Ship management method, fleet management method and related devices

Abstract

The invention relates to a ship management method, a fleet management method and related devices. The ship management method comprises the following steps: acquiring first information of a ship, wherein the first information comprises an oil consumption condition associated with a navigational speed; acquiring second information of an energy-saving device which can be used by the ship; third information of fuel capable of being used by the ship is obtained; an integer programming model is constructed, the integer programming model comprises one or more of a first sub-model, a second sub-model and a third sub-model, the first sub-model is used for determining the navigational speed of the ship, the second sub-model is used for determining an energy-saving device to be used by the ship, and the third sub-model is used for determining fuel to be used by the ship, the integer programming model comprises a constraint that a carbon intensity index CII of the ship does not exceed a target CII; the integer programming model is solved using respective one or more of the first information, the second information, and the third information and a respective one or more of a speed, an energy saver, and fuel to be used is determined for the vessel.

Description

Technical Field

[0001] The present disclosure relates to the technical field of ship management, and more particularly, to a method for managing a ship, a method for managing a fleet, and electronic devices, non-transitory storage media, and computer program products associated therewith. Background Art

[0002] The shipping industry produces 3% of global greenhouse gas emissions annually, emitting approximately 833 million tons of CO2 in 2021. Faced with the severe challenges of global climate change and driven by the dual mandates of "carbon neutrality and peak carbon emissions" and industry regulations, the shipping industry must embark on a green transformation. Summary of the Invention

[0003] A brief overview of the present disclosure is provided below to provide a basic understanding of some aspects of the present disclosure. However, it should be understood that this overview is not an exhaustive overview of the present disclosure. It is not intended to identify key or important parts of the present disclosure, nor is it intended to limit the scope of the present disclosure. Its purpose is simply to present certain concepts of the present disclosure in a simplified form as a prelude to the more detailed description that will be given later.

[0004] According to a first aspect of the present disclosure, a method for managing a ship is provided, comprising: obtaining first information about the ship, the first information including fuel consumption associated with navigation speed; obtaining second information about energy-saving devices that can be used by the ship; obtaining third information about fuel that can be used by the ship; constructing an integer programming model, the integer programming model including one or more of a first sub-model, a second sub-model, and a third sub-model, the first sub-model being used to determine the navigation speed of the ship, the second sub-model being used to determine the energy-saving device to be used by the ship, and the third sub-model being used to determine the fuel to be used by the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using corresponding one or more of the first information, the second information, and the third information and determining corresponding one or more of the navigation speed, energy-saving device, and fuel to be used for the ship based on the solution results.

[0005] According to a second aspect of the present disclosure, a method for managing a ship is provided, comprising: obtaining first information about the ship, the first information including fuel consumption associated with the navigation speed; constructing an integer programming model, the integer programming model including a first sub-model, the first sub-model being used to determine the navigation speed of the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using the first information and determining the navigation speed to be used for the ship based on the solution result.

[0006] According to a third aspect of the present disclosure, a method for managing a ship is provided, comprising: obtaining second information about energy-saving devices that can be used for the ship; constructing an integer programming model, the integer programming model including a second sub-model, the second sub-model being used to determine the energy-saving device to be used by the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using the second information and determining the energy-saving device to be used for the ship based on the solution result.

[0007] According to a fourth aspect of the present disclosure, a method for managing a ship is provided, comprising: obtaining third information about fuel that can be used by the ship; constructing an integer programming model, the integer programming model including a third sub-model, the third sub-model being used to determine the fuel to be used by the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using the third information and determining the fuel to be used for the ship based on the solution result.

[0008] According to a fifth aspect of the present disclosure, there is provided a method for managing a fleet, comprising executing the method according to any one of the first to fourth aspects of the present disclosure on each ship in the fleet.

[0009] According to the sixth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-executable instructions, wherein the computer-executable instructions, when executed by the processor, enable the processor to execute the method according to any one of the first to fifth aspects of the present disclosure.

[0010] According to a seventh aspect of the present disclosure, a non-volatile storage medium having computer-executable instructions stored thereon is provided. When the computer-executable instructions are executed by a computer, the computer executes the method according to any one of the first to fifth aspects of the present disclosure.

[0011] According to an eighth aspect of the present disclosure, a computer program product is provided. The computer program product includes instructions, and when the instructions are executed by a processor, the method according to any one of the first to fifth aspects of the present disclosure is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The foregoing and other features and advantages of the present disclosure will become apparent from the following description of the embodiments of the present disclosure taken in conjunction with the accompanying drawings, which are incorporated herein and form a part of the specification and serve to further explain the principles of the present disclosure and enable those skilled in the art to make and use the present disclosure.

[0013] Figure 1 A flowchart illustrating a method for managing a vessel according to some embodiments of the present disclosure is shown;

[0014] Figures 2 to 8 exemplarily showing a data graph obtained by applying an integer programming model according to some embodiments of the present disclosure;

[0015] Figure 9 A schematic block diagram of an electronic device according to some embodiments of the present disclosure is shown;

[0016] Figure 10 A schematic block diagram of a computer system is shown on which embodiments of the present disclosure may be implemented.

[0017] Note that in the embodiments described below, the same reference numerals are sometimes used in common across different drawings to denote the same parts or parts having the same functions, and their repeated descriptions are omitted. In some cases, similar reference numerals and letters are used to denote similar items, so once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0018] For ease of understanding, the positions, sizes, and ranges of various structures shown in the drawings and the like may not represent actual positions, sizes, and ranges, etc. Therefore, the present disclosure is not limited to the positions, sizes, and ranges disclosed in the drawings and the like. DETAILED DESCRIPTION

[0019] Various exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0020] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the present disclosure, its application, or use. In other words, the structures and methods herein are presented in an exemplary manner to illustrate various embodiments of the structures and methods of the present disclosure. However, those skilled in the art will appreciate that these are merely exemplary of the disclosure that may be implemented, and are not exhaustive. Furthermore, the drawings are not necessarily drawn to scale, and some features may be exaggerated to illustrate details of specific components.

