Method for estimating a ship waiting time and device using said method

Abstract

The present application relates to a method for estimating a waiting time of a ship and an apparatus using the same, and the method can include the steps of obtaining specification information and destination information of a specific ship, and inputting the specification information and the destination information to a waiting time estimation model to output an estimated waiting time which is a result of estimating an interval from a first time point at which a state of the specific ship is switched to a first state in a first region corresponding to a destination to a second time point at which the state of the specific ship is switched to a second state in a second region corresponding to the destination.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This invention claims priority to Korean Patent Application No. 10-2024-0045558, filed on April 3, 2024. All disclosures in the Korean patent application are incorporated herein by reference as part of this specification. Technical Field

[0003] The embodiments disclosed herein relate to a method for estimating ship waiting time and an apparatus for using the method. Background Technology

[0004] Typically, due to insufficient port facilities, ships remain anchored in anchorage areas while waiting to berth. This results in waiting time for ships.

[0005] Traditionally, the average waiting time at a specific port over a specific period (e.g., the most recent month) is used to generate waiting time prediction information, which is then provided to ships arriving at the anchorage area.

[0006] However, following this existing approach would present several problems.

[0007] For example, if the average waiting time over a specific period is used as the waiting time prediction information according to existing methods, there is a problem that the average waiting time is very inaccurate. For example, the waiting time prediction information based on the average is 10 days, while the actual waiting time is much shorter or much longer than the average.

[0008] For example, if waiting time prediction information is provided to vessels that arrive at the anchorage area later, there is a problem that the waiting time prediction information is not of great significance except as a simple reference.

[0009] Therefore, a method is needed to solve the aforementioned problem. Summary of the Invention

[0010] Technical issues

[0011] The embodiments disclosed in this disclosure provide a method and apparatus for estimating ship waiting time.

[0012] The embodiments disclosed herein provide a method and apparatus for deriving the optimal route for a ship based on estimated waiting time.

[0013] The embodiments disclosed in this disclosure provide a method and apparatus for establishing an efficient operating plan for a ship.

[0014] The embodiments disclosed herein provide a method and apparatus for reducing greenhouse gas emissions from ships.

[0015] The technical problems to be solved by this disclosure are not limited to those described above, and any other technical problems not mentioned herein will be clearly understood by those skilled in the art to which this disclosure pertains from the following description.

[0016] Technical solution

[0017] The method according to the embodiments disclosed in this disclosure may include: acquiring specification information and destination information of a specific vessel; and inputting the specification information and the destination information into a waiting time estimation model to output an estimated waiting time, wherein the estimated waiting time is the result of estimating the interval from a first time point to a second time point, wherein at the first time point the specific vessel switches to a first state in a first region corresponding to the destination, and at the second time point the specific vessel switches to a second state in a second region corresponding to the destination.

[0018] In an embodiment, before outputting the estimated waiting time, the method may further include: training the waiting time estimation model using a training dataset that includes training specification information and training destination information of multiple training vessels.

[0019] In an embodiment, training the waiting time estimation model may include: inputting the training specification information and the training destination information into the waiting time estimation model to output a training estimated waiting time, wherein the training estimated waiting time is the result of estimating the interval from a first training time point to a second training time point, wherein the state of the training vessel switches to a first training state at the first training time point and the state of the training vessel switches to a second training state at the second training time point; generating a loss using the training estimated waiting time and its corresponding true ground truth (GT) waiting time; and updating at least some parameters of the waiting time estimation model based on the loss.

[0020] In an embodiment, the first training state can be a state in which the position of the training vessel is within a first training area and the speed of the training vessel is less than or equal to a first reference speed, the first training area corresponding to the training destination; and the second training state can be a state in which the position of the training vessel is within a second training area and the speed of the training vessel is less than or equal to a second reference speed, the second training area corresponding to the training destination.

[0021] In an embodiment, the first training area and the second training area may be set based on Automatic Identification System (AIS) data corresponding to the plurality of training vessels.

[0022] In an embodiment, the first reference speed may be greater than the second reference speed.

[0023] In an embodiment, the destination information may include at least some of the following: port location information, port operation information, congestion information of the first area, and congestion information of the second area; and the training destination information may include at least some of the following: training port location information, training port operation information, congestion information of the first training area, and congestion information of the second training area.

