormal values and noise points are analyzed by adopting a box diagram detection method, the abnormal values are processed by a method of deleting data and filling interpolation after analysis, and the sorted data samples are stored in an unmanned ship energy efficiency digital twin system database.
Because the obtained data has large dimensionality, when a machine learning algorithm and a swarm intelligence optimization algorithm are used for data analysis, dimensionless processing needs to be carried out on the data in the unmanned ship energy efficiency digital twin system database, namely, the data are converted into dimensionless expressions through the following formula:
in the formula, x represents original data to be converted; x is the number ofminRepresenting the minimum value in the dimension data sample; x is the number ofmaxRepresenting the maximum value in the dimensional data sample.
According to the change of the actual navigation environment of the unmanned ship, data mining analysis is carried out on meteorological data and sea condition data in the selected route, and route section division is carried out on the route based on an improved k-means clustering algorithm, and the specific implementation process is as follows:
1) based on meteorological sea state data set samples { x1，x2，…xmAnd selecting initialized k samples as an initial flight segment clustering center, wherein the center points of the classes are as follows: a is1，a2，a3…ak∈RnWherein a iskRepresenting the kth initial cluster center point; k is the total number of the given clustering categories, and an optimal solution is searched by a grid search method to realize the value optimization of k;
2) by adopting a method of introducing a kernel function, the meteorological sea state data points in the input space are mapped into a higher-dimensional feature space through nonlinear mapping, and clustering is performed in a new feature space, so that the improvement of an algorithm is realized, and a more accurate clustering result is obtained.
3) In the high dimensional feature space, x for each sample in the meteorological sea state datasetiThe distances from the cluster centers of the k segments to the cluster centers of the k segments are calculated by the following formula and are classified into the class corresponding to the cluster center with the smallest distance.
In the formula, ciRepresenting the class with the closest distance between the sample i and the k classes;
4) for each class, the center point of the class is recalculated by:
in the formula, bjRepresenting the center point of the class for each update step.
5) And repeating the iteration steps 3) and 4) until the set maximum iteration number is reached or the minimum error change is reached.
According to meteorological sea condition characteristics of different voyage sections, an unmanned ship energy consumption model of the voyage section is established by adopting a machine learning algorithm based on data driving, the input characteristics of the energy consumption model are multivariable information such as wind speed, wind direction, water flow speed, wave height, host rotating speed, shaft power, voyage speed and the like, the output characteristics of the model are ship host oil consumption, and the accurate unmanned ship energy consumption model is finally obtained by repeatedly carrying out iterative training and optimization and comparing with actual ship operation data.
The establishing process of the unmanned ship energy consumption model based on the machine learning algorithm is as follows:
1) firstly, the output result x of the j node of the first layer hidden layer of the energy consumption modeljAnd output result y of h-th node of second layer hidden layerhObtainable by the formula (5) and the formula (6), respectively:
in the formula (f)1、f2Transfer functions of input parameters and output parameters respectively; b1，b2，...，bj、θ1，θ2，...，θhRepresents a bias of the network; w is aij、wjhIs the weight of the network; piIs the input parameter of the network, i.e. the input characteristic of the energy consumption model.
2) Obtaining the daily oil consumption of the ship main engine by the formula (7):
in the formula, q is the daily oil consumption of the ship main engine; f. of3Is a transfer function of the input parameter and the output parameter; w is ahkRepresents a bias of the network; beta represents the bias of the network.
Establishing an unmanned ship energy efficiency optimization model of the branch flight according to different flight segments, and reflecting the energy efficiency level change condition of each flight segment by solving the average EEOI value of each flight segment, wherein the average EEOI value can be obtained by an equation (8):
in the formula: EEOIaveIs the average EEOI value; j is a fuel type;
CO2 conversion factor for fuel oil;
fuel consumption per unit time of the host；
The oil consumption of the auxiliary engine in unit time; v. ofsIs the ship speed to the ground; m is the ship cargo capacity; t is the integration period.