[0021] In addition, technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0023] The shipping industry's green transformation is constrained by multiple factors, including energy choice, technological maturity, and cost-effectiveness. Currently, green fuel technologies for ships are rapidly developing, and future fuel options will become increasingly diverse. Furthermore, the continuous emergence of energy-saving and emission-reduction technologies for ships, improvements in operational management, and the widespread application of digital technologies offer the shipping industry a variety of possible transformation paths. Different energy and technology combinations offer varying emission reduction and cost-effectiveness for the shipping industry's green transformation. The goal is to identify, from among these numerous transformation paths, those that are technically feasible, risk-controlled, and cost-effective.

[0024] To this end, the present disclosure provides a method for managing ships, which constructs an integer programming model and inputs the ship's operating data and information about energy-saving devices and fuels available for the ship into the integer programming model to determine one or more of the ship's speed, energy-saving devices, and fuels to be used while meeting a target Carbon Intensity Index (CII), thereby helping ships make scientific and reasonable management decisions in a complex and changing environment.

[0025] Below, the ship management method according to various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It will be appreciated that the actual ship management method may include other steps, but to avoid obscuring the key points of the present disclosure, these other steps are not discussed herein and are not shown in the accompanying drawings.

[0026] Additionally, terms such as "first," "second," and the like may also be used herein for reference purposes only and are not intended to be limiting. For example, the terms "first," "second," and other numerical terms referring to structures or elements do not imply a sequence or order unless the context clearly indicates otherwise.

[0027] Figure 1 FIG. 1 is a flow chart of a method 100 for managing a vessel (hereinafter referred to as “method 100”) according to some embodiments of the present disclosure. Figure 1 As shown, the method 100 includes steps S102 to S110.

[0028] At step S102, first information about the vessel is obtained. The first information includes fuel consumption associated with the vessel's speed. In some embodiments, the first information may include a fuel consumption prediction model for the vessel, which can be used to determine the vessel's fuel consumption at a specified speed. In some embodiments, the first information may also include information such as the vessel's model and tonnage.

[0029] For example, the first information may include basic vessel information, operational information, and other data. Basic vessel information may include the vessel's International Maritime Organization (IMO) number (International Maritime Organization vessel number), ship name, ship type, summer deadweight tonnage or gross tonnage, nominal container capacity, ship management, operating region, installed energy-saving devices, current fuel usage, and the like, or any combination thereof. Operational vessel information may include the vessel's annual port time percentage, operating speed range, current operating route, a list of adjustable routes, and the like, or any combination thereof.

[0030] At step S104, second information about energy-saving devices available for use on the ship is obtained. In some embodiments, the second information may include the type and quantity of energy-saving devices available for use on the ship, the year of technical maturity of the energy-saving devices (from which year the energy-saving devices can be put into use), and other information.

[0031] For example, the second information may include data on ship energy-saving technologies, operational management technologies, and ship-technology matching relationships. Data on ship energy-saving technologies and operational management technologies may include technology name, year of maturity, service life, emission reduction effect, economic cost, applicable ship type, or any combination thereof. The ship-technology matching relationship may include applicable lists of energy-saving technologies, emission reduction technologies, operational management technologies, and green energy technologies, as well as mutually exclusive lists of ship technologies, docking time, and scrapping year, or any combination thereof.

[0032] At step S106, third information about the fuel that can be used by the ship is obtained. In some embodiments, the third information may include information such as the type and quantity of the fuel that can be used by the ship, and the cost of the fuel.

[0033] For example, the third data may include fuel characteristics and prices, ship-new energy technology matching relationships, and other data. Fuel characteristics and prices may include fuel name, calorific value, emissions (CO2, CH4, SO2, etc.), fuel weight, technology maturity year, historical prices (for example, from the initial year to the target year), or any combination thereof. Ship-new energy technology matching relationships may include applicable lists of energy-saving technologies, emission reduction technologies, operations management, and green energy technologies, as well as mutually exclusive lists of ship technologies, docking time, and scrapping year, or any combination thereof.

[0034] It can be understood that the information included in the first information, the second information and the third information can be flexibly adjusted according to the input parameters required by the integer programming model.

[0035] At step S108, an integer programming model is constructed. The integer programming model includes a constraint that the ship's CII does not exceed a target CII. In some embodiments, the target CII can be determined based on, for example, the ship's CII target line and / or the IMO's regulatory control line.

[0036] The integer programming model may include one or more of a first sub-model, a second sub-model, and a third sub-model. Here, the first sub-model is used to determine the ship's speed, the second sub-model includes a sub-model for determining the energy-saving devices to be used by the ship, and the third sub-model is used to determine the fuel to be used by the ship. Furthermore, in some embodiments, the integer programming model may also include an objective function for minimizing management costs.

[0037] At step S110, the integer programming model is solved using one or more of the first information, the second information, and the third information, and one or more of the speed, energy-saving device, and fuel to be used for the ship are determined based on the solution results. In some examples, the integer programming model may include a first sub-model and a second sub-model, and the integer programming model may be solved using the corresponding first information and the second information, and the speed and energy-saving device to be used for the ship may be determined based on the solution results. In other examples, the integer programming model may include a first sub-model, and the integer programming model may be solved using the corresponding first information, and the speed to be used for the ship may be determined based on the solution results. It will be understood that the type of information used and the specific parameters to be used for the ship depend on the type of sub-models included in the integer programming model.

[0038] Therefore, the integer programming model constructed in this disclosure can simultaneously optimize one or more aspects of the ship's speed, energy-saving devices, and fuel and output a management plan, thereby realizing the ship's carbon emission reduction transformation path so that the ship's CII can meet the target CII.

[0039] In some embodiments, the first information, the second information, and the third information are associated with a specified year, and a constraint of the integer programming model includes a requirement that the ship's CII in the specified year not exceed a target CII for the specified year. Method 100 may include solving the integer programming model using one or more of the first information, the second information, and the third information, and determining one or more of the speed, energy-saving device, and fuel to be used for the ship in the specified year based on the solution. This ensures that the ship's CII in the specified year meets the requirements.

[0040] In some embodiments, the target CII for a given year is determined based on the vessel's baseline CII for the given year. Specifically, in some examples, the target CII for a given year is set to Among them, CII tar CII is the target CII for a given year.ref is the base CII of the ship in the specified year, and Z is the reduction parameter corresponding to the specified year.

[0041] Refer to CII Baseline Guidelines (G2), the ship's baseline CII (CII ref ) can be determined based on the ship type and carrying capacity. For example, the benchmark CII can be calculated by the following formula: CII ref =a*Capacity -c , where a and c are parameters that can be calibrated according to different ship types and sizes, and Capacity is the carrying capacity.