[0024] In this embodiment, the training dataset may further include weather information for training. Weather information may also be acquired when obtaining the specification information and the destination information. When outputting the estimated waiting time, the specification information, the destination information, and the weather information may be input into the waiting time estimation model to output the estimated waiting time.

[0025] In an embodiment, after outputting the estimated waiting time, the method may further include: generating optimal route information for the specific vessel to reach the destination with reference to the estimated waiting time.

[0026] In an embodiment, after generating the optimal route information, the process may further include: generating at least some of the estimated carbon intensity index (CII) rating and estimated fuel consumption for the specific vessel based on the optimal route information.

[0027] In an embodiment, after generating at least some of the estimated carbon intensity rating and the estimated fuel consumption, the method may further include providing at least some of the estimated waiting time, the optimal route information, the estimated carbon intensity rating, and the estimated fuel consumption to the user of the vessel via a human-machine interface.

[0028] In one embodiment, after generating the optimal route information, the method may further include: controlling the vessel with reference to the optimal route information.

[0029] An apparatus according to embodiments disclosed in this disclosure may include: a memory storing computer-executable instructions; and at least one or more processors accessing the memory and executing the instructions. The at least one or more processors may input specific vessel specification information and destination information into a waiting time estimation model to output an estimated waiting time, which is the time taken for the specific vessel to berth in the destination-corresponding anchorage area from the time it enters the anchorage area corresponding to the destination.

[0030] In an embodiment, the at least one or more processors may generate optimal route information for the specific vessel to reach the destination, with reference to the estimated waiting time.

[0031] In an embodiment, the at least one or more processors may generate at least a portion of the estimated carbon intensity index (CII) rating and estimated fuel consumption for the particular vessel based on the optimal route information.

[0032] Beneficial effects

[0033] According to this disclosure, methods and apparatus for estimating ship waiting times can be provided.

[0034] According to this disclosure, a method and apparatus for deriving the optimal route for a ship based on estimated waiting time can be provided.

[0035] According to this disclosure, methods and apparatus for establishing efficient operating plans for ships can be provided.

[0036] According to this disclosure, methods and apparatus for reducing greenhouse gas emissions from ships can be provided.

[0037] In addition, various effects can be provided, directly or indirectly, through this disclosure. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a method for estimating ship waiting time according to an embodiment of the present disclosure;

[0039] Figure 2 This is a flowchart illustrating the process of training a latency estimation model according to an embodiment of the present disclosure;

[0040] Figures 3a to 4b The accompanying drawings illustrate the process of setting up a first training region and a second training region according to an embodiment of the present disclosure;

[0041] Figures 5a to 5c These are accompanying drawings illustrating the process of determining a first time point and a second time point for training according to embodiments of the present disclosure;

[0042] Figure 6 This is a flowchart illustrating the process of generating optimal line information using estimated waiting time information according to embodiments of the present disclosure; and

[0043] Figure 7 This is a block diagram illustrating an apparatus according to an embodiment of the present disclosure.

[0044] Regarding the description of the accompanying drawings, identical or similar parts will be indicated by identical or similar reference numerals. Detailed Implementation

[0045] In the following, some embodiments of the present disclosure will be described in detail with reference to the exemplary accompanying drawings. When adding reference numerals to components in the various drawings, it should be noted that even if the same component is shown in other drawings, it is indicated by the same numerals. Furthermore, in describing embodiments of the present disclosure, detailed descriptions of well-known features or functions will be omitted so as not to unnecessarily obscure the spirit of the disclosure. In particular, various embodiments of the present disclosure may be described with reference to the accompanying drawings. However, it should be understood that this is not intended to limit the embodiments described in this disclosure to a particular form of disclosure, and includes various modifications, equivalents, and / or alternatives to the embodiments of the present disclosure. Similar numerals may be used for similar components in the description of the drawings.

[0046] In describing components according to embodiments of the present disclosure, terms such as “first,” “second,” “A,” “B,” “(a),” and “(b)” may be used. These terms are intended only to distinguish one component from another, and they do not limit the nature, order, or sequence of the respective components. Furthermore, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms such as those defined in general dictionaries should be interpreted as having the same meaning as in the context of the relevant technical field and should not be interpreted as having an idealized or overly formalized meaning unless expressly defined as such in this application. For example, terms such as “first,” “second,” “1st,” and “2nd” used in this disclosure may be used to refer to various components, regardless of order and / or priority, and to distinguish one component from another, but do not limit these components. For example, “first user device” and “second user device” may refer to different user devices regardless of their order or priority. For example, a first component may be referred to as a second component without departing from the scope of this disclosure, and similarly, a second component may be referred to as a first component.

