WO2020253976A1 - Procédé de planification d'un trajet d'un navire marin - Google Patents

Procédé de planification d'un trajet d'un navire marin Download PDF

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Publication number
WO2020253976A1
WO2020253976A1 PCT/EP2019/073082 EP2019073082W WO2020253976A1 WO 2020253976 A1 WO2020253976 A1 WO 2020253976A1 EP 2019073082 W EP2019073082 W EP 2019073082W WO 2020253976 A1 WO2020253976 A1 WO 2020253976A1
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WIPO (PCT)
Prior art keywords
vessel
data
route
trip
weather
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PCT/EP2019/073082
Other languages
English (en)
Inventor
Ethan FAGHANI
Jon WINGREN
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Volvo Penta Corporation
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Publication date
Application filed by Volvo Penta Corporation filed Critical Volvo Penta Corporation
Publication of WO2020253976A1 publication Critical patent/WO2020253976A1/fr

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Classifications

    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B79/00—Monitoring properties or operating parameters of vessels in operation
    • B63B79/10—Monitoring properties or operating parameters of vessels in operation using sensors, e.g. pressure sensors, strain gauges or accelerometers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING 
    • B63B79/00—Monitoring properties or operating parameters of vessels in operation
    • B63B79/20—Monitoring properties or operating parameters of vessels in operation using models or simulation, e.g. statistical models or stochastic models
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T70/00—Maritime or waterways transport

