CN110967022B - Ship navigational speed optimization auxiliary decision-making system - Google Patents
Ship navigational speed optimization auxiliary decision-making system Download PDFInfo
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- CN110967022B CN110967022B CN201911345506.5A CN201911345506A CN110967022B CN 110967022 B CN110967022 B CN 110967022B CN 201911345506 A CN201911345506 A CN 201911345506A CN 110967022 B CN110967022 B CN 110967022B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
A ship navigational speed optimization auxiliary decision-making system comprises a weather forecast analyzer, a fuel consumption prediction model, an ocean route reconstruction model, a genetic algorithm solver, a data acquisition platform arranged at a ship end and a ship end interface, wherein the fuel consumption prediction model is used for predicting the fuel consumption of a host machine per hour under actual sea conditions; the ocean route reconstruction model is used for equally dividing the route of the target ship and re-planning the rest route according to the current position of the target ship; the genetic algorithm solver is used for calculating an optimal rotation speed solution of minimum fuel consumption of the route; the ship end interface is used for inputting predefined route information and displaying route and speed optimization suggestions. The advantages are that: establishing a main engine hourly oil consumption estimation model and a navigational speed optimization model of a specific ship, wherein the main engine comprises wind wave factors, according to the test data; and the optimal energy-saving navigation state adapting to the navigation environment of the ship and the running rotating speed of the main engine of each navigation section are provided by taking the minimum fuel consumption of the navigation times as a target and through the optimization decision of the full-range navigation speed, so that the safety and energy saving of the ship are realized.
Description
Technical Field
The invention relates to the technical field of ship route speed optimization, in particular to a ship speed optimization auxiliary decision-making system.
Background
The optimization of the ship sailing speed plays an important role in reducing the fuel cost and the greenhouse gas emission. Cost control is a necessary means for many maritime logistics enterprises to survive in a strong market competition. Mainly in 2008, the international maritime logistics market is still affected by the financial crisis of 2008. Many researchers have utilized different technological innovations to help companies save costs, including speed optimization. For many maritime logistics companies such as Damascus, finding the optimal speed solution with the lowest fuel consumption is a cost control solution. In addition, the speed optimization helps to reduce greenhouse gas emissions. According to united nations reports, marine logistics occupy more than 70% of world transportation. Therefore, reducing greenhouse gas emissions from operating ships helps to prevent global warming. As is well known, global warming has become a common challenge for humans. The gradual rise of global air temperature causes the sea level to rise and the ecosystem to change drastically, resulting in a series of serious problems endangering human survival. Under the trend of global energy conservation and emission reduction, the society of each community puts forward higher emission reduction requirements on ocean logistics industry, and establishes more carbon dioxide emission limits and standards. According to carbon emission agreements signed by the International Maritime Organization (IMO) at 2018, 4, 13 and london, the carbon dioxide emissions of the shipping industry should be reduced by 50% to 2050. Furthermore, the goal of zero emissions is to further reduce carbon emissions in marine logistics.
At present, energy-saving methods mainly adopted by various shipping companies during speed-down navigation are proved to have certain energy-saving efficacy. However, facing the owner's demand, employing a slow down sailing strategy in a single pass may result in delays in the lead time.
Accordingly, ship speed optimization is applied to improve ship management of shipping companies, including time management, cost control management, fueling management, and fleet management. Expert scholars at home and abroad make more sufficient researches on the optimization of navigational speed. When the output power of the host or the fuel consumption of the host per hour is estimated, a plurality of foreign students mainly adopt an empirical formula, and some of the students can consider the ship performance and combine with historical meteorological data to construct an hourly fuel consumption model. In addition, the navigation speed optimization studied by the scholars at home and abroad is mainly to perform navigation speed optimization before navigation, and corresponding navigation speed suggestions are provided according to optimization targets. Many scholars utilize their own improved genetic algorithm or ant colony algorithm to plan the course speed.
However, the existing route speed optimization scheme has the following problems:
1. problems in the research of the estimated oil consumption model per hour of the host machine
(1) The basic performance data of the target ship is lacked as calculation support, many oil consumption estimation models are based on empirical formulas or collected data of similar ships, and the empirical formulas are not applicable when the ship characteristics of each ship are different.
