Carbon emission accounting method, system, equipment and medium in port and aviation container transportation process

Abstract

The invention provides a carbon emission accounting method, system, equipment and medium in a port and aviation container transportation process, and belongs to the technical field of logistics carbon management and intelligent algorithm crossing. The method comprises the following steps: collecting transportation and energy consumption data of roads, waterways, railways and logistics storage links in a classified manner, and preprocessing the transportation and energy consumption data to form a standardized data set; a carbon emission factor parameter library is created in combination with industrial standards, different transportation modes and cargo types, and dynamic updating is performed through a machine learning algorithm. And special accounting sub-models are constructed for different transportation modes, and carbon emission accounting is carried out by using corresponding formulas and parameters. And inputting the standardized data into the nuclear operator model, calculating the carbon emission and screening an optimal path. And model parameters are calibrated by using historical data to ensure accuracy. And finally, collecting data in real time, inputting the data into the accounting sub-model, generating a decision support report comprising implementation steps, resource demands and expected effects, and assisting in realizing low-carbon and efficient management of the transportation process.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of logistics carbon management and intelligent algorithm intersection, more specifically, relates to a port and shipping container transportation process carbon emission accounting method, system, device and medium. BACKGROUND

[0002] Under the promotion of global low-carbon development trend and national green energy and "double carbon" strategy, the demand for carbon emission management in the field of port and shipping is increasingly urgent. Energy regulatory agencies continue to strengthen carbon emission control requirements, and port and shipping related enterprises actively explore the field of low-carbon and zero-carbon. The demand for green gathering and distribution carbon emission reduction evaluation services is particularly prominent in container logistics transportation business. Establishing a carbon emission reduction accounting system for green gathering and distribution modes such as sea-rail intermodal transport and water-water transfer, and providing accurate multi-modal transport carbon emission reduction accounting optimization screening services, has become a key measure to improve logistics efficiency, reduce costs, reduce carbon emissions and enhance the green competitiveness of enterprises. However, the current carbon emission accounting technology in the field of port and shipping container transportation has significant limitations, and has not formed a full-process carbon management capability. Container cargo turnover involves complex scenarios such as diverse transportation vehicles, various transportation types, and numerous transfer locations, making it difficult to track cargo carbon emissions and posing many challenges to carbon management, making it difficult to meet the actual needs of fine management. One of the core defects of existing carbon emission accounting technology is the single source of data. Current accounting relies heavily on internal statistical data, lacking effective support from upstream and downstream supply chain data and third-party detection data, resulting in one-sided accounting dimensions and low data accuracy. This data acquisition mode cannot fully reflect the real situation of carbon emissions throughout the transportation chain, resulting in deviations between accounting results and actual emissions, making it difficult to serve as a reliable basis for carbon management decisions. Another key problem lies in the limitations of algorithm models. Existing accounting models have fixed logic and cannot dynamically adjust calculation logic according to the production characteristics and transportation scenarios of different industries, making it difficult to effectively link carbon emission data at each link and reducing the model's versatility. At the same time, there is a lack of uniform standards for carbon emission factor values, and there is a lack of a regular updating mechanism, further reducing the scientificity and accuracy of the accounting results. In addition, for the mainstream multi-modal transportation mode, existing technologies have not been able to comprehensively cover and accurately account for carbon emission links at each logistics node, which is prone to missing details and accounting results that are easily influenced by human factors or data loss, severely restricting the improvement of port and shipping container transportation carbon management level. SUMMARY In view of the above problems, the purpose of the present application is to provide a port container transportation process carbon emission accounting method, system, device and medium, which integrates internal and external multi-source data, constructs an industry adaptation mechanism and introduces an intelligent AI model, thereby improving the comprehensiveness, universality and anti-interference ability of carbon accounting, and providing high-precision data support for container green transportation carbon emission control.

[0003] To achieve the above purpose, the present application realizes the following technical solutions: In a first aspect, the present application provides a port container transportation process carbon emission accounting method, comprising: The transportation data and energy consumption data of the highway, waterway, railway transportation and logistics storage links are classified and collected, and after preprocessing, the data is stored in a classified manner to form a standardized data set; According to the industry standard, a parameter library containing various carbon emission factors is created in combination with different transportation modes and cargo types, and a dynamic updating mechanism is established, historical data is analyzed by a machine learning algorithm, and various carbon emission factors are updated regularly; For the transportation modes of highway, waterway and railway, sub-models are constructed respectively, so that each sub-model can perform carbon emission accounting according to the characteristics and data of the specific transportation mode by using the corresponding formula and parameters; The standardized data set is input into the corresponding sub-model, the carbon emission is calculated, and the optimal carbon emission path is selected; The sub-models are calibrated by using multiple sets of historical data to adjust the model parameters; Real-time collection of transportation data and energy consumption data of highway, waterway, railway transportation and logistics storage links, preprocessing and inputting into the corresponding sub-model, running the sub-model to obtain the carbon emission results of each path, selecting the optimal carbon emission path, and forming a decision support report containing implementation steps, resource requirements and expected effects.

[0004] In an optional embodiment, the classification and collection of transportation data and energy consumption data of highway, waterway, railway transportation and logistics storage links, after preprocessing, are stored in a classified manner to form a standardized data set, comprising: The transportation data and energy consumption data of the highway transportation link are collected, including vehicle fuel supply type, cold box refrigerant filling amount table, vehicle average fuel consumption, route information; The transportation data and energy consumption data of the waterway transportation link are collected, including ship fuel supply type, cold box refrigerant filling amount table, ship average fuel consumption, route information; The transportation data and energy consumption data of the railway transportation link are collected, including train fuel supply type, cold box refrigerant filling amount table, train average fuel consumption, route information; Collecting transportation data and energy consumption data of the logistics storage link, including energy consumption of the ship in port and shore power usage of the ship in port; Collecting transportation mode information, historical carbon emission data and related environmental data; For the collected data, perform the cleaning process of removing outliers and filling missing values, and unify the data units and formats, and store them according to different links and data types in the cargo transportation process to form a standardized data set.