Definitions

  • the invention relates to a method for planning a trip of a marine vessel.
  • the invention also relates to a computer program, a computer readable medium, a control unit, and a marine vessel.
  • the invention is not restricted to any particular type of marine vessel. Instead it may be used on any type and any size of marine vessel.
  • the invention is advantageously used for water surface vessels.
  • the invention may be used for a passenger marine vessel.
  • the marine vessel may be a commercial vessel.
  • the marine vessel may be a vessel, e.g. a boat or a yacht, intended for pleasure use.
  • US2013124088 discloses a method for routing a vessel from a start point to an end point. Initial routes are generated, and an evaluation of the routes is performed to generate values for a number of objectives for the routes.
  • the objectives may include lateness, fuel consumption, safety of the cargo, safety of the vessel, comfort, maintenance, detectability, and gas emissions.
  • the evaluation is performed by means of a vessel model to simulate the performance of the vessel.
  • the model is likely unable to predict the comfort level with any accuracy.
  • the model will not be very helpful in route planning, with an aim to avoid discomfort for the passengers.
  • a model aimed to predict the comfort of passengers will be complex, and will take a long time of research to be developed. For example, wave direction changes are complicated in model work. Further, if there is a change of some design feature of a vessel, e.g. of a propeller, or a load change, the model would not be valid anymore, and the model work has to start from the beginning.
  • An object of the invention is to improve known marine vessel trip planning methods.
  • a particular object of the invention is to improve, in marine vessel trip planning, predictions of the comfort of passengers.
  • the objects are reached with a method according to claim 1.
  • the objects are reached with a method for planning a trip of a marine vessel, from a departure position to a destination, the method comprising
  • predicting one or more accelerations of the vessel during the trip along the route based at least partly on the forecast, the weather data, and the acceleration data.
  • the accelerations of the vessel during the trip may be predicted by means of a vessel model.
  • the vessel model may be provided in a computer.
  • the vessel model may form a part of a computer based trip planning model.
  • the vessel model may form a part of a data based trip planning model.
  • the vessel model may simulate the performance of the vessel.
  • Data on the trip route, and the weather condition forecast may be used as input data for the vessel model.
  • the model By the gathering, by means of signals from one or more sensors, acceleration data indicative of accelerations of the vessel, and/or at least one other vessel, the model can be improved.
  • the weather data, and the acceleration data may be gathered before the trip route is selected.
  • the weather data, and the acceleration data, gathered by means of the sensor signals may be used to adjust, or update, the model. This model adjustment may be done before the trip route is selected. Thereby, a particularly accurate prediction of accelerations of the vessel during the trip along the route may be obtained. Predicting the one or more accelerations of the vessel during the trip along the route, may involve predicting one acceleration during the trip, or a plurality of accelerations during the trip.
  • Predicting the accelerations of the vessel during the trip along the route may involve predicting a medium acceleration during the trip, and/or a maximum acceleration during the trip.
  • the vessel accelerations may be indicative of the comfort of passengers on the vessel.
  • the invention may provide an improved prediction of the passenger comfort level.
  • the invention provides a fast adaption to design changes. For example, where a design change is formed by a change of one or more propellers on the vessel, with a different propeller design.
  • the weather data, and the acceleration data may be gathered while the vessel is operating after the design change.
  • This weather data, and acceleration data may be used to adjust the model in view of the design change.
  • a time consuming development of a new data based model in view of the design change may be omitted, and instead the existing model may be adjusted in a relatively short time span.
  • an accurate prediction of accelerations of the vessel during the trip along the route may be obtained.
  • the weather data may be stored so as to be indicative of a history of the weather conditions surrounding the vessel, and/or at least one other vessel.
  • the weather data may be stored in a data storage.
  • the weather data may be used to adjust a data based, or a data based, trip planning model. Such an adjustment is herein understood as storing the weather data.
  • the storing of the weather data may be done by an adjustment of a trip planning model.
  • the weather data may be stored as recorded by the sensor(s). Thereby, the stored weather data may be used to adjust the trip planning model.
  • the weather data may be based at least partly on information from public databases, e.g. Viva.
  • the weather data may include one or more of wind direction, and wind strength.
  • the weather data may include sea data. Such sea data may include one or more of wave height, wave direction, wave pattern, and current.
  • the weather data may also be referred to as weather and sea data.
  • the weather data may also include visibility, and/or traffic information.
  • the weather data may include one or more of a wind map, and a current map.
  • the weather data may at least partly be obtained by means of one or more onboard sensors. Weather data obtained by means of one or more sensors are herein referred to as sensor based weather data, or sensor recorded weather data.
  • the sensors may be arranged to detect ambient conditions.
  • the sensors may include one or more of a wind sensor, a sonar, and a camera at least one other vessel.
  • the weather data may be based at least partly on data from previous operations of the vessel.
  • the weather data may be based at least partly on data received from at least one other vessel.
  • the at least one other vessel may be one or more vessels ahead of the vessel for which the trip is planned.
