A method, device and equipment for evaluating greenhouse gas emissions from ships

Abstract

The present invention provides a method, device and equipment for evaluating greenhouse gas emissions of ships, wherein the method includes: obtaining static ship data of a target ship; obtaining the navigation track and meteorological sea condition data of the target ship within a preset driving period; determining at least one target segment of the navigation track of the target ship within the preset driving period according to the dynamic data corresponding to the navigation track of the target ship within the preset driving period and the meteorological sea condition data; determining a greenhouse gas emission evaluation model for at least one of the target segments; obtaining the target greenhouse gas emissions corresponding to at least one of the target segments of the target ship within the preset driving period according to the greenhouse gas emission evaluation model. The scheme provided by the present invention can realize the evaluation of greenhouse gas emissions of intelligent ships in different time periods corresponding to different segments, and reduce the evaluation cost of greenhouse gas emissions of intelligent ships.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse gas emission testing of intelligent ships, and in particular to a method, device and equipment for evaluating greenhouse gas emissions of ships. Background Art

[0002] Scientific research shows that greenhouse gas (GHG) emissions from human production and daily life are the primary cause of global warming. Global industries are committed to reducing GHG emissions to achieve the Paris Agreement's goal of limiting global warming to well below 2°C by the end of the century, and striving to achieve the desired 1.5°C target. As the primary mode of transport for global trade, the shipping industry, while providing convenient and efficient transportation, also emits significant amounts of greenhouse gases (primarily carbon dioxide). With the increase in extreme weather events linked to global climate change, the pressure to reduce emissions is increasing. The International Maritime Organization is working to achieve its shipping emissions reduction targets through technology, management, and market mechanisms. However, effectively assessing emissions reductions is fundamental to achieving these goals and a practical need facing the industry.

[0003] The existing measurement and quota of greenhouse gas emissions from ships have the following shortcomings:

[0004] 1. Bunkering Note (BDN) and Tank Inventory Method: This method is subject to uncertainty in fuel consumption measurement. This is due, on the one hand, to the moisture, impurities, and non-combustion substances in the fuel. On the other hand, there is a discrepancy between the BDN refueling volume and the actual amount received, which leads to differences in emissions.

[0005] 2. Onboard oil tank monitoring method: This method is essentially the same as method 1 above, with the difference being that the measurement frequency is increased, generally requiring daily measurements, which greatly increases the crew's workload. Inaccurate measurements during navigation, especially in bad weather, affect the accurate calculation of fuel quantity, resulting in large errors in the calculation results.

[0006] 3. Flow meter monitoring method for combustion process: This method requires investment in configuring metering equipment for energy-consuming equipment, which is relatively costly. The metering accuracy depends on the accuracy of the metering equipment, and the maintenance cost of the metering equipment is high.

[0007] 4. Direct measurement of greenhouse gas emissions: This method requires continuous measurement of exhaust gas flow and the carbon dioxide content (percentage) in the exhaust gas. Therefore, a measuring device must be installed in the exhaust pipe of each energy-consuming equipment. The initial investment and subsequent maintenance costs of the measuring equipment are high, and the measurement accuracy depends on the precision of the measuring equipment. At the same time, the accuracy of the calculation of carbon dioxide greenhouse gas emissions usually requires third-party verification and verification. The mobile characteristics of ships bring time and labor costs to third-party verification. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method, device and equipment for evaluating greenhouse gas emissions of ships, so as to improve the accuracy of greenhouse gas emissions evaluation of smart ships in different sections of navigation and reduce the evaluation cost of greenhouse gas emissions of smart ships.

[0009] To solve the above technical problems, an embodiment of the present invention provides a method for evaluating greenhouse gas emissions from ships, comprising:

[0010] Obtain static data of the target ship;

[0011] Obtaining the navigation track and weather and sea condition data of the target ship within a preset navigation period;

[0012] determining at least one target segment of the navigation track of the target ship within the preset navigation period based on the dynamic data corresponding to the navigation track of the target ship within the preset navigation period and the meteorological and sea condition data;

[0013] determining a greenhouse gas emissions assessment model for at least one of the target flight segments;

[0014] According to the greenhouse gas emission assessment model, a target greenhouse gas emission corresponding to at least one target segment of the target ship within the preset travel period is obtained.

[0015] Optionally, determining at least one target segment of the navigation trajectory of the target ship within the preset navigation period according to the dynamic data corresponding to the navigation trajectory of the target ship within the preset navigation period and the meteorological and sea condition data includes:

[0016] Determining the weather and sea condition data corresponding to each track point in the navigation track;

[0017] Obtaining at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track according to the dynamic data and the meteorological and sea condition data corresponding to each track point;

[0018] At least one target segment of the navigation trajectory is determined based on at least one of the indicator data and the indicator threshold.

[0019] Optionally, determining the meteorological and sea condition data corresponding to each track point in the navigation track includes:

[0020] Gridding the navigation track within a preset longitude and latitude range according to a preset spatial resolution to obtain a gridded track of the navigation track;

[0021] The meteorological and sea condition data corresponding to each track point in the navigation track is determined according to a preset algorithm, the meteorological and sea condition data, and each grid cell in the gridded track of the navigation track.

[0022] Optionally, obtaining at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track based on the dynamic data and the meteorological and sea condition data corresponding to each track point includes:

[0023] Obtaining at least one first indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point according to the dynamic data;

[0024] At least one second indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point is obtained according to the meteorological and sea condition data corresponding to each trajectory point.

[0025] Optionally, determining a greenhouse gas emissions assessment model for at least one target flight segment includes:

[0026] determining, based on the dynamic data of at least one of the target segments, a corresponding navigation state and a corresponding berthing state of the target vessel in the at least one target segment;

[0027] Obtaining a first greenhouse gas emissions assessment model for the target ship based on the first dynamic data corresponding to the navigation state and the static data of the ship;

[0028] A second greenhouse gas emission assessment model for the target ship is obtained according to the second dynamic data corresponding to the mooring state and the static data of the ship.