[0005] In an optional embodiment, the transportation modes of highway, waterway and railway are respectively constructed to account for the sub-models, including: For the transportation modes of highway, waterway and railway, highway transportation carbon accounting sub-model, logistics storage carbon accounting sub-model, waterway transportation carbon accounting sub-model and railway transportation carbon accounting sub-model are respectively constructed. The highway transportation carbon accounting sub-model calculates the carbon dioxide emissions by using the driving distance method, including:

[0006] In the formula, C 公路,i is the carbon dioxide emissions of the i-th type of vehicle, F 油耗,i is the average fuel consumption per unit of the i-th type of vehicle, E 燃料,i is the greenhouse gas emission factor of the fuel used by the vehicle, C 逸散,j is the direct greenhouse gas emissions of the j-th fugitive source, T i is the freight turnover of the i-th type of vehicle, and the calculation formula is T i =W i ×D i , wherein W i is the weight of the goods, and D i is the transportation distance. For refrigerated container transportation, the fugitive source emissions are calculated by the following formula:

[0007] In the formula, R j is the charging amount of the j-th refrigerant, P GWP,j is the global warming potential of the j-th fugitive source, K 逸散,j is the fugitive coefficient of the j-th fugitive source. The logistics storage carbon accounting sub-model includes a loading and unloading operation carbon accounting model and a refrigerated storage carbon accounting model. The loading and unloading operation carbon accounting model includes:

[0008] In the formula, C 装卸 is the greenhouse gas emissions of the loading and unloading operation, C 燃料,kFk is the average fuel consumption of the kth handling equipment 油耗,k Ek is the unit fuel consumption of the kth handling equipment 燃料,k C is the greenhouse gas emission factor of the fuel used by the handling equipment The refrigerated storage carbon accounting model comprises:

[0009] wherein C 冷藏 R is the direct greenhouse gas emission of the refrigerated storage process l P is the charging amount of the refrigerant in the refrigeration equipment GWP,l K is the global warming potential of the refrigerant 逸散,l is the fugitive coefficient of the refrigerant.

[0010] In an optional embodiment, the waterway transportation carbon accounting sub-model comprises a voyage process carbon accounting model and a port stay process carbon accounting model; The voyage process carbon accounting model comprises:

[0011] wherein C 水路,m Tm is the carbon dioxide emission of the mth type of ship m Fm is the freight turnover of the mth type of ship 油耗,m Em is the unit ton-kilometer fuel consumption of the mth type of ship 燃料,m C is the greenhouse gas emission factor of the fuel used by the ship 逸散,n is the direct greenhouse gas emission of the n th fugitive source The port stay process carbon accounting model comprises: When the ship uses shore power, When the ship does not use shore power, ; wherein C 靠港 E is the greenhouse gas emission of the port stay process 岸电 E is the shore power consumption during the port stay of the ship 电力 t is the greenhouse gas emission factor of the electricity o t is the port stay duration of the ship o E is the auxiliary machinery power of the ship 燃油,o C is the greenhouse gas emission factor of the fuel of the ship The emission reduction amount of using shore power to replace fuel oil is calculated by the following formula:

[0012] wherein R 替代 T is the carbon dioxide emission reduction amount of using shore power to replace fuel oil p E is the unit power generation fuel consumption rate of the diesel generator of the ship燃油,p Greenhouse gas emission factor of ship fuel.

[0013] In an optional embodiment, the railway transportation carbon accounting sub-models include a diesel locomotive carbon accounting model and an electrified railway carbon accounting model. The diesel locomotive carbon accounting model includes:

[0014] wherein C 铁路,q is the amount of greenhouse gas emission of the diesel locomotive, V q is the estimated consumption of the qth fuel of the locomotive, F 油耗,q is the fuel consumption rate of the locomotive, E 燃料,q is the greenhouse gas emission factor of the fuel, E 供应,q is the greenhouse gas emission factor of the fuel supply process; The electrified railway carbon accounting model includes:

[0015] wherein C 电气,r is the amount of greenhouse gas emission of the electrified railway locomotive, V r is the estimated power consumption of the rth type of locomotive, E 电力,r is the greenhouse gas emission factor of the purchased power.

[0016] In an optional embodiment, the inputting of the standardized data set into the corresponding accounting sub-models, the calculation of the carbon emission amount and the screening of the optimal carbon emission path include: The inputting of the standardized data set into the corresponding accounting sub-models, the determination of the objective function and the constraint condition by using the linear programming, the nonlinear programming or the dynamic programming optimization method, and the obtaining of the preliminary optimal carbon emission path by solving the optimal solution; and the screening of the optimal carbon emission path from the preliminary optimal carbon emission path by using the machine learning algorithm to train the historical data so that the model can predict the carbon emission result of different paths.

[0017] In an optional embodiment, the calibration of the accounting sub-models by using multiple sets of historical data and the adjustment of the model parameters include: The calibration of the accounting sub-models by using multiple sets of historical data, the comparison of the carbon emission amount simulated by the accounting sub-models with the actual carbon emission amount in a certain time period in the past, and the adjustment of the related parameters if the difference between the carbon emission amounts exceeds a preset threshold; The verification of the prediction accuracy of the accounting sub-models for future carbon emission by using the reserved historical data or the cited research data, and the continuous adjustment of the model parameters if the verification fails.