[0042] TargetCII(CII tar ) changes dynamically based on the baseline CII. Different years can have different reduction parameters. For example, a company can set a target year for net zero emissions and dynamically adjust its target line based on that target year. In order to meet the target line, companies can set reduction parameters based on international regulations and their own goals, thereby flexibly adjusting their carbon emission control strategies. Specifically, the CII Reduction Factor Guidelines (G3) stipulate the CII of ships. tar Relative to CII during 2023-2026 ref The specific value of the reduction parameter Z can be referred to the content shown in Table 1 below.

[0043] Table 1

[0044] years Z years Z 2023 5 2025 9 2024 7 2026 11

[0045] In addition, starting from 2027, companies can also make adjustments based on their own target lines and calculate the CII for subsequent years through linear interpolation. tar Specifically, companies can set a net-zero emissions target year as a benchmark and gradually adjust the reduction parameters each year based on actual conditions, so that the target line can transition smoothly each year while avoiding excessive fluctuations, ensuring the achievement of emission control targets.

[0046] In some embodiments, the first sub-model may include a constraint that a first CII determined based on the speed of the ship does not exceed a first preset CII, the second sub-model may include a constraint that a second CII determined based on the energy-saving device to be used by the ship does not exceed a second preset CII, and the third sub-model includes a constraint that a third CII determined based on the fuel to be used by the ship does not exceed a third preset CII.

[0047] For example, the first preset CII, the second preset CII, and the third preset CII can each be set equal to the target CII. Thus, the CII of the ship modified based on each of the three aspects—speed determined by the first sub-model, energy-saving devices to be used determined by the second sub-model, and fuel to be used—can meet the target CII. This ensures that the CII of the ship modified based on the combined speed, energy-saving devices, and fuel meet the target CII.

[0048] Alternatively, the sum of one or more of the first preset CII, the second preset CII, and the third preset CII can be set equal to the target CII. For example, when the integer programming model includes a first sub-model, a second sub-model, and a third sub-model, the sum of the first preset CII, the second preset CII, and the third preset CII can be set equal to the target CII. Thus, the CII required to be reduced for the ship is shared by one or more of the first sub-model, the second sub-model, and the third sub-model in terms of one or more of speed, energy-saving devices, and fuel, respectively. This allows the CII of the ship after the modification is determined based on the corresponding one or more of speed, energy-saving devices, and fuel to meet the target CII requirement. In this way, by sharing the CII required to be reduced for the ship in terms of one or more of speed, energy-saving devices, and fuel, the cost and technical requirements for ship modification can be reduced.

[0049] For illustrative purposes, the following describes in detail exemplary constructions of the first, second, and third sub-models. Throughout this document, when describing the first, second, and third sub-models, the same or similar characters may be used to represent the same or similar variables. Therefore, once a variable is defined in one embodiment, it need not be repeatedly described in subsequent embodiments.

[0050] In some embodiments, a first sub-model is constructed by the following operations: setting a first constraint, the first constraint including that a first CII determined based on the speed of the ship does not exceed a first preset CII; setting a second constraint, the second constraint including that the speed of the ship is not less than the minimum speed of the ship; and setting a first objective function, the first objective function including minimizing the cost of adjusting the speed.

[0051] Specifically, in some examples, the first objective function may include The first constraint may include Satisfy f(x t )≤A1 t , the second constraint may include Satisfy x t ≥v min, where T0 is the initial year and T is the target year. For example, if you want to plan the ship's speed, energy-saving devices, and fuel from 2024 to 2050, you can set T0 to 2024 and T to 2050. In addition, the min function is used to find the minimum value, c1 t is the speed adjustment cost of the ship in year t, x t is the ship's speed in year t, f(x t ) indicates the ship’s speed x t Fuel consumption prediction model under fuel consumption conditions, A1 t is the first preset CII in year t, and v min The minimum speed.

[0052] When planning capacity, a fleet usually introduces two types of new ships: 1. New ships of the same model and tonnage introduced due to the elimination of old ships; 2. New ships of the same model but different tonnage as old ships introduced due to capacity scale adjustments.

[0053] For the first scenario, in some embodiments, when a vessel is newly introduced into a fleet, the vessel's fuel consumption prediction model can be determined by the following operation: In response to the fleet including an older vessel of the same model and tonnage as the vessel, the vessel's fuel consumption prediction model is determined to be the fuel consumption prediction model of the older vessel of the same model and tonnage. Because the new vessel and the older vessel have the same model and tonnage, even if historical data for the new vessel is lacking, the historical data of the older vessel can be directly mapped to the new vessel to accurately predict the new vessel's fuel consumption.

[0054] For the second scenario, in some embodiments, when the vessel is newly introduced into the fleet, the fuel consumption prediction model for the vessel may be determined by the following operations: In response to the fleet not including an older vessel of the same model and tonnage as the vessel, at least one older vessel of the same model and closest tonnage as the vessel is selected from the fleet, and the fuel consumption prediction model for the vessel is determined based on the fuel consumption prediction model of the at least one older vessel. Because such new and older vessels have certain differences and lack historical data, a similarity matching method may be employed to compare the tonnage of the new vessel with that of older vessels in the fleet, selecting one or more vessels with tonnage closest to the new vessel for use in inferring the fuel consumption prediction model for the new vessel.

[0055] Specifically, after determining the difference between the tonnage of the new ship and the tonnage of the old ship, the sorting algorithm R = argsort can be used to sort the tonnage of the new ship and the old ship. v (|DWT0-DWT v |)[:N R ] Select at least one old ship that is closest to the tonnage of the new ship, where N Ris the number of the at least one old ship that is closest to the tonnage of the new ship, DWT0 is the tonnage of the new ship, DWT v is the tonnage of the vth old ship in the fleet, and R is the set of at least one old ship whose tonnage is closest to that of the new ship.

[0056] In some embodiments, determining the fuel consumption prediction model of the ship based on the fuel consumption prediction model of the at least one old ship that is closest to the tonnage of the new ship includes: determining the fuel consumption prediction model of the ship based on the fuel consumption prediction model of each old ship in the at least one old ship and the tonnage similarity between the old ship and the ship. In some examples, the fuel consumption prediction model of the ship (new ship) is determined as Among them, x is the speed, r is the old ship in the set R, DWT r is the tonnage of the old ship r, and f r (x) is the fuel consumption prediction model of the old ship r.