[0029] Optionally, obtaining a first greenhouse gas emissions assessment model for the target ship according to the first dynamic data corresponding to the navigation state and the static data of the ship includes:

[0030] determining a first correction coefficient of the main engine output power of the target ship according to the speed in the first dynamic data corresponding to the navigation state;

[0031] A first greenhouse gas emission assessment model of the target ship is obtained according to the ship speed in the first dynamic data corresponding to the navigation state, the first correction coefficient, and the rated output power of the target ship in the ship static data.

[0032] Optionally, obtaining, according to the greenhouse gas emissions assessment model, a target greenhouse gas emissions corresponding to at least one target segment of the target ship within the preset travel period includes:

[0033] According to the first greenhouse gas emission assessment model and / or the second greenhouse gas emission assessment model, a target greenhouse gas emission corresponding to at least one target segment of the target ship within the preset travel period is obtained.

[0034] An embodiment of the present invention further provides a device for evaluating greenhouse gas emissions from a ship, comprising:

[0035] The first acquisition module is used to acquire static data of the target ship;

[0036] The second acquisition module is used to obtain the navigation track and weather and sea condition data of the target ship within a preset travel period;

[0037] A processing module is used to determine at least one target segment of the navigation trajectory of the target ship within the preset navigation period based on the dynamic data corresponding to the navigation trajectory of the target ship within the preset navigation period and the meteorological and sea condition data; determine a greenhouse gas emissions assessment model for at least one of the target segments; and obtain target greenhouse gas emissions corresponding to at least one of the target segments of the target ship within the preset navigation period based on the greenhouse gas emissions assessment model.

[0038] An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the computer program executes any one of the above methods.

[0039] An embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0040] The above solution of the present invention includes at least the following beneficial effects:

[0041] The above-mentioned scheme of the present invention obtains the static data of the target ship; obtains the navigation track and meteorological and sea condition data of the target ship within a preset driving period; determines at least one target segment of the navigation track of the target ship within the preset driving period based on the dynamic data corresponding to the navigation track of the target ship within the preset driving period and the meteorological and sea condition data; determines a greenhouse gas emission assessment model for at least one of the target segments; and obtains the target greenhouse gas emissions corresponding to at least one of the target segments of the target ship within the preset driving period based on the greenhouse gas emission assessment model, so as to improve the accuracy of greenhouse gas emission assessment of smart ships in different segments and reduce the assessment cost of greenhouse gas emissions of smart ships. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1This is a flow chart of a method for evaluating greenhouse gas emissions from ships provided by an embodiment of the present invention;

[0043] Figure 2 This is a flowchart of determining at least one target segment in a method for evaluating greenhouse gas emissions of a ship provided by an optional embodiment of the present invention;

[0044] Figure 3 This is a flowchart of determining the meteorological and sea condition data corresponding to each trajectory point in a method for evaluating greenhouse gas emissions of a ship provided by an optional embodiment of the present invention;

[0045] Figure 4 This is a block diagram of a module of a device for evaluating greenhouse gas emissions from ships provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0047] like Figure 1 An embodiment of the present invention provides a method for evaluating greenhouse gas emissions from ships, which may include:

[0048] Step 11, obtaining the static data of the target ship;

[0049] Step 12: Obtain the navigation track and weather and sea condition data of the target ship within a preset navigation period;

[0050] Step 13, determining at least one target segment of the navigation track of the target ship within the preset navigation period based on the dynamic data corresponding to the navigation track of the target ship within the preset navigation period and the meteorological and sea condition data;

[0051] Step 14, determining a greenhouse gas emissions assessment model for at least one of the target flight segments;

[0052] Step 15: obtaining target greenhouse gas emissions corresponding to at least one target segment of the target ship within the preset travel period according to the greenhouse gas emissions assessment model.

[0053] In this embodiment, the static data of the target ship and the dynamic data corresponding to its navigation track can be collected in real time through the AIS ship automatic identification system of the target ship; the dynamic data may include: the latitude and longitude of the target ship (used to characterize the current position information of the target ship), speed, heading, turning rate (the rate at which the target ship turns), draft, positioning time (UTC) of the track point, etc. It should be known that each track point in the navigation track corresponds to a positioning time, and at this positioning time, each track point has a certain latitude and longitude, speed, heading, Dynamic information such as turning rate; the static data may include: the target ship's name, call sign, MMSI number (World Maritime Radio Special Identification Code), IMO number (target ship identity number), country of registry, type (such as oil tanker, fishing vessel), size (such as length, width and other dimensional parameters), main and auxiliary engines and boiler parameter information (such as rated power, etc.); the meteorological sea condition data can be collected by meteorological collection equipment, and the meteorological sea condition data may include: wind speed, wind direction, wave height, wave direction, wave period, ocean current speed, ocean current direction, etc.;

[0054] The target ship has unique greenhouse gas emission characteristics under different navigation states. Based on the dynamic data corresponding to the navigation track within the preset driving period, the meteorological and sea condition data, and the static data of the ship, the navigation track is segmented into grids to obtain at least one target segment of the track, so as to distinguish the different navigation states corresponding to the target ship and facilitate subsequent refined greenhouse gas emission assessment for any target segment.