(2) The influence of the calculated weather factors is lacking. Weather factors
(3) Correction of base performance data in the absence of real ship data
(4) The estimated accuracy of the oil consumption per hour for a specific ship sailing under a specific weather is difficult to ensure
(5) Less consideration is given to the relation between the fuel consumption rate of a host and the power
2. Problems of speed optimization research for ocean routes
(1) There is a lack of accurate weather forecast.
(2) The dynamic navigational speed optimization function is lacking.
3. Problems with recommended voyage optimization advice
(1) Host rotational speed between steering points of the route is too great
(2) Without taking into account actual sailing habits of crews
(3) The optimization algorithm may cause the search result to be in local optimum, rather than the whole route optimum state to the defects existing in the prior art, and the invention is proposed.
Disclosure of Invention
Aiming at the defects of the prior art structure, the ship navigational speed optimization auxiliary decision-making system is provided, and the system aims to obtain the ship rotational speed on each navigation section on the premise of reaching a destination port on time so as to minimize the fuel consumed by a host machine when completing the voyage tasks. The system can automatically give the host machine rotating speed and the fuel quantity required to be consumed by the host machine on each navigation section from the current position according to the current ship position, provide scientific basis for ship drivers, and reduce blindness of setting the rotating speed by experience. The key technology involves two aspects: the method is accurate in fuel consumption calculation model and can quickly obtain the calculation method of the optimized rotating speed on each section of the whole route.
In order to achieve the aim of the invention, the ship navigational speed optimization auxiliary decision-making system is realized by the following technical scheme:
a marine vessel navigational speed optimization aid decision making system, the system comprising:
the data acquisition platform is arranged at the ship end and is used for acquiring real-time navigation information of the target ship;
the weather forecast analyzer is arranged at the shore end and is used for receiving the weather forecast original data and analyzing the weather forecast original data into weather forecast information related to the target ship;
the oil consumption prediction model is arranged at the shore end and is used for predicting the fuel consumption of the host machine in each hour under the actual sea condition according to the navigation information acquired by the data acquisition platform;
the ocean route reconstruction model is arranged at the shore end and is used for equally dividing a given target ship route to design a large circular route which is approximately in an arc shape and re-planning the rest route according to the current position of the target ship;
the genetic algorithm solver is arranged at the shore end and is used for calculating the optimal rotation speed solution of the target ship with minimum fuel consumption of the route according to the navigation information of the target ship, the weather forecast information related to the target ship, the fuel consumption per hour of the host predicted by the fuel consumption prediction model and the route set by the ocean route reconstruction model;
and the ship end interface is arranged at the ship end and is used for inputting predefined route information and displaying route and speed optimization suggestions of the target ship.
The system also comprises a shore database arranged at a shore and a ship database arranged at a ship, wherein the shore database is in communication connection with the oil consumption prediction model, the ocean route reconstruction model, the genetic algorithm solver and the gas image prediction analyzer, receives and stores the data communicated by the oil consumption prediction model, the ocean route reconstruction model, the genetic algorithm solver and the gas image prediction analyzer; the ship end database is in communication connection with the shore end database, the ship end interface and the data acquisition platform, and receives and stores the data of the shore end database and the data acquisition platform.
The navigation information acquired by the data acquisition platform comprises: load state, draft, trim value, propulsion efficiency η of target vessel D Density ρ of air A Wind speed at reference altitude V WR Wind direction ψ at reference height WR Density ρ of sea water s Gravitational acceleration g, sense wave height H 1/3 Flow velocity V c Flow direction beta, heading ψ.
The fuel consumption prediction model comprises:
a host output power calculation unit predicting a target ship host output power according to the following formulas 1-4;
P ＝ P s + P add (1)
P add ＝(R wind +R wave )V s /η D (2)
the ground speed calculation unit predicts the ground speed of the target ship according to the following formula 5-6;
ΔV s1 ＝V c cos(β-ψ) (5)
V g ＝V s +ΔV s1 (6)
the fuel consumption calculation unit obtains the fuel consumption rate of a specific power value based on the fuel consumption rate curve of the host according to the calculation results of the host output power calculation unit and the ground speed calculation unit;
wherein P is the output power of the host, P s In still waterPower, P add For environmentally induced power increase, R wind Resistance increase due to wind, R wave For increasing drag caused by waves, V s For the speed of water navigation, V g To navigate to the ground, H 1/3 For sense wave height, B is the target ship shape width, L BWL Is the ship length from the bow to 95% of the type width, delta V s1 The speed of the ocean current increases.