[0057] This allows the fuel consumption prediction model for existing ships in the fleet to be effectively transferred to new vessels, ensuring that the integer programming model maintains high accuracy and reliability even when new ships are added to the fleet. Through appropriate similarity matching and fuel consumption prediction model mapping, the integer programming model provides scientific and actionable fuel consumption predictions for ships, thereby optimizing speed control strategies. This provides important support for optimizing speed, reducing carbon emissions, and reducing operating costs in the context of energy transition.

[0058] In some embodiments, a second sub-model is constructed by the following operations: setting one or more of the third to eighth constraints, the third constraint including that the second CII determined based on the energy-saving device to be used by the ship does not exceed the second preset CII, the fourth constraint including that the number of types of energy-saving devices installed each time the ship enters dock does not exceed a preset threshold, the fifth constraint including that the ship cannot repeatedly install the same type of energy-saving device, the sixth constraint including that the ship cannot simultaneously install technologically mutually exclusive energy-saving devices, the seventh constraint including that the ship can only install energy-saving devices when entering dock, and the eighth constraint including that each time the ship enters dock, only energy-saving devices that are technologically mature at this time can be installed; and setting a second objective function, the second objective function including minimizing the cost of using the energy-saving device.

[0059] Specifically, in some examples, the second objective function includes Among them, min function is used to find the minimum value, n is the number of energy-saving devices that can be used on ships, c2 i,t is the cost of installing energy-saving device i in year t, and y i,t Indicates whether energy-saving device i is installed in year t. i,t =1 means that energy-saving device i, y is installed in year t i,t= 0 means that energy-saving device i is not installed in year t. For example, the second objective function can include the one-time capital expenditure CAPEX of the year, the subsequent operating expenditure OPEX, and the loss of revenue caused by suspension of navigation during the docking and installation period. These factors directly affect the economic benefits of the ship.

[0060] In some examples, the third constraint includes satisfy Among them, I1 t is the CII of the ship before the energy-saving device modification in year t, h i is the emission reduction effect of energy-saving device i, A2 t is the second preset CII for year t, and tol is a tuning parameter. For example, a larger tol indicates that the second sub-model is more inclined to minimize the cost of energy-saving devices, while a smaller tol indicates that the second sub-model is more inclined to minimize total ship emissions. The third constraint can be used to limit the annual CII index to not exceed the control line for that year.

[0061] In some examples, the fourth constraint includes satisfy Where Yr is the set of ship docking years, and N Th1 The fourth constraint can be used to limit the number of modifications for each ship during docking. For example, it can be set that a maximum of 4 emission reduction options can be selected each time a ship enters dock. In this case, N Th1 = 4. For years with different renovation quantity limits, they can be grouped and corresponding constraints can be imposed.

[0062] In some examples, the fifth constraint includes satisfy Where S can only install N Th2 The fifth constraint can be used to limit the number of times an energy-saving device can be used on a ship. Information on the service life of the technology can be stored in the ship database. For example, for a certain energy-saving device, there are N Th2 =1, it means that a ship that has already installed the energy-saving device cannot install the same energy-saving device again during its service life. For energy-saving devices with different usage limits, they can be grouped separately and corresponding constraints can be applied.

[0063] In some examples, the sixth constraint includes Satisfy i,t +y j,t ≤1, where M0 is the set of all mutually exclusive energy-saving devices, and i and j are energy-saving devices in M0. The sixth constraint can be used to impose technology mutual exclusion constraints. For example, some technologies may not be able to be used in combination with other technologies due to their specificity.

[0064] In some examples, the seventh constraint includes Satisfy i,t = 0. The seventh constraint can be used to impose a retrofit time constraint, which indicates that the energy-saving device must be installed in a specified docking year.

[0065] In some examples, the eighth constraint includes Satisfy i,t =0, where Y i The eighth constraint can be used to restrict each energy-saving device to be used on ships only after the corresponding technology maturity year. In other words, energy-saving devices can only be used on ships after the technology is mature, thereby improving safety and effectiveness.

[0066] Since ships are docked for inspection and maintenance every few years (for example, every five years), energy-saving devices can be installed during docking. Based on the matching relationship between ships and energy-saving devices, as well as information such as the year of technical maturity, effective duration, and reusability of the energy-saving devices, a second sub-model is established, with the goal of minimizing the cost of energy-saving device technology, subject to the constraints that the ship's CII cannot exceed the target CII, energy-saving device technologies are mutually exclusive, the number of years of technical maturity of energy-saving devices, the number of times the same energy-saving device can be installed at the same time, and the total number of different energy-saving devices installed. This allows the optimal energy-saving device installation strategy to be found, maximizing cost-effectiveness and minimizing environmental impact.

[0067] In some embodiments, if the ship's construction year is later than a specified year, the integer programming model constructed by method 100 may not include the second sub-model, or the second sub-model may be disabled during actual ship management, for example, by setting its input parameters to null. For example, for new ships introduced in the future, considering technological advancements and new shipbuilding planning, it may no longer be necessary to optimize the application of energy-saving devices. Specifically, it is projected that by 2040, the energy efficiency of new ships will increase by 10%, primarily due to the application of new technologies, design optimization, and improved energy management. After 2050, with further technological development and the advancement of energy transition, the energy efficiency of new ships is expected to increase by a further 15% (compared to a 5% increase for ships built before 2040). This assumption is based on current trends in ship design and energy technology and reflects the shipbuilding industry's continued progress in low-carbon and efficient shipping. Therefore, during the optimization process, for ships built after 2040, it is assumed that the improved energy efficiency of these ships can partially replace the role of traditional energy-saving devices, thereby avoiding repeated energy-saving retrofits on new ships and reflecting the expected green performance of future ships. For new shipbuilding, the efficiency of solving the integer programming model can be improved by disabling or omitting the second sub-model.

[0068] In some embodiments, a third sub-model is constructed by the following operations: setting one or more of the ninth to tenth constraints, the ninth constraint including that a third CII determined based on the fuel to be used by the ship does not exceed a third preset CII, and the tenth constraint including meeting the fuel usage requirements of the ship; and setting a third objective function, the third objective function including minimizing the fuel usage cost.