  • the historic weather data may be particularly useful when synchronized with the acceleration data.
  • the weather data comprises a plurality of historic forecasts of weather conditions.
  • the weather conditions may include wind data.
  • the weather conditions may include sea conditions, such as wave data.
  • the weather conditions may also be referred to as weather and sea conditions.
  • Gathering the weather data may be done partly by recording weather data with one or more sensors.
  • the recorded weather data may be synchronized with weather forecast data for the time of the recording. Synchronizing data may involve connecting data for events which occur substantially at the same time.
  • the accelerations of the vessel during the trip along the route may be predicted based partly on at least one of the historic forecasts.
  • the obtained forecast of weather conditions, for the route may be mapped to a historic forecast which is the same, or similar, to the forecast for the route.
  • sensor recorded weather data synchronized with the historic forecast may be extracted, along with acceleration data synchronized with the sensor recorded weather data. Thereby, the accelerations of the vessel during the trip along the route, may be accurately predicted.
  • the gathering of acceleration data, by means of signals from one or more sensors, may be done by recording the acceleration data by means on the one or more sensor.
  • the sensor recorded acceleration data may be synchronized with weather forecast data for the time of the recording.
  • the obtained forecast of weather conditions, for the route may be mapped to a historic forecast which is the same, or similar, to the forecast for the route.
  • Sensor recorded acceleration data, synchronized with the historic forecast may be extracted. Based on the extracted acceleration data, the accelerations of the vessel during the trip along the route, may be predicted.
  • the historic weather data may be used to improve the obtained forecast of weather conditions.
  • the forecast may be refined.
  • sensor recorded weather data may be synchronized with weather forecast data for the time of the recording.
  • the recorded weather data may also be synchronized with position data for the vessel.
  • synchronizing data may involve connecting data for events which occur substantially at the same time.
  • a recorded weather condition at a point in time and at a position, may be connected with data of a forecast for said point in time, and said position.
  • the recorded weather data may be mapped to the forecast data.
  • the obtained forecast may be improved by selecting a historic forecast which is similar to the obtained forecast, and extracting the sensor based weather conditions recorded in conjunction with the historic forecast.
  • a historic forecast, and/or the forecast for the route may include wind information, sea wave information, current information, and/or tidal data.
  • the acceleration data may be stored so as to be indicative of a history of accelerations of the vessel, and/or at least one other vessel.
  • the acceleration data may be stored in a data storage.
  • the acceleration data may be used to adjust a computer based, or data based, trip planning model. Such an adjustment is herein understood as storing the acceleration data.
  • the storing of the acceleration data may be done by an adjustment of a trip planning model.
  • the acceleration data may be stored in a data storage. Thereby, the stored acceleration data may be used to adjust the trip planning model.
  • the acceleration data may be based on signals from one or more onboard sensors.
  • the acceleration data may be gathered by means of one or more of an accelerometer, an Inertial Measurement Unit (IMU), and a vibration sensor.
  • the acceleration data may be based at least partly on data from previous operations of the vessel.
  • the acceleration data may be based at least partly on data received from at least one other vessel.
  • the at least one other vessel may be one or more vessels ahead of the vessel for which the trip is planned.
  • Accelerations of the vessel during the trip may be predicted based partly on the stored weather data, and the stored acceleration data.
  • the acceleration data is synchronized with the weather data.
  • synchronizing data may involve connecting data for events which occur substantially at the same time.
  • a recorded weather condition at a point in time, may be connected with acceleration data recorded at said point in time.
  • the recorded weather data may be mapped to the acceleration data.
  • the improvement of the passenger comfort level during the trip along the route, compared to prior art vessel trip planning solutions, is unexpectedly high.
  • the obtained forecast could be mapped to a similar, stored, historic forecast.
  • the historic forecast may be mapped to historic sensor based weather data, recorded at the time of the historic forecast.
  • the historic weather data may be mapped to acceleration data, recorded at the time of the recording of the weather data.
  • the method comprises predicting a passenger comfort level during the trip along the route, based at least partly on the predicted one or more accelerations.
  • an accurate comfort level prediction may be made based partly on the weather data, and the acceleration data.
  • the acceleration data may comprise data on accelerations of the vessel perpendicular to a direction of travel of the vessel.
  • the predicted one or more accelerations comprise one or more accelerations of the vessel perpendicular to a direction of travel of the vessel.
  • the method comprises gathering speed data, indicative of a plurality of speeds of the vessel.
  • the speed data may be the result of measurements, e.g. by a manual gauge, or a GPS (Global Positioning System) unit.
  • the speed data may be stored so as to be indicative of a history of speeds of the vessel.
  • the speed data is preferably synchronized with the weather data.
  • the speed data is preferably synchronized with the acceleration data.
  • the method comprises predicting at least one speed of the vessel along the route.
  • the prediction of accelerations of the vessel during the trip may be based partly on the predicted speed of the vessel along the route, and the speed data.