The genetic algorithm solver is realized by the following method:
initializing a plurality of groups of assumed optimal rotation speed solutions, wherein each group of assumed optimal rotation speed solutions comprises assumed rotation speed values of each navigation segment;
step two, calculating the total fuel consumption and the assumed rotating speed value of the route of the assumed optimal rotating speed solution according to the weather forecast information and the navigation information, and the waypoints, the timetable, the limited rotating speed, the limited navigational speed and the latitude and longitude of the steering point of each leg;
step three, selecting a group with the lowest total fuel consumption of the route in the assumed optimal rotation speed solutions calculated in the step two;
step four, executing step two again on the selected assumed optimal rotation speed solution, and obtaining a group of new assumed optimal rotation speed solutions with the assumed rotation speed values changed and the assumed rotation speed values of each leg changed in a crossing way compared with the selected assumed optimal rotation speed solution; and selecting the new assumed optimal rotational speed solution;
step five, repeating the step four until the stopping condition is met: the iteration times reach a set value, or the minimum oil consumption value is not changed any more; then judging whether the finally obtained assumed optimal rotation speed solution meets the limiting condition or not: the rotating speed cannot exceed the maximum rotating speed of the host machine, the navigational speed cannot exceed the maximum rotating speed, and the rotating speed cannot be in the rotating speed forbidden zone range of the host machine; if yes, the assumed optimal rotation speed solution is an optimal rotation speed solution with the minimum fuel consumption of the residual route of the target ship, and the optimal rotation speed solution is output to a ship end interface; if not, repeating the second to fourth steps until the optimal rotation speed solution meeting the stop condition and the limiting condition simultaneously is obtained.
The specific calculation steps of the step two are as follows:
A. initializing i=0, starting with the first segment of the assumed optimal rotation solution; calculating the i-th distance D i And determining the time T to the start of the ith segment id Acquiring weather conditions corresponding to the time according to the weather forecast information;
B. inputting navigation information of the ith navigation section into the oil consumption prediction model to obtain oil consumption q, host output power and ground speed per hour;
C. calculating the target ship oil consumption Q according to the following formulas 7-9 i And gets the time T to reach the i+1th segment end (i+1)d ：
T i ＝ D i /V g (7)
T (i+1)d ＝T id + T i (8)
Q i ＝q*T i (9)
D. Checking whether the limiting condition is met or not, and checking whether the calculation of the whole route is completed or not; if the limiting condition is met and the calculation of the whole route is completed, outputting the total fuel consumption F and the ground speed; and if the limiting condition is not met or the calculation of the whole route is not completed, returning to the step A to continue the calculation.
Compared with the prior art, the invention has the beneficial effects that:
(1) The influence of sea conditions on the optimal navigational speed is comprehensively considered;
(2) The influence of the ship attitude on the optimal navigational speed is accurately considered;
(3) The energy-saving navigational speed decision of the ship comprehensively considers the navigational order plan, the wind and wave conditions of the navigation area and the navigational performance of the ship, aims at minimizing the navigational order fuel consumption of the ship, takes the safety and the navigational order time of the ship as constraint conditions, gives out the optimal energy-saving navigational state adapting to the navigational environment of the ship and the optimal navigational speed of each navigation segment through the full range navigational speed optimization decision, and realizes the safe energy-saving navigational speed control of the ship;
(4) And adjusting the energy-saving navigational speed of the rest navigation section in real time according to the navigational plan adjustment and the change of the wind and the wave of the navigation area, and guiding the ship to complete the production task.
(5) The ship energy-saving navigational speed auxiliary decision-making system quantifies the current fuzzy practice of speed reduction and energy saving of the ship, so that the energy-saving effect is more considerable;
(6) The system gives optimal results from the whole course situation rather than looking at the current state, so that the final results will maximize energy savings or voyage profits.
(7) The dynamic speed optimization of the ocean route is realized, and the latest speed optimization suggestion can be provided according to the latest meteorological conditions.
Drawings
The above features and advantages of the present invention will become more apparent and readily appreciated from the following description of exemplary embodiments thereof, taken in conjunction with the accompanying drawings.