The established unmanned ship energy efficiency dynamic optimization model takes the average EEOI value as an objective function, and the constraint conditions comprise that:
nmin＜ne≤nmax (11)
in the formula, n represents the number of divided segments; l isiThe voyage is the voyage of the ith voyage section; l is0Is the total voyage of the airline; vsiOptimizing the speed for the ship in the ith voyage section; t is0Representing a voyage time limit; n isminIndicating a host minimum speed limit; n ismaxRepresenting a maximum speed limit of the main engine; n iseThe rotating speed of the main engine after optimization.
And considering the influence of multiple influence factors on the ship speed, dynamically optimizing the ship speed in each navigation section based on a group intelligent algorithm, and obtaining the ship speed corresponding to the minimum objective function value as the optimal solution of the intelligent decision, thereby realizing the dynamic intelligent optimization decision of the unmanned ship speed.
The initial modeling of the three-dimensional ship body is carried out through Solidworks, the software is convenient to draw geometric dimensions, a ship body model, a host computer and a propeller model can be efficiently drawn according to a real ship model drawing paper in proportion, on the basis, the software such as 3DMax and Blender is combined to carry out three-dimensional rendering, the processing including material quality, texture, mapping, color, light effect and the like is carried out, the model has higher fidelity, a three-dimensional virtual ship body model, a three-dimensional virtual host computer model and a three-dimensional virtual propeller model based on data driving are built, and the ship body parts are distinguished by adjusting the rendering of the material quality.
The three-dimensional virtual scene rendering of the unmanned ship navigation environment is carried out through software such as 3DMax and Blender, a three-dimensional virtual navigation environment scene is constructed, and the three-dimensional virtual navigation environment scene is driven based on navigation environment data acquired in real time and can be highly consistent with a real navigation environment.
Converting the longitude and latitude coordinates and the steering points of the real ship of the selected air route, and mapping an air route track in a virtual navigation environment scene through a Blender to enable the established three-dimensional model of the ship body to navigate on the air route, wherein fig. 4 is an implementation illustration of mapping the air route track in the virtual navigation environment scene.
The interaction function is realized by using a built-in Python script language of the Blender, so that the established three-dimensional virtual hull model reflects the real-time navigation track; meanwhile, virtual environment scenes at different positions and different times of a navigation track are matched with actual navigation environment scene information, and the real-time dynamic change state of the navigation environment of the unmanned ship is displayed in real time in the virtual animation of the window interface of the twin body.
And realizing an interactive function by using a built-in Python scripting language of the Blender, and realizing real-time dynamic change of the navigation speed of the unmanned ship in real time in the virtual animation of the window interface of the twin body by matching the navigation speed of the established three-dimensional virtual ship body model with the real ship navigation speed data information.
The interaction function is realized by using a built-in Python scripting language of the Blender, and the real-time dynamic change of the rotating speed of the unmanned ship host is displayed in real time in the virtual animation of the twin body window interface by matching the rotating speed of the established three-dimensional virtual host model with the rotating speed data information of the real ship host; meanwhile, the real-time dynamic change of the rotating speed of the propeller of the unmanned ship is displayed in the virtual animation in real time by matching the rotating speed of the established three-dimensional virtual propeller model with the rotating speed data information of the propeller of the real ship;
matching the corresponding three-dimensional virtual ship body model, the three-dimensional virtual host model and the three-dimensional virtual propeller model, reflecting the navigation environment information, the navigation track information, the navigation speed information, the host rotating speed information, the propeller rotating speed information and the ship energy efficiency level information of the unmanned ship in real time in the virtual animation of the twin-organism window interface, realizing three-dimensional synchronous display of the navigation environment virtual scene, the virtual model and the ship energy efficiency level, and realizing remote monitoring of real ship navigation conditions and energy efficiency intelligent optimization auxiliary decision on a shore basis. In addition, the twin window interface has a three-dimensional multi-view viewing function, and the viewing view adjustment and the specific detail information display of the three-dimensional animation can be realized through the three-dimensional view adjusting function and the local zooming-in and zooming-out function.