[0055] Furthermore, based on the dynamic data and the static data of the ship, a greenhouse gas emissions assessment model for the target ship is constructed, and based on the greenhouse gas emissions assessment model, the greenhouse gas emissions of the target ship at any time point and time period in any target section within the preset driving period can be assessed, and the greenhouse gas emissions of all ships sailing on the route and port area can also be assessed; at the same time, based on the greenhouse gas emissions assessment model, the accuracy and reliability of ship greenhouse gas emissions monitoring can be improved, decision support can be provided for third-party verification agencies and industry authorities, and the cost of ship greenhouse gas emissions assessment can be reduced.

[0056] In an optional embodiment of the present invention, since the dynamic data is collected based on the AIS automatic identification system, it has the problems of large data volume and high noise data; therefore, after obtaining the dynamic data corresponding to the navigation trajectory of the target ship within a preset driving period, it is necessary to pre-process the dynamic data. The pre-processing may include: removing redundant data, filtering abnormal information, smoothing the target ship trajectory, etc., so as to reduce the data volume and improve the efficiency and accuracy of subsequent data processing;

[0057] Here, the filtering of abnormal information is mainly aimed at abnormal conditions existing in the transmission process of AIS signals, and the abnormal data exceeding the speed threshold and exceeding the longitude and latitude range in the dynamic data are filtered out;

[0058] The smoothed target ship trajectory means that the longitude and latitude of the target ship in the dynamic data may fluctuate greatly in a short period of time, and these data need to be smoothed to ensure the continuity and rationality of the longitude and latitude corresponding to the trajectory points in the navigation trajectory of the target ship; here, a preset smoothing algorithm can be used to smooth the trajectory points of the navigation trajectory. The preset smoothing algorithm is not limited, and any algorithm that can achieve smoothing operations can be used, such as a cubic spline interpolation algorithm.

[0059] In an optional embodiment of the present invention, after preprocessing the dynamic data, the method may further include: fusing the preprocessed dynamic data, the meteorological and sea conditions data, and the ship static data to obtain a basic data set containing the dynamic data, the meteorological and sea conditions data, and the ship static data. The subsequent navigation trajectory segmentation and greenhouse gas emission assessment model establishment are all based on the data in the basic data set. The fusion processing may include: combining the preprocessed dynamic data with the main and auxiliary engines and the static data according to the MMSI number or IMO number in the static data. The parameter information of the boiler is matched (power consumption information in the main and auxiliary engines and boiler parameters); after parsing and cleaning the meteorological and sea condition data, the meteorological and sea condition data are matched according to the positioning time of the trajectory points in the dynamic data; since the sources of the meteorological and sea condition data and the dynamic data are different, when they are integrated and matched, they are coarse matches, and the data with similar collection times are matched here; by fusing the pre-processed dynamic data, the meteorological and sea condition data, and the static data of the ship, the unified integration of data from different sources can be achieved, thereby ensuring the accuracy of the subsequent navigation trajectory segmentation and carbon emission model establishment.

[0060] like Figure 2 As shown, in an optional embodiment of the present invention, the above step 13 may include:

[0061] Step 131, determining the weather and sea condition data corresponding to each track point in the navigation track;

[0062] Step 132: obtaining at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track based on the dynamic data and the meteorological and sea condition data corresponding to each track point;

[0063] Step 133: Determine at least one target segment of the navigation trajectory based on at least one of the indicator data and the indicator threshold.

[0064] In this embodiment, since the weather and sea condition data have a significant impact on the navigation of the target ship, it is first necessary to confirm the weather and sea condition data corresponding to each track point in the navigation track to ensure the accuracy of the subsequent navigation track segmentation and greenhouse gas emission assessment model establishment;

[0065] Further, according to the dynamic data corresponding to each track point in the navigation track and the meteorological and sea condition data corresponding to each track point, at least one type of indicator data between two adjacent track points is obtained;

[0066] The navigation trajectory is segmented according to at least one of the index data and the index threshold corresponding to the index data, which is to divide the original navigation trajectory into multiple segments with similar characteristics to obtain multiple target segments; the navigation trajectory is composed of a series of trajectory points of the target ship and is a multivariate time series; assuming that the navigation trajectory of the target ship is T = {t1, t2, ... t i …, t n}, where t i represents the i-th track point of the target ship, i = 1, 2, ..., n, which is a positive integer;

[0067] t i ={time i ,lat i ,lon i , Draught i , cog i , sog i , WindSpeed i , WindDirection i ,

[0068] WaveHigh i , WaveDirection i , WavePeriod i , CurrentSpeed i , CurrentDirection i}, timei Indicates the positioning time (UTC) of the track point, lat i Indicates the latitude of the track point, loni indicates the longitude of the track point, cog i Indicates the navigation angle of the target ship at the current track point, sogi indicates the speed of the target ship at the current track point, WindSpeed i Indicates the wind speed corresponding to the target ship at the current track point, WindDirection i Indicates the wind direction corresponding to the target ship at the current track point, WaveHigh i Indicates the wave height corresponding to the target ship at the current track point, WaveDirection i Indicates the wave direction corresponding to the target ship at the current track point, WavePeriod i Indicates the wave period corresponding to the target ship at the current track point, CurrentSpeed i Indicates the ocean current velocity corresponding to the target ship at the current track point, CurrentDirection i Indicates the ocean current direction corresponding to the target ship's current track point;

[0069] It should be known that each track point in the navigation track of the target ship and the track points in the multiple target segments obtained after segmenting the navigation track all contain the relevant data information in the above-mentioned track point ti; by associating the meteorological and sea condition data of each track point, the navigation track is further divided based on the associated track points to ensure the accuracy of the navigation track division, and at the same time provide refined segment information corresponding to the subsequent assessment of the greenhouse gas emissions of the target ship, thereby ensuring the accuracy of the greenhouse gas emissions assessment.