[0018] In a second aspect, the embodiments of the present application further provide a port and shipping container transportation process carbon emission accounting system, comprising: A multi-source data acquisition and preprocessing module is configured to acquire transportation data and energy consumption data of road, waterway, and railway transportation and logistics storage links, and to store the data after preprocessing to form a standardized data set; A carbon factor parameter library construction module is configured to create a parameter library containing various carbon emission factors according to industry standards and in combination with different transportation modes and cargo types, and to establish a dynamic updating mechanism to regularly update various carbon emission factors by analyzing historical data through a machine learning algorithm; An intelligent accounting model construction module is configured to construct an accounting sub-model for each of road, waterway, and railway transportation modes to calculate carbon emissions according to the characteristics and data of the specific transportation mode by using corresponding formulas and parameters through each accounting sub-model; A model training and optimization module is configured to input the standardized data set into the corresponding accounting sub-model to calculate carbon emissions and select the optimal carbon emission path; A model calibration and verification module is configured to calibrate the accounting sub-model by using multiple sets of historical data to adjust the model parameters; A model running module is configured to acquire transportation data and energy consumption data of road, waterway, and railway transportation and logistics storage links in real time, input the data into the corresponding accounting sub-model after preprocessing, run the accounting sub-model to obtain carbon emission results of each path, select the optimal carbon emission path, and form a decision support report containing implementation steps, resource requirements, and expected effects.

[0019] In a third aspect, the embodiments of the present application further provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the port and shipping container transportation process carbon emission accounting method according to any one of the above.

[0020] In a fourth aspect, the embodiments of the present application further provide a storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the port and shipping container transportation process carbon emission accounting method according to any one of the above.

[0021] As can be seen from the above technical solutions, the present application has the following advantages: The port container transportation process carbon emission accounting method provided by the application, through multi-source data acquisition and preprocessing, combined with industry standards to build a dynamically updated carbon factor parameter library, respectively constructs an accounting sub-model for different transportation modes, uses optimization methods and machine learning algorithms to screen the optimal carbon emission path, and ensures the accuracy of the results through model calibration and verification, so as to realize the accurate accounting and effective optimization of the carbon emission of the port container transportation process, provide scientific basis for decision-making, help to reduce carbon emission, improve transportation efficiency, reduce environmental impact, and have significant environmental and economic benefits.

[0022] The application ensures the high quality of the input data through multi-source data acquisition and standardized preprocessing, provides a solid data foundation for subsequent carbon emission accounting, reduces the accounting deviation caused by data quality problems, and improves the credibility of the entire accounting process.

[0023] The application constructs a special accounting sub-model for different transportation modes such as highway, waterway and railway, and combines specific transportation characteristics and professional formulas to calculate carbon emission, realizes the accurate quantification of carbon emission in each link of the transportation process, and helps to identify the key links and main sources of carbon emission.

[0024] The application uses optimization algorithms and machine learning techniques to analyze the accounting results, which can screen the optimal carbon emission path, provide a scientific basis for transportation decision-making, and help enterprises to meet transportation needs while minimizing carbon emissions, and achieve the goal of green transportation.

[0025] The application promotes the development of low-carbon port container transportation through accurate accounting and path optimization, effectively reduces greenhouse gas emissions, and has important significance for mitigating climate change and improving environmental quality, in line with the trend and requirements of sustainable development.

[0026] The optimized transportation path of the application not only reduces the carbon emission cost, but also may bring direct economic benefits such as fuel consumption reduction. At the same time, enterprises that actively practice low-carbon transportation can improve their social image and market competitiveness, and better adapt to increasingly stringent environmental protection policies and market demand for green logistics. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the application, the drawings needed to be used in the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0028] Figure 1 The flowchart of the port container transportation process carbon emission accounting method provided by the application.

[0029] Figure 2 A structure diagram of a port and shipping container transportation process carbon emission accounting system is provided.

[0030] Figure 3 A structure diagram of an electronic device is provided. DETAILED DESCRIPTION

[0031] Various embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to encompass all adjustments, equivalents, and / or alternatives falling within the spirit and scope of various embodiments of the present disclosure.

[0032] Hereinafter, the term "include" or "may include" used in various embodiments of the present disclosure indicates the presence of the disclosed functions, operations, or elements, and does not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their equivalents are merely intended to indicate the presence of specific features, numbers, steps, operations, elements, components, or combinations thereof, and should not be understood as first excluding the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0034] Please refer to Figure 1 The method flowchart of a port and shipping container transportation process carbon emission accounting method in a specific embodiment is shown, and the method includes: S1: Collecting transportation data and energy consumption data of highway, waterway, railway transportation and logistics storage links, and storing them after preprocessing, forming a standardized data set.

[0035] In the specific implementation, data collection is the basis for carbon emission accounting in port container transportation. First, for the road transportation link, we need to collect vehicle fuel supply type, cold box refrigerant filling table, vehicle average fuel consumption, and route information. These information can help us understand the energy consumption of road transportation. For example, the vehicle fuel supply type determines the potential of carbon emissions, while the vehicle average fuel consumption directly reflects the energy utilization efficiency during transportation. At the same time, the starting point, ending point, passing point, driving distance, road type and traffic restriction information in the route information are of great significance to the evaluation of carbon emissions of road transportation.

[0036] For water transportation, ship fuel supply type, cold box refrigerant filling table, ship average fuel consumption, and route information are key data points. Ship fuel supply type and average fuel consumption can reflect the energy utilization of the ship, while route information can help evaluate the carbon emissions of the ship on different routes. The data collection of railway transportation is similar to road and water transportation, including train fuel supply type, cold box refrigerant filling table, train average fuel consumption, and route information. These data can help us understand the energy consumption and carbon emission characteristics of railway transportation.

