Route creation method and apparatus for low-carbon operations

Abstract

A method and apparatus for generating a navigation route for low-carbon operation of a vessel are disclosed. The disclosed method for generating a navigation route for low-carbon operation includes the steps of: estimating greenhouse gas emissions of a target vessel based on the waiting time of the target vessel at an anchorage and the navigation route of the target vessel; and updating the navigation route using the estimated greenhouse gas emissions.

Description

Technology Field

[0001] The present invention relates to a method and apparatus for generating a navigation route of a ship, and more specifically, to a method and apparatus for generating a navigation route for low-carbon navigation of a ship. Background Technology

[0003] Currently, the International Maritime Organization (IMO) has raised its greenhouse gas reduction target from 50% to 100% to achieve '2050 carbon neutrality'. Accordingly, Korea has adopted the '2023 Greenhouse Gas Reduction Strategy', and regulations on the Energy Efficiency Index (EEXI) and Carbon Intensity Index (CII) are being implemented to curb greenhouse gas emissions.

[0004] The energy efficiency index of existing ships refers to a value calculated in advance and indexed using ship specifications such as engine output and tonnage, which represents the amount of carbon dioxide emitted when transporting 1 ton of cargo 1 mile, and the carbon intensity index represents a value calculated retrospectively and indexed using ship operation information such as fuel consumption and sailing distance, which represents the amount of carbon dioxide emitted when transporting 1 ton of cargo 1 nautical mile.

[0005] Therefore, a method for generating navigation routes for low-carbon ship operation is required.

[0006] Related prior art includes patent literature such as Korean Registered Patent No. 10-2006925, Korean Published Patent No. 2021-0149023, and No. 2022-0036953, and non-patent literature such as "A Study on the Calculation of Greenhouse Gas Emissions in the Ship Sector, Journal of the Korean Society of Transportation Vol. 28, No. 6, December 2010." The problem to be solved

[0008] The present invention is intended to provide a method and apparatus for generating a navigation path that can further reduce greenhouse gases emitted by a ship. means of solving the problem

[0010] According to one embodiment of the present invention for achieving the above-mentioned purpose, a method for generating a navigation route for low-carbon navigation is provided, comprising: a step of estimating greenhouse gas emissions of a target vessel based on the waiting time at the anchorage of the target vessel and the navigation route of the target vessel; and a step of updating the navigation route using the estimated greenhouse gas emissions.

[0011] In addition, according to another embodiment of the present invention for achieving the above-mentioned purpose, a navigation route generation device for low-carbon navigation is provided, comprising: a memory; and at least one processor electrically connected to the memory, wherein the processor predicts the waiting time of a target vessel at an anchorage, estimates the greenhouse gas emissions of the target vessel according to the navigation route of the target vessel and the waiting time, and updates the navigation route using the estimated greenhouse gas emissions. Effects of the invention

[0013] One embodiment of the present invention can generate a navigation route that further reduces greenhouse gas emissions by optimizing the navigation route of a ship, taking into account even the greenhouse gas emissions generated during the ship's waiting time. Brief explanation of the drawing

[0015] FIG. 1 is a drawing for illustrating a low-carbon navigation support system according to an embodiment of the present invention. FIG. 2 is a diagram illustrating a method for generating a navigation path for low-carbon navigation according to an embodiment of the present invention. FIG. 3 is a diagram showing an optimized navigation path according to an embodiment of the present invention. Specific details for implementing the invention

[0016] The present invention is susceptible to various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0018] When a vessel arrives at a port ahead of its scheduled work time, it waits at the anchorage. During this waiting period, the vessel operates auxiliary engines and other equipment to maintain operations; consequently, greenhouse gases are emitted from the ship even when it is not in operation. These greenhouse gases pollute the air within the port. Furthermore, prolonged waiting times imply that the vessel operated at unnecessarily high speeds until arrival, resulting in additional fuel loss and greenhouse gas emissions due to speeding.

[0019] Based on this, the present invention proposes a method for generating a ship's navigation route that can reduce greenhouse gas emissions by taking into account greenhouse gas emissions generated during waiting time at an anchorage.