[0069] Specifically, in some examples, the third objective function includes Among them, m is the number of fuel types that can be used by the ship, c3 k,t is the cost of using fuel k in year t, z k,t is the proportion of fuel k used in year t.

[0070] In some examples, the ninth constraint includes Among them, I2 t is the CII of the ship before fuel conversion in year t, p k is the emission reduction effect of fuel k, and A3 t The third preset CII for year t. The ninth constraint can be used to limit the CII of each year to not exceed the control line of that year.

[0071] For example, the emission reduction effect of a fuel can be obtained based on the calorific value and carbon factor of the fuel and the reference fuel. Specifically, in some embodiments, the emission reduction effect of fuel k is set to Among them, LCV k is the calorific value of fuel k, LCV kr is the reference fuel k r The calorific value of fuel k is C(k), and C(k r ) is the reference fuel k r For example, the carbon factor of the reference fuel k r It can be HFO fuel, its calorific value LCV kr is 40.5. k The smaller it is, the less carbon emissions the fuel emits compared to the reference fuel at the same calorific value, and the better the emission reduction effect.

[0072] In some examples, the tenth constraint includes The tenth constraint can be used to ensure that the fuel blend ratios sum to 1 each year.

[0073] As a non-limiting example, during the green transformation of ships, based on different ship types and operational requirements, dual-fuel ship conversion options may include liquefied natural gas (LNG) engines (3.1% slip), LNG engines (1% slip), methanol engines, and ammonia engines. Furthermore, considering the mutual exclusivity between dual-fuel ship conversion and the use of other clean fuels, the third sub-model compares the total costs of the fuel types and fuel mixes corresponding to four different options (including: no conversion to dual-fuel, LNG conversion, ammonia conversion, and methanol conversion). Based on the aforementioned ninth and tenth constraints, the option with the lowest cost is selected as the final option.

[0074] Furthermore, for new ships introduced after 2025 and before 2035, methanol will be preferred as a retrofit fuel option, helping to reduce carbon emissions and maintain technological adaptability during the fleet transition period. As a low-carbon fuel, methanol offers high technical feasibility and low transition costs, aligning with the mid-term goals of the energy transition. After 2035, as all dual-fuel technologies mature, the third sub-model will freely determine the fuel conversion type for newbuildings based on economic and technical feasibility, enabling more flexible and efficient energy transition planning.

[0075] In some embodiments, the CII of a ship before retrofitting in year t may be set to Among them, M is the carbon emission of the ship in the tth year, W is the transportation workload of the ship in the tth year, C Fk is the carbon content per unit heat, FC k is the consumption of fuel k used by the ship, ∑ k represents the sum of all fuel currently used by the ship, C is the carrying capacity of the ship, D t is the route distance of the ship in year t. For example, the above I1 t and I2 t According to the I t For example, FC k This can include both the ship's fuel consumption in port and the ship's fuel consumption during navigation. The ship's fuel consumption in port can include all fuel consumption at anchor and in port, for example, calculated from the ship's average daily fuel consumption and total time in port. The ship's fuel consumption during navigation can be calculated from the ship's average daily fuel consumption and navigation time, and the average daily fuel consumption during navigation can be derived based on the aforementioned fuel consumption prediction model.

[0076] Therefore, the integer programming model can combine fuel consumption predictions to quantify and design the optimal speed reduction plan. It can also analyze docking time and energy-saving device candidates to obtain a recommended energy-saving device selection combination for each docking. It can also compare clean fuels and fuel modification options to select the most economical and regulatory-compliant options. Ultimately, the integer programming model can comprehensively consider speed, energy-saving devices, and fuel to provide a recommended energy transition path for ships, providing more scientific and detailed support for ship energy efficiency management and the development of environmental protection and emission reduction strategies.

[0077] The present disclosure also provides a method for managing a ship, comprising: obtaining first information about the ship, the first information including fuel consumption associated with the navigation speed; constructing an integer programming model, the integer programming model including a first sub-model, the first sub-model being used to determine the navigation speed of the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using the first information and determining the navigation speed to be used for the ship based on the solution result.

[0078] Various embodiments of this method can refer to the relevant embodiments in method 100 and will not be described in detail here.

[0079] The present disclosure also provides a method for managing a ship, comprising: obtaining second information about energy-saving devices that can be used for the ship; constructing an integer programming model, the integer programming model including a second sub-model, the second sub-model being used to determine the energy-saving device to be used by the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using the second information and determining the energy-saving device to be used for the ship based on the solution result.

[0080] Various embodiments of this method can refer to the relevant embodiments in method 100 and will not be described in detail here.

[0081] The present disclosure also provides a method for managing a ship, comprising: obtaining third information about fuel that can be used by the ship; constructing an integer programming model, the integer programming model including a third sub-model, the third sub-model being used to determine the fuel to be used by the ship, wherein the integer programming model includes a constraint that the carbon intensity index CII of the ship does not exceed the target CII; and solving the integer programming model using the third information and determining the fuel to be used for the ship based on the solution result.

[0082] Various embodiments of this method can refer to the relevant embodiments in method 100 and will not be described in detail here.

[0083] The present disclosure also provides a method 200 for managing a fleet, comprising executing the method 100 described in any of the aforementioned embodiments on each vessel in the fleet. It will be appreciated that, because a fleet comprises at least one vessel, the method 100 described in any of the aforementioned embodiments can be applied to each vessel in the fleet to achieve carbon emission reductions for the entire fleet, thereby improving the fleet's economy and compliance.

[0084] For illustrative purposes, we can take a company's four major fleets, namely, container fleet, bulk carrier fleet, tanker fleet and special transport fleet (referred to as "container, bulk carrier, tanker and special transport fleet") as an example, apply the fleet / ship management method proposed in this disclosure, and analyze its plans and effects of achieving carbon emission reduction through multiple strategies such as reducing speed, energy-saving device technology, green fuel and fuel transformation optimization, so as to evaluate the performance of the integer programming model of this disclosure.

