A ship carbon intensity index level auxiliary decision support system and method

Abstract

The present invention provides a ship carbon intensity index grade auxiliary decision support system and method. The system includes an information collection module, a data quality monitoring module, a fuel consumption model establishment module, a grade monitoring module, a grade estimation module, a grade auxiliary decision support module and a human-computer interaction module. The human-computer interaction module is respectively connected to the information collection module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision support module. The information collection module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision support module are connected in sequence. The modules cooperate with each other. The present invention can effectively improve the monitoring and estimation capabilities of the ship's CII value, avoid the phenomenon of ship violations due to excessive CII grades, and formulate a reasonable plan for ship navigation in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of ship energy conservation and emission reduction, and in particular to a ship carbon intensity index level auxiliary decision support system and method. Background Art

[0002] With increasing pressure to address global climate change, the International Maritime Organization (IMO) developed a greenhouse gas (GHG) emissions reduction strategy in 2018. The primary objective of the IMO's initial strategy was to reduce the carbon intensity of ships, which would be achieved through increasingly stringent energy efficiency and emissions reduction requirements. In June 2021, the Marine Environment Protection Committee (MEPC) adopted new amendments to the IMO's MARPOL Convention. These additions include new energy efficiency requirements—the Energy Efficiency Index for Existing Ships (EEXI) and the Carbon Intensity Index (CII)—which are key measures to reduce greenhouse gas (GHG) emissions from shipping.

[0003] Faced with increasingly stringent emission reduction requirements in the shipping industry, reducing carbon emissions during the operational phase of ships is imperative. To reduce the impact of greenhouse gas emissions on the environment, all sectors have formulated decarbonization requirements. During container ship operations, due to the implementation of the Carbon Intensity Index (CII) emission regulations, the requirements for ship CII values ​​are becoming increasingly stringent. CII levels are assessed annually based on fuel consumption and operational performance. In the mandatory CII assessment system, ships are divided into five grades: A, B, C, D, and E, based on the calculated CII values. The assessment criteria are becoming increasingly stringent each year. From 2023, if a ship is rated D or E, it will be deemed to be insufficiently compliant with CII and not actively addressing climate change. Shipowners will be required to update the ship's Energy Efficiency Management Plan (SEEMP) to improve the ship's CII grade.

[0004] Therefore, ship operators need to implement reasonable plans to prevent a series of punitive measures that may affect ship operations due to a reduction in CII level. Summary of the Invention

[0005] To address the issues of penalizing ship operations caused by a reduction in a ship's CII rating, a system for supporting ship carbon intensity index rating decisions is provided. By employing specific calculation and judgment methods, this system effectively improves the ability to monitor and estimate ship CII values, preventing violations caused by exceeding CII limits and enabling the development of appropriate navigation plans. The present invention also relates to a method for supporting ship carbon intensity index rating decisions.

[0006] The technical solutions of the present invention are as follows:

[0007] A ship carbon intensity index grade auxiliary decision support system, characterized by comprising an information collection module, a data quality monitoring module, a fuel consumption model establishment module, a grade monitoring module, a grade estimation module, a grade auxiliary decision support module, and a human-computer interaction module, wherein the human-computer interaction module is respectively connected to the information collection module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module, and the grade auxiliary decision support module, and the information collection module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module, and the grade auxiliary decision support module are sequentially connected;

[0008] The information collection module collects the historical voyage data of the ship and the future voyage plan data and the ship's target carbon intensity index value selected by the user in the human-computer interaction module;

[0009] The data quality monitoring module includes a data quality abnormality reminder submodule and a voyage plan abnormality reminder submodule. The data quality abnormality reminder submodule automatically determines whether the sailing fuel consumption data, berthing fuel consumption data and sailing mileage data in the historical voyage data are abnormal. If the historical voyage data is judged to be abnormal, a historical voyage data quality abnormality reminder is pushed; the voyage plan abnormality reminder submodule automatically determines whether the port sequence of the ship's current voyage is consistent with the port sequence of the future voyage plan. If they are inconsistent, a reminder of failure to execute according to the voyage plan is pushed;

[0010] The fuel consumption model establishment module removes abnormal historical voyage data determined by the data quality abnormality reminder submodule, and establishes a sailing fuel consumption model and a berthing fuel consumption model based on the removed historical voyage data;

[0011] The grade monitoring module calculates the total sailing distance of the ship in the current year and each voyage in the year based on the eliminated historical voyage data, and calculates the total fuel consumption of the ship in the current year and each voyage in the current year based on the sailing fuel consumption model and the berthing fuel consumption model. The carbon intensity index value of the ship in the current year and each voyage in the current year is calculated based on the total fuel consumption and total sailing distance, and the carbon intensity index grade is determined based on the carbon intensity index value;

[0012] The grade estimation module automatically determines whether the order of ports passed by the ship's current voyage in the voyage plan abnormality reminder submodule is in accordance with the future voyage plan. If not, no estimation result is output. If it is in accordance with the future voyage plan, the estimated carbon intensity index value of the ship in each future year and each voyage in each future year is output;

[0013] The grade auxiliary decision-making module monitors the estimated carbon intensity index values ​​for each future year in real time and automatically determines whether the estimated carbon intensity index value exceeds the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, an auxiliary decision-making recommendation is generated for the target ship to improve the ship's carbon intensity index grade;

[0014] The human-computer interaction module has ship target carbon intensity index values, future voyage plans and selection confirmation instructions for users to select, and displays the output results of each module.

[0015] Preferably, the data quality monitoring module further includes a data delay reminder submodule and a voyage plan update reminder submodule, wherein the data delay reminder submodule extracts the latest data of the ship leaving the destination port from the historical voyage data, and automatically determines at regular intervals whether the difference between the departure time of the destination port of the latest data and the current time exceeds a preset time threshold, and pushes a historical voyage data delay reminder if the difference exceeds the preset time threshold;

[0016] The data delay reminder submodule automatically determines whether the difference between the end time of the future voyage plan and the current time exceeds a preset time threshold. If it does not exceed the preset time threshold or there is no future voyage plan, it pushes a reminder to update the future voyage plan in time.