[0037] The data collection of logistics storage focuses on the energy consumption of the ship during port stay, including the energy consumption of loading and unloading (such as loading and unloading ships, horizontal transportation, and storage in the yard), the use of shore power during port stay (if used), and the duration of port stay. These data can reflect the energy consumption during logistics storage and are crucial for accurate carbon emission accounting.

[0038] In addition to the above data of transportation links, we also need to collect transportation mode information (such as single mode or multimodal combination of road, railway, and waterway), historical carbon emission data, and related environmental data (such as air temperature and wind speed). These data can help us better understand the carbon emissions during transportation and provide support for subsequent model training and optimization.

[0039] After collecting these data, preprocessing is needed. The preprocessing process includes removing outliers, filling missing values, unifying data units and formats, and classifying and storing data according to different links and data types in the cargo transportation process, finally forming a standardized data set. For example, in removing outliers, we can use statistical methods to identify and remove data points that are obviously outside the normal range; in filling missing values, we can use interpolation or estimation based on historical data to supplement missing data. Unifying data units and formats ensures that data from different sources can be integrated and analyzed under the same accounting framework. Classification storage is to organize data according to transportation mode, cargo type, etc., so that subsequent sub-models can quickly and accurately obtain the required data.

[0040] Through this step, a high-quality, standardized dataset can be obtained, providing a solid data foundation for subsequent carbon emission accounting.

[0041] S2: According to industry standards, combined with different transportation modes and cargo types, create a parameter library containing various carbon emission factors, and establish a dynamic updating mechanism. Analyze historical data through machine learning algorithms to regularly update various carbon emission factors.

[0042] In the specific embodiment, constructing the carbon factor parameter library is a key step in carbon emission accounting. Specifically, according to national, industry and international standards, combined with different transportation modes and cargo types, a comprehensive parameter library is created, which includes carbon emission factor table, carbon intensity table, global warming potential value table of fugitive sources, fuel greenhouse gas emission coefficient, fuel supply greenhouse gas emission coefficient, refrigerant fugitive coefficient, power greenhouse emission coefficient, and alternative fuel greenhouse emission coefficient.

[0043] The construction process of this parameter library needs to fully consider various factors affecting carbon emissions. For example, for fuel greenhouse gas emission coefficient, the amount of carbon dioxide emitted during the combustion process of different types of fuel (such as gasoline, diesel, natural gas, etc.) needs to be accurately measured and recorded. For the global warming potential value table of fugitive sources, the impact of substances such as refrigerants on the climate after fugitive needs to be considered, and their global warming potential values are determined through scientific research and experimental data.

[0044] In order to ensure the accuracy and timeliness of the parameter library, this method establishes a dynamic updating mechanism. Through machine learning algorithms (such as time series analysis) to analyze historical data, the trend of carbon emission factors can be predicted, and the database can be updated regularly according to these predictions. For example, when new research results show that the greenhouse gas emission coefficient of a certain fuel has changed, or when policy regulations make new requirements for carbon emission accounting methods, the corresponding data in the parameter library can be adjusted in time to reflect the latest situation.

[0045] This dynamic updating mechanism enables the carbon factor parameter library to adapt to changing actual conditions, providing reliable parameter support for carbon emission accounting.

[0046] S3: For road, waterway and railway transportation modes, respectively construct accounting sub-models to calculate carbon emissions according to the characteristics and data of each transportation mode using corresponding formulas and parameters.

[0047] In the specific embodiment, when constructing intelligent accounting models, we construct special accounting sub-models for different transportation modes such as road, waterway and railway. Each sub-model calculates carbon emissions according to the characteristics and data of the specific transportation mode using corresponding formulas and parameters.

[0048] For the road transport carbon accounting sub-model, we use the distance travelled method to calculate the CO2 emissions. The formula is:

[0049] where C 公路,i is the CO2 emissions of the i-th vehicle type, T i is the freight turnover of the i-th vehicle type (calculated as T i = W i x D i , where W i is the weight of the freight and D i is the distance travelled), F i is the average fuel consumption per unit of the i-th vehicle type, E i is the greenhouse gas emission factor of the fuel used by the vehicle, and C 逸散,j is the direct greenhouse gas emissions of the j-th fugitive source. For refrigerated container transport, we also need to calculate the fugitive source emissions using the formula: where R j is the charge of the j-th refrigerant, P GWP,j is the global warming potential of the j-th fugitive source, and K 逸散,j is the fugitive coefficient of the j-th fugitive source.

[0050] In the logistics storage carbon accounting sub-model, we include the loading and unloading carbon accounting model and the refrigerated storage carbon accounting model. The formula for the loading and unloading carbon accounting model is:

[0051] where C 装卸 is the greenhouse gas emissions of the loading and unloading operation, C 燃料,k is the average fuel consumption of the k-th loading and unloading equipment, F 油耗,k is the unit fuel consumption of the k-th loading and unloading equipment, and E 燃料,k is the greenhouse gas emission factor of the fuel used by the loading and unloading equipment.

[0052] For the refrigerated storage carbon accounting model, we use the formula:

[0053] where C 冷藏 is the direct greenhouse gas emissions of the refrigerated storage process, R l is the charge of the refrigerant in the refrigerated equipment, P GWP,l is the global warming potential of the refrigerant, and K 逸散,l is the fugitive coefficient of the refrigerant.