[0085] On the one hand, by bringing the data of the four major fleets of container, bulk, oil and special vessels into the integer programming model, the overall carbon emissions of the four fleets in 2024-2050 can be obtained under various carbon emission reduction optimization strategies. Figure 2 As shown, the overall carbon emissions have reached a peak of 36.1159 million tons in 2025, and carbon emissions will be zero in 2050, meeting the goal of "striving to peak overall carbon emissions from shipping before 2030 and achieve carbon neutrality before 2050". Figure 3 As can be seen from the figure, the overall greenhouse gas (GHG) emissions have reached a peak of 37.2603 million tons in 2025, and then continued to decrease, reaching 1.565 million tons in 2050. Figure 4 As can be seen from the figure, the overall life cycle GHG emissions reached a peak of over 400 million tons in 2025, and then continued to decline. By 2050, the life cycle GHG emissions were 3.526 million tons.

[0086] On the other hand, by bringing the data of the four major fleets of container, bulk, oil and special vessels into the integer programming model, the annual carbon emission intensity of the four fleets from 2024 to 2050 can be obtained under various carbon emission reduction optimization strategies. Figure 5 As shown, the carbon emission intensity of ships = carbon emissions / (deadweight tonnage * annual sailing speed * annual sailing ratio * 365 * 24), the unit is 100 million tons * nautical miles, and the carbon emission intensity reduction rate is obtained by the ratio of the carbon intensity of each year from 2024 to 2050 to the carbon intensity of 2020. According to statistics, the carbon intensity in 2020 was 554.4 million tons * nautical miles. Therefore, the carbon emission intensity of ships in 2025 will decrease by about 26.43% compared with 2020, exceeding 3.8%; the carbon emission intensity of ships in 2030 will decrease by about 33.22% compared with 2020, exceeding 6.5% and meeting the requirements. Continue to refer to Figure 6The calculation method of carbon emission intensity of international ships is the same as that of overall ships. By 2030, the carbon emission intensity of international shipping will be reduced by 35.68% compared with 2020. Figure 7 , where GHG emission intensity of ships = GHG emissions / (deadweight tonnage * annual sailing speed * annual sailing ratio * 365 * 24), in units of 100 million tons * nautical miles. The GHG emission intensity reduction rate is obtained by comparing the GHG intensity of each year from 2024 to 2050 with the GHG intensity in 2020. It can be seen that the GHG emission intensity of ships in 2025 will decrease by about 16.96% compared with 2020; and the GHG emission intensity of ships in 2030 will decrease by about 24.45% compared with 2020, which meets the requirements. Figure 8 The lifecycle GHG emissions intensity of a ship is calculated as follows: lifecycle GHG emissions / (deadweight tonnage * annual navigation speed * annual navigation ratio * 365 * 24), expressed in 100 million tons * nautical miles. The lifecycle GHG emissions intensity reduction rate is calculated by taking the ratio of the lifecycle GHG intensity in each year from 2024 to 2050 to the lifecycle GHG intensity in 2024. This indicates that the lifecycle GHG emissions intensity of ships in 2025 will decrease by approximately 12.16% compared to 2020, and by approximately 17.88% in 2030 compared to 2020, meeting the requirements.

[0087] The present disclosure also provides an electronic device, which may include: a processor; and a memory storing computer-executable instructions, where the computer-executable instructions, when executed by the processor, enable the processor to perform the method according to any of the aforementioned embodiments.

[0088] refer to Figure 9 , which shows a schematic block diagram of an electronic device 400 according to some embodiments of the present disclosure. Figure 9As shown, electronic device 400 includes a processor 402 and a memory 404 storing computer-executable instructions that, when executed by processor 402, cause processor 402 to perform a method according to any of the aforementioned embodiments. Processor 402 may, for example, be a central processing unit (CPU) of electronic device 400. Processor 402 may be any type of general-purpose processor, or may be a processor specifically designed for managing a vessel or fleet, such as an application-specific integrated circuit ("ASIC"). Memory 404 may be coupled to processor 402 and may include various computer-readable media accessible by processor 402. In various embodiments, memory 404 described herein may include volatile and non-volatile media, removable and non-removable media. For example, memory 404 may include any combination of random access memory ("RAM"), dynamic RAM ("DRAM"), static RAM ("SRAM"), read-only memory ("ROM"), flash memory, cache memory, and / or any other type of non-transitory computer-readable media. The memory 404 may store instructions that, when executed by the processor 402 , cause the processor 402 to perform the method according to any of the aforementioned embodiments of the present disclosure.

[0089] The electronic device 400 is configured to execute the method described in any of the aforementioned embodiments, and therefore reference may be made to the aforementioned descriptions of various embodiments of the method 100 and the method 200, which will not be repeated here.

[0090] The present disclosure also provides a non-transitory storage medium having computer-executable instructions stored thereon, which, when executed by a computer, causes the computer to perform the method according to any of the aforementioned embodiments of the present disclosure.

[0091] The present disclosure also provides a computer program product that may include instructions that, when executed by a processor, may implement the method according to any of the aforementioned embodiments of the present disclosure. The instructions may be any set of instructions to be executed directly by one or more processors, such as machine code, or any set of instructions to be executed indirectly, such as a script. The instructions may be stored in an object code format for direct processing by one or more processors, or in any other computer language, including a script or collection of independent source code modules that are interpreted on demand or compiled in advance.