[0047] In this disclosure, the expressions “have,” “may have,” “include,” and “contain,” or “may include” and “may contain” indicate the presence of a corresponding feature (such as a component like a number, function, operation, or part), but do not exclude the presence of additional features.

[0048] It will be understood that when a component (e.g., the first component) is referred to as "(operationally or communicatively) coupled / connected to" or "connected to" another component (e.g., the second component), the component may be directly coupled / connected to or connected to the other component, or there may be an intermediate component (e.g., the third component). Conversely, when a component (e.g., the first component) is referred to as "directly coupled / connected to" or "directly connected to" another component (e.g., the second component), it should be understood that there is no intermediate component (e.g., the third component).

[0049] Depending on the circumstances, the expression “configured as” as used in this disclosure may be used interchangeably, for example, with the expressions “applicable to,” “capable of,” “designed to,” “suitable for,” “manufactured as,” or “capable of.”

[0050] The term "configured as" does not necessarily mean "specifically designed" in hardware. Rather, the expression "a device configured as..." can mean that the device is "capable" of operating with other devices or other components. For example, "a processor configured to perform A, B, and C" can refer to a dedicated processor (e.g., an embedded processor) or a general-purpose processor (e.g., a central processing unit (CPU) or application processor) for performing the respective operations, which can perform the respective operations by executing one or more software programs stored in a memory device. The terminology used in this disclosure is used to describe specific embodiments and is not intended to limit the scope of this disclosure. Unless otherwise stated, terms in the singular form may include plural forms. All terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by those skilled in the art as described in this disclosure. It will be further understood that terms defined in dictionaries and commonly used should also be interpreted as having their usual meaning in the relevant field and not in an idealized or over-formalized manner, unless expressly so defined in the various embodiments of this disclosure. In some cases, even if a term is defined in the specification, it may not be construed as excluding embodiments of this disclosure.

[0051] In this disclosure, expressions such as “A or B,” “at least one of A and / or B,” or “one or more of A and / or B” can include any and all combinations of the associated listed items. For example, the terms “A or B,” “at least one of A and B,” or “at least one of A or B” can refer to all of the following cases: case (1), which includes at least one A; case (2), which includes at least one B; or case (3), which includes both at least one A and at least one B. Furthermore, in describing embodiments of this disclosure, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, and C,” “at least one of A, B, or C,” and “at least one of A, B, or C or any combination thereof” can include any one or all possible combinations of the items listed together in the corresponding phrase. In particular, phrases such as “at least one of A, B, or C or any combination thereof” can include “A,” “B,” or “C,” or “AB” or “ABC” as combinations thereof.

[0052] In the following text, reference will be made to Figures 1 to 7 The embodiments of this disclosure are described in detail.

[0053] Figure 1 This is a flowchart illustrating a method according to an embodiment of the present disclosure.

[0054] First, it is possible to obtain the specifications and destination information of a specific vessel (S110).

[0055] At this point, destination information may include the location information of the ship's target port.

[0056] As another example, destination information may include at least some of the following: the location of the vessel’s target port, the required berthing time of the vessel, port operation information (e.g., operating time information), the anchorage area corresponding to the target port, congestion information of the berthing area, the vessel’s departure position, the vessel’s planned draft, international oil price information, and freight index information.

[0057] In addition, in step S110, weather information corresponding to the target port can also be acquired. For example, the weather information could be about the area located at a predetermined distance from the target port. Another example is weather information about the anchorage and berthing areas corresponding to the target port. Yet another example is weather information about the area corresponding to the vessel's route.

[0058] If the specification information and destination information are obtained according to step S110, the estimated waiting time can be output, which is the result of estimating the time it takes for a specific vessel to berth at the destination (S120).

[0059] For example, specification information and destination information can be input into a waiting time estimation model to output an estimated waiting time, which is the result of estimating the interval from a first time point to a second time point. At the first time point, in a first region corresponding to the destination, the state of a specific vessel switches to a first state, and at the second time point, in a second region corresponding to the destination, the state of the specific vessel switches to a second state.