  • the method comprises gathering load data, indicative of a plurality of loads of the vessel. Gathering the load data may involve, for example, hull waterline measurements on the vessel.
  • the load data may be stored so as to be indicative of a history of loads of the vessel.
  • the load data is preferably synchronized with the weather data.
  • the load data is preferably synchronized with the acceleration data.
  • the method may involve determining a load of the vessel along the route. Thereby, the prediction of accelerations of the vessel during the trip is based partly on the determined load of the vessel along the route, and the load data.
  • the determined load may be matched with one or more portions of the load data being indicative of the determined load, or a load close to the determined load.
  • a historic forecast which is similar to the obtained forecast may be selected.
  • Sensor based weather conditions recorded in conjunction with the historic forecast may be extracted.
  • the extraction of the weather data may be allowed by synchronization of the sensor based weather data and historic forecasts.
  • a portion of the extracted sensor based weather data, synchronized with said one or more portions of the load data being indicative of the determined load, or a load close to the determined load may be mapped with a portion of the acceleration data, recorded simultaneously with said portion of the extracted weather data. This portion of the
  • acceleration data could be used for the prediction of accelerations of the vessel during the trip.
  • the weather data comprises wave data, indicative of at least one property of waves surrounding the vessel, and/or at least one other vessel.
  • the wave data may comprise wave direction data, indicative of a wave direction in relation to the direction of travel of the vessel.
  • the weather data may further comprise wind direction data, indicative of a wind direction in relation to the direction of travel of the vessel.
  • the weather data may further comprise wind strength data, indicative of a wind strength.
  • the accelerations of a vessel may be largely dependent on the wave direction in relation to the direction of travel of the vessel. Therefore, the wave direction data may allow an improved accuracy of the vessel accelerations prediction.
  • the wave data comprises wave height data, indicative of a wave height.
  • an improved accuracy of the vessel accelerations prediction may be allowed.
  • the wave data comprises wave pattern data, indicative of a pattern of waves surrounding the vessel, or at least one other vessel.
  • the wave pattern data may include data on the frequency of waves, and/or data on groups of waves, for example the number of waves in groups of waves.
  • the wave data is gathered, at least partly, by means of one or more cameras arranged to provide images of waves surrounding the vessel, and/or at least one other vessel.
  • the camera may be used for determining wave directions, wave heights, and/or wave patterns.
  • the method may comprise categorizing data based on images from the camera, for categorizing waves recorded by the camera. Thereby, information on categories of waves may be obtained.
  • an image processing tool, and a categorizing tool may be used to process the camera signals.
  • the prediction of accelerations of the vessel during the trip is based at least partly on the wave data.
  • a historic forecast which is similar to the obtained forecast may be selected.
  • Sensor based weather data, comprising wave data, recorded in conjunction with the historic forecast may be extracted.
  • the extracted weather data may be mapped with a portion of the acceleration data, recorded simultaneously with said portion of the extracted weather data. This portion of the acceleration data could be used for the prediction of accelerations of the vessel during the trip.
  • the method may comprise predicting a duration of the trip along the route.
  • the method comprises repeating the steps of selecting a route for the trip, and obtaining a forecast of weather conditions for the route, one or more times, each time with a different selected route.
  • the method may comprise choosing, from the selected routes, a route, based on a balance between an aim to minimize a duration of the trip, and an aim to minimize the accelerations of the vessel. This may provide an optimal balance between the duration of the trip, and the passenger comfort level.
  • the vessel is controlled so as to travel along chosen route.
  • the method may comprise gathering, by means of signals from one or more sensors, fuel and speed data indicative of fuel consumption and speed of the vessel, and/or at least one other vessel.
  • the fuel and speed data may be stored so as to be indicative of a history of fuel consumption and speed of the vessel, and/or at least one other vessel.
  • the fuel and speed data may be based on signals from one or more onboard sensors.
  • the fuel and speed data may indicate one or more of a fuel flow, an engine torque, an engine speed, and power consumption of one or more auxiliary devices of the vessel.
  • the fuel and speed data may be based at least partly on data from previous operations of the vessel.
  • the fuel and speed data may be based at least partly on data received from at least one other vessel.
  • the at least one other vessel may be one or more vessels ahead of the vessel for which the trip is planned.
  • the fuel and speed data is synchronized with the weather data.
  • the method may comprise predicting a duration of the trip along the route, and the total fuel consumption, based at least partly on the forecast, the weather data, and the fuel and speed data.
  • the method may comprise choosing, from the selected routes, a route based on a balance between an aim to minimize a duration of the trip, an aim to minimize the fuel consumption, and an aim to minimize the accelerations of the vessel. This may provide an optimal balance between the duration of the trip, the total fuel consumption, and the passenger comfort level
  • an adaptive learning system may be provided. The system may be used to predict one or more of the time of arrival, the total energy consumption and the comfort level.
  • Embodiments of the invention may use a combination of public data, onboard sensors and an experience based database for an optimization based on an adaptive learning system using machine learning or Artificial Intelligence. Results and a suggested operation involving speed and route, may be shown to an operator of the vessel.