[0017] Preferably, in the grade estimation module, outputting the estimated carbon intensity index value of the ship in each future year and each voyage in each future year includes:

[0018] S1: Calculate the total fuel consumption and total sailing distance of each leg of each voyage in the order of ports that the ship has passed through in the current year according to the future voyage plan based on the historical voyage data after elimination;

[0019] S2: Obtain the port information of the ship when it leaves the destination port in the historical voyage data, calculate the total fuel consumption and total sailing distance of each section after the port that has not been executed in the port sequence, and calculate the estimated carbon intensity index value of the ship in the current year and future years based on the total fuel consumption and total sailing distance of each section that has been executed and has not been executed in the port sequence;

[0020] S3: Automatically determine whether the end time of the future voyage plan exceeds the last day of the ship's current year. If so, estimate the ship's carbon intensity index value from the first leg of the unexecuted port sequence to the leg on the last day of the current year. Then calculate the estimated carbon intensity index value of the ship in that year according to steps S1 and S2, and determine the carbon intensity index level based on the estimated carbon intensity index value;

[0021] S4: If the end time of the future voyage plan does not exceed the last day of the current year for the ship, the last voyage plan is copied and added to the last voyage plan until the end time of the future voyage plan exceeds the last day of the current year for the ship. Then, the estimated carbon intensity index value of the ship in that year is calculated according to step S3, and the carbon intensity index level is determined based on the estimated carbon intensity index value.

[0022] Preferably, in the grade auxiliary decision module, generating auxiliary decision suggestions for the target ship to improve the carbon intensity index grade of the ship includes:

[0023] S1': Increase the sailing time of the remaining legs in the future voyage plan to improve the ship's carbon intensity index level;

[0024] S2': Calculate the carbon intensity index value of a single voyage of other voyage plans and compare it with the carbon intensity index value of the target ship's current voyage. If the carbon intensity index value of a single voyage of other voyage plans is less than the carbon intensity index value of the target ship's current voyage within a certain range, change to another route;

[0025] S3': The energy-saving ratios of various energy-saving appendages calculated according to meteorological conditions are incorporated into the calculation of the carbon intensity index value of a single voyage of the target ship's current route, and the carbon intensity index level of the ship is improved by combining the energy-saving ratios of various energy-saving appendages;

[0026] S4': For ships whose carbon intensity index level exceeds the preset level threshold, an energy efficiency management plan report for the ship is generated and modified, saved and downloaded in the human-computer interaction module.

[0027] Preferably, it also includes a data storage module and a user management module. The human-computer interaction module is connected to the information acquisition module, data quality monitoring module, fuel consumption model establishment module, grade monitoring module, grade estimation module and grade auxiliary decision module through the data storage module, and the user management module is connected to the human-computer interaction module.

[0028] A ship carbon intensity index level auxiliary decision support method, characterized by comprising the following steps:

[0029] Information collection step: collecting the ship's historical voyage data, as well as the future voyage plan data and the ship's target carbon intensity index value selected by the user in the human-computer interaction module;

[0030] Data quality monitoring steps: Automatically determine whether the navigation fuel consumption data, berthing fuel consumption data, and mileage data in the historical voyage data are abnormal. If the historical voyage data is determined to be abnormal, a historical voyage data quality abnormality reminder will be pushed; then automatically determine whether the port sequence of the ship's current voyage is consistent with the port sequence of the future voyage plan. If not, a reminder will be pushed that the voyage plan has not been followed;

[0031] Fuel consumption model establishment step: remove the abnormal historical voyage data determined in the data quality monitoring step, and establish the navigation fuel consumption model and the berthing fuel consumption model based on the removed historical voyage data;

[0032] Grade monitoring steps: Calculate the total sailing distance of the ship in the current year and each voyage in that year based on the eliminated historical voyage data, and calculate the total fuel consumption of the ship in that year and each voyage in that year based on the sailing fuel consumption model and the berthing fuel consumption model. Calculate the carbon intensity index value of the ship in that year and each voyage in that year based on the total fuel consumption and total sailing distance, and determine the carbon intensity index grade based on the carbon intensity index value;

[0033] Grade estimation step: Automatically determine whether the order of ports the ship passes through on its current voyage is in accordance with the future voyage plan. If not, no estimation result will be output. If it is in accordance with the future voyage plan, the estimated carbon intensity index value for the ship in each future year and each voyage in each future year will be output;

[0034] Grade-assisted decision-making steps: Real-time monitoring of the estimated carbon intensity index values ​​for each future year, and automatic determination of whether the estimated carbon intensity index values ​​exceed the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, then an auxiliary decision-making recommendation is generated for the target ship to improve the ship's carbon intensity index grade;

[0035] Human-computer interaction steps: It provides users with the ability to select target carbon intensity index values ​​for ships, future voyage plans, and selection confirmation instructions, and displays the output results of each module.

[0036] Preferably, in the data quality monitoring step, the latest data of the ship leaving the destination port in the historical voyage data is also extracted, and it is automatically determined at regular intervals whether the difference between the departure time of the destination port of the latest data and the current time exceeds a preset time threshold. If it exceeds the preset time threshold, a historical voyage data delay reminder is pushed; it is also automatically determined whether the difference between the end time of the future voyage plan and the current time exceeds the preset time threshold. If it does not exceed the preset time threshold or there is no future voyage plan, a timely update reminder for the future voyage plan is pushed.