[0092] Figure 10A schematic block diagram of a computer system 500 on which embodiments of the present disclosure may be implemented is shown. The computer system 500 includes a bus 502 or other communication mechanism for transmitting information, and a processing device 504 coupled to the bus 502 for processing information. The computer system 500 also includes a memory 506 coupled to the bus 502 for storing instructions to be executed by the processing device 504. The memory 506 may be a random access memory (RAM) or other dynamic storage device. The memory 506 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processing device 504. The computer system 500 also includes a read-only memory (ROM) 508 or other static storage device coupled to the bus 502 for storing static information and instructions for the processing device 504. A storage device 510, such as a magnetic disk or optical disk, is provided and coupled to the bus 502 for storing information and instructions. Computer system 500 may be coupled via bus 502 to output devices 512 for providing output to a user, such as, but not limited to, a display (such as a cathode ray tube (CRT) or liquid crystal display (LCD)), speakers, and the like. Input devices 514, such as a keyboard, mouse, microphone, and the like, are coupled to bus 502 for communicating information and command selections to processing device 504. Computer system 500 may perform embodiments of the present disclosure. Consistent with certain implementations of the present disclosure, results are provided by computer system 500 in response to processing device 504 executing one or more sequences of one or more instructions contained in memory 506. Such instructions may be read into memory 506 from another computer-readable medium, such as storage device 510. Execution of the sequences of instructions contained in memory 506 causes processing device 504 to perform the methods described herein. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present disclosure are not limited to any specific combination of hardware circuitry and software. In various embodiments, computer system 500 can be connected to one or more other computer systems like computer system 500 across a network via network interface 516 to form a networked system. The network can include a private network or a public network such as the Internet. In a networked system, one or more computer systems can store data and supply data to other computer systems. As used herein, the term "computer-readable medium" refers to any medium that participates in providing instructions to processing device 504 for execution. Such media can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks such as storage device 510. Volatile media include dynamic memory such as memory 506. Transmission media include coaxial cables, copper wire, and optical fibers, including the wiring comprising bus 502.Common forms of computer-readable media or computer program products include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic medium, CD-ROMs, digital video disks (DVDs), Blu-ray discs, any other optical media, thumb drives, memory cards, RAM, PROMs and EPROMs, flash EPROMs, any other memory chips or cartridges, or any other tangible medium from which a computer can read. Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processing device 504 for execution. For example, the instructions may initially be carried on a diskette of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 500 may receive the data on the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector coupled to bus 502 may receive the data carried in the infrared signal and place the data on bus 502. Bus 502 carries the data to memory 506, from which processing device 504 retrieves the instructions and executes them. Optionally, the instructions received by memory 506 may be stored on storage device 510 either before or after execution by processing device 504 .

[0093] According to various embodiments, instructions configured to be executed by a processing device to perform a method are stored on a computer-readable medium. A computer-readable medium can be a device that stores digital information. For example, a computer-readable medium includes a compact disk read-only memory (CD-ROM) as known in the art for storing software. The computer-readable medium is accessed by a processor adapted to execute the instructions configured to be executed.

[0094] The foregoing description describes one or more exemplary embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the exemplary embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a server system. Of course, the present disclosure does not exclude that with the future development of computer technology, the computer that implements the functions of the above embodiments may be, for example, a personal computer, a laptop computer, an in-vehicle human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0096] Although one or more embodiments of the present disclosure provide method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When an actual device or terminal product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, in an environment of parallel processors or multi-threaded processing, or even in a distributed data processing environment).

[0097] The terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, product, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, product, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, product, or apparatus that includes the elements is not precluded. For example, if words such as "first," "second," etc. are used to indicate names, they do not imply any particular order.

[0098] For the convenience of description, the above devices are described in terms of functions divided into various modules. Of course, when implementing one or more embodiments of the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0099] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.

[0100] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0102] Those skilled in the art will appreciate that one or more embodiments of the present disclosure may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] One or more embodiments of the present disclosure may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of the present disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0104] The same or similar parts between the various embodiments of the present disclosure can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of the present disclosure, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present disclosure, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the present disclosure and the features of the different embodiments or examples without contradiction.

[0105] In addition, when used in this disclosure, the words "herein," "above," "below," "hereunder," "supra," and words of similar meaning shall refer to the disclosure as a whole and not to any particular portions of the disclosure. Furthermore, unless expressly stated otherwise or otherwise understood in the context of use, conditional language used herein, such as "may," "might," "for example," "such as," and the like, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements, and / or states. Thus, such conditional language is generally not intended to imply that one or more embodiments in any way require features, elements, and / or states, or whether such features, elements, and / or states are included or performed in any particular embodiment.

Claims

1. A method for managing a vessel, comprising: Acquiring first information about the ship, the first information including fuel consumption associated with the ship speed; obtaining second information about energy-saving devices available for the ship; obtaining third information about fuel usable by the ship; constructing an integer programming model, the integer programming model including one or more of a first sub-model, a second sub-model, and a third sub-model, wherein the first sub-model is used to determine a speed of the ship, the second sub-model is used to determine an energy-saving device to be used by the ship, and the third sub-model is used to determine a fuel to be used by the ship, wherein the integer programming model includes a constraint that a carbon intensity index (CII) of the ship does not exceed a target CII; and The integer programming model is solved using corresponding one or more of the first information, the second information, and the third information, and corresponding one or more of a speed, an energy-saving device, and a fuel to be used for the ship are determined according to the solution result.

2. The method according to claim 1, wherein The first information, the second information, and the third information are associated with a specified year, and a constraint of the integer programming model includes that the CII of the ship in the specified year does not exceed a target CII for the specified year, The method includes solving the integer programming model using corresponding one or more of the first information, the second information, and the third information and determining corresponding one or more of the speed, energy-saving device, and fuel to be used for the ship in the specified year based on the solution results.

3. The method according to claim 2, wherein: The target CII for the specified year is determined based on the baseline CII for the vessel in the specified year.

4. The method according to claim 3, wherein: The target CII for the specified year is set to Among them, CII tar The target CII for the specified year, CII ref is the base CII of the vessel in the specified year, and Z is the reduction parameter corresponding to the specified year.

5. The method according to claim 1, wherein The first sub-model includes a constraint that a first CII determined based on the speed of the ship does not exceed a first preset CII, the second sub-model includes a constraint that a second CII determined based on the energy-saving device to be used by the ship does not exceed a second preset CII, and the third sub-model includes a constraint that a third CII determined based on the fuel to be used by the ship does not exceed a third preset CII.

6. The method according to claim 5, wherein: The first preset CII, the second preset CII, and the third preset CII are respectively equal to the target CII; or A sum of corresponding one or more of the first preset CII, the second preset CII, and the third preset CII is equal to the target CII.

7. The method according to claim 1 , comprising constructing the first sub-model by: Setting a first constraint, wherein the first constraint includes that a first CII determined based on the speed of the ship does not exceed a first preset CII; Setting a second constraint, wherein the second constraint includes that the speed of the ship is not less than the minimum speed of the ship; and A first objective function is set, wherein the first objective function includes minimizing the cost of adjusting the ship speed.

8. The method according to claim 7, wherein: The first objective function includes The first constraint includes Satisfy f(x t )≤A1 t , The second constraint includes Satisfy x t ≥v min , Among them, the min function is used to find the minimum value, T0 is the initial year, T is the target year, c1 t is the speed adjustment cost of the ship in year t, x t is the speed of the ship in year t, f(x t ) indicates the ship’s speed x t Fuel consumption prediction model under fuel consumption conditions, A1 t is the first preset CII in year t, and v min The minimum speed.