FIG. 1 is a general structure diagram of a ship navigational speed optimization auxiliary decision-making system;
FIG. 2 is a flow chart of calculation of total fuel consumption of a route of the ship speed optimization auxiliary decision-making system according to the embodiment of the invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings, to facilitate understanding by those skilled in the art:
referring to fig. 1-2, an embodiment of the present invention provides a ship navigational speed optimization auxiliary decision-making system, which aims to obtain a ship rotational speed on each leg on the premise of reaching a destination port on time, so that fuel consumed by a host computer when completing a voyage task is minimum. The system can automatically give the host machine rotating speed and the fuel quantity required to be consumed by the host machine on each navigation section from the current position according to the current ship position, provide scientific basis for ship drivers, and reduce blindness of setting the rotating speed by experience. The key technology involves two aspects: the method is accurate in fuel consumption calculation model and can quickly obtain the calculation method of the optimized rotating speed on each section of the whole route.
The system mainly comprises a ship end system and a shore end system.
The ship end system is divided into three parts: the system comprises a ship end interface, a data acquisition platform and a ship end database. The ship terminal interface on the ship is in communication connection with the ship terminal database and is used for inputting predefined route information and displaying route and speed optimization suggestions of the target ship. The ship end database is in communication connection with the data acquisition platform, receives the data of the data acquisition platform and stores the data. The data acquisition platform is a real-time navigation information acquisition platform, and the acquired navigation information comprises: load state, draft, trim value, propulsive efficiency η D Density ρ of air A Wind speed at reference altitude V WR Wind direction ψ at reference height WR Density ρ of sea water s Gravitational acceleration g, sense wave height H 1/3 Flow velocity V c Flow direction beta, heading ψ. The collected data are collected in a ship end database.
Furthermore, the shore end system mainly includes five parts: the system comprises a shore database, a weather forecast analyzer, an ocean route reconstruction model, a ship performance-based fuel consumption prediction model and a genetic algorithm solver. The weather forecast analyzer is used for receiving the weather forecast raw data and analyzing the weather forecast raw data into weather forecast information related to the target ship. The predefined route information and the collected data are sent to a shore database, which combines weather forecast data with target ship performance data to optimize ship speed. The optimization results are sent to the ocean vessel via a satellite network and displayed on the vessel interface. The shore end database is in communication connection with the oil consumption prediction model, the ocean route reconstruction model, the genetic algorithm solver and the gas image prediction analyzer, receives and stores data communicated with the oil consumption prediction model, the ocean route reconstruction model, the genetic algorithm solver and the gas image prediction analyzer. The shore database is in wireless communication connection with the ship database, and the ship database receives and stores the data of the shore database.
The bank database, weather forecast analyzer, ship interface, data acquisition platform and ship database are known to those skilled in the art, so the principle and structure thereof will not be described herein. The following describes the fuel consumption prediction model, the ocean route reconstruction model and the genetic algorithm solver in detail:
1. fuel consumption prediction model
The model predicts the fuel consumption (q) per hour of the host under actual sea conditions by inputting meteorological condition parameters (data of wind and wave currents) and actual load condition data (draft, trim and rotation speed). In order to predict the fuel consumption per hour of the host under specific sailing conditions, two steps are divided: firstly, predicting the output power and the ground speed of a host; the second step is to calculate the fuel consumption per kilowatt-hour in combination with the host's fuel consumption rate curve. The fuel consumption rate of the specific power value can be obtained by using a B spline curve method. Thus, the model can predict the fuel consumption per hour and the speed to ground under actual sea conditions. The device mainly comprises a host output power calculating unit, a ground speed calculating unit and an hourly fuel consumption calculating unit.
The host output power calculation unit is used for predicting the output power of the host of the target ship according to the following formulas 1-4 mainly by referring to a direct power method proposed by ISO 2015;
P＝P s +P add (1)
P add ＝(R wind +R wave )V s /η D (2)
the ground speed calculation unit predicts the ground speed of the target ship according to the following formula 5-6;
ΔV s1 ＝V c cos(β-ψ) (5)
V g ＝V s +ΔV s1 (6)
wave-induced power increase (P add ) The increase in drag caused by the wave is calculated as shown in equations 3 and 4, mainly using empirical equation 2. Calculating still waterAccording to the test data of the static water model of the target ship in the towing tank, the output power (P) of the host in the static water can be accurately calculated by using the actual front draft, rear draft and rotating speed s ) Speed of sailing water (V s ) Propulsion efficiency (eta) D ). Due to irregularities in test data of the still water model, the model predicts the host output power (P) in still water by adopting a random forest algorithm in an artificial intelligence algorithm s ). Obtaining the ground speed (V) according to the formula (5) and the formula (6) g )。
The fuel consumption calculation unit obtains the fuel consumption rate of a specific power value based on the fuel consumption rate curve of the host according to the calculation results of the host output power calculation unit and the ground speed calculation unit; as shown in the table below. The fuel consumption rate of the specific power value can be obtained by using a B spline curve method.