In addition, the chart is used for displaying the physical energy consumption information and the energy efficiency level information of the unmanned ship, and the chart is a dynamic chart based on real-time data driving, so that dynamic visualization of corresponding information can be realized.
The specific virtual experiment verification steps are as follows:
the method comprises the following steps: selecting historical voyage times, and acquiring navigation information including requirements of a ship departure port, a destination port, departure time, arrival time, a course distance and voyage time, and main engine oil consumption, main engine rotating speed, propeller rotating speed, main engine shaft power, ship speed, ship course, longitude and latitude, weather and sea condition data of actual navigation of the ship;
step two: constructing a three-dimensional scene of an actual navigation environment of the ship through a model layer, constructing two three-dimensional virtual ships with the same parameters under the same scene, and respectively corresponding to a virtual ship driven by historical actual navigation data and a virtual ship driven by energy efficiency intelligent optimization decision data;
step three: establishing real-time mapping of historical actual navigation data virtual ships and historical actual storage data, and displaying the running state and the energy efficiency of the virtual ships in a three-dimensional dynamic manner; establishing real-time mapping between the virtual ship based on the energy efficiency intelligent optimization decision data and the energy efficiency intelligent optimization decision output result data, displaying the operation state and the energy efficiency of the virtual ship in a three-dimensional dynamic mode, comparing the operation state and the energy efficiency level of two virtual ships, and verifying the effectiveness and the applicability of the proposed ship energy efficiency intelligent optimization decision algorithm, namely, under the same navigation environment condition, if the energy efficiency level of the virtual ship driven based on the energy efficiency intelligent optimization decision data is superior to the energy efficiency level of the virtual ship driven by actual navigation data, the ship energy efficiency intelligent optimization decision algorithm is effective and applicable; meanwhile, according to the comparative analysis of the energy efficiency level, the ship energy efficiency optimization effect based on the ship energy efficiency intelligent optimization decision algorithm, namely the percentage of the energy efficiency optimization, can be verified.
The beneficial effects of the whole are as follows:
the unmanned ship energy efficiency dynamic optimization model considering various influence factors is established by taking data driving as a modeling basis, unmanned ship energy efficiency intelligent optimization decision is realized through a group intelligent algorithm, an unmanned ship energy efficiency digital twin system is established through relevant professional software, and real-time mapping of data is realized, the established unmanned ship energy efficiency digital twin system can reflect navigation environment scene, ship speed, host rotating speed, propeller rotating speed and energy efficiency level in real time, and the system is convenient for a shipping company to realize unmanned ship energy efficiency intelligent decision and management; meanwhile, the system can be applied to an unmanned ship energy efficiency intelligent optimization control virtual experiment, experiment tests and verification which are difficult to perform on an actual ship are realized, and the actual ship navigation condition is reflected by a data-driven high-precision virtual model, so that the effectiveness of an intelligent optimization decision algorithm and the energy efficiency optimization effect of the intelligent optimization decision algorithm can be verified.
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 (9)
1. A data-driven unmanned ship energy efficiency digital twin system comprises an entity base layer, a data layer, a model layer, a dynamic visualization layer and a functional application layer,
an entity basic layer used for data information interaction, software and hardware configuration and interface design,
the data layer is used for data cleaning, data processing and database construction,
the model layer is used for establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model and carrying out energy efficiency intelligent optimization decision,
a dynamic visualization layer used for three-dimensional model construction, data real-time mapping and three-dimensional model dynamic display,
and the functional application layer is used for energy efficiency remote monitoring, remote intelligent optimization management and virtual experiments.
2. The unmanned ship energy efficiency digital twin system based on data driving of claim 1, wherein the interface design refers to designing the interface layout of a navigational speed information window, a course information window, a host rotational speed information window, a navigational speed optimization decision window and a twin window,
the navigation speed information window is used for displaying real-time dynamic navigation speed information of the ship;
the course information window is used for displaying real-time dynamic course information of the ship;
the main engine rotating speed information window is used for displaying real-time dynamic rotating speed information of the ship main engine;
the navigation speed optimization decision window is used for displaying navigation speed dynamic optimization result information;
the twin window is used for displaying three-dimensional ship model and virtual navigation environment information, and can be used for displaying three-dimensional multi-view angles.