[0020] An embodiment of the present invention estimates greenhouse gas emissions of a ship based on the ship's waiting time and the ship's operating route, and can optimize the ship's operating route so that such greenhouse gas emissions can be reduced, that is, so that the ship can operate at a low carbon level. In other words, while existing operating route generation methods primarily optimize operating routes by considering only greenhouse gas emissions generated during the ship's operation, an embodiment of the present invention optimizes the ship's operating route by considering greenhouse gas emissions generated during waiting time as well. Therefore, according to an embodiment of the present invention, an operating route can be generated that further reduces greenhouse gas emissions.

[0021] Hereinafter, embodiments according to the present invention will be described in detail with reference to the attached drawings.

[0023] FIG. 1 is a drawing for illustrating a low-carbon navigation support system according to an embodiment of the present invention.

[0024] Referring to FIG. 1, a low-carbon navigation support system according to one embodiment of the present invention includes a navigation route generating device (110), a database (120), and a user (130).

[0025] The navigation route generating device (110) includes a memory (111) and at least one processor (112) electrically connected to the memory (111), and the processor (112) can perform a series of processes to generate a navigation route to support low-carbon navigation of the vessel.

[0026] The processor (112) estimates the greenhouse gas emissions of the target vessel based on the waiting time at the target vessel's anchorage and the target vessel's navigation route, and updates the target vessel's navigation route based on the estimated greenhouse gas emissions. The processor (112) can periodically estimate the greenhouse gas emissions of the target vessel and optimize the navigation route to minimize the greenhouse gas emissions. Greenhouse gas emissions can be estimated through various known algorithms, and the navigation route can also be optimized through various optimization algorithms.

[0027] The waiting time of the target vessel can be calculated from the estimated time of arrival (ETA) according to the target vessel's sailing route and the pre-planned time of entry of the target vessel. However, since situations frequently occur where it is difficult for vessels to enter the port at the pre-planned time due to various variables in the ocean or port, according to an embodiment, the processor (112) can predict the waiting time of the target vessel from the dwell time of other vessels at the target port and estimate greenhouse gas emissions based on the predicted waiting time.

[0028] The navigation route generating device (110) provides the updated navigation route to the user (130), and the user (130) may be a shipping company, a navigator, or a port manager, etc.

[0029] The database (120) provides data necessary for generating a navigation route or predicting waiting times to the navigation route generation device (110). The database (120) can store ship operation information, ship specification information, port entry / exit information, port operation information, marine weather information, marine topographic feature information, etc. Ship operation information or ship specification information, etc. can be obtained from the Automatic Identification System (AIS), and port entry / exit information or port operation information, etc. can be obtained from the Port Management Information System (Port MIS).

[0031] FIG. 2 is a diagram illustrating a method for generating a navigation route for low-carbon navigation according to an embodiment of the present invention, and FIG. 3 is a diagram showing an optimized navigation route according to an embodiment of the present invention.

[0032] A method for generating a flight path according to one embodiment of the present invention may be performed in a computing device comprising a memory and a processor, and the aforementioned flight path generating device is an example of such a computing device.

[0033] A computing device according to an embodiment of the present invention estimates the greenhouse gas emissions of a target vessel based on the waiting time at the target vessel's anchorage and the target vessel's navigation route (S210). That is, the estimated greenhouse gas emissions correspond to the sum of the greenhouse gas emissions due to the waiting time and the greenhouse gas emissions due to the navigation route. The greenhouse gas emissions due to the initially set navigation route of the vessel may correspond to the amount of greenhouse gas emitted from the current estimation point to the scheduled arrival time, and the computing device may periodically estimate the greenhouse gas emissions of the target vessel.

[0034] As an example, the computing device can estimate greenhouse gas emissions by adding the value obtained by multiplying the fuel consumption during waiting time by the greenhouse gas emission factor and the value obtained by multiplying the fuel consumption during flight time by the greenhouse gas emission factor. The flight time may correspond to the interval between the current estimation point and the estimated time of arrival.