[0054] The carbon accounting sub-model for waterway transportation includes the carbon accounting model for sailing process and the carbon accounting model for port process. The formula of the carbon accounting model for sailing process is:

[0055] where C 水路,m is the carbon dioxide emission of the mth type of ship, T m is the freight turnover of the mth type of ship, F 油耗,m is the unit ton-kilometer fuel consumption of the mth type of ship, E 燃料,m is the greenhouse gas emission factor of the fuel used by the ship, C 逸散,n is the direct greenhouse gas emission of the nth type of escape source.

[0056] The carbon accounting model for port process is divided into two cases according to whether the ship uses shore power. When the ship uses shore power, ; when the ship does not use shore power, . Where C 靠港 is the greenhouse gas emission of the port process, E 岸电 is the shore power consumption during the ship's port stay, E 电力 is the greenhouse gas emission factor of electricity, t o is the length of the ship's port stay, P o is the power of the ship's auxiliary machinery, E 燃油,o is the greenhouse gas emission factor of the ship's fuel. In addition, we can also calculate the emission reduction amount of using shore power to replace fuel by the formula R 替代 =E 岸电 ×B p ×E 燃油,p , where R 替代 is the carbon dioxide emission reduction amount of using shore power to replace fuel, B p is the unit power generation fuel consumption rate of the ship's diesel generator, E 燃油,p is the greenhouse gas emission factor of the ship's fuel.

[0057] The carbon accounting sub-model for railway transportation includes the carbon accounting model for diesel locomotive and the carbon accounting model for electrified railway. The formula of the carbon accounting model for diesel locomotive is:

[0058] where C 铁路,q is the greenhouse gas emission of the diesel locomotive, V q is the estimated consumption of the qth fuel of the locomotive, F 油耗,q is the fuel consumption rate of the locomotive, E 燃料,q is the greenhouse gas emission factor of the fuel, E 供应,q is the greenhouse gas emission factor of the fuel supply process.

[0059] For the carbon accounting model of electrified railways, the formula is used:

[0060] Here, C 电气,r is the greenhouse gas emissions of the electric locomotive of the electrified railway, V r is the estimated power consumption of the rth type of locomotive, E 电力,r is the greenhouse gas emission factor of purchased electricity.

[0061] By constructing these detailed accounting sub-models, this method can accurately account for the carbon emissions characteristics of different transportation modes, providing a scientific basis for subsequent path optimization and decision support.

[0062] S4: Input the standardized data set into the corresponding accounting sub-model, calculate the carbon emissions and select the optimal carbon emission path.

[0063] In the specific implementation, after completing data collection, preprocessing and intelligent accounting model construction, the standardized data set is input into the corresponding accounting sub-model to start calculating carbon emissions and selecting the optimal carbon emission path.

[0064] First, use optimization methods such as linear programming, nonlinear programming or dynamic programming to determine the objective function and constraints. For example, in linear programming, the total carbon emissions can be used as the objective function to minimize; at the same time, a series of constraints are set according to the actual transportation requirements and limitations (such as freight transportation time, transportation cost, etc.). By solving this optimization model, the preliminary optimal carbon emission path can be obtained.

[0065] However, in order to further improve the accuracy and adaptability of path optimization, machine learning algorithms are also used to train historical data. Machine learning models (such as neural networks, decision trees, etc.) can learn patterns and trends in historical carbon emission data, and thus predict the carbon emission results of different paths. Based on these prediction results, a better path can be selected from the preliminary optimal carbon emission path, making it more consistent with actual conditions and future trends.

[0066] This path optimization strategy that combines optimization methods and machine learning techniques not only helps us find the optimal carbon emission path under current conditions, but also continuously improves the optimization results as data accumulates and models update, providing strong support for carbon emission management in port and container shipping.

[0067] S5: Use multiple sets of historical data to calibrate the accounting sub-model and adjust the model parameters.

[0068] In the specific implementation, in order to ensure the accuracy and reliability of the accounting sub-model, it is necessary to use multiple sets of historical data to calibrate and verify it. In the calibration process, this step compares the carbon emissions simulated by the accounting sub-model in a certain period of time in the past with the actual carbon emissions. If the difference in carbon emissions exceeds the preset threshold, the relevant parameters need to be adjusted. For example, if the model predicts that the carbon emissions are significantly lower than the actual value, it may be necessary to re-examine the input carbon emission factors or transportation data to check if there is an underestimation, and adjust the model parameters accordingly.

[0069] At the same time, the prediction accuracy of the accounting sub-model for future carbon emissions is also verified through reserved historical data or data from other authoritative research. If the verification result shows that the prediction performance of the model is not good, it is necessary to return to the previous step to further adjust and optimize the model framework or parameters. This process may need to be iterated several times until the model can stably output accurate prediction results.

[0070] Through this strict calibration and verification process, the accuracy and reliability of the accounting sub-model in actual application can be ensured, providing a solid guarantee for its decision support role in carbon emission management.

[0071] S6: Real-time collection of transportation data and energy consumption data of highway, waterway, railway transportation and logistics storage links, pre-processing and inputting into the corresponding accounting sub-model, running the accounting sub-model to obtain carbon emission results of each path, screening out the optimal carbon emission path, and forming a decision support report containing implementation steps, resource requirements and expected effects.

[0072] In the specific implementation, this step is the real-time data collection and decision support phase. In this phase, real-time transportation data and energy consumption data of highway, waterway, railway transportation and logistics storage links need to be collected. These real-time data, after the same pre-processing process as before, are input into the corresponding accounting sub-model.

[0073] After the accounting sub-model runs, the carbon emission results of each path can be obtained. Through the analysis of these results, the optimal carbon emission path can be screened out. Based on this optimal path, a detailed decision support report can be formed. This report not only contains the implementation steps of the optimal path, but also lists the required resources and the expected effects. For example, the report may suggest increasing the freight transportation volume of a certain route because it performs best in terms of carbon emissions; or suggest replacing a certain mode of transportation because its carbon emission factor is lower, which can effectively reduce the total carbon emissions.