[0070] like Figure 3 As shown, in an optional embodiment of the present invention, the above step 131 may include:

[0071] Step 1311 , gridding the navigation track within a preset latitude and longitude range according to a preset spatial resolution to obtain a gridded track of the navigation track;

[0072] Step 1312: Determine the meteorological and sea condition data corresponding to each track point in the navigation track according to a preset algorithm, the meteorological and sea condition data, and each grid cell in the gridded track of the navigation track.

[0073] In this embodiment, the preset spatial resolution and the preset longitude and latitude range can be set according to the obtained longitude and latitude of the track points in the navigation track;

[0074] According to the preset spatial resolution and the preset longitude and latitude range, the navigation track is gridded in the geographic space to obtain the gridded track of the navigation track; for example, the preset spatial resolution is 1°×1°, and the preset longitude and latitude range is: the latitude preset range is: [-90°, 90°], and the longitude preset range is: [-180°, 180°], then the track points in the navigation track start from the latitude preset range [-90°, 90°] and the longitude preset range [-180°, 180°], and the navigation track is gridded every 1° to obtain the gridded track of the navigation track; each unit grid in the gridded track corresponds to a track point, the longitude and latitude values ​​of the track point, and the longitude and latitude range of the unit grid;

[0075] Furthermore, the meteorological and sea condition data are distributed to each grid cell in the gridded track of the navigation track according to a preset algorithm to match the track points in the unit grid; preferably, the preset algorithm can be an interpolation algorithm, which determines the weight of the track point in each grid cell or the corresponding interpolation function according to the properties of the meteorological and sea condition data and the spatial latitude and longitude characteristics of each grid cell, and the weight or the interpolation function will determine how to assign the meteorological and sea condition data near the positioning time corresponding to the track point to the grid cell where the track point is located;

[0076] Furthermore, interpolation calculation is performed in the gridded space according to the selected interpolation method and weight. For each grid cell, the interpolation result of the meteorological and sea condition data corresponding to the grid cell is calculated according to the value and weight of the meteorological and sea condition data near the positioning time corresponding to the track point in the unit grid (here, the meteorological and sea condition data near the positioning time corresponding to the track point refers to the meteorological and sea condition data within a preset time range from the positioning time corresponding to the track point), and the calculated interpolation result is filled into the corresponding grid cell to determine the meteorological and sea condition data corresponding to each track point in the navigation track, thus completing the interpolation of the meteorological and sea condition data into each track point.

[0077] Here, the interpolation function may include any one of the following:

[0078] Nearest Neighbor Interpolation: Assign the weather and sea condition data with the closest positioning time corresponding to the track point in the grid cell to the grid cell;

[0079] Bilinear Interpolation: Performs linear interpolation calculations in the horizontal and vertical directions based on a preset number of nearest neighbor meteorological and sea condition data around the grid cell.

[0080] Cubic Spline Interpolation: Utilizes more neighboring meteorological and sea condition data around the grid cell and performs interpolation calculations using a cubic polynomial.

[0081] Selecting an appropriate interpolation function and weight determination is very important for the precision and accuracy of the final interpolation result, which can ensure the accuracy of the subsequent target segment division.

[0082] In an optional embodiment of the present invention, the above step 132 may include:

[0083] Step 1321: Obtain at least one first indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point based on the dynamic data;

[0084] Step 1322: Obtain at least one second indicator data of the current trajectory point and the previous trajectory point adjacent to the current trajectory point based on the meteorological and sea condition data corresponding to each trajectory point.

[0085] In this embodiment, after determining the meteorological and sea condition data corresponding to each track point in the navigation track, at least one first indicator data of the current track point and the previous track point adjacent to the current track point in the navigation track, and at least one second indicator data of the current track point and the previous track point adjacent to the current track point are respectively calculated based on the dynamic data and the meteorological and sea condition data corresponding to each track point;

[0086] At least one of the first indicator data is a first difference in dynamic data between the current trajectory point and the previous trajectory point adjacent to the current trajectory point; at least one of the second indicator data is a second difference in meteorological and sea condition data between the current trajectory point and the previous trajectory point adjacent to the current trajectory point; the first indicator data may include: speed difference, heading difference, and target ship draft difference; the second indicator data may include: wind direction difference, wind speed difference, wave height difference, wave direction difference, wave period difference, ocean current speed difference, and ocean current direction difference;

[0087] Furthermore, based on at least one of the first indicator data and at least one of the second indicator data and the corresponding indicator threshold, the dividing point of the target segment in the navigation trajectory is determined; specifically, when any one of the speed difference, heading difference, target ship draft difference in the first indicator data and the wind direction difference, wind speed difference, wave height difference, wave direction difference, wave period difference, ocean current speed difference, and ocean current direction difference in at least one of the second indicator data is greater than the corresponding indicator threshold, the current trajectory point is determined as the dividing point of the target segment in the navigation trajectory, and the dividing point is the end point of the previous target segment and the starting point of the next target segment, thereby determining multiple target segments in the navigation trajectory; the target segment may include one trajectory point or multiple trajectory points; when the carbon emission model is subsequently established and calculated, the average value of the dynamic data corresponding to the trajectory points in the target segment is used;

[0088] Here, the corresponding indicator thresholds may include: speed threshold, heading threshold, draft threshold, wind direction threshold, wind speed threshold, wave height threshold, wave direction threshold, wave period threshold, ocean current speed threshold, and ocean current direction threshold; the indicator thresholds may be set according to actual needs. In an implementable example of the present invention, the settings of the corresponding indicator thresholds may be shown in the following table:

[0089]

[0090] Table 1, indicator threshold comparison table.

[0091] In an optional embodiment of the present invention, the above step 14 may include:

[0092] Step 141, determining the navigation state and berthing state of the target vessel in the at least one target segment according to the dynamic data of the at least one target segment;

[0093] Step 142: obtaining a first greenhouse gas emission assessment model of the target ship based on the first dynamic data corresponding to the navigation state and the static data of the ship;

[0094] Step 143 : obtaining a second greenhouse gas emission assessment model of the target ship according to the second dynamic data corresponding to the mooring state and the static data of the ship.