[0037] Preferably, in the level estimation step, outputting the estimated carbon intensity index value of the ship in each future year and each voyage in each future year includes:

[0038] S1: Calculate the total fuel consumption and total sailing distance of each leg of each voyage in the order of ports that the ship has passed through in the current year according to the future voyage plan based on the historical voyage data after elimination;

[0039] S2: Obtain the port information of the ship when it leaves the destination port in the historical voyage data, calculate the total fuel consumption and total sailing distance of each section after the port that has not been executed in the port sequence, and calculate the estimated carbon intensity index value of the ship in the current year and future years based on the total fuel consumption and total sailing distance of each section that has been executed and has not been executed in the port sequence;

[0040] S3: Automatically determine whether the end time of the future voyage plan exceeds the last day of the ship's current year. If so, estimate the ship's carbon intensity index value from the first leg of the unexecuted port sequence to the leg on the last day of the current year. Then calculate the estimated carbon intensity index value of the ship in that year according to steps S1 and S2, and determine the carbon intensity index level based on the estimated carbon intensity index value;

[0041] S4: If the end time of the future voyage plan does not exceed the last day of the current year for the ship, the last voyage plan is copied and added to the last voyage plan until the end time of the future voyage plan exceeds the last day of the current year for the ship. Then, the estimated carbon intensity index value of the ship in that year is calculated according to step S3, and the carbon intensity index level is determined based on the estimated carbon intensity index value.

[0042] Preferably, in the grade auxiliary decision-making step, generating auxiliary decision-making suggestions for the target ship to improve the carbon intensity index grade of the ship includes:

[0043] S1': Increase the sailing time of the remaining legs in the future voyage plan to improve the ship's carbon intensity index level;

[0044] S2': Calculate the carbon intensity index value of a single voyage of other voyage plans and compare it with the carbon intensity index value of the target ship's current voyage. If the carbon intensity index value of a single voyage of other voyage plans is less than the carbon intensity index value of the target ship's current voyage within a certain range, change to another route;

[0045] S3': Different types of energy-saving ratios calculated based on meteorological conditions are incorporated into the calculation of the carbon intensity index value of a single voyage of the target ship's current route. The carbon intensity index level of the ship is improved by combining different types of energy-saving ratios.

[0046] S4': For ships whose carbon intensity index level exceeds the preset level threshold, an energy efficiency management plan report for the ship is generated and modified, saved and downloaded in the human-computer interaction module.

[0047] Preferably, in the grade-assisted decision-making step, the estimated carbon intensity index value of the ship in future years and each voyage in future years is also compared with the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, an alarm message is pushed and an alarm prompt is generated.

[0048] The beneficial effects of the present invention are:

[0049] The present invention provides a ship carbon intensity index grade auxiliary decision support system, comprising an information collection module, a data quality monitoring module, a fuel consumption model establishment module, a grade monitoring module, a grade estimation module, a grade auxiliary decision support module and a human-computer interaction module. The modules work in coordination with each other, firstly collecting the ship's historical voyage data and the future voyage plan data and the ship's target carbon intensity index value selected by the user in the human-computer interaction module, then judging whether the sailing fuel consumption data, berthing fuel consumption data and sailing mileage data in the historical voyage data are abnormal, and if it is judged that the historical voyage data are abnormal, a historical voyage data quality abnormality reminder is pushed; and judging whether the order of ports passed by the current voyage of the ship is consistent with the order of ports passed by the future voyage plan, and if they are inconsistent, a reminder of not following the voyage plan is pushed; then the abnormal historical voyage data judged by the data quality abnormality reminder submodule is eliminated, and a sailing fuel consumption model and a berthing fuel consumption model are established based on the eliminated historical voyage data, and then the ship's current annual fuel consumption is calculated based on the eliminated historical voyage data. The total fuel consumption of the ship for the year and for each voyage in the year is calculated based on the sailing fuel consumption model and the berthing fuel consumption model. The carbon intensity index value of the ship for the year and for each voyage in the year is calculated based on the total fuel consumption and total sailing distance. The carbon intensity index level is determined based on the carbon intensity index value. Finally, it is determined whether the port sequence of the ship's current voyage in the voyage plan abnormality reminder submodule is executed in accordance with the future voyage plan. If not, no estimation result is output. If it is executed in accordance with the future voyage plan, the estimated carbon intensity index value of the ship in each future year and for each voyage in each future year is output. Finally, the estimated carbon intensity index value of each future year is monitored in real time, and it is automatically determined whether the estimated carbon intensity index value exceeds the target carbon intensity index value selected by the user. If the estimated carbon intensity index value exceeds or the difference with the target carbon intensity index value does not exceed a certain range of the target carbon intensity index value, an auxiliary decision-making recommendation for the target ship is generated to improve the carbon intensity index level of the ship. This invention can be used to monitor container ship CII ratings in real time, providing downgrade risk warnings based on actual conditions and offering guidance and decision-making assistance for upgrading the rating. This system is highly convenient and efficient. Users simply enter search criteria and click a button in the human-computer interaction module to retrieve and filter out ship information that highly meets the relevant criteria. This significantly improves the ability to monitor and estimate ship CII values, preventing ship violations due to excessive CII ratings and allowing for the development of appropriate navigation plans in advance.

[0050] The present invention also relates to a method for supporting ship carbon intensity index level decision-making assistance. This method corresponds to the above-mentioned ship carbon intensity index level decision-making assistance support system and can be understood as an implementation method of the above-mentioned ship carbon intensity index level decision-making assistance support system. The method includes an information collection step, a data quality monitoring step, a fuel consumption model establishment step, a level monitoring step, a level estimation step, a level decision-making assistance step, and a human-computer interaction step. By adopting specific calculation and judgment methods, the monitoring and estimation capabilities of ship CII values ​​can be improved, the phenomenon of ship violations due to excessive CII levels can be avoided, and reasonable ship navigation plans can be formulated in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a structural diagram of the ship carbon intensity index level auxiliary decision support system of the present invention.

[0052] Figure 2 This is a schematic diagram of the preferred structure of the ship carbon intensity index level auxiliary decision support system of the present invention.