  • Embodiments of the invention may provide an ad-on kit for marine vessels for one or more of energy optimization, arrival time estimation and optimization, and comfort of passengers.
  • Embodiments of the invention may involve the use of a suitable Human Machine Interface (HMI), which shows one or more of total fuel consumption, axillary energy consumption, average of fleet, e.g. for commercial ferries for example, time of arrival and current comfort level.
  • HMI Human Machine Interface
  • Embodiments of the invention may involve the use of a computer based, or data based, trip planning model.
  • a base model, and an adaptive learning model may be provided. Thereby, the outputs of the models may be compared, and one of them selected.
  • Fig. 1 is a schematic side view of a marine vessel.
  • Fig. 2 is a schematic view of a longitudinal, and vertical cross-section of the vessel in fig. 1.
  • Fig. 3 is a block diagram depicting steps in a method according to an embodiment of the invention, for planning a trip of the vessel in fig. 1.
  • Fig. 4 is a map with weather forecast information and routes for the vessel.
  • Fig. 5 is a block diagram depicting steps in a method according to a more general embodiment of the invention.
  • Fig. 1 shows a marine surface vessel 1.
  • a marine surface vessel 1
  • a boat for transport of people such as a commercial ship, a boat for transport of people, a power boat, a leisure boat or another type of marine vessel.
  • the vessel comprises a propulsion system 2, for the propulsion of the vessel.
  • the propulsion system comprises an internal combustion engine. It should be noted however, that the invention is equally applicable to vessels with other types of propulsion, e.g. purely electric propulsion.
  • the vessel comprises a control system.
  • the control system comprises a control unit 3, which may be provided as one physical unit, or a plurality of physical units arranged to send and receive control signals to and from each other.
  • the control unit 3 may comprise computing means such as a CPU or other processing device, and storing means such as a
  • the control system further comprises a storage device 31 , such as a hard disk or a flash memory. Data in the storage device 31 is accessible to the control unit 3. In some embodiments, the storage device may form a part of the control unit.
  • the control unit 3 comprises an autopilot.
  • a user command input device (not shown) is provided in the form of a switch, which is arranged to be manipulated by a user, so as to selectively activate the autopilot.
  • the autopilot is arranged receive input commands from a user regarding a desired course over ground, and to use signals from the Global Positioning System (GPS) for adjustments of a steering function of the vessel.
  • GPS Global Positioning System
  • the control unit 10 is arranged to adjust the vessel steering to align an actual course over ground of the vessel with the desired course over ground.
  • the control unit 3 is arranged to control the propulsion system 2.
  • the control may be based at least partly on commands from the autopilot.
  • the control system comprises sensors, each arranged to send signals to the control unit 3.
  • the sensors include a wind sensor 301 , an accelerometer 302, an IMU 303, a vibration sensor 304, a manual speed gauge 305, a GPS unit 306, a fuel flow sensor 307, an engine speed sensor 308, and a camera 310.
  • the control system further comprises communication equipment 309.
  • the vessel may receive data from public databases, and/or data from other vessels.
  • the control unit is arranged to use an adaptive trip planning model.
  • the model may be stored in the storage device 31.
  • Control signals in the control system may be sent through communication lines or wirelessly.
  • the method comprises gathering weather data WSD indicative of weather conditions surrounding the vessel, and at least one other vessel.
  • the weather conditions may include sea conditions.
  • the weather data may also be referred to as weather and sea data.
  • a portion of the weather data is gathered by means of the wind sensor 301 , the camera 310, and the communications equipment 309.
  • the weather data WSD is stored in the storage device 31 , so as to be indicative of a history of the weather conditions surrounding the vessel, and at least one other vessel.
  • the storing is done by adapting the adaptive trip planning model.
  • the weather data may be stored in the control unit 3.
  • the weather data WSD may include wind direction, wind strength, wave height, wave direction, wave pattern, and current.
  • the weather data is at least partly obtained S1 by means of one or more onboard sensors.
  • Such weather data is herein referred to as sensor based weather data SWSD.
  • the sensors are arranged to detect ambient conditions.
  • the weather data may be based on data from previous operations of the vessel.
  • gathering weather data comprises gathering S2 a plurality of historic forecasts HF of weather conditions.
  • the sensor based weather data SWSD is synchronized S3 with weather forecast data for the time of the recording.
  • the weather data WSD comprises wind direction data, and wind strength data, gathered by means of the wind sensor 301.
  • the weather data WSD comprises wave data WAD, indicative of a plurality of properties of waves surrounding the vessel.
  • the wave data comprises wave direction data, indicative of a wave direction in relation to the direction of travel of the vessel.
  • the wave data comprises wave height data, indicative of a wave height.
  • the wave data further comprises wave pattern data, indicative of a pattern of waves surrounding the vessel.
  • the wave pattern data may include data on the frequency of waves, and/or data on groups of waves, for example the number of waves in groups of waves.
  • the wave data WAD is gathered by means of the camera 310.
  • the camera 310 is arranged to provide images of waves surrounding the vessel.
  • the camera is used for determining wave directions, wave heights, and wave patterns.
  • the method comprises categorizing data based on images from the camera, for categorizing waves recorded by the camera.