[0095] In this embodiment, the rules for classifying the navigation status and berthing status of the target ship in the target segment are shown in Table 2 below.

[0096]

[0097] Table 2, table of target ship status classification rules within the target segment;

[0098] By dividing the states of the target ship, the accuracy of the greenhouse gas emission assessment model constructed in different states and the accuracy of the greenhouse gas emission assessment in different states are ensured;

[0099] Furthermore, based on the first dynamic data corresponding to the navigation state of the target ship in the target segment and the static data of the ship, a first greenhouse gas emission assessment model for the navigation state of the target ship in the target segment can be constructed;

[0100] Specifically, it may include:

[0101] Step 1421, determining a first correction coefficient of the main engine output power of the target ship according to the speed in the first dynamic data corresponding to the navigation state;

[0102] Step 1422: Obtain a first sub-greenhouse gas emission assessment model for the target ship based on the ship speed in the first dynamic data corresponding to the navigation state, the first correction coefficient, and the rated output power of the target ship in the ship static data.

[0103] In this embodiment, considering that the speed of the target ship is affected by the meteorological and sea conditions data in the navigation state, and the influence on the speed can be reflected in the main engine output power of the target ship, the first correction coefficient for the main engine output power of the target ship can be determined based on the speed in the first dynamic data and the speed threshold corresponding to the target ship, so as to correct the main engine output power of the target ship in the navigation state, thereby reducing the influence of the meteorological and sea conditions data and ensuring the accuracy of subsequent greenhouse gas emission calculations.

[0104] In an optional embodiment of the present invention, the first correction coefficient can be calculated by the following formula:

[0105]

[0106] Wherein, α represents the first correction coefficient, v j represents the average speed of the jth target segment in the navigation trajectory, j=1, 2, ..., n is a positive integer, v0 represents the design or service speed of the target ship, and a is a set constant parameter.

[0107] In an optional embodiment of the present invention, in the navigation state, the main engine output power of the target ship also affects the fuel consumption rate of the main engine; here, the fuel consumption rate of the main engine can be corrected by a second correction coefficient; preferably, the second correction coefficient can be obtained by fitting the fuel consumption rate-power curve reported by the main engine bench test of the target ship.

[0108] The second correction coefficient can be expressed as:

[0109]

[0110] Wherein, β represents the second correction coefficient, L1 represents the main engine load rate of the target ship, b, c, and d are all constant parameters obtained by fitting;

[0111] Furthermore, based on the speed in the first dynamic data corresponding to the navigation state, the first correction coefficient, the second correction coefficient, and the rated output power of the target ship in the static ship data, a first greenhouse gas emission assessment model for the target ship is constructed as follows:

[0112] E 1g =P1×L1×α×T1×(S1×β)×CF g +P2×L2×T2×S2×CF g ;

[0113] Among them, E 1g represents the greenhouse gas emissions of the target ship under the navigation state, P1 represents the rated power of the main engine of the target ship, T1 represents the operating time of the main engine of the target ship, S1 represents the fuel consumption rate of the main engine of the target ship, P2 represents the rated power of the auxiliary engine of the target ship, L2 represents the auxiliary engine load factor of the target ship (under the navigation state, the auxiliary engine load factor can be set to a constant parameter, such as 0.5), T2 represents the operating time of the auxiliary engine of the target ship, S2 represents the fuel consumption rate of the auxiliary engine of the target ship, CF g represents the emission factor of the greenhouse gas g; here, the greenhouse gas may include any one of CO2, CH4, and N2O (that is, g can be expressed as any one of CO2, CH4, and N2O). T1 and T2 can be determined by the time of the current target segment, that is, the positioning time of the starting track point and the positioning time of the ending track point in the target segment. The main engine fuel consumption rate and the auxiliary engine fuel consumption rate can both be obtained from the bench test report of the target ship;

[0114] Here, the greenhouse gas emission factors corresponding to any type of fuel are shown in Table 3 below.

[0115]

[0116] Table 3, Greenhouse gas emission factors corresponding to different fuel types;

[0117] In an optional embodiment of the present invention, a second greenhouse gas emission assessment model for the target ship is constructed based on the second dynamic data corresponding to the mooring state and the static data of the ship as follows:

[0118] E 2g =P2×L2×T2×S2×CF g +P3×T3×S3×CF g ;

[0119] Among them, E 2g represents the greenhouse gas emissions of the target ship in the moored state, P3 represents the boiler output power of the target ship, T3 represents the operating time of the target ship's boiler (which can be determined by the time of the current target segment, that is, the positioning time of the starting track point and the positioning time of the ending track point in the target segment), and S3 represents the boiler fuel consumption rate of the target ship, which can be obtained from the fuel report of the target ship;

[0120] Here, the boiler output power of the target ship is a set value. The boiler output power of target ships of different types and tonnages in the moored state is shown in Table 4 below:

[0121]

[0122] Table 4, boiler output power corresponding to different types of ships;

[0123] By establishing a greenhouse gas emission assessment model for a target ship under different conditions within the corresponding target voyage, the greenhouse gas emissions of the target ship within the corresponding target voyage can be accurately measured.

[0124] In an optional embodiment of the present invention, the above step 15 may include:

[0125] Step 151: Obtain target greenhouse gas emissions corresponding to at least one target segment of the target ship within the preset travel period according to the first greenhouse gas emissions assessment model and / or the second greenhouse gas emissions assessment model.