Wherein P is the output power of the host, P s For power in still water, P add For environmentally induced power increase, R wind Resistance increase due to wind, R wave For increasing drag caused by waves, V s For the speed of water navigation, V g To navigate to the ground, H 1/3 For sense wave height, B is the target ship shape width, L BWL Is the ship length from the bow to 95% of the type width, delta V s1 The speed of the ocean current increases.
The oil consumption prediction model fully utilizes the resistance, the propulsion performance and the water opening performance of the propeller in the ship still water obtained by the test or the numerical calculation, derives the influence of wind and waves on the ship resistance and the working point of the propeller through the corresponding relation of the resistance and the thrust, is equivalent to considering the increase of the ship resistance and the decrease of the efficiency of the propeller caused by the wind and the waves at the same time, accords with the actual situation better than other methods, and has better prediction precision.
2. Model for reconstructing ocean route
The model of ocean route reconstruction is to further subdivide a given ocean route to improve the accuracy of the voyage optimization. The course range given by the crewman is too long to ensure the accuracy of the speed optimization. For ocean routes, the crew would prefer to consider the leg as a great circle route if the difference in altitude and longitude between the starting and ending points of the leg is great. Therefore, the crews must design a route that meets the rules of sailing so that the ship can traverse the vast ocean with the shortest voyage. Therefore, for the large circular route, the large circular route similar to the circular arc shape can be effectively designed by equidistant segmentation, and the habit of driving the ocean route by a shipman is met. Further, for this model, the segments are not divided twice when the distances between the start and end points of the segments differ by no more than 600 knots.
Achieving dynamic speed optimization is another important function of the system, and therefore requires re-planning of the remaining course of the ocean-going vessel. During sailing, ocean going vessels inevitably deviate from a predetermined course due to being influenced by various factors.
Therefore, according to the current position of the sea ship, the splicing of the remaining routes is a key step for realizing dynamic optimization of the voyage. The specific splicing steps of the rest lines are as follows.
The first step: if the planned route is the ocean route, dividing the planned route and inserting a newly added route point;
and a second step of: calculating the distance between the current position of the ship and each waypoint of the divided route;
and a third step of: according to the current position of the ship, determining two steering points closest to the current position of the ship;
fourth step: determining a next turning point according to the course angle among the three points;
fifth step: and recombining the remaining routes according to the current position of the ship.
3. Genetic algorithm solver
The genetic algorithm solver solves the navigational speed optimization problem as shown in the following steps.
Initializing m groups of assumed optimal rotation speed solutions, wherein each group of assumed optimal rotation speed solutions comprises assumed rotation speed values of each navigation segment; here, if the entire path is divided into N segments, each set of assumed rotational speed solutions contains N values.
Step two, calculating the total fuel consumption and the assumed rotating speed value of the route of the assumed optimal rotating speed solution according to the weather forecast data and the real-time navigation information, and the waypoints, the timetable, the limited rotating speed, the limited navigational speed and the latitude and longitude of the steering point of each leg; this step may determine the suitability of each set of hypothetical rotational speed solutions and the overall fuel consumption of the route may be considered an fitness value. In calculating the oil time for each leg, we need to determine the exact time T to reach each leg id Because we predict the fuel consumption Q of each segment in combination with the weather forecast information updated in real time i . The oil consumption of the whole route is calculated as follows:
A. initializing i=0, starting with the first segment of the assumed optimal rotation solution; calculating the i-th distance D i And determining the time T to the start of the ith segment id And obtaining weather conditions for the time according to the weather forecast data;
B. inputting navigation information (mainly wind wave flow data, assumed rotating speed, draft and trim value of the navigation section) of the ith navigation section into the oil consumption prediction model to obtain oil consumption q, host output power and ground speed per hour;
C. calculating the target ship oil consumption Q according to the following formulas 7-9 i And gets the time T to reach the i+1th segment end (i+1)d ：
T i ＝ D i /V g (7)
T (i+1)d ＝T id + T i (8)
Q i ＝q*T i (9)
D. Checking whether the limiting condition is met or not, and checking whether the calculation of the whole route is completed or not; if the limiting condition is met and the calculation of the whole route is completed, outputting the total fuel consumption F and the ground speed; and if the limiting condition is not met or the calculation of the whole route is not completed, returning to the step A to continue the calculation.