[0053] Figure 3 It is a flow chart of the ship carbon intensity index level auxiliary decision support method of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described below with reference to the accompanying drawings.

[0055] The present invention relates to a ship carbon intensity index level auxiliary decision support system, the structural diagram of the system is as follows: Figure 1 As shown, the system includes an information acquisition module, a data quality monitoring module, a fuel consumption model establishment module, a grade monitoring module, a grade estimation module, a grade auxiliary decision-making module and a human-computer interaction module. The human-computer interaction module is directly (or indirectly) connected to the information acquisition module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision-making module respectively. The information acquisition module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision-making module are connected in sequence. The modules work together and adopt specific calculation and judgment methods to make the system very convenient and efficient to use. The user only needs to enter the search conditions and click the button in the human-computer interaction module to retrieve and filter out the ship information that highly meets the relevant conditions. It can greatly improve the monitoring and estimation capabilities of the ship's carbon intensity index value (i.e., CII value), avoid the phenomenon of ship violations due to excessive carbon intensity index levels (i.e., CII levels), and formulate reasonable ship navigation plans in advance.

[0056] Specifically, if Figure 2As shown in the preferred structural diagram, the system includes an information acquisition module, a data quality monitoring module, a fuel consumption model establishment module, a grade monitoring module, a grade estimation module, a grade auxiliary decision module, a data storage module, a human-computer interaction module and a user management module. The human-computer interaction module of this embodiment is indirectly connected to the information acquisition module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision module, and is indirectly connected through the data storage module, that is, the information acquisition module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision module are all connected to the human-computer interaction module through the data storage module. The data storage module is used to store the basic data of the ship, the historical voyage data of the ship, the future voyage plan data of the ship, the route port time plan data, and store and provide the main data generated by each module (such as the current CII value, the estimated CII value, the auxiliary decision recommendation and the energy efficiency management plan SEEMP report, etc.); the user management module is connected to the human-computer interaction module for managing the permissions and functions of each user under the human-computer interaction module.

[0057] Among them, the information collection module is used to obtain the ship's historical voyage data from the data storage module, and collect the future voyage plan data and the ship's target carbon intensity index value (i.e., CII value) selected by the user in the human-computer interaction module.

[0058] The data quality monitoring module includes a data delay reminder submodule, a voyage plan update reminder submodule, a data quality anomaly reminder submodule and a voyage plan anomaly reminder submodule. Among them, the data delay reminder submodule extracts the latest data of the ship leaving the destination port in the historical voyage data from the data storage module, and automatically determines at intervals (i.e., 10 o'clock every day) whether the difference between the departure time of the destination port of the latest data and the current time is greater than the preset time threshold, i.e., whether it is greater than 30 days. If it is greater than 30 days, the historical voyage data delay reminder is pushed; the data delay reminder submodule is used to automatically determine the end time of the future voyage plan in the data storage module and the time difference between the departure time and the current time. Whether the difference between the current time is greater than the preset time threshold, that is, whether it is greater than 30 days; if the difference between the current time and the end time is less than or equal to 30 days or there is no future voyage plan, a reminder for timely update of the future voyage plan will be pushed; the data quality abnormality reminder submodule is used to automatically determine whether the navigation fuel consumption data, berthing fuel consumption data and navigation mileage data in the historical voyage data are abnormal. If the historical voyage data is judged to be abnormal, a reminder of historical voyage data quality abnormality will be pushed; the voyage plan abnormality reminder submodule is used to automatically determine whether the order of ports passed by the current voyage of the ship is consistent with the order of ports passed by the future voyage plan. If they are inconsistent, a reminder of failure to execute according to the voyage plan will be pushed.

[0059] The fuel consumption model building module analyzes the regularity of the ship's sailing fuel consumption and berthing fuel consumption in the historical voyage data. Specifically, the abnormal historical voyage data identified by the data quality abnormality reminder submodule is first eliminated. Then, based on the eliminated historical voyage data, the sailing fuel consumption model is established by year, and the relationship formula between speed and hourly fuel consumption is fitted, as shown in the following formula:

[0060] F=ax 2 +bx+c (1)

[0061] In the above formula, F is the fuel consumption per hour, x is the speed, and a, b, and c are the coefficients of the fitting formula.

[0062] Then, based on the historical voyage data after elimination, a berthing fuel consumption model was established by year, and the hourly berthing fuel consumption formula was fitted, as shown in the following formula:

[0063] T=ax b +bx+c (2)

[0064] In the above formula, T is the fuel consumption per hour at berth, x is the ship's draft, and a, b, and c are the coefficients of the fitting formula.

[0065] The grade monitoring module calculates the total sailing distance of the ship in the current year and each voyage in the year based on the historical voyage data after the above-mentioned elimination, and calculates the total fuel consumption of the ship in the current year and each voyage in the current year based on the speed-hourly fuel consumption relationship formula fitted by the sailing fuel consumption model and the hourly berthing fuel consumption formula fitted by the berthing fuel consumption model. The carbon intensity index value (i.e., CII value) of the ship in the current year and each voyage in the current year is calculated based on the total fuel consumption and total sailing distance. The carbon intensity index grade (i.e., CII grade) is determined based on the carbon intensity index value and the requirements of the International Maritime Organization (IMO). The CII value and CII grade are displayed through the human-computer interaction module. At the same time, the CII value and CII grade of each voyage, as well as the cumulative CII value and CII grade of each voyage, are displayed. For example, in 2022, ship A underwent voyages 001, 002, and 003. The total fuel consumption and total sailing distance from voyage 001 to 003 are calculated, and the CII value and CII grade for that year are then calculated. The total fuel consumption and sailing distance at the end of voyage 001 and 002 are then calculated, and the cumulative CII value after the two voyages and the corresponding CII grade are obtained. At the same time, the total fuel consumption and total sailing distance of each of the three voyages are calculated, and the CII value and CII grade for each voyage are obtained. The above calculation results are stored in the data storage module and provided to the human-computer interaction module for interface display.