[0060] For example, the first area can be an anchorage area, and the second area can be a berthing area. In other words, the estimated waiting time can be the time from a first point in time when a particular vessel switches from an anchorage state in the anchorage area to a second point in time when the particular vessel switches from a berthing state in the berthing area.

[0061] For example, if additional weather information is available, specification information, destination information, and weather information can be input into the waiting time estimation model to output an estimated waiting time.

[0062] In this case, the waiting time estimation model can be a machine learning-based estimation model.

[0063] For example, the waiting time estimation model can be in a state where it is pre-trained using a training dataset that includes training specification information and training destination information for multiple training vessels.

[0064] Figure 2 This is a flowchart illustrating the process of training a waiting time estimation model according to an embodiment of the present disclosure.

[0065] First, the training specifications and training destination information can be input into the waiting time estimation model to output the training estimated waiting time, which is the result of estimating the time spent by the training vessel berthing at the training destination (S210).

[0066] In detail, training specification information and training destination information can be input into the waiting time estimation model to output the training estimated waiting time, which is the result of estimating the interval between the first training time point when the state of the training vessel switches to the first training state and the second training time point when the state of the training vessel switches to the second training state.

[0067] At this point, the training destination information may include the location information of the training port that serves as the target for the training vessel.

[0068] As another example, training destination information may include at least some of the following: location information of the training port that serves as the target of the training vessel, training berthing time information required by the training vessel, operational information of the training port (e.g., operating time information), congestion information of the anchorage area (training first area) and berthing area (training second area) corresponding to the training port, training departure position information of the training vessel, training planned draft information of the training vessel, training international oil price information, and training freight index information.

[0069] If the estimated waiting time for training is output according to step S210, the error between the estimated waiting time for training and its corresponding true (GT) waiting time can be used to generate the loss (S220).

[0070] If a loss is generated according to step S220, at least some parameters of the waiting time estimation model can be updated based on the loss (S230).

[0071] At this time, the first training state can be a state in which the training vessel is located in the first training area and the speed of the training vessel is less than or equal to the first reference speed, wherein the first training area corresponds to the training destination. For example, the first training area can be an anchorage area.

[0072] For example, when the first reference speed is set to 1.1 knots, the point in time when the training vessel enters the first training area and its speed decreases to 1.1 knots or lower can be the first training time point when the training vessel switches to the first training state.

[0073] Furthermore, the second training state can be a state in which the training vessel is located in the second training area and its speed is less than or equal to the second reference speed, wherein the second training area corresponds to the training destination. The second training area can be a berthing area.

[0074] For example, when the second reference speed is set to 0.5 knots, the point in time when the training vessel enters the second training area and its speed decreases to 0.5 knots or lower can be the second training time point when the training vessel switches to the second training state.

[0075] For example, the first reference velocity corresponding to the first training region can be greater than the second reference velocity corresponding to the second training region.

[0076] For example, the first training zone and the second training zone can be set up based on Automatic Identification System (AIS) data corresponding to multiple training vessels.

[0077] Figures 3a to 4b The accompanying diagram illustrates the process of setting up the first training region and the second training region.

[0078] First, refer to Figure 3a and Figure 3b It can be confirmed that the process of setting up a first training area (e.g., an anchorage area) and a second training area (e.g., a berthing area) based on Automatic Identification System (AIS) data of training vessels around the first training port can be obtained from multiple AIS data collected from multiple training vessels.

[0079] In detail, such as Figure 3a As shown, a filtering process can be applied to the Automatic Identification System (AIS) data of training vessels around the first training port to generate polygon 311. Figure 3b As shown, a grouping process can be applied to the polygon 311, and anchoring and berthing areas 310 can be set.

[0080] For example, polygon groups 310 located within a predetermined distance (or line length) from the first training port can be designated as berthing areas, and polygon groups 310 located outside the predetermined distance can be designated as anchorage areas.

[0081] In addition, refer to Figure 4a and Figure 4b Similar to the description in Figure 3, it can be confirmed that the process of setting up a first training area (e.g., an anchorage area) and a second training area (e.g., a berthing area) based on the Automatic Identification System (AIS) data of training vessels around the second training port can be obtained from multiple AIS data collected from multiple training vessels.

[0082] In detail, such as Figure 4a As shown, a filtering process can be applied to the Automatic Identification System (AIS) data of training vessels around the second training port to generate polygon 411. Figure 4b As shown, a grouping process can be applied to the polygon 411, and anchoring and berthing areas 410 can be set.