  • the method further comprises gathering S4, by means of signals from the accelerometer 302, the I MU 303, and the vibration sensor 304, acceleration data AD indicative of accelerations of the vessel.
  • the acceleration data is stored so as to be indicative of a history of accelerations of the vessel.
  • the acceleration data is stored in the storage device 31. The storing is done by adapting the adaptive trip planning model.
  • the acceleration data AD is synchronized S5 with the weather data WSD.
  • the acceleration data AD comprises data on accelerations of the vessel perpendicular to a direction of travel of the vessel.
  • a coordinate system is depicted.
  • the x-axis extends along the direction of travel of the vessel.
  • the y-axis extends horizontally (when the vessel is at rest) and perpendicularly to the direction of travel.
  • the z-axis extends vertically (when the vessel is at rest) and perpendicularly to the direction of travel. Accelerations of the vessel perpendicular to a direction of travel of the vessel, may in a plane defined by the y-axis and the z-axis.
  • the acceleration data AD may also comprise data on roll movements of the vessel. Such movements may be around the x-axis.
  • the acceleration data AD may also comprise data on pitch movements of the vessel. Such movement may be around the y-axis.
  • the method further comprises gathering S6 speed data SD, indicative of a plurality of speeds of the vessel.
  • the speed data is gathered by means of the speed gauge 305, and the GPS unit 306.
  • the speed data is stored so as to be indicative of a history of speeds of the vessel.
  • the speed data SD is stored in the storage device 31. The storing is done by adapting the adaptive trip planning model.
  • the speed data SD is synchronized with the weather data WSD.
  • the speed data SD is further synchronized S7 with the acceleration data AD.
  • a route RA for the trip is selected S8.
  • the route RA extends the departure position PA to the destination PB.
  • a forecast of weather conditions, for the route, is obtained S9.
  • the forecast is obtained by means of the communications equipment 309.
  • the forecast may include one or more of wind direction, wind strength, wave height, and current. In fig. 4, only the wind direction, and the wind strength are indicated by the wind symbols WS.
  • At least one speed of the vessel along the route RA is predicted S10.
  • a duration of the trip along the route is also predicted.
  • the method further comprises predicting S11 one or more accelerations of the vessel during the trip along the route RA, based at least partly on the forecast, the weather data WSD, the predicted speed of the vessel along the route, the speed data SD, and the acceleration data AD.
  • the method further comprises predicting S12 a passenger comfort level during the trip along the route, based at least partly on the predicted accelerations.
  • the predicted speed for the route RA is mapped to a portion of the speed data SD, indicating a speed which is the same or close to the predicted speed.
  • the obtained forecast of weather conditions, for the route is mapped to a historic forecast HF which is the same, or similar, to the forecast for the route.
  • Sensor based weather data SWSD synchronized with the historic forecast HF, is extracted.
  • Acceleration data AD synchronized with the sensor based weather data SWSD, and with said portion of the speed data, is also extracted. This synchronized acceleration data is used for the prediction of accelerations of the vessel during the trip. Based on the predicted accelerations a passenger comfort level during the trip along the route, is predicted.
  • the method further comprises repeating the steps of selecting a route for the trip, and obtaining a forecast of weather conditions for the route, a plurality of times, each time with a different selected route S13.
  • a further route RB is exemplified.
  • the selected routed RA, RB may be regarded as candidates for a route of the vessel.
  • a route is chosen S14, from the selected routes RA, RB, based on a balance between an aim to minimize a duration of the trip, and an aim to minimize the accelerations of the vessel.
  • the method comprises gathering, by means of signals from the fuel flow sensor 307, the engine speed sensor 308, the speed gauge 305, and the GPS unit 306, fuel and speed data FSD indicative of fuel consumption and speed of the vessel.
  • the fuel and speed data is stored so as to be indicative of a history of fuel consumption and speed of the vessel.
  • the storing is done by adapting the adaptive trip planning model.
  • the fuel and speed data is synchronized with the weather data WSD.
  • the method further comprises predicting a duration of the trip along the route, and the total fuel consumption, based at least partly on the forecast, the weather data WSD, and the fuel and speed data FSD.
  • the method comprises repeating the steps of selecting a route for the trip, and obtaining a forecast of weather conditions for the route, one or more times, each time with a different selected route.
  • the method further comprises choosing, from the selected routes, a route based on a balance between an aim to minimize a duration of the trip, an aim to minimize the fuel consumption, and an aim to minimize the accelerations of the vessel.
  • Fig. 5 is a block diagram depicting steps in a method according to a more general embodiment of the invention.
  • the method comprises, for planning a trip of a marine vessel from a departure position to a destination.
  • the vessel could be e.g. as described above with reference to fig. 1.
  • the method comprises gathering S1 , S2 weather data indicative of weather conditions surrounding the vessel, and/or at least one other vessel.
  • the method further comprises gathering S4, by means of signals from one or more sensors, acceleration data indicative of accelerations of the vessel, and/or at least one other vessel.
  • the method further comprises selecting S8 a route for the trip, and obtaining S9 a forecast of weather conditions, for the route. Based at least partly on the forecast, the weather data, and the acceleration data, one or more accelerations of the vessel during the trip along the route are predicted S11.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • Ocean & Marine Engineering (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Navigation (AREA)