[0066] The grade estimation module provides the estimated CII values ​​and estimated CII grades for each future year, as well as the estimated CII values ​​and estimated CII grades for each voyage, based on the future voyage plan. It first automatically determines whether the order of ports the ship's current voyage passes through in the voyage plan anomaly reminder submodule is in accordance with the future voyage plan. If not, no estimation results are output. If it is, the estimated carbon intensity index values ​​for the ship in each future year and for each voyage within each future year are output. The estimation is performed once a day, and the estimation results are stored in the data storage module. Furthermore, outputting the estimated carbon intensity index values ​​specifically includes the following steps:

[0067] S1: Calculate the total fuel consumption and total sailing distance of each leg of the ship's current year's voyages in the order of ports it has passed through in the future voyage plan based on the historical voyage data after elimination (ports constitute a leg, and multiple legs constitute a voyage);

[0068] S2: Obtain the port information of the latest data when the ship departs from the destination port in the historical voyage data, calculate the total fuel consumption and total sailing distance of each section of the port sequence that has not been executed from that port to the last day of the current year (that is, calculate the berthing fuel consumption, sailing fuel consumption and sailing distance between each port), and calculate the estimated CII value and CII grade of the ship in the current year and future years based on the total fuel consumption and total sailing distance of each section of the port sequence that has been executed and not executed;

[0069] S3: Automatically determine whether the end time of the future voyage plan exceeds the last day of the ship's current year (i.e., the New Year's Eve). If so, find the voyage segments in the future voyage plan where "departure time at the departure port < New Year's Eve" and "arrival time at the destination port ≥ New Year's Eve", and estimate the ship's carbon intensity index value from the first segment in the sequence of unexecuted ports to the segment on the last day of the current year. Then, calculate the estimated carbon intensity index value (CII value) of the ship in that year according to steps S1 and S2, and determine the estimated carbon intensity index level (estimated CII level) based on the estimated carbon intensity index value;

[0070] S4: If the end time of the future voyage plan does not exceed the last day of the current year for the ship, the last voyage plan is copied. The number of copies is (cross-year time - end time of the future voyage plan) / total duration of the last voyage plan, and the copy is rounded up and added to the end of the last planned voyage (with the end time of the last voyage plan as the departure time) until the end time of the future voyage plan exceeds the last day of the current year for the ship. Then, the estimated carbon intensity index value of the ship in that year is calculated according to step S3, and the carbon intensity index level is determined based on the estimated carbon intensity index value.

[0071] The grade support decision module monitors the estimated carbon intensity index values ​​for future years in real time and automatically determines whether the estimated carbon intensity index values ​​exceed the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, it generates support decision recommendations for the target ship to improve the ship's carbon intensity index grade;

[0072] Finally, the estimated carbon intensity index values ​​of the ship in future years and each voyage in future years are compared with the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, an alarm message is pushed and an alarm prompt is generated.

[0073] The grade auxiliary decision-making module is used to monitor the estimated carbon intensity index values ​​of future years in real time and automatically determine whether the estimated carbon intensity index values ​​exceed the target carbon intensity index values ​​selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, that is, the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed 3% of the target carbon intensity index value, then an auxiliary decision-making recommendation is generated for the target ship to improve the ship's carbon intensity index grade;

[0074] If the target ship has an over-limit warning, relevant suggestions for auxiliary decision-making are generated. Specifically,

[0075] 1) Adjust the sailing time of the remaining segments in the future voyage plan, increase the sailing time of the remaining segments in the future voyage plan, and estimate the estimated CII value of each future year after the adjustment according to the grade estimation module. By increasing the sailing time, the estimated annual CII value will be more than 10% away from the set CII value, thereby improving the carbon intensity index level of the ship;

[0076] 2) Calculate the carbon intensity index value of a single voyage of other voyage plans in the data storage module and compare it with the carbon intensity index value of the target ship's current voyage. If the carbon intensity index value of a single voyage of other voyage plans is less than the carbon intensity index value of the target ship's current route by a certain range, that is, less than the carbon intensity index value of the current route by more than 10%, it is recommended to change to another route.

[0077] 3) The energy-saving ratios calculated for different types of energy-saving appendages based on meteorological conditions are incorporated into the CII value calculation for a single voyage on the target ship's current route. The energy-saving ratios of multiple energy-saving appendages can be combined during the calculation process to provide recommendations for installing energy-saving appendages that are 10% less than the carbon intensity index value for the target ship's current route, thereby improving the ship's carbon intensity index level. For example, if a ship's CII value corresponds to a 3% difference in CII level, reaching Class C or above, and the energy-saving ratio of a first-type energy-saving appendage is 1% and that of a second-type energy-saving appendage is 2%, then by adding both first-type and second-type energy-saving appendages, the ship's carbon intensity index (CII value) can be raised to Class C or above.

[0078] 4) For ships whose carbon intensity index level exceeds the preset level threshold, that is, for ships with CII levels of D and E, a ship energy efficiency management plan report (SEEMP) is generated and can be modified, saved and downloaded in the human-computer interaction module.

[0079] The human-computer interaction module allows users to select target carbon intensity index values ​​for ships, future voyage plans, and confirm their selections. It also displays the output of each module. Specifically, the module displays real-time CII values ​​and CII grades for each ship, voyage and annual CII estimates, and monitoring information provided by the data quality monitoring module. Furthermore, the user can set target CII values ​​and grades for each ship, enter future voyage plans, display decision-making support information, and modify, save, and download SEEMP reports.