Step three, selecting a group with the lowest total fuel consumption of the route in the assumed optimal rotation speed solutions calculated in the step two;
step four, executing step two again on the selected assumed optimal rotation speed solution, and obtaining a group of new assumed optimal rotation speed solutions with the assumed rotation speed values changed and the assumed rotation speed values of each leg changed in a crossing way compared with the selected assumed optimal rotation speed solution; and selecting the new assumed optimal rotational speed solution;
step five, repeating the step four until the stopping condition is met: the iteration times reach the set value or the fuel consumption minimum value and are not changed any more; then judging whether the obtained assumed optimal rotation speed solution meets the limiting condition or not: the rotating speed cannot exceed the maximum rotating speed of the host machine, the navigational speed cannot exceed the maximum rotating speed, and the rotating speed cannot be in the rotating speed forbidden zone range of the host machine; if yes, assuming the optimal rotation speed solution to be the optimal rotation speed solution of the minimum fuel consumption of the residual route of the target ship, and outputting the optimal rotation speed solution to a ship end interface; and if not, repeating the second step until obtaining the optimal rotation speed solution meeting the stop condition and the limiting condition.
Compared with the prior art, the invention has the beneficial effects that:
(1) The influence of sea conditions on the optimal navigational speed is comprehensively considered;
(2) The influence of the ship attitude on the optimal navigational speed is accurately considered;
(3) The energy-saving navigational speed decision of the ship comprehensively considers the navigational order plan, the wind and wave conditions of the navigation area and the navigational performance of the ship, aims at minimizing the navigational order fuel consumption of the ship, takes the safety and the navigational order time of the ship as constraint conditions, gives out the optimal energy-saving navigational state adapting to the navigational environment of the ship and the optimal navigational speed of each navigation segment through the full range navigational speed optimization decision, and realizes the safe energy-saving navigational speed control of the ship;
(4) And adjusting the energy-saving navigational speed of the rest navigation section in real time according to the navigational plan adjustment and the change of the wind and the wave of the navigation area, and guiding the ship to complete the production task.
(5) The ship energy-saving navigational speed auxiliary decision-making system quantifies the current fuzzy practice of speed reduction and energy saving of the ship, so that the energy-saving effect is more considerable;
(6) The system gives optimal results from the whole course situation rather than looking at the current state, so that the final results will maximize energy savings or voyage profits.
(7) The dynamic speed optimization of the ocean route is realized, and the latest speed optimization suggestion can be provided according to the latest meteorological conditions.
While the intent and embodiments of the present invention have been described in detail by way of examples, those skilled in the art to which the invention pertains will appreciate that the foregoing examples are merely illustrative of the preferred embodiments of the present invention, and that it is not intended to list all embodiments individually and that any implementation embodying the technical scheme of the present invention is within the scope of the present invention.
Claims (5)
1. A marine vessel navigational speed optimization aid decision making system, the system comprising:
the data acquisition platform is arranged at the ship end and is used for acquiring real-time navigation information of the target ship;
the weather forecast analyzer is arranged at the shore end and is used for receiving the weather forecast original data and analyzing the weather forecast original data into weather forecast information related to the target ship;
the oil consumption prediction model is arranged at the shore end and is used for predicting the fuel consumption of the host machine in each hour under the actual sea condition according to the navigation information acquired by the data acquisition platform;
the ocean route reconstruction model is arranged at the shore end and is used for equally dividing a given target ship route to design a large circular route which is approximately in an arc shape and re-planning the rest route according to the current position of the target ship;
the genetic algorithm solver is arranged at the shore end and is used for calculating the optimal rotation speed solution of the target ship with minimum fuel consumption of the route according to the navigation information of the target ship, the weather forecast information related to the target ship, the fuel consumption per hour of the host predicted by the fuel consumption prediction model and the route set by the ocean route reconstruction model;
the ship end interface is arranged at the ship end and is used for inputting predefined route information and displaying route and speed optimization suggestions of the target ship;
the genetic algorithm solver is realized by the following method:
initializing a plurality of groups of assumed optimal rotation speed solutions, wherein each group of assumed optimal rotation speed solutions comprises assumed rotation speed values of each navigation segment;