3. The unmanned ship energy efficiency digital twin system based on data driving of claim 1, wherein the three-dimensional model building means modeling of a three-dimensional ship body, a host and a propeller of a virtual ship through Solidworks, and then three-dimensional rendering of the three-dimensional ship body, the host, the propeller and a navigation environment is performed by using 3DMax and Blender software.
4. The unmanned ship energy efficiency digital twin system based on data driving according to claim 1, wherein the real-time data mapping is to map data in a database and output data of an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model into visual elements, including visual space scenes, visual marks and visual animations;
the visual space scene is visually developed by adopting a mode of combining three dimensions and two dimensions;
the visual mark is matched into the established virtual space scene after coordinate conversion based on longitude and latitude coordinates and steering point coordinates of the real ship route;
the visual animation is real-time mapping from the ship speed, the main engine rotating speed, the propeller rotating speed and the data value of the air route to the three-dimensional model display.
5. The unmanned ship energy efficiency digital twin system based on data driving of claim 2, wherein the three-dimensional multi-view display is performed by adjusting a three-dimensional view and local enlargement and reduction, so that viewing view adjustment of three-dimensional animation and specific detail information display are realized.
6. A data-driven unmanned ship energy efficiency digital twinning method is characterized by comprising the following steps:
the method comprises the following steps: acquiring data including weather, sea conditions, flight paths, operation and energy consumption data through a European middle-term weather forecast center and ship data acquisition equipment based on a target course and a target ship;
step two: performing data cleaning and data analysis on the acquired data, and constructing an unmanned ship energy efficiency digital twin system database;
step three: based on an unmanned ship energy efficiency digital twin system database, carrying out navigation segment division on a target air route, establishing an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model, and carrying out optimization decision analysis according to optimal ship energy efficiency;
step four: the method comprises the steps that real-time mapping is carried out on output data and ship acquisition data based on an unmanned ship energy consumption model and an unmanned ship energy efficiency dynamic optimization model and a ship body, a host, a propeller and a navigation environment three-dimensional model, and three-dimensional dynamic visualization is achieved;
step five: the ship energy efficiency is remotely monitored based on a system design interface, ship navigation information, ship speed, host rotating speed, propeller rotating speed and ship energy efficiency level are displayed in real time through a three-dimensional model and a chart, decision analysis results are transmitted to a ship speed control system, and the decision analysis results are verified through virtual experiments.
7. The unmanned ship energy efficiency digital twinning method based on data driving of claim 6, wherein the unmanned ship energy consumption model is established by taking multivariable of wind speed, wind direction, wave height, rotating speed, shaft power and the like as input characteristics and taking unmanned ship host machine oil consumption as output characteristics and obtaining the unmanned ship energy consumption model through repeated iterative training and optimization of a machine learning algorithm.
8. The unmanned ship energy efficiency digital twinning method based on data driving according to claim 6, wherein the establishing of the unmanned ship energy efficiency dynamic optimization model comprises the following steps:
the method comprises the following steps: analyzing weather and sea condition data of a target route through a clustering algorithm, and dividing the target route into sections;
step two: calculating an average ship energy efficiency operation index of each navigation section through an unmanned ship energy consumption model, wherein the average ship energy efficiency operation index is obtained through an equation (1):
in the formula: EEOIaveThe average ship energy efficiency operation index is obtained; j is a fuel type;
CO as fuel oil2A conversion factor;
is the oil consumption of the host machine in unit time;
the oil consumption of the auxiliary engine in unit time; v. ofsIs the ship speed to the ground; m is the ship cargo capacity; t is an integration period;
step three: dynamically optimizing and solving the ship speed by using a group intelligent optimization algorithm by taking the ship speed in each navigation section as an optimization variable, taking an average ship energy efficiency operation index as an optimization target and taking the navigation time and the rotating speed of a host as constraint conditions to obtain the optimal ship speed corresponding to the optimal objective function value;
wherein the constraint conditions are as follows:
nmin＜ne≤nmax (4)
in the formula, n represents the number of divided segments; l isiThe voyage is the voyage of the ith voyage section; l is0Is the total voyage of the airline; vsiOptimizing the speed for the ship in the ith voyage section; t is0Representing a voyage time limit; n isminIndicating a host minimum speed limit; n ismaxRepresenting a maximum speed limit of the main engine; n iseThe rotating speed of the main engine after optimization.