[0074] This decision support report provides clear and specific guidance for port and shipping container transport decision-makers, helping them minimize carbon emissions while meeting transportation needs, contributing to the sustainable development of enterprises and environmental protection. In this embodiment, by implementing the port and shipping container transport process carbon emission accounting method, multi-source data can be comprehensively and accurately collected and processed, a dynamically updated carbon factor parameter library can be constructed, and special accounting sub-models can be established for different transportation modes, realizing the accurate quantification of carbon emissions throughout the transportation process. Combined with optimization algorithms and machine learning techniques, this method can filter out the optimal carbon emission path, providing a scientific basis for transportation decision-making. Through model calibration and verification, the accuracy and reliability of the accounting results are ensured. Ultimately, through real-time data collection and decision support, the port and shipping container transport is promoted towards low-carbon and high-efficiency transformation, achieving the multiple goals of reducing carbon emissions, saving costs, improving enterprise competitiveness and environmental benefits.

[0075] As Figure 2 shown below is an embodiment of a port and shipping container transport process carbon emission accounting system provided by the present disclosure, which belongs to the same inventive concept as the port and shipping container transport process carbon emission accounting method described above. Details not described in the embodiment of the port and shipping container transport process carbon emission accounting system can be referred to the embodiment of the port and shipping container transport process carbon emission accounting method described above.

[0076] A port and shipping container transport process carbon emission accounting system, comprising: A multi-source data collection and preprocessing module for collecting and preprocessing transportation data and energy consumption data of road, waterway, railway transportation and logistics storage links, and storing the data after preprocessing to form a standardized data set.

[0077] A carbon factor parameter library construction module for creating a parameter library containing various carbon emission factors according to industry standards, combining different transportation modes and cargo types, and establishing a dynamic updating mechanism to update various carbon emission factors regularly through machine learning algorithm analysis of historical data.

[0078] An intelligent accounting model construction module for constructing accounting sub-models for road, waterway and railway transportation modes to calculate carbon emissions according to the characteristics and data of each transportation mode using corresponding formulas and parameters through each accounting sub-model.

[0079] A model training and optimization module for inputting the standardized data set into the corresponding accounting sub-models to calculate the carbon emissions and filter out the optimal carbon emission path.

[0080] A model calibration and verification module for calibrating the accounting sub-models using multiple sets of historical data to adjust the model parameters.

[0081] The model running module is configured to collect transportation data and energy consumption data of road, waterway and railway transportation and logistics storage in real time, input the preprocessed data into corresponding accounting sub-models, run the accounting sub-models to obtain carbon emission results of each path, screen out an optimal carbon emission path, and form a decision support report including implementation steps, resource requirements and expected effects.

[0082] The port and shipping container transportation process carbon emission accounting system provided by the embodiment realizes accurate quantification of carbon emissions in the whole transportation process by accurate collection and processing of multi-source data, construction of a dynamically updated carbon factor parameter library, and special accounting sub-models for different transportation modes; the optimal path is screened out by combining optimization algorithms and machine learning techniques, which not only significantly improves the accuracy and reliability of carbon emission accounting, but also provides scientific support for transportation decision-making, effectively promotes the transformation of the port and shipping industry to low carbon and high efficiency, and realizes the win-win of environmental and economic benefits.

[0083] Figure 3 A hardware structure schematic diagram of an electronic device for implementing various embodiments of the present application.

[0084] The port and shipping container transportation process carbon emission accounting method provided by the embodiment of the present application can be applied to an electronic device. Those skilled in the art can understand that the electronic device structure involved in the embodiment of the present application does not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than the illustration, or combine certain components, or different component arrangements. In the embodiment of the present application, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or claimed herein.

[0085] The electronic device can include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charge management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, a key, a camera, a display screen, and a SIM card interface, etc.

[0086] The processor can include one or more processing units, such as: the processor can include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video code, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units can be independent devices, or can be integrated in one or more processors.

[0087] The processor can be the nerve center and command center of the electronic device. The controller can generate operation control signals according to instruction operation codes and timing signals, and complete the control of fetching and executing instructions.

[0088] The processor can also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory can save instructions or data that the processor has just used or repeatedly uses. If the processor needs to use the instruction or data again, it can be directly called from the memory. Avoiding repeated access reduces the waiting time of the processor, thus improving the efficiency of the system.

[0089] The external memory interface can be used to connect an external storage card, such as a MicroSD card, to realize the expansion of the storage capacity of the electronic device. The external storage card communicates with the processor through the external memory interface to realize the data storage function. For example, files such as music and video are saved in the external storage card.

[0090] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory can include a program storage area and a data storage area. The internal memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0091] The wireless communication function of the electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor, etc.

[0092] The wireless communication module can provide a wireless communication solution applied to the electronic device, including wireless local area networks (WLAN) such as a wireless fidelity (Wi-Fi) network, Bluetooth (BT), a global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), and the like.

[0093] The electronic device can realize an audio function and the like through an audio module, a speaker, a receiver, a microphone, a headset interface, an application processor, and the like.

[0094] The electronic device can realize a photographing function through an ISP, a camera, a video codec, a GPU, a display screen, an application processor, and the like.

[0095] The electronic device can realize a display function through a GPU, a display screen, an application processor, and the like.

[0096] The GPU is a microprocessor for image processing, connected to the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor can include one or more GPUs that execute program instructions to generate or change display information.

[0097] The display screen is used to display images, videos, and the like. The display screen includes a display panel.