[0083] For example, polygon groups 410 located within a predetermined distance (or line length) from the second training port can be designated as berthing areas, and polygon groups 410 located outside the predetermined distance can be designated as anchorage areas.

[0084] Figures 5a to 5c The accompanying diagram is used to describe the process of determining the first and second time points for training.

[0085] First, refer to Figure 5a It can be confirmed that the training first area 510 and training second area 520 around the third training port are set based on the Automatic Identification System data 500 of the training vessels around the third training port.

[0086] For ship anchoring, this may involve a process to reduce speed within the anchoring area. For example... Figure 5b As shown, the vessel's trajectory can be confirmed via the Automatic Identification System (AIS) data 500 to be concentrated in a portion of the anchorage area.

[0087] Therefore, if the automatic identification system data 500 confirms that the speed of the training vessel in the training first area 510 (anchorage area) is less than or equal to the first reference speed at a first time point, the corresponding time point can be set as the training first time point (i.e., the anchorage time point).

[0088] Similarly, for ship berthing, there may be a process required to reduce speed within the berthing area. For example... Figure 5c As shown, data 500 from the automatic identification system confirms that the speed of the vessel is 0 in a portion of the berthing area.

[0089] Therefore, if the automatic identification system data 500 confirms that the speed of the training vessel in the training second area 520 (berthing area) is less than or equal to the second reference speed at a first time point, the corresponding time point can be set as the training second time point (i.e., the berthing time point).

[0090] Meanwhile, while the foregoing has illustratively described specification information and destination information as input variables for training the waiting time estimation model, the method according to the embodiments disclosed in this disclosure is not limited to this example.

[0091] For example, the training dataset can also include weather information used for training.

[0092] Meanwhile, if the estimated waiting time is output according to step S120, the optimal route information for a specific ship to reach its destination can be generated by referring to the estimated waiting time.

[0093] At this point, the optimal route information can be, but is not limited to, route information for reaching the first area (anchorage area) corresponding to the destination. For example, the optimal route information could be route information for reaching the second area (berthing area) corresponding to the destination.

[0094] Meanwhile, if optimal route information reflecting the estimated waiting time is generated, the ship can be controlled with reference to the optimal route information.

[0095] For example, if optimal route information reflecting the estimated waiting time is generated, at least a portion of the estimated carbon intensity index (CII) rating and estimated fuel consumption for a particular vessel can be generated based on the optimal route information.

[0096] At least some of the estimated waiting time, the optimal route information, the estimated carbon intensity rating, and the estimated fuel consumption can be provided to the ship's user via a human-machine interface.

[0097] Since optimal route information that does not take into account vessel waiting time is traditionally provided to vessel users, the optimal route information generated by the method according to the embodiments disclosed in this disclosure provides users with waiting time optimized for each individual port, thus enabling users to efficiently plan vessel operations.

[0098] Figure 6 This is a flowchart illustrating a process for generating optimal line information using estimated waiting time information according to embodiments disclosed in this disclosure.

[0099] Reference Figure 6 Ship specifications and weather information (such as weather information provided by NOAA) can be input into the ship performance estimation module to estimate the ship's performance (S610_1). For example, the ship's hydrostatic resistance and additional resistance can be estimated. In addition, the relationship between the ship's speed, output, engine RPM, and fuel can be modeled.

[0100] In addition, vessel specifications, destination information, and required berthing time can be input into the waiting time estimation model to output the estimated waiting time for the vessel (S610_2). For example, recommended arrival times for the vessel at the port can also be output.

[0101] In addition, the ship's departure point information, destination information, departure time information, arrival time information, and loading status information can be input into the optimal route generation device to generate an initial optimal route solution for the ship (S610_3). For example, the optimal route generation device can generate an initial optimal route solution based on a graph search algorithm, and the initial optimal route solution can be defined as the parent solution of the evolutionary strategy algorithm.

[0102] The optimal route generation device can evaluate the initial solution of the optimal route using the ship's performance information and estimated waiting time information generated according to steps S610_1 and S610_2 (S620). For example, the optimal route generation device can determine the ship's optimal RPM by estimating the output of each segment and can predict the fuel consumption based on the initial solution of the optimal route.

[0103] The optimal route generation device can add normally distributed random values ​​to the coordinates of any path point to generate an optimal route sub-solution (S630).