step two, calculating the total fuel consumption and the assumed rotating speed value of the route of the assumed optimal rotating speed solution according to the weather forecast information and the navigation information, and the waypoints, the timetable, the limited rotating speed, the limited navigational speed and the latitude and longitude of the steering point of each leg;
step three, selecting a group with the lowest total fuel consumption of the route in the assumed optimal rotation speed solutions calculated in the step two;
step four, executing step two again on the selected assumed optimal rotation speed solution, and obtaining a group of new assumed optimal rotation speed solutions with the assumed rotation speed values changed and the assumed rotation speed values of each leg changed in a crossing way compared with the selected assumed optimal rotation speed solution; and selecting the new assumed optimal rotational speed solution;
step five, repeating the step four until the stopping condition is met: the iteration times reach a set value, or the minimum oil consumption value is not changed any more; then judging whether the finally obtained assumed optimal rotation speed solution meets the limiting condition or not: the rotating speed cannot exceed the maximum rotating speed of the host machine, the navigational speed cannot exceed the maximum rotating speed, and the rotating speed cannot be in the rotating speed forbidden zone range of the host machine; if yes, the assumed optimal rotation speed solution is an optimal rotation speed solution with the minimum fuel consumption of the residual route of the target ship, and the optimal rotation speed solution is output to a ship end interface; if not, repeating the second to fourth steps until the optimal rotation speed solution meeting the stop condition and the limiting condition simultaneously is obtained.
2. The vessel voyage optimization aid decision making system according to claim 1, wherein: the system also comprises a shore database arranged at a shore and a ship database arranged at a ship, wherein the shore database is in communication connection with the oil consumption prediction model, the ocean route reconstruction model, the genetic algorithm solver and the gas image prediction analyzer, receives and stores the data communicated by the oil consumption prediction model, the ocean route reconstruction model, the genetic algorithm solver and the gas image prediction analyzer; the ship end database is in communication connection with the shore end database, the ship end interface and the data acquisition platform, and receives and stores the data of the shore end database and the data acquisition platform.
3. The vessel voyage optimization aid decision making system according to claim 2, wherein: the navigation information acquired by the data acquisition platform comprises: load state, draft, trim value, propulsion efficiency η of target vessel D Density ρ of air A Wind speed at reference altitude V WR Wind direction ψ at reference height WR Density ρ of sea water s Gravitational acceleration g, sense wave height H 1/3 Flow velocity V c Flow direction beta, heading ψ.
4. A vessel voyage optimization aid decision-making system according to claim 3, wherein the fuel consumption prediction model comprises:
a host output power calculation unit predicting a target ship host output power according to the following formulas 1-4;
P ＝ P s + P add (1)
P add ＝(R wind +R wave )V s /η D (2)
the ground speed calculation unit predicts the ground speed of the target ship according to the following formula 5-6;
ΔV s1 ＝V c cos(β-ψ) (5)
V g ＝V s +ΔV s1 (6)
the fuel consumption calculation unit obtains the fuel consumption rate of a specific power value based on the fuel consumption rate curve of the host according to the calculation results of the host output power calculation unit and the ground speed calculation unit;
wherein P is the output power of the host, P s For power in still water, P add For environmentally induced power increase, R wind Resistance increase due to wind, R wave For increasing drag caused by waves, V s For the speed of water navigation, V g To navigate to the ground, H 1/3 For sense wave height, B is the target ship shape width, L BWL Is the ship length from the bow to 95% of the type width, delta V s1 The speed of the ocean current increases.
5. The ship navigational speed optimization aid decision-making system according to claim 4, wherein the specific calculation step of the step two is:
A. initializing i=0, starting with the first segment of the assumed optimal rotation solution; calculating the i-th distance D i And determining the time T to the start of the ith segment id Acquiring weather conditions corresponding to the time according to the weather forecast information;
B. inputting navigation information of the ith navigation section into the oil consumption prediction model to obtain oil consumption q, host output power and ground speed per hour;
C. calculating the target ship oil consumption Q according to the following formulas 7-9 i And gets the time T to reach the i+1th segment end (i+1)d ：
T i ＝D i /V g (7)
T (i+1)d ＝T id +T i (8)
Q i ＝q*T i (9)
D. Checking whether the limiting condition is met or not, and checking whether the calculation of the whole route is completed or not; if the limiting condition is met and the calculation of the whole route is completed, outputting the total fuel consumption F and the ground speed; and if the limiting condition is not met or the calculation of the whole route is not completed, returning to the step A to continue the calculation.
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