Abstract

L'invention concerne un procédé de planification d'un trajet d'un navire marin (1), d'une position de départ (PA) à une destination (PB), le procédé consistant - à collecter (S1, S2) des données météorologiques (WSD) indiquant des conditions météorologiques environnantes du navire, et/ou d'au moins un autre navire, - à collecter (S4), au moyen de signaux provenant d'un ou de plusieurs capteurs (302, 303, 304), des données d'accélération indiquant des accélérations du navire, et/ou d'au moins un autre navire, - à sélectionner (S8) une route (RA, RB) du trajet, - à obtenir (S9) une prévision des conditions météorologiques, par rapport à la route, - à prédire une ou plusieurs accélérations (S11) du navire sur la route pendant le trajet, sur la base, au moins en partie, de la prévision, des données météorologiques (WSD) et des données d'accélération (AD).
PCT/EP2019/073082 2019-06-20 2019-08-29 Procédé de planification d'un trajet d'un navire marin WO2020253976A1 (fr)

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SE1950770-6 2019-06-20

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112949932A (zh) * 2021-03-18 2021-06-11 自然资源部第二海洋研究所 船舶交通流预测方法、装置、计算机设备及存储介质

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008152613A2 (fr) * 2007-06-14 2008-12-18 Fincantieri Cantieri Navali Italiani S.P.A. Dispositif et procédé de guidage pour l'homme de quart d'un navire
EP2498056A1 (fr) * 2009-11-04 2012-09-12 Kawasaki Jukogyo Kabushiki Kaisha Procédé de commande de manoeuvre et système de commande de manoeuvre
US20130124088A1 (en) 2011-11-16 2013-05-16 The Boeing Company Vessel Routing System

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008152613A2 (fr) * 2007-06-14 2008-12-18 Fincantieri Cantieri Navali Italiani S.P.A. Dispositif et procédé de guidage pour l'homme de quart d'un navire
EP2498056A1 (fr) * 2009-11-04 2012-09-12 Kawasaki Jukogyo Kabushiki Kaisha Procédé de commande de manoeuvre et système de commande de manoeuvre
US20130124088A1 (en) 2011-11-16 2013-05-16 The Boeing Company Vessel Routing System

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112949932A (zh) * 2021-03-18 2021-06-11 自然资源部第二海洋研究所 船舶交通流预测方法、装置、计算机设备及存储介质
CN112949932B (zh) * 2021-03-18 2024-02-02 自然资源部第二海洋研究所 船舶交通流预测方法、装置、计算机设备及存储介质

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