[0080] The present invention also relates to a method for evaluating ship operation efficiency, which corresponds to the above-mentioned ship operation efficiency evaluation system and can be understood as an implementation method of the above-mentioned ship operation efficiency evaluation system. The method includes an information collection step, a data quality monitoring step, a fuel consumption model establishment step, a level monitoring step, a level estimation step, a level auxiliary decision-making step, and a human-computer interaction step. Figure 3 As shown,

[0081] Information collection step: collecting the ship's historical voyage data, as well as the future voyage plan data and the ship's target carbon intensity index value selected by the user in the human-computer interaction module;

[0082] Data quality monitoring steps: Automatically determine whether the navigation fuel consumption data, berthing fuel consumption data, and mileage data in the historical voyage data are abnormal. If the historical voyage data is determined to be abnormal, a historical voyage data quality abnormality reminder will be pushed; then automatically determine whether the port sequence of the ship's current voyage is consistent with the port sequence of the future voyage plan. If not, a reminder will be pushed that the voyage plan has not been followed;

[0083] Fuel consumption model establishment step: remove the abnormal historical voyage data determined in the data quality monitoring step, and establish the navigation fuel consumption model and the berthing fuel consumption model based on the removed historical voyage data;

[0084] Grade monitoring steps: Calculate the total sailing distance of the ship in the current year and each voyage in that year based on the eliminated historical voyage data, and calculate the total fuel consumption of the ship in that year and each voyage in that year based on the sailing fuel consumption model and the berthing fuel consumption model. Calculate the carbon intensity index value of the ship in that year and each voyage in that year based on the total fuel consumption and total sailing distance, and determine the carbon intensity index grade based on the carbon intensity index value;

[0085] Grade estimation step: Automatically determine whether the order of ports the ship passes through on its current voyage is in accordance with the future voyage plan. If not, no estimation result will be output. If it is in accordance with the future voyage plan, the estimated carbon intensity index value for the ship in each future year and each voyage in each future year will be output;

[0086] Grade-assisted decision-making steps: Real-time monitoring of the estimated carbon intensity index values ​​for each future year, and automatic determination of whether the estimated carbon intensity index values ​​exceed the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, then an auxiliary decision-making recommendation is generated for the target ship to improve the ship's carbon intensity index grade;

[0087] Human-computer interaction steps: It provides users with the ability to select target carbon intensity index values ​​for ships, future voyage plans, and selection confirmation instructions, and displays the output results of each module.

[0088] Preferably, in the data quality monitoring step, the latest data of the ship leaving the destination port in the historical voyage data is also extracted, and it is automatically determined at regular intervals whether the difference between the departure time of the destination port of the latest data and the current time exceeds a preset time threshold. If it exceeds the preset time threshold, a historical voyage data delay reminder is pushed; it is also automatically determined whether the difference between the end time of the future voyage plan and the current time exceeds the preset time threshold. If it does not exceed the preset time threshold or there is no future voyage plan, a timely update reminder for the future voyage plan is pushed.

[0089] Preferably, in the level estimation step, outputting the estimated carbon intensity index value of the ship in each future year and each voyage in each future year includes:

[0090] S1: Calculate the total fuel consumption and total sailing distance of each leg of each voyage in the order of ports that the ship has passed through in the current year according to the future voyage plan based on the historical voyage data after elimination;

[0091] S2: Obtain the port information of the ship when it leaves the destination port in the historical voyage data, calculate the total fuel consumption and total sailing distance of each section after the port that has not been executed in the port sequence, and calculate the estimated carbon intensity index value of the ship in the current year and future years based on the total fuel consumption and total sailing distance of each section that has been executed and has not been executed in the port sequence;

[0092] S3: Automatically determine whether the end time of the future voyage plan exceeds the last day of the ship's current year. If so, estimate the ship's carbon intensity index value from the first leg of the unexecuted port sequence to the leg on the last day of the current year. Then calculate the estimated carbon intensity index value of the ship in that year according to steps S1 and S2, and determine the carbon intensity index level based on the estimated carbon intensity index value;

[0093] S4: If the end time of the future voyage plan does not exceed the last day of the current year for the ship, the last voyage plan is copied and added to the last voyage plan until the end time of the future voyage plan exceeds the last day of the current year for the ship. Then, the estimated carbon intensity index value of the ship in that year is calculated according to step S3, and the carbon intensity index level is determined based on the estimated carbon intensity index value.

[0094] Preferably, in the grade auxiliary decision-making step, generating auxiliary decision-making suggestions for the target ship to improve the carbon intensity index grade of the ship includes:

[0095] S1': Increase the sailing time of the remaining legs in the future voyage plan to improve the ship's carbon intensity index level;

[0096] S2': Calculate the carbon intensity index value of a single voyage of other voyage plans and compare it with the carbon intensity index value of the target ship's current voyage. If the carbon intensity index value of a single voyage of other voyage plans is less than the carbon intensity index value of the target ship's current voyage by a certain range, it is recommended to change other routes;

[0097] S3': Different types of energy-saving ratios calculated based on meteorological conditions are incorporated into the calculation of the carbon intensity index value of a single voyage of the target ship's current route. The carbon intensity index level of the ship is improved by combining different types of energy-saving ratios.

[0098] S4': For ships whose carbon intensity index level exceeds the preset level threshold, an energy efficiency management plan report for the ship is generated and can be modified, saved and downloaded in the human-computer interaction module.

[0099] Preferably, in the grade-assisted decision-making step, the estimated carbon intensity index value of the ship in future years and each voyage in future years is also compared with the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, an alarm message is pushed and an alarm prompt is generated.

[0100] The present invention provides an objective and scientific ship carbon intensity index rating decision-making support system and method. By adopting specific calculation and judgment methods, it can be used to monitor the CII rating of container ships in real time, provide downgrade risk warnings based on actual conditions, and provide decision-making guidance for rating upgrades. This can improve the monitoring and estimation capabilities of ship CII values, avoid ship violations due to CII rating exceeding the limit, and formulate reasonable ship navigation plans in advance.