[0035] And as previously described, the waiting time can be calculated from the target vessel's scheduled arrival time and the pre-planned entry time of the target vessel, or predicted from the working time of at least one prior vessel that enters the target port, which is the target vessel's destination, before the target vessel. Here, the working time corresponds to the time from the time of the prior vessel's berthing to the time of its departure from the target port. Since the waiting time of the target vessel increases when the prior vessel's working time increases, and the waiting time of the target vessel decreases when the prior vessel's working time decreases, the computing device can predict the waiting time from the prior vessel's working time and estimate greenhouse gas emissions based on the predicted waiting time.

[0036] The computing device can calculate the working time of a vessel from port entry / exit information or port operation information, or predict the working time using various prediction models, and can estimate the waiting time from the target vessel's scheduled arrival time and the predicted working time.

[0037] According to an embodiment, the computing device can predict the work time of a previous vessel using a pre-trained machine learning model. The computing device can predict the work time by inputting the operation information of vessels within the port, the specifications information of the previous vessel and port entry / exit information, and the operation information and weather information of the target port into the machine learning model.

[0038] Here, the operational information of vessels within the port is information indicating the congestion status of the target port, and includes at least one of the latitude and longitude coordinates, speed, and heading information of the vessels within the target port at the time of departure of the previous vessel, i.e., the time when the previous vessel's cargo handling operation is completed. Additionally, the specification information includes at least one of the length, gross tonnage, and width of the previous vessel, and the port entry and exit information includes at least one of the estimated time of arrival (ETA) and cargo volume information of the previous vessel. Furthermore, the operational information includes at least one of the actual time of berthing (ATB), actual time of departure (ATD), and berth and crane status at the target port, and the weather information includes at least one of the wind speed, current, wave height, etc. at the target port.

[0039] Operational information of vessels within the port, vessel specifications, and port arrival and departure data, as well as operational and weather information of the target port, are parameters that significantly affect the vessel's working time. For example, as the vessel's cargo volume increases and weather conditions deteriorate, the working time may increase beyond the pre-planned time. Furthermore, depending on the current operational status of vessels within the port, if the port is congested, the vessel may depart from the port at a different time than the pre-planned departure time, thereby increasing the working time. Therefore, the computing device predicts the working time of the previous vessel by utilizing the operational information of vessels within the port, the specifications of the previous vessel, port arrival and departure data, and the operational and weather information of the target port.

[0040] As an example, the machine learning model may be a regression algorithm, and the machine learning model may be trained to predict work time from input data. Furthermore, the training data for training the machine learning model may include specification information of various reference vessels, port entry and exit information and operation information within the port, and operational information and weather information of the reference port, along with the work times of the reference vessels. The reference port corresponds to the port where the reference vessel enters.

[0041] The computing device can predict the previous vessel's working time using a machine learning model and estimate the target vessel's waiting time from the target vessel's estimated arrival time and the predicted working time. For example, if the predicted previous vessel's working time at the time of the current greenhouse gas emission estimation is one day, and the target vessel's estimated arrival time is one day and two hours after the time of the current greenhouse gas emission estimation, the waiting time can be estimated as two hours. If,

[0042] The computing device updates the target vessel's navigation route (S220) using the estimated greenhouse gas emissions. The computing device can optimize the navigation route to minimize the estimated greenhouse gas emissions, and can determine the navigation route that minimizes the value of the objective function for greenhouse gas emissions as the optimal navigation route. At this time, weather conditions, marine topographic features, etc., may be used as constraints.

[0043] Alternatively, the computing device can update the navigation route so that the target vessel's carbon intensity index rating becomes above a preset threshold rating. Since a penalty is imposed if the carbon intensity index rating is lower than Grade C, the threshold rating can be set to Grade C.

[0044] The navigation route includes the latitude and longitude coordinates, speed, and heading of the target vessel, and the computing device can update at least one of the latitude and longitude coordinates, speed, and heading of the target vessel. As shown in FIG. 3, if the current speed of the target vessel is 21.1 knots, the computing device can update the speed of the target vessel to 15.2 knots, at which time the waiting time is reduced from 12 hours and 20 minutes to 1 hour and 30 minutes, and greenhouse gas emissions can also be reduced by about half.