9. The unmanned ship energy efficiency digital twinning method based on data driving according to claim 6, wherein the virtual experiment comprises:
the method comprises the following steps: selecting historical voyage times, and acquiring navigation information including requirements of a ship departure port, a destination port, departure time, arrival time, a course distance and voyage time, and main engine oil consumption, main engine rotating speed, propeller rotating speed, main engine shaft power, ship speed, ship course, longitude and latitude, weather and sea condition data of actual navigation of the ship;
step two: constructing a three-dimensional scene of an actual navigation environment of a ship, constructing two three-dimensional virtual ships with the same parameters under the same scene, and respectively corresponding to a virtual ship driven by historical actual navigation data and a virtual ship driven by energy efficiency intelligent optimization decision data;
step three: establishing real-time mapping of historical actual navigation data virtual ships and historical actual storage data, and displaying the running state and the energy efficiency of the virtual ships in a three-dimensional dynamic manner; the method comprises the steps of establishing real-time mapping of a virtual ship based on energy efficiency intelligent optimization decision data and energy efficiency intelligent optimization decision output result data, displaying the operation state and energy efficiency of the virtual ship in a three-dimensional dynamic mode, and comparing and analyzing the operation state and energy efficiency level of two virtual ships, so that the effectiveness of an energy efficiency management method is verified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110198914.3A CN113033073A (en) | 2021-02-22 | 2021-02-22 | Unmanned ship energy efficiency digital twinning method and system based on data driving |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110198914.3A CN113033073A (en) | 2021-02-22 | 2021-02-22 | Unmanned ship energy efficiency digital twinning method and system based on data driving |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113033073A true CN113033073A (en) | 2021-06-25 |
Family
ID=76461136
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110198914.3A Pending CN113033073A (en) | 2021-02-22 | 2021-02-22 | Unmanned ship energy efficiency digital twinning method and system based on data driving |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113033073A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487760A (en) * | 2021-07-28 | 2021-10-08 | 大连海事大学 | Turbine system of intelligent ship |
| CN113487913A (en) * | 2021-08-10 | 2021-10-08 | 上海奇梦网络科技有限公司 | Digital twin remote operation and maintenance system for ship management |
| CN113627780A (en) * | 2021-08-09 | 2021-11-09 | 中国海洋石油集团有限公司 | Integrated digital twin system based on deepwater floating platform state monitoring and evaluation |
| CN113742331A (en) * | 2021-10-11 | 2021-12-03 | 浙江数智交院科技股份有限公司 | Digital twin ship driving method and device |
| CN113869783A (en) * | 2021-10-15 | 2021-12-31 | 中远海运科技股份有限公司 | Ship operation efficiency evaluation system and method |
| CN113888088A (en) * | 2021-09-30 | 2022-01-04 | 武汉理工大学 | Digital twin-based ship energy efficiency management system function verification platform |
| CN114441126A (en) * | 2022-02-10 | 2022-05-06 | 上海电气集团股份有限公司 | Vibration test method, system, equipment and medium based on digital twinning |
| CN114444716A (en) * | 2022-01-06 | 2022-05-06 | 中国电子科技集团公司电子科学研究院 | Multi-agent game training method and system in virtual environment |