[0126] In this embodiment, when all the trajectory points in the target segment indicate that the target ship is in a sailing state, the target greenhouse gas emissions of the target ship in the current target segment can be expressed as:

[0127]

[0128] Wherein, E1 represents the target greenhouse gas emissions of the target ship in the current target segment. represents the corresponding CO2, N2O, and CH4 emissions calculated by the first greenhouse gas emission assessment model; represents the global warming potential of CO2, represents the global warming potential of N2O, Indicates the global warming potential of CH4.

[0129] When all the trajectory points in the target segment indicate that the target ship is at anchor, the target greenhouse gas emissions of the target ship in the current target segment can be expressed as:

[0130]

[0131] Wherein, E2 represents the target greenhouse gas emissions of the target ship when it is moored in the current target segment. represents the corresponding CO2, N2O, and CH4 emissions calculated by the second greenhouse gas emission assessment model;

[0132] When the trajectory points within the target segment indicate that the target ship is in both the sailing state and the moored state, the target greenhouse gas emissions of the target ship within the current target segment can be expressed as:

[0133] E3=E1+E2; wherein E3 represents the target greenhouse gas emissions of the target ship in the moored state and the sailing state within the current target segment.

[0134] The above-mentioned embodiment of the present invention obtains static ship data of a target ship and the navigation track and meteorological and sea condition data of the target ship within a preset travel period; determines at least one target segment of the navigation track of the target ship within the preset travel period based on the dynamic data corresponding to the navigation track of the target ship within the preset travel period and the meteorological and sea condition data; and constructs a greenhouse gas emissions assessment model based on the dynamic data corresponding to the target segment and the static ship data; obtains the target greenhouse gas emissions corresponding to at least one target segment of the target ship within the preset travel period based on the greenhouse gas emissions assessment model, thereby enabling the assessment of greenhouse gas emissions of intelligent ships in different segments and reducing the assessment cost of greenhouse gas emissions of intelligent ships;

[0135] The above-mentioned solution of the present invention can realize the greenhouse gas emission assessment at any time point and time period on the track of a single ship, and can also realize the historical greenhouse gas emission assessment of all ships passing through the route and port area; the solution provided by the present invention is low-cost, high-efficiency, and short-time, and can provide a convenient greenhouse gas emission accounting and assessment tool for government authorities, industry organizations, and third-party verification agencies, saving a lot of human resource costs.

[0136] like Figure 4 As shown, an embodiment of the present invention further provides a device 40 for evaluating greenhouse gas emissions from a ship, comprising:

[0137] A first acquisition module 41 is used to acquire static data of a target ship;

[0138] The second acquisition module 42 is used to obtain the navigation track and weather and sea condition data of the target ship within a preset travel period;

[0139] The processing module 43 is used to determine at least one target segment of the navigation trajectory of the target ship within the preset driving period based on the dynamic data corresponding to the navigation trajectory of the target ship within the preset driving period and the meteorological and sea condition data; determine a greenhouse gas emission assessment model for at least one of the target segments; and obtain the target greenhouse gas emissions corresponding to at least one of the target segments of the target ship within the preset driving period based on the greenhouse gas emission assessment model.

[0140] Optionally, the processing module 43 is configured to determine at least one target segment of the navigation trajectory of the target ship within the preset navigation period based on the dynamic data corresponding to the navigation trajectory of the target ship within the preset navigation period and the meteorological and sea condition data, including:

[0141] Determining the weather and sea condition data corresponding to each track point in the navigation track;

[0142] Obtaining at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track according to the dynamic data and the meteorological and sea condition data corresponding to each track point;

[0143] At least one target segment of the navigation trajectory is determined based on at least one of the indicator data and the indicator threshold.

[0144] Optionally, the processing module 43 is configured to determine the meteorological and sea condition data corresponding to each track point in the navigation track, including:

[0145] Gridding the navigation track within a preset longitude and latitude range according to a preset spatial resolution to obtain a gridded track of the navigation track;

[0146] The meteorological and sea condition data corresponding to each track point in the navigation track is determined according to a preset algorithm, the meteorological and sea condition data, and each grid cell in the gridded track of the navigation track.

[0147] Optionally, the processing module 43 is configured to obtain at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track based on the dynamic data and the meteorological and sea condition data corresponding to each track point, including:

[0148] Obtaining at least one first indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point according to the dynamic data;

[0149] At least one second indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point is obtained according to the meteorological and sea condition data corresponding to each trajectory point.

[0150] Optionally, the processing module 43 is configured to determine a greenhouse gas emission assessment model for at least one target flight segment, including:

[0151] determining, based on the dynamic data of at least one of the target segments, a corresponding navigation state and a corresponding berthing state of the target vessel in the at least one target segment;

[0152] Obtaining a first greenhouse gas emissions assessment model for the target ship based on the first dynamic data corresponding to the navigation state and the static data of the ship;

[0153] A second greenhouse gas emission assessment model for the target ship is obtained according to the second dynamic data corresponding to the mooring state and the static data of the ship.

[0154] Optionally, the processing module 43 is configured to obtain a first greenhouse gas emission assessment model of the target ship based on the first dynamic data corresponding to the navigation state and the static data of the ship, including:

[0155] determining a first correction coefficient of the main engine output power of the target ship according to the speed in the first dynamic data corresponding to the navigation state;

[0156] A first greenhouse gas emission assessment model of the target ship is obtained according to the ship speed in the first dynamic data corresponding to the navigation state, the first correction coefficient, and the rated output power of the target ship in the ship static data.

[0157] Optionally, the processing module 43 is configured to obtain, according to the greenhouse gas emission assessment model, a target greenhouse gas emission corresponding to at least one target segment of the target ship within the preset travel period, including:

[0158] According to the first greenhouse gas emission assessment model and / or the second greenhouse gas emission assessment model, a target greenhouse gas emission corresponding to at least one target segment of the target ship within the preset travel period is obtained.