[0104] The optimal route generation device can evaluate the optimal route sub-solution using the ship's performance information and estimated waiting time information generated according to steps S610_1 and S610_2 (S640). For example, the optimal route generation device can determine the ship's optimal RPM by estimating the output of each segment and can predict the fuel consumption based on the optimal route sub-solution.

[0105] The optimal route generation device can compare the optimal route parent solution with the optimal route child solution (S650). For example, the optimal route generation device can compare the fuel consumption of the optimal route parent solution with the fuel consumption of the optimal route child solution.

[0106] If the fuel consumption of the optimal route parent solution is greater than the fuel consumption of the optimal route child solution, then the optimal route parent solution can be updated using the optimal route child solution (S660).

[0107] It can be determined whether the iteration criterion (S670) is met.

[0108] If the iteration criteria are met, the final updated solution can be defined as the solution for the optimal route.

[0109] If, in step S650, it is determined that the fuel consumption of the optimal route parent solution is equal to or less than the fuel consumption of the optimal route child solution, a new optimal route child solution can be generated. Furthermore, if the iteration criterion is not met in step S670, a new optimal route child solution can be generated.

[0110] For reference only, and for ease of description, in Figure 6 The present disclosure illustratively describes a scheme using a genetic algorithm as a method for generating optimal route information; however, the method for generating optimal route information according to the embodiments disclosed herein is not limited to genetic algorithms. For example, optimal route information can be generated based on optimization algorithms different from genetic algorithms.

[0111] Figure 7 This is a block diagram illustrating an apparatus according to an embodiment of the present disclosure.

[0112] An apparatus 100 according to an embodiment may include: a memory 120 storing computer-executable instructions 122; and at least one or more processors 110 for accessing the memory 120 and executing the instructions 122.

[0113] At this time, the processor 110 can input the specific vessel's specifications and destination information into the waiting time estimation model to output an estimated waiting time, which is the time spent by the specific vessel from the time it enters the anchorage area corresponding to the destination to the time it berths in the berthing area corresponding to the destination.

[0114] The above description is merely an example of the technical concept of this disclosure, and those skilled in the art can make various modifications and variations without departing from the essential characteristics of this disclosure.

[0115] The above embodiments can be implemented using hardware components, software components, and / or combinations of hardware and software components. For example, the apparatus, methods, and components described in the embodiments can be implemented using a general-purpose or special-purpose computer such as a processor, controller, arithmetic logic unit (ALU), digital signal processor, microcomputer, field-programmable gate array (FPGA), programmable logic unit (PLU), microprocessor, or any device capable of executing instructions and responding. The processing unit can execute an operating system (OS) or a software application running on said operating system. Furthermore, the processing unit can access, store, manipulate, process, and generate data in response to the execution of software. Those skilled in the art will understand that although a single processing unit may be shown for ease of understanding, the processing unit can include multiple processing elements and / or multiple types of processing elements. For example, the processing unit can include multiple processors, or one processor and one controller. Additionally, other processing configurations are possible, such as parallel processors.

[0116] Software may include computer programs, code, instructions, or one or more combinations thereof, and may configure a processing unit to operate in a desired manner, or may independently or jointly instruct the processing unit. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual device, computer storage medium or unit, or transmitted signal waves, for interpretation by the processing unit or to provide instructions or data to the processing unit. Software may be distributed across computer systems connected via a network and stored or executed in a distributed manner. Software and data may be stored in computer-readable storage media.

[0117] The method according to the embodiments can be implemented using program instructions executable by various computer devices and can be recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., individually or in combination, and the program instructions recorded on the medium may be specifically designed and configured for the embodiments or may be known and available to those skilled in the art of computer software. Examples of computer-readable media include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as optical disc read-only memory (CD-ROM) disks and digital versatile optical discs (DVDs); magneto-optical media, such as floppy disks; and hardware devices specifically configured to store and execute program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, etc. Examples of computer programs include not only machine language code created by a compiler but also high-level language code that can be executed by a computer using an interpreter or similar means.

[0118] The aforementioned hardware device can be configured to act as one or more software modules to perform the operations of the embodiments, and vice versa.

[0119] Although embodiments have been described with reference to the limited accompanying drawings, it will be apparent to those skilled in the art that various changes or modifications can be made to the embodiments based on the above description. For example, appropriate effects may be achieved even if the foregoing processes and methods are performed in a different order than described above, and / or the foregoing components, such as systems, structures, devices, or circuits, are connected or combined in a different form and manner than described above, or are replaced or switched by other components or equivalents.