9. The method according to claim 8, wherein In the case where the ship is a new ship newly introduced into the fleet, the fuel consumption prediction model of the ship is determined by the following operations: In response to the fleet including an old ship of the same model and tonnage as the ship, determining the fuel consumption prediction model of the ship to be the fuel consumption prediction model of the old ship of the same model and tonnage; or In response to the fleet not including an old ship of the same model and tonnage as the ship, at least one old ship of the same model and closest tonnage as the ship is selected from the fleet, and a fuel consumption prediction model of the ship is determined based on the fuel consumption prediction model of the at least one old ship.

10. The method according to claim 9, wherein: Determining the fuel consumption prediction model of the ship based on the fuel consumption prediction model of the at least one old ship includes: The fuel consumption prediction model of the ship is determined based on the fuel consumption prediction model of each old ship of the at least one old ship and the tonnage similarity between the old ship and the ship.

11. The method according to claim 9, wherein: In response to the fleet not including an old ship of the same model and tonnage as the ship, the fuel consumption prediction model of the ship is determined to be Among them, x is the speed, N R is the number of the at least one old ship, R is the set of the at least one old ship, r is the old ship in the set R, DWT0 is the tonnage of the ship, DWT r is the tonnage of the old ship r, and f r (x) is the fuel consumption prediction model of the old ship r.

12. The method according to claim 1, comprising constructing the second sub-model by: Set one or more of the following constraints: A third constraint, wherein the second CII determined based on the energy-saving device to be used by the ship does not exceed a second preset CII, The fourth constraint includes that the number of types of energy-saving devices installed on the ship each time it enters dock does not exceed a preset threshold, The fifth constraint includes that the ship cannot repeatedly install the same type of energy-saving device. The sixth constraint includes that the ship cannot simultaneously install energy-saving devices that are mutually exclusive in technology. A seventh constraint, wherein the seventh constraint includes that the energy-saving device can only be installed when the vessel is docked, and An eighth constraint, wherein each time the ship enters dock, only energy-saving devices with mature technology at that time can be installed; and A second objective function is set, wherein the second objective function includes minimizing the cost of using the energy-saving device.

13. The method according to claim 12, wherein: The second objective function includes The third constraint includes satisfy The fourth constraint includes satisfy The fifth constraint includes satisfy The sixth constraint includes Satisfy i,t +y j,t ≤1; The seventh constraint includes Satisfy i,t =0; The eighth constraint includes Satisfy i,t =0, Among them, min function is used to find the minimum value, n is the number of energy-saving devices that can be used on the ship, T0 is the initial year, T is the target year, c2 i,t is the cost of installing energy-saving device i in year t, y i,t Indicates whether energy-saving device i, y is installed in year t i,t =1 means that energy-saving device i, y is installed in year t i,t =0 means that energy-saving device i is not installed in year t, I1 t is the CII of the ship before the energy-saving device modification in year t, h i is the emission reduction effect of energy-saving device i, A2 t is the second preset CII for year t, tol is the adjustment parameter, Yr is the set of docking transformation years of the ship, N Th1 is the preset threshold for the number of energy-saving devices installed each time the ship enters the dock, S is the number of types of energy-saving devices that can only be installed N Th2 The energy-saving device set of times, M0 is the energy-saving device set of all technologies mutually exclusive, and Y i The year in which the technology of energy-saving device i reaches maturity.

14. The method according to claim 1, comprising constructing the third sub-model by: Set one or more of the following constraints: A ninth constraint is set, wherein the ninth constraint includes that a third CII determined based on the fuel to be used by the ship does not exceed a third preset CII, setting a tenth constraint, the tenth constraint comprising satisfying a fuel usage requirement of the vessel; and A third objective function is set, the third objective function including minimizing fuel usage cost.

15. The method according to claim 14, wherein The third objective function includes The ninth constraint includes The tenth constraint includes Among them, min function is used to find the minimum value, m is the number of fuel types that can be used by the ship, T0 is the initial year, T is the target year, c3 k,t is the cost of using fuel k in year t, z k,t is the proportion of fuel k used in year t, I2 t is the CII of the ship before fuel conversion in year t, p k is the emission reduction effect of fuel k, and A3 t The third preset CII for year t.

16. The method according to claim 15, wherein The emission reduction effect of a fuel is based on the calorific value and carbon factor of the fuel and a reference fuel.

17. The method according to claim 16, wherein: The emission reduction effect of fuel k is set to Among them, LCV k is the calorific value of fuel k, LCV kr is the reference fuel k r The calorific value of fuel k is C(k), and C(k r ) is the reference fuel k r The carbon factor.

18. A method for managing a vessel, comprising: Acquiring first information about the ship, the first information including fuel consumption associated with the ship speed; Constructing an integer programming model, the integer programming model including a first sub-model, the first sub-model being used to determine a speed of the ship, wherein the integer programming model includes a constraint that a carbon intensity index (CII) of the ship does not exceed a target CII; as well as The integer programming model is solved using the first information and a speed to be used for the ship is determined according to the solution result.

19. A method for managing a vessel, comprising: obtaining second information about energy-saving devices available for the ship; constructing an integer programming model, the integer programming model including a second sub-model, the second sub-model being used to determine an energy-saving device to be used by the ship, wherein the integer programming model includes a constraint that a carbon intensity index (CII) of the ship does not exceed a target CII; as well as The integer programming model is solved using the second information, and an energy-saving device to be used for the ship is determined according to the solution result.

20. A method for managing a vessel, comprising: obtaining third information about fuel usable by the ship; constructing an integer programming model, the integer programming model including a third sub-model, the third sub-model being used to determine a fuel to be used by the ship, wherein the integer programming model includes a constraint that a carbon intensity index (CII) of the ship does not exceed a target CII; and The integer programming model is solved using the third information and fuel to be used for the ship is determined according to the solution result.

21. A method for managing a fleet, comprising performing the method of any one of claims 1 to 20 on each vessel in the fleet.

22. An electronic device comprising: processor; as well as A memory storing computer executable instructions which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 21.

23. A non-transitory storage medium having stored thereon computer-executable instructions, which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 21.

24. A computer program product comprising instructions which, when executed by a processor, implement the method according to any one of claims 1 to 21.

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