[0159] It should be noted that this device is a device corresponding to the above-mentioned method for evaluating greenhouse gas emissions from ships. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of this device and can achieve the same technical effects.

[0160] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0161] An embodiment of the present invention further provides a computer-readable storage medium comprising instructions, which, when executed on a computer, cause the computer to execute the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0162] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0163] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0166] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0167] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0168] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0169] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0170] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for evaluating greenhouse gas emissions from ships, characterized in that: include: Obtain static ship data of the target ship; Obtaining the navigation track and weather and sea condition data of the target ship within a preset travel period; Determining at least one target segment of the navigation track of the target ship within the preset navigation period according to the dynamic data corresponding to the navigation track of the target ship within the preset navigation period and the meteorological sea condition data; determining a greenhouse gas emissions assessment model for at least one of the target flight segments; According to the greenhouse gas emission assessment model, obtaining a target greenhouse gas emission corresponding to at least one target segment of the target ship within the preset travel period; Wherein, determining at least one target segment of the navigation track of the target ship within the preset navigation period according to the dynamic data corresponding to the navigation track of the target ship within the preset navigation period and the meteorological sea condition data includes: Determine the meteorological sea condition data corresponding to each track point in the navigation track; According to the dynamic data and the meteorological and sea condition data corresponding to each track point, at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track is obtained, including: According to the dynamic data, obtaining at least one first indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point; According to the meteorological and sea condition data corresponding to each trajectory point, obtaining at least one second indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point; The navigation trajectory is composed of the trajectory points of the target ship; the expression of the navigation trajectory is T = {t1, t2, ... t i …, t n }, where t i is the i-th track point of the target ship, i is a positive integer; t i ={time i ,lat i ,lon i ,Draught i , cog i , sog i , WindSpeed i , WindDirection i , WaveHigh i , WaveDirection i , WavePeriod i , CurrentSpeed i , CurrentDirection i }, time i is the positioning time of the trajectory point, lat i is the latitude of the trajectory point, lon i is the longitude of the trajectory point, cog i is the navigation angle of the target ship at the current track point, sog i WindSpeed ​​is the speed of the target ship at the current track point, i WindDirection is the wind speed corresponding to the target ship at the current track point. i The wind direction corresponding to the target ship at the current track point, WaveHigh i The wave height corresponding to the target ship at the current track point, WaveDirection i WavePeriod is the wave direction corresponding to the target ship at the current track point. i CurrentSpeed ​​is the wave period corresponding to the current trajectory point of the target ship, i CurrentDirection is the ocean current velocity corresponding to the target ship at the current track point. i The ocean current direction corresponding to the target ship at the current track point; Determine at least one target segment of the navigation trajectory according to at least one of the first indicator data and at least one of the second indicator data and corresponding indicator thresholds; Determining a greenhouse gas emission assessment model for at least one of the target flight segments includes: Determining the navigation status and berthing status of the target ship in at least one of the target segments according to the dynamic data of at least one of the target segments; Obtaining a first greenhouse gas emission assessment model for the target ship according to the first dynamic data corresponding to the navigation state and the static data of the ship; According to the second dynamic data corresponding to the berthing state and the static data of the ship, a second greenhouse gas emission assessment model of the target ship is obtained; the expression of the second greenhouse gas emission assessment model of the target ship is: E 2g =P2×L2×T2×S2×CF g +P3×T3×S3×CF g ; Among them, E 2g is the greenhouse gas emissions of the target ship in the moored state, P2 is the rated power of the auxiliary engine of the target ship, L2 is the auxiliary engine load rate of the target ship, T2 is the operating time of the auxiliary engine of the target ship, S2 is the auxiliary engine fuel consumption rate of the target ship, CF g is the greenhouse gas emission factor, P3 is the boiler output power of the target ship, T3 is the operating time of the boiler of the target ship, and S3 is the boiler fuel consumption rate of the target ship; Wherein, obtaining a first greenhouse gas emission assessment model of the target ship according to the first dynamic data corresponding to the navigation state and the static data of the ship includes: Determining a first correction coefficient of the main engine output power of the target ship according to the speed in the first dynamic data corresponding to the navigation state; According to the speed in the first dynamic data corresponding to the navigation state, the first correction coefficient and the rated output power of the target ship in the ship static data, a first greenhouse gas emission assessment model of the target ship is obtained; the expression of the first greenhouse gas emission assessment model of the target ship is: E 1g =P1×L1× α ×T1×(S1×β)×CF g +P2×L2×T2×S2×CF g ; Among them, E 1g is the greenhouse gas emissions of the target ship under the navigation state, P1 is the rated power of the main engine of the target ship, T1 is the operating time of the main engine of the target ship, S1 is the fuel consumption rate of the main engine of the target ship, is the first correction coefficient, is the second correction coefficient, which is used to correct the fuel consumption rate of the main engine. It is obtained by fitting the fuel consumption rate-power curve reported by the main engine bench test of the target ship. is the main engine load factor of the target ship, and: ； ； ； in, is the average speed of the jth target segment in the navigation trajectory, j=1, 2, ..., n is a positive integer, is the design or service speed of the target ship, a is the set constant parameter, b, c, d are the constant parameters obtained by fitting; Wherein, obtaining the target greenhouse gas emissions corresponding to at least one target segment of the target ship within the preset travel period according to the greenhouse gas emissions assessment model includes: According to the first greenhouse gas emission assessment model and / or the second greenhouse gas emission assessment model, the target greenhouse gas emissions corresponding to at least one of the target segments of the target ship within the preset travel period are obtained.