[0045] Meanwhile, since navigation route optimization requires significant computational resources, the computing device may determine the parameters to be updated among latitude and longitude coordinates, speed, and heading in step S220, based on the current carbon intensity index grade of the target vessel, in order to use computational resources efficiently. If the current carbon intensity index grade of the target vessel is Grade C or higher, the computing device may update one or more of the latitude and longitude coordinates, speed, and heading; if the current carbon intensity index grade of the target vessel is lower than Grade C, it may update all of the latitude and longitude coordinates, speed, and heading. The fewer the number of parameters to be updated, the lower the amount of computation required for navigation route optimization can be.

[0047] The technical details described above may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiments, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. Hardware devices may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

[0049] As described above, the present invention has been explained by specific details such as specific components, limited embodiments, and drawings; however, this is provided merely to aid in a more comprehensive understanding of the invention, and the invention is not limited to the above embodiments. A person skilled in the art to which the invention pertains can make various modifications and variations from this description. Therefore, the scope of the invention should not be limited to the described embodiments, and all things equivalent to or having equivalent variations to the claims set forth below, as well as the claims themselves, shall be considered to fall within the scope of the concept of the invention.

Claims

Claim 1 A method for generating a navigation route for low-carbon operation, comprising the step of estimating greenhouse gas emissions of a target vessel according to the waiting time at the anchorage of the target vessel and the navigation route of the target vessel; A method for generating a navigation route for low-carbon operation, comprising the step of updating the navigation route using the estimated greenhouse gas emissions, wherein the method further comprises the step of predicting the working time of at least one prior vessel that enters the target port, which is the destination of the target vessel, before the target vessel, and estimating the waiting time from the estimated working time of the target vessel's scheduled arrival time and the predicted working time, wherein the working time is the time from the time of the prior vessel's berthing to the time of departure from the target port, wherein the navigation route includes the latitude and longitude coordinates, speed, and heading of the target vessel, wherein the step of updating the navigation route updates at least one of the latitude and longitude coordinates, speed, and heading, wherein if the current carbon intensity index grade of the target vessel is Grade C or higher, one of the latitude and longitude coordinates, speed, and heading is updated, and if the current carbon intensity index grade of the target vessel is lower than Grade C, all of the latitude and longitude coordinates, speed, and heading are updated. Claim 2 In claim 1, the step of predicting the waiting time involves inputting the operation information of vessels within the port, the specification information of the previous vessel and port entry / exit information, and the operation information and weather information of the target port into a pre-trained machine learning model to predict the work time, thereby generating a navigation route for low-carbon navigation. Claim 3 A method for generating a navigation path for low-carbon navigation according to claim 2, wherein the navigation information of the vessels within the port includes at least one of the latitude and longitude coordinates, speed, and heading information of the vessels within the target port at the time of completion of the cargo handling operation of the previous vessel. Claim 4 In claim 1, the step of updating the flight path optimizes the flight path so that the estimated greenhouse gas emissions are minimized. A method for generating a flight path for low-carbon operation. Claim 5 Memory; and includes at least one processor electrically connected to the memory, wherein the processor predicts the waiting time of the target vessel at the anchorage, estimates the greenhouse gas emissions of the target vessel based on the target vessel's sailing route and the waiting time, updates the sailing route using the estimated greenhouse gas emissions, predicts the working time of at least one prior vessel that arrives at the target port, which is the destination of the target vessel, before the target vessel, and estimates the waiting time from the target vessel's estimated arrival time and the predicted working time, wherein the working time is the time from the time of the prior vessel's berthing to the time of departure from the target port, the sailing route includes the latitude and longitude coordinates, speed, and heading of the target vessel, and the processor updates at least one of the latitude and longitude coordinates, speed, and heading, and if the target vessel's current carbon intensity index grade is Grade C or higher, updates one of the latitude and longitude coordinates, speed, and heading, and if the target vessel's current carbon intensity index grade is lower than Grade C, updates all of the latitude and longitude coordinates, speed, and heading Navigation route generation device for updating low-carbon navigation. Claim 6 In claim 5, the processor inputs the specification information and operation information of the previous vessel and the operation information and weather information of the target port into a pre-trained machine learning model to predict the operation time, thereby forming a navigation route generation device for low-carbon navigation.

Images