[0101] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents. In short, all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be included in the scope of protection of the patent for the present invention.

Claims

1. A ship carbon intensity index level auxiliary decision support system, characterized by: It includes an information collection module, a data quality monitoring module, a fuel consumption model establishment module, a grade monitoring module, a grade estimation module, a grade decision support module and a human-computer interaction module. The human-computer interaction module is respectively connected to the information collection module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade decision support module. The information collection module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade decision support module are connected in sequence. The information collection module collects the historical voyage data of the ship and the future voyage plan data and the ship's target carbon intensity index value selected by the user in the human-computer interaction module; The data quality monitoring module includes a data quality abnormality reminder submodule and a voyage plan abnormality reminder submodule. The data quality abnormality reminder submodule automatically determines whether the sailing fuel consumption data, berthing fuel consumption data and sailing mileage data in the historical voyage data are abnormal. If the historical voyage data is judged to be abnormal, a historical voyage data quality abnormality reminder is pushed; the voyage plan abnormality reminder submodule automatically determines whether the port sequence of the ship's current voyage is consistent with the port sequence of the future voyage plan. If they are inconsistent, a reminder of failure to execute according to the voyage plan is pushed; The fuel consumption model establishment module removes abnormal historical voyage data determined by the data quality abnormality reminder submodule, and establishes a sailing fuel consumption model and a berthing fuel consumption model based on the removed historical voyage data; The grade monitoring module calculates the total sailing distance of the ship in the current year and each voyage in the year based on the eliminated historical voyage data, and calculates the total fuel consumption of the ship in the current year and each voyage in the current year based on the sailing fuel consumption model and the berthing fuel consumption model. The carbon intensity index value of the ship in the current year and each voyage in the current year is calculated based on the total fuel consumption and total sailing distance, and the carbon intensity index grade is determined based on the carbon intensity index value; The grade estimation module automatically determines whether the order of ports passed by the ship's current voyage in the voyage plan abnormality reminder submodule is in accordance with the future voyage plan. If not, no estimation result is output. If it is in accordance with the future voyage plan, the estimated carbon intensity index value of the ship in each future year and each voyage in each future year is output; The grade auxiliary decision-making module monitors the estimated carbon intensity index values ​​for each future year in real time and automatically determines whether the estimated carbon intensity index value exceeds the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, an auxiliary decision-making recommendation is generated for the target ship to improve the ship's carbon intensity index grade; The human-computer interaction module has ship target carbon intensity index values, future voyage plans and selection confirmation instructions for users to select, and displays the output results of each module.

2. The ship carbon intensity index level auxiliary decision support system according to claim 1 is characterized in that: The data quality monitoring module also includes a data delay reminder submodule and a voyage plan update reminder submodule. The data delay reminder submodule extracts the latest data of the ship leaving the destination port in the historical voyage data, and automatically determines at regular intervals whether the difference between the departure time of the destination port of the latest data and the current time exceeds a preset time threshold. If the difference exceeds the preset time threshold, a historical voyage data delay reminder is pushed. The data delay reminder submodule automatically determines whether the difference between the end time of the future voyage plan and the current time exceeds a preset time threshold. If it does not exceed the preset time threshold or there is no future voyage plan, it pushes a reminder to update the future voyage plan in time.

3. The ship carbon intensity index level auxiliary decision support system according to claim 1 is characterized in that: In the grade estimation module, the output of the estimated carbon intensity index value of the ship in each future year and each voyage in each future year includes: S1: Calculate the total fuel consumption and total sailing distance of each leg of each voyage in the order of ports that the ship has passed through in the current year according to the future voyage plan based on the historical voyage data after elimination; S2: Obtain the port information of the ship when it leaves the destination port in the historical voyage data, calculate the total fuel consumption and total sailing distance of each section after the port that has not been executed in the port sequence, and calculate the estimated carbon intensity index value of the ship in the current year and future years based on the total fuel consumption and total sailing distance of each section that has been executed and has not been executed in the port sequence; S3: Automatically determine whether the end time of the future voyage plan exceeds the last day of the ship's current year. If so, estimate the ship's carbon intensity index value from the first leg of the unexecuted port sequence to the leg on the last day of the current year. Then calculate the estimated carbon intensity index value of the ship in that year according to steps S1 and S2, and determine the carbon intensity index level based on the estimated carbon intensity index value; S4: If the end time of the future voyage plan does not exceed the last day of the current year for the ship, the last voyage plan is copied and added to the last voyage plan until the end time of the future voyage plan exceeds the last day of the current year for the ship. Then, the estimated carbon intensity index value of the ship in that year is calculated according to step S3, and the carbon intensity index level is determined based on the estimated carbon intensity index value.

4. The ship carbon intensity index level auxiliary decision support system according to any one of claims 1 to 3, characterized in that: In the grade auxiliary decision module, generating auxiliary decision suggestions for the target ship to improve the carbon intensity index grade of the ship includes: S1': Increase the sailing time of the remaining legs in the future voyage plan to improve the ship's carbon intensity index level; S2': Calculate the carbon intensity index value of a single voyage of other voyage plans and compare it with the carbon intensity index value of the target ship's current voyage. If the carbon intensity index value of a single voyage of other voyage plans is less than the carbon intensity index value of the target ship's current voyage within a certain range, change to another route; S3': The energy-saving ratios of various energy-saving appendages calculated according to meteorological conditions are incorporated into the calculation of the carbon intensity index value of a single voyage of the target ship's current route, and the carbon intensity index level of the ship is improved by combining the energy-saving ratios of various energy-saving appendages; S4': For ships whose carbon intensity index level exceeds the preset level threshold, an energy efficiency management plan report for the ship is generated and modified, saved and downloaded in the human-computer interaction module.