[0120] Therefore, other implementations, other embodiments, and solutions equivalent to the claims are all within the scope of the appended claims.

[0121] Therefore, the embodiments disclosed herein are provided to explain the spirit and scope of this disclosure, and not to limit them, so that the spirit and scope of this disclosure are not limited by the embodiments. The scope of this disclosure should be interpreted based on the appended claims, and all technical ideas within the scope of the claims should be included within the scope of this disclosure.

Claims

1. A method comprising: Obtain specific vessel specifications and destination information; as well as The specification information and the destination information are input into the waiting time estimation model to output the estimated waiting time, which is the result of estimating the interval from the first time point to the second time point. At the first time point, the state of the specific vessel in the first region corresponding to the destination switches to the first state, and at the second time point, the state of the specific vessel in the second region corresponding to the destination switches to the second state.

2. The method according to claim 1, further comprising: Before outputting the estimated waiting time, the waiting time estimation model is trained using a training dataset that includes training specification information and training destination information for multiple training vessels.

3. The method according to claim 2, wherein, Training the waiting time estimation model includes: The training specification information and the training destination information are input into the waiting time estimation model to output the estimated waiting time for training. The estimated waiting time is the result of estimating the interval from the first training time point to the second training time point. At the first training time point, the state of the training vessel is switched to the first training state, and at the second training time point, the state of the training vessel is switched to the second training state. The loss is generated using the estimated waiting time for training and its corresponding ground truth (GT) waiting time; and At least some parameters of the waiting time estimation model are updated based on the loss.

4. The method according to claim 3, wherein, The first training state is a state in which the position of the training vessel is within the first training area and the speed of the training vessel is less than or equal to a first reference speed, wherein the first area corresponds to the training destination, and The second training state is a state in which the position of the training vessel is within the second training area and the speed of the training vessel is less than or equal to the second reference speed, wherein the second area corresponds to the training destination.

5. The method according to claim 4, wherein, The first training area and the second training area are set based on Automatic Identification System (AIS) data corresponding to the plurality of training vessels.

6. The method according to claim 4, wherein, The first reference speed is greater than the second reference speed.

7. The method according to claim 4, wherein, The destination information includes at least some of the following: port location information, port operation information, congestion information of the first area, and congestion information of the second area. The training destination information includes at least some of the following: training port location information, training port operation information, congestion information of the first training area, and congestion information of the second training area.

8. The method according to claim 2, wherein, The training dataset also includes weather information for training. In obtaining the specification information and the destination information, weather information is also obtained, and Specifically, when outputting the estimated waiting time, the specification information, the destination information, and the weather information are input into the waiting time estimation model to output the estimated waiting time.

9. The method according to claim 1, further comprising: After outputting the estimated waiting time, optimal route information for the specific vessel to reach the destination is generated with reference to the estimated waiting time.

10. The method of claim 9, further comprising: After generating the optimal route information, at least some of the estimated carbon intensity index (CII) rating and estimated fuel consumption for the specific vessel are generated based on the optimal route information.

11. The method of claim 10, further comprising: After generating at least some of the estimated carbon intensity rating and the estimated fuel consumption, the estimated waiting time, the optimal route information, the estimated carbon intensity rating, and the estimated fuel consumption are provided to the ship's user via a human-machine interface.

12. The method according to claim 9, further comprising: After generating the optimal route information, the vessel is controlled with reference to the optimal route information.

13. An apparatus for estimating ship waiting time, the apparatus comprising: Memory, the memory storing computer-executable instructions; as well as At least one or more processors configured to access the memory and execute the instructions. Wherein, the at least one or more processors are configured as follows: The specific vessel's specifications and destination information are input into the waiting time estimation model to output an estimated waiting time, which is the time spent by the specific vessel from the time it enters the anchorage area corresponding to the destination until it berths in the berthing area corresponding to the destination.

14. The apparatus according to claim 13, wherein, The at least one or more processors are configured to: The estimated waiting time is used to generate optimal route information for the specific vessel to reach the destination.

15. The apparatus according to claim 14, wherein, The at least one or more processors are configured to: Based on the optimal route information, at least a portion of the estimated carbon intensity index (CII) rating and estimated fuel consumption of the specific vessel is generated.

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