[0098] The above electronic device realizes the port and shipping container transportation process carbon emission accounting method, which collects and preprocesses the data of road, waterway, railway transportation and logistics storage links by classification, constructs a dynamically updated carbon emission factor parameter library, establishes a special accounting sub-model for different transportation modes, uses optimization and machine learning algorithms to screen the optimal carbon emission path, achieves the beneficial effects of precise quantification of carbon emission, optimization of transportation decision, reduction of carbon emission cost, improvement of economic benefit and enterprise competitiveness, and realization of green and low-carbon transformation of port and shipping container transportation.

[0099] In the storage medium provided in the present application, a program product capable of realizing the port and shipping container transportation process carbon emission accounting method is stored.

[0100] The port and shipping container transportation process carbon emission accounting method comprises: The transportation data and energy consumption data of road, waterway, railway transportation and logistics storage links are collected and classified, and after preprocessing, they are stored in a standardized data set; According to industry standards, a parameter library containing various carbon emission factors is created in combination with different transportation modes and cargo types, and a dynamic updating mechanism is established. The historical data is analyzed by a machine learning algorithm to update the various carbon emission factors regularly. For the transportation modes of highways, waterways, and railways, accounting sub-models are constructed to calculate carbon emissions according to the characteristics and data of specific transportation modes using corresponding formulas and parameters. The standardized data set is input into the corresponding accounting sub-models to calculate the carbon emissions and select the optimal carbon emission path. A plurality of historical data is used to calibrate the accounting sub-models and adjust the model parameters. Real-time transportation data and energy consumption data of highway, waterway, and railway transportation and logistics storage links are collected, preprocessed, and input into the corresponding accounting sub-models. The carbon emission results of each path are obtained by running the accounting sub-models, the optimal carbon emission path is selected, and a decision support report containing implementation steps, resource requirements, and expected effects is formed.

[0101] In some possible implementations, the container shipping process carbon emission accounting method of the present disclosure can be implemented in the form of a program product, which includes program code for causing a terminal device to perform the steps described in the "Exemplary Method" section above when the program product is run on the terminal device.

[0102] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0103] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for carbon emission accounting in port and shipping container transportation, characterized in that, include: Transportation data and energy consumption data from road, waterway, and rail transportation and logistics storage are collected in categories, preprocessed, and then stored in categories to form a standardized dataset. In accordance with industry standards, and taking into account different modes of transportation and types of goods, a parameter library containing various carbon emission factors was created, and a dynamic update mechanism was established. Historical data was analyzed through machine learning algorithms, and various carbon emission factors were updated regularly. For each mode of transportation—road, waterway, and rail—a separate accounting sub-model is constructed. Each sub-model is used to calculate carbon emissions based on the characteristics and data of the specific mode of transportation, employing appropriate formulas and parameters. The standardized dataset is input into the corresponding accounting sub-model to calculate carbon emissions and select the optimal carbon emission path. The sub-model was calibrated using multiple sets of historical data, and the model parameters were adjusted. Real-time data collection of transportation and energy consumption data from highway, waterway, and railway transportation and logistics storage is conducted. After preprocessing, the data is input into the corresponding accounting sub-model. The accounting sub-model is then run to obtain carbon emission results for each route, select the optimal carbon emission route, and generate a decision support report that includes implementation steps, resource requirements, and expected effects.

2. The carbon emission accounting method for port and shipping container transportation according to claim 1, characterized in that, The data collected in the categorized manner, including transportation data and energy consumption data from highway, waterway, and railway transportation and logistics storage, are preprocessed and then categorized and stored to form a standardized dataset, including: Collect transportation data and energy consumption data in the road transportation process, including vehicle fuel supply type, refrigerant filling table for cold boxes, average vehicle fuel consumption, and route information; Collect transportation data and energy consumption data in the waterway transportation process, including ship fuel supply type, refrigerant filling table for cold boxes, average ship fuel consumption, and route information; Collect transportation data and energy consumption data in the railway transportation process, including train fuel supply type, refrigerant filling table for cold boxes, average train fuel consumption, and route information; Collect transportation and energy consumption data in the logistics and storage process, including energy consumption of ships at port and shore power usage of ships at port; Collect information on transportation methods, historical carbon emission data, and related environmental data; The collected data undergoes a cleaning process to remove outliers and fill in missing values. Data units and formats are standardized, and the data is categorized and stored according to different stages and data types in the cargo transportation process to form a standardized dataset.

3. The carbon emission accounting method for port and shipping container transportation according to claim 2, characterized in that, For the transportation modes of highway, waterway, and railway, separate accounting sub-models are constructed, including: For road, waterway, and rail transportation modes, carbon accounting operator models are constructed for road transportation, logistics and storage, waterway transportation, and rail transportation, respectively. The road transport carbon accounting sub-model uses the mileage method to calculate carbon dioxide emissions, including: In the formula, C 公路,i For the carbon dioxide emissions of vehicle type i, F 油耗,i Let E be the unit average fuel consumption of vehicle type i. 燃料,i The greenhouse gas emission factor of fuels used by vehicles, C 逸散,j T represents the direct greenhouse gas emissions from the j-th fugitive source. i The freight turnover of vehicle type i is calculated using the formula T. i =W i ×D i W i D represents the weight of the goods. i For transportation distance; For refrigerated container transportation, the emissions from fugitive sources are calculated using the following formula: Among them, R j Let P be the charge amount of the j-th refrigerant. GWP,j Let K be the global warming potential of the j-th escape source. 逸散,j Let be the dissipation coefficient of the j-th dissipation source; The logistics storage carbon accounting sub-model includes a loading and unloading operation carbon accounting model and a cold storage carbon accounting model; The carbon accounting model for loading and unloading operations includes: Among them, C 装卸 For greenhouse gas emissions from loading and unloading operations, C 燃料,k Let F be the average fuel consumption of the k-th type of loading and unloading equipment. 油耗,k Let E be the unit fuel consumption of the k-th loading and unloading equipment. 燃料,k Greenhouse gas emission factors of fuel used for loading and unloading equipment; The carbon accounting models for refrigerated storage include: Among them, C 冷藏 R represents the direct greenhouse gas emissions from the cold storage process. l P is the amount of refrigerant added to the refrigeration equipment. GWP,l K represents the global warming potential of the refrigerant. 逸散,l This is the refrigerant dispersion coefficient.