5. The ship carbon intensity index level auxiliary decision support system according to any one of claims 1 to 3, characterized in that: It also includes a data storage module and a user management module. The human-computer interaction module is connected to the information acquisition module, the data quality monitoring module, the fuel consumption model establishment module, the grade monitoring module, the grade estimation module and the grade auxiliary decision module through the data storage module. The user management module is connected to the human-computer interaction module.

6. A ship carbon intensity index level auxiliary decision support method, characterized in that: The following steps are involved: Information collection step: collecting the ship's historical voyage data, as well as the future voyage plan data and the ship's target carbon intensity index value selected by the user in the human-computer interaction module; Data quality monitoring steps: Automatically determine whether the voyage fuel consumption data, berthing fuel consumption data, and mileage data in the historical voyage data are abnormal. If the historical voyage data is abnormal, a reminder of the abnormal quality of the historical voyage data will be pushed; Then, it automatically determines whether the order of ports visited by the ship on the current voyage is consistent with the order of ports visited by the future voyage plan. If they are inconsistent, a reminder is pushed indicating that the voyage plan has not been followed. Fuel consumption model establishment step: remove the abnormal historical voyage data determined in the data quality monitoring step, and establish the navigation fuel consumption model and the berthing fuel consumption model based on the removed historical voyage data; Grade monitoring steps: Calculate the total sailing distance of the ship in the current year and each voyage in that year based on the eliminated historical voyage data, and calculate the total fuel consumption of the ship in that year and each voyage in that year based on the sailing fuel consumption model and the berthing fuel consumption model. Calculate the carbon intensity index value of the ship in that year and each voyage in that year based on the total fuel consumption and total sailing distance, and determine the carbon intensity index grade based on the carbon intensity index value; Grade estimation step: Automatically determine whether the order of ports the ship passes through on its current voyage is in accordance with the future voyage plan. If not, no estimation result will be output. If it is in accordance with the future voyage plan, the estimated carbon intensity index value for the ship in each future year and each voyage in each future year will be output; Grade-assisted decision-making steps: Real-time monitoring of the estimated carbon intensity index values ​​for each future year, and automatic determination of whether the estimated carbon intensity index values ​​exceed the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, or the estimated carbon intensity index value is less than or equal to the target carbon intensity index value and the difference between the two does not exceed a certain threshold range, then an auxiliary decision-making recommendation is generated for the target ship to improve the ship's carbon intensity index grade; Human-computer interaction steps: It provides users with the ability to select target carbon intensity index values ​​for ships, future voyage plans, and selection confirmation instructions, and displays the output results of each module.

7. The ship carbon intensity index level auxiliary decision support method according to claim 6 is characterized in that: In the data quality monitoring step, the latest data of the ship leaving the destination port in the historical voyage data is also extracted, and it is automatically determined at regular intervals whether the difference between the departure time of the latest data from the destination port and the current time exceeds a preset time threshold. If it exceeds the preset time threshold, a historical voyage data delay reminder is pushed; it is also automatically determined whether the difference between the end time of the future voyage plan and the current time exceeds the preset time threshold. If it does not exceed the preset time threshold or there is no future voyage plan, a reminder for timely update of the future voyage plan is pushed.

8. The ship carbon intensity index level auxiliary decision support method according to claim 6 is characterized in that: In the level estimation step, the output of the estimated carbon intensity index value of the ship in each future year and each voyage in each future year includes: S1: Calculate the total fuel consumption and total sailing distance of each leg of each voyage in the order of ports that the ship has passed through in the current year according to the future voyage plan based on the historical voyage data after elimination; S2: Obtain the port information of the ship when it leaves the destination port in the historical voyage data, calculate the total fuel consumption and total sailing distance of each section after the port that has not been executed in the port sequence, and calculate the estimated carbon intensity index value of the ship in the current year and future years based on the total fuel consumption and total sailing distance of each section that has been executed and has not been executed in the port sequence; S3: Automatically determine whether the end time of the future voyage plan exceeds the last day of the ship's current year. If so, estimate the ship's carbon intensity index value from the first leg of the unexecuted port sequence to the leg on the last day of the current year. Then calculate the estimated carbon intensity index value of the ship in that year according to steps S1 and S2, and determine the carbon intensity index level based on the estimated carbon intensity index value; S4: If the end time of the future voyage plan does not exceed the last day of the current year for the ship, the last voyage plan is copied and added to the last voyage plan until the end time of the future voyage plan exceeds the last day of the current year for the ship. Then, the estimated carbon intensity index value of the ship in that year is calculated according to step S3, and the carbon intensity index level is determined based on the estimated carbon intensity index value.

9. The ship carbon intensity index level auxiliary decision support method according to claim 6 is characterized in that: In the grade auxiliary decision-making step, generating auxiliary decision-making suggestions for the target ship to improve the carbon intensity index grade of the ship includes: S1': Increase the sailing time of the remaining legs in the future voyage plan to improve the ship's carbon intensity index level; S2': Calculate the carbon intensity index value of a single voyage of other voyage plans and compare it with the carbon intensity index value of the target ship's current voyage. If the carbon intensity index value of a single voyage of other voyage plans is less than the carbon intensity index value of the target ship's current voyage within a certain range, change to another route; S3': Different types of energy-saving ratios calculated based on meteorological conditions are incorporated into the calculation of the carbon intensity index value of a single voyage of the target ship's current route. The carbon intensity index level of the ship is improved by combining different types of energy-saving ratios. S4': For ships whose carbon intensity index level exceeds the preset level threshold, an energy efficiency management plan report for the ship is generated and modified, saved and downloaded in the human-computer interaction module.

10. The ship carbon intensity index level auxiliary decision support method according to any one of claims 6 to 9, characterized in that: In the grade-assisted decision-making step, the estimated carbon intensity index values ​​of the ship in future years and each voyage in future years are also compared with the target carbon intensity index value selected by the user. If the estimated carbon intensity index value is greater than the target carbon intensity index value, an alarm message is pushed and an alarm prompt is generated.

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