2. The method for evaluating greenhouse gas emissions from ships according to claim 1, characterized in that: Determining the meteorological and sea condition data corresponding to each track point in the navigation track, including: According to a preset spatial resolution, gridding the navigation track within a preset longitude and latitude range to obtain a gridded track of the navigation track; The meteorological sea condition data corresponding to each track point in the navigation track is determined according to a preset algorithm, the meteorological sea condition data and each grid unit in the gridded track of the navigation track.

3. A device for evaluating greenhouse gas emissions from a ship, characterized in that: include: A first acquisition module is used to acquire static ship data of a target ship; The second acquisition module is used to acquire the navigation track and meteorological and sea condition data of the target ship within a preset driving period; A processing module, configured to determine at least one target segment of the navigation track of the target ship within the preset travel period according to the dynamic data corresponding to the navigation track of the target ship within the preset travel period and the meteorological and sea condition data; Determining a greenhouse gas emission assessment model for at least one of the target segments; obtaining a target greenhouse gas emission corresponding to at least one of the target segments of the target ship within the preset travel period according to the greenhouse gas emission assessment model; Wherein, determining at least one target segment of the navigation track of the target ship within the preset navigation period according to the dynamic data corresponding to the navigation track of the target ship within the preset navigation period and the meteorological sea condition data includes: Determine the meteorological sea condition data corresponding to each track point in the navigation track; According to the dynamic data and the meteorological and sea condition data corresponding to each track point, at least one indicator data of a current track point and a previous track point adjacent to the current track point in the navigation track is obtained, including: According to the dynamic data, obtaining at least one first indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point; According to the meteorological and sea condition data corresponding to each trajectory point, obtaining at least one second indicator data of the current trajectory point and a previous trajectory point adjacent to the current trajectory point; The navigation trajectory is composed of the trajectory points of the target ship; the expression of the navigation trajectory is T = {t1, t2, ... t i …, t n }, where t i is the i-th track point of the target ship, i is a positive integer; t i ={time i ,lat i ,lon i ,Draught i , cog i , sog i , WindSpeed i , WindDirection i , WaveHigh i , WaveDirection i , WavePeriod i , CurrentSpeed i , CurrentDirection i }, time i is the positioning time of the trajectory point, lat i is the latitude of the trajectory point, lon i is the longitude of the trajectory point, cog i is the navigation angle of the target ship at the current track point, sog i WindSpeed ​​is the speed of the target ship at the current track point, i WindDirection is the wind speed corresponding to the target ship at the current track point. i The wind direction corresponding to the target ship at the current track point, WaveHigh i The wave height corresponding to the target ship at the current track point, WaveDirection i WavePeriod is the wave direction corresponding to the target ship at the current track point. i CurrentSpeed ​​is the wave period corresponding to the current trajectory point of the target ship, i CurrentDirection is the ocean current velocity corresponding to the target ship at the current track point. i The ocean current direction corresponding to the target ship at the current track point; Determine at least one target segment of the navigation trajectory according to at least one of the first indicator data and at least one of the second indicator data and corresponding indicator thresholds; Determining a greenhouse gas emission assessment model for at least one of the target flight segments includes: Determining the navigation status and berthing status of the target ship in at least one of the target segments according to the dynamic data of at least one of the target segments; Obtaining a first greenhouse gas emission assessment model for the target ship according to the first dynamic data corresponding to the navigation state and the static data of the ship; According to the second dynamic data corresponding to the berthing state and the static data of the ship, a second greenhouse gas emission assessment model of the target ship is obtained; the expression of the second greenhouse gas emission assessment model of the target ship is: E 2g =P2×L2×T2×S2×CF g +P3×T3×S3×CF g ; Among them, E 2g is the greenhouse gas emissions of the target ship in the moored state, P2 is the rated power of the auxiliary engine of the target ship, L2 is the auxiliary engine load rate of the target ship, T2 is the operating time of the auxiliary engine of the target ship, S2 is the auxiliary engine fuel consumption rate of the target ship, CF g is the greenhouse gas emission factor, P3 is the boiler output power of the target ship, T3 is the operating time of the boiler of the target ship, and S3 is the boiler fuel consumption rate of the target ship; Wherein, obtaining a first greenhouse gas emission assessment model of the target ship according to the first dynamic data corresponding to the navigation state and the static data of the ship includes: Determining a first correction coefficient of the main engine output power of the target ship according to the speed in the first dynamic data corresponding to the navigation state; According to the speed in the first dynamic data corresponding to the navigation state, the first correction coefficient and the rated output power of the target ship in the ship static data, a first greenhouse gas emission assessment model of the target ship is obtained; the expression of the first greenhouse gas emission assessment model of the target ship is: E 1g =P1×L1× α ×T1×(S1×β)×CF g +P2×L2×T2×S2×CF g ; Among them, E 1g is the greenhouse gas emissions of the target ship under the navigation state, P1 is the rated power of the main engine of the target ship, T1 is the operating time of the main engine of the target ship, S1 is the fuel consumption rate of the main engine of the target ship, is the first correction coefficient, is the second correction coefficient, which is used to correct the fuel consumption rate of the main engine. It is obtained by fitting the fuel consumption rate-power curve reported by the main engine bench test of the target ship. is the main engine load factor of the target ship, and: ； ； ； in, is the average speed of the jth target segment in the navigation trajectory, j=1, 2, ..., n is a positive integer, is the design or service speed of the target ship, a is the set constant parameter, b, c, d are the constant parameters obtained by fitting; Wherein, obtaining the target greenhouse gas emissions corresponding to at least one target segment of the target ship within the preset travel period according to the greenhouse gas emissions assessment model includes: According to the first greenhouse gas emission assessment model and / or the second greenhouse gas emission assessment model, the target greenhouse gas emissions corresponding to at least one of the target segments of the target ship within the preset travel period are obtained.

4. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 2 is performed.

5. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 2.

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