| CN115205706A (en) * | 2022-08-17 | 2022-10-18 | 数字太空(北京)科技股份公司 | Remote sensing data acquisition method and device, electronic equipment and computer readable medium |
| CN115277743A (en) * | 2022-06-22 | 2022-11-01 | 上海万舟通海事技术有限公司 | Internet of things-based ship energy consumption and carbon emission monitoring system |
| CN115407786A (en) * | 2022-11-01 | 2022-11-29 | 中国船舶集团有限公司第七〇七研究所 | Method, device, equipment and medium for deciding automatic navigation of ship in full voyage |
| CN116756837A (en) * | 2023-08-17 | 2023-09-15 | 广东省科学院广州地理研究所 | Digital twin resource data database building management method and device |
| CN113888088B (en) * | 2021-09-30 | 2024-06-04 | 武汉理工大学 | Digital twinning-based ship energy efficiency management system function verification platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110737986A (en) * | 2019-10-15 | 2020-01-31 | 大连海事大学 | unmanned ship energy efficiency intelligent optimization simulation system and method |
| CN110967022A (en) * | 2019-12-24 | 2020-04-07 | 上海船舶运输科学研究所 | Ship speed optimization aid decision-making system |
| CN111292419A (en) * | 2020-01-19 | 2020-06-16 | 智慧航海（青岛）科技有限公司 | Intelligent ship navigation digital sand table system based on electronic chart |
| CN112307688A (en) * | 2020-10-29 | 2021-02-02 | 中国舰船研究设计中心 | System and method for monitoring, managing and optimizing energy efficiency of electric propulsion ship |
-
2021
- 2021-02-22 CN CN202110198914.3A patent/CN113033073A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110737986A (en) * | 2019-10-15 | 2020-01-31 | 大连海事大学 | unmanned ship energy efficiency intelligent optimization simulation system and method |
| CN110967022A (en) * | 2019-12-24 | 2020-04-07 | 上海船舶运输科学研究所 | Ship speed optimization aid decision-making system |
| CN111292419A (en) * | 2020-01-19 | 2020-06-16 | 智慧航海（青岛）科技有限公司 | Intelligent ship navigation digital sand table system based on electronic chart |
| CN112307688A (en) * | 2020-10-29 | 2021-02-02 | 中国舰船研究设计中心 | System and method for monitoring, managing and optimizing energy efficiency of electric propulsion ship |
Non-Patent Citations (3)
| Title |
|---|
| 孙超等: "基于航速与实时能效综合目标的船舶主机转速优化", 《大连海事大学学报》, vol. 46, no. 3, pages 60 - 67 * |
| 蔡笑驰;姚怡芝;李鑫;: "数字孪生技术在船舶全生命周期的应用架构", 船舶设计通讯, no. 02 * |
| 黄连忠等: "基于模拟退火算法的船舶航速优化研究", 《船舶》, no. 176, pages 8 - 17 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487760A (en) * | 2021-07-28 | 2021-10-08 | 大连海事大学 | Turbine system of intelligent ship |
| CN113487760B (en) * | 2021-07-28 | 2023-07-21 | 大连海事大学 | Turbine system of intelligent ship |
| CN113627780A (en) * | 2021-08-09 | 2021-11-09 | 中国海洋石油集团有限公司 | Integrated digital twin system based on deepwater floating platform state monitoring and evaluation |
| CN113487913A (en) * | 2021-08-10 | 2021-10-08 | 上海奇梦网络科技有限公司 | Digital twin remote operation and maintenance system for ship management |
| CN113888088A (en) * | 2021-09-30 | 2022-01-04 | 武汉理工大学 | Digital twin-based ship energy efficiency management system function verification platform |
| CN113888088B (en) * | 2021-09-30 | 2024-06-04 | 武汉理工大学 | Digital twinning-based ship energy efficiency management system function verification platform |
| CN113742331A (en) * | 2021-10-11 | 2021-12-03 | 浙江数智交院科技股份有限公司 | Digital twin ship driving method and device |
| CN113869783A (en) * | 2021-10-15 | 2021-12-31 | 中远海运科技股份有限公司 | Ship operation efficiency evaluation system and method |
| CN114444716A (en) * | 2022-01-06 | 2022-05-06 | 中国电子科技集团公司电子科学研究院 | Multi-agent game training method and system in virtual environment |
| CN114441126A (en) * | 2022-02-10 | 2022-05-06 | 上海电气集团股份有限公司 | Vibration test method, system, equipment and medium based on digital twinning |
| CN115277743B (en) * | 2022-06-22 | 2023-10-20 | 上海万舟通海事技术有限公司 | Ship energy consumption and carbon emission monitoring system based on Internet of things |