4. The carbon emission accounting method for port and shipping container transportation according to claim 3, characterized in that, The waterway transportation carbon accounting sub-model includes a navigation process carbon accounting model and a port berthing process carbon accounting model; The carbon accounting model for navigation includes: Among them, C 水路,m T represents the carbon dioxide emissions of type m ships. m For the freight turnover of type m ships, F 油耗,m E represents the fuel consumption per ton-kilometer for type m ships. 燃料,m Greenhouse gas emission factor of fuel used in ships, C 逸散,n Let n be the direct greenhouse gas emissions from the nth fugitive source. The carbon accounting model for the berthing process includes: When ships use shore power When the ship is not using shore power, ; Among them, C 靠港 E represents greenhouse gas emissions during the berthing process. 岸电 E represents shore power usage during ship berthing. 电力 For the greenhouse gas emission factor of electricity, t o P represents the duration of a ship's berthing in port. o For the power of marine auxiliary machinery, E 燃油,o Greenhouse gas emission factors for marine fuel oil; The emission reductions from replacing fuel oil with shore power can be calculated using the following formula: Among them, R 替代 To reduce carbon dioxide emissions by using shore power instead of fuel oil, B p E represents the unit fuel consumption rate for power generation of marine diesel generators. 燃油,p Greenhouse gas emission factors for ship fuel oil.

5. The carbon emission accounting method for port and shipping container transportation according to claim 4, characterized in that, The railway transportation carbon accounting sub-model includes a diesel locomotive carbon accounting model and an electrified railway carbon accounting model; The carbon accounting model for internal combustion locomotives includes: Among them, C 铁路,q V represents the greenhouse gas emissions from internal combustion locomotives. q F represents the estimated consumption of the q-th type of fuel for the locomotive. 油耗,q E represents the fuel consumption rate of the locomotive. 燃料,q E is the greenhouse gas emission factor for fuels. 供应,q Greenhouse gas emission factors in the fuel supply process; The carbon accounting model for electrified railways includes: Among them, C 电气,r V represents greenhouse gas emissions from electrified railway locomotives. r E represents the estimated power consumption of the r-th type of locomotive. 电力,r Greenhouse gas emission factors for purchased electricity.

6. The carbon emission accounting method for port and shipping container transportation according to claim 5, characterized in that, The process of inputting a standardized dataset into the corresponding accounting sub-model to calculate carbon emissions and select the optimal carbon emission pathway includes: The standardized dataset is input into the corresponding sub-model. First, the objective function and constraints are determined using linear programming, nonlinear programming, or dynamic programming optimization methods. The preliminary optimal carbon emission path is obtained by solving for the optimal solution. Then, machine learning algorithms are used to train the model on historical data so that the model can predict the carbon emission results of different paths. The optimal carbon emission path is then selected from the preliminary optimal carbon emission path.

7. The carbon emission accounting method for port and shipping container transportation according to claim 6, characterized in that, The calibration of the sub-model using multiple sets of historical data and the adjustment of model parameters include: The accounting sub-model is calibrated using multiple sets of historical data. The carbon emissions simulated by the accounting sub-model for a certain period in the past are compared with the actual carbon emissions. If the difference in carbon emissions exceeds a preset threshold, the relevant parameters are adjusted. The accuracy of the accounting sub-model's prediction of future carbon emissions is verified by using reserved historical data or cited research data. If the verification fails, the model parameters are adjusted.

8. A carbon emission accounting system for port and shipping container transportation, characterized in that, The system adopts the carbon emission accounting method for port and shipping container transportation as described in any one of claims 1 to 7; The system includes: The multi-source data acquisition and preprocessing module is used to collect transportation data and energy consumption data from highway, waterway, and railway transportation and logistics storage in a classified manner. After preprocessing, the data is classified and stored to form a standardized dataset. The carbon factor parameter library construction module is used to create a parameter library containing various carbon emission factors according to industry standards and in combination with different modes of transportation and cargo types, and to establish a dynamic update mechanism. It analyzes historical data through machine learning algorithms and updates various carbon emission factors regularly. The intelligent accounting model construction module is used to build accounting sub-models for different modes of transportation such as road, waterway, and railway. Each accounting sub-model is used to calculate carbon emissions based on the characteristics and data of the specific mode of transportation, using corresponding formulas and parameters. The model training and optimization module is used to input standardized datasets into the corresponding sub-models, calculate carbon emissions, and select the optimal carbon emission pathways. The model calibration and validation module is used to calibrate the sub-model using multiple sets of historical data and adjust the model parameters. The model execution module is used to collect real-time transportation data and energy consumption data from highway, waterway, and railway transportation and logistics storage. After preprocessing, the data is input into the corresponding accounting sub-model, which runs to obtain carbon emission results for each path, selects the optimal carbon emission path, and generates a decision support report that includes implementation steps, resource requirements, and expected effects.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the carbon emission accounting method for port and shipping container transportation as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the carbon emission accounting method for port and shipping container transportation as described in any one of claims 1 to 7.

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