| CN115277743A (en) * | 2022-06-22 | 2022-11-01 | 上海万舟通海事技术有限公司 | Internet of things-based ship energy consumption and carbon emission monitoring system |
| CN115205706A (en) * | 2022-08-17 | 2022-10-18 | 数字太空(北京)科技股份公司 | Remote sensing data acquisition method and device, electronic equipment and computer readable medium |
| CN115407786A (en) * | 2022-11-01 | 2022-11-29 | 中国船舶集团有限公司第七〇七研究所 | Method, device, equipment and medium for deciding automatic navigation of ship in full voyage |
| CN116756837A (en) * | 2023-08-17 | 2023-09-15 | 广东省科学院广州地理研究所 | Digital twin resource data database building management method and device |
| CN116756837B (en) * | 2023-08-17 | 2023-11-28 | 广东省科学院广州地理研究所 | Digital twin resource data database building management method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113033073A (en) | Unmanned ship energy efficiency digital twinning method and system based on data driving | |
| Wang et al. | A Three-Dimensional Dijkstra's algorithm for multi-objective ship voyage optimization | |
| CN110737986B (en) | Unmanned ship energy efficiency intelligent optimization simulation system and method | |
| Tsou et al. | An Ant Colony Algorithm for efficient ship routing | |
| CN116644608B (en) | Real sea area ship motion forecasting method and system based on marine environment data | |
| Wu et al. | Long-voyage route planning method based on multi-scale visibility graph for autonomous ships | |
| CN108680163A (en) | A kind of unmanned boat route search system and method based on topological map | |
| Christensen et al. | A risk-based approach for determining the future potential of commercial shipping in the Arctic | |
| CN111552299B (en) | Intelligent optimization management system and optimization method for wind wing navigation-aiding ship energy efficiency | |
| KR101982582B1 (en) | System for evaluation of vessel based by automatic identification system data and marine environment data connected big data | |
| CN112947485A (en) | Path planning method and system | |
| CN114596731B (en) | AIS-based ship navigation process meteorological sea condition data fusion processing system and method | |
| Khayyal et al. | Creation and spatial analysis of 3D city modeling based on GIS data | |
| Cristea et al. | Operational shipping intelligence through distributed cloud computing | |
| CN112348867A (en) | Method and system for constructing city high-precision three-dimensional terrain based on LiDAR point cloud data | |
| CN109949197A (en) | A kind of ship-shore cooperation system towards Haihe River through transport | |
| CN112215395B (en) | Underwater equipment adaptability information guarantee system based on ocean big data | |
| Dong et al. | RETRACTED ARTICLE: Research on North Gulf distributed big data submarine 3D terrain computing system based on remote sensing and multi-beam | |
| Marie et al. | Sail-assisted motor vessels weather routing using a fuzzy logic model | |
| Pan et al. | Development of ship weather routing system with higher accuracy using SPSS and an improved genetic algorithm | |
| TWI805048B (en) | Planning method for deployment of operating stations | |
| Büyüksalih et al. | Design for 3D city model management using remote sensing and GIS: A case study for the Golden Horn in Istanbul, Turkey | |
| Jin et al. | Research on ship route planning method based on neural network wave data forecast | |
| Filippopoulos et al. | Multi-sensor data fusion for the vessel trim analyzer and optimization platform | |
| CN112734684A (en) | Ocean environment information superposition and fusion display method based on electronic chart |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |