A port container wharf export single container carbon footprint accounting method and system

Abstract

The present application provides a port container wharf export single container carbon footprint accounting method and system, relating to the technical field of carbon footprint accounting. In view of the problems of coarse accounting granularity, unfair energy consumption allocation and difficulty in accurately correlating process data in the prior art, the present application obtains the full-process operation instruction sequence by taking the container number as the index, performs space-time dual correlation matching combined with the spatial trajectory, adopts direct metering method, operation frequency allocation method, operation volume-mileage allocation method and storage time length proportion allocation method for different equipment types to perform differentiated energy consumption allocation, accounts the carbon footprint of each link based on the allocated energy consumption and carbon emission factor, and accumulates to obtain the total carbon footprint, while generating a visual report containing structure proportion, benchmark comparison and abnormal early warning. The present application significantly improves the space-time correlation accuracy of energy consumption data and single container operation, improves the accuracy and fairness of energy consumption allocation, and enhances the refinement degree and decision support capability of carbon footprint accounting.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint accounting technology, and in particular to a method and system for calculating the carbon footprint of a single container exported from a port container terminal. Background Technology

[0002] As a key node in the global supply chain, the carbon emission management capabilities of container terminals are directly related to carbon cost accounting and green competitiveness in international trade. Against this backdrop, developing a method capable of accurately calculating the carbon footprint of a single export container throughout its entire operational process has become an urgent need for port enterprises to optimize their energy structure, implement refined carbon management, and provide data support for green shipping. This has significant practical implications and broad application prospects.

[0003] However, existing technologies still have the following shortcomings: First, the granularity of the calculation is too coarse. Most existing technologies use the average carbon emission per container calculated by dividing the total carbon emissions of the terminal by the total throughput, which cannot reflect the differences in carbon emissions between different container types, container weights, yard locations, and operating processes, leading to inaccurate carbon cost accounting. Second, the accounting boundaries are not uniform. Most studies only consider the carbon emissions of major loading and unloading equipment such as quay cranes, failing to fully include the emissions of horizontal transport vehicles and auxiliary production facilities, thus failing to comprehensively reflect the carbon footprint of the entire export container operation process. Third, the energy consumption allocation method is unscientific. For situations where the same equipment serves multiple containers simultaneously, and for the common energy consumption of horizontal transport vehicles and auxiliary production facilities, existing technologies mostly use a simple average allocation method based on the workload, without considering key differentiating factors such as transport distance, number of operations, and storage time, resulting in inaccurate allocation results.

[0004] Therefore, this invention is proposed. Summary of the Invention

[0005] To address this, the present invention provides a method and system for calculating the carbon footprint of a single container exported from a port container terminal, in order to overcome the problems of coarse calculation granularity, incomplete calculation boundaries, and unfair energy consumption allocation in the prior art.

[0006] To achieve the above objectives, this invention provides a method for calculating the carbon footprint of a single container exported from a port container terminal, comprising: S1, obtain the full-process operation instruction sequence of the target container, and obtain the spatial data of the terminal, the operation data of the terminal equipment, and the time-series energy consumption data; S2, using the equipment identifier and operation time interval in the operation instruction sequence as association conditions, matches the fragmented energy consumption data that is associated with each operation instruction of the target container in both time and space from the time-series energy consumption data; S3, when the same equipment serves multiple containers, including the target container, within the same operating time interval, a differentiated energy consumption allocation rule is executed according to the equipment type, and the part of the fragmented energy consumption data that belongs to common consumption is allocated to the target container. S4, based on the energy consumption data allocated to the target container and the corresponding carbon emission factors, calculates the carbon footprint components generated in the loading and unloading process, horizontal transportation process and auxiliary production process respectively; S5, summing up the carbon footprint components of each stage, yields the total carbon footprint of the entire operation process of the target container.

[0007] Furthermore, based on the container number of the target container, the equipment number, operation start timestamp, and operation end timestamp corresponding to each operation instruction are extracted from the sequence of operation instructions throughout the entire process, forming a composite key of time window consisting of the equipment number, operation start timestamp, and operation end timestamp; Based on the time window composite key, query and extract time-series energy consumption data to obtain the continuous energy consumption data sequence of the corresponding device within the time window; The obtained energy consumption data sequence is subjected to integrity verification and outlier removal.

[0008] Furthermore, the implementation of differentiated energy consumption allocation rules based on device type includes: For stationary loading and unloading equipment of the electric type, a direct metering method is adopted that fully includes the matched fragmented energy consumption data; For mobile loading and unloading equipment handling fuel, the operation frequency allocation method is adopted, which is based on the ratio of the number of operations to the target container to the total number of operations of the equipment on that day. For horizontal transport vehicles, the workload-mileage allocation method is adopted, which is based on the ratio of the theoretical mileage of a single transport task of the target container to the total mileage of the vehicle on that day. For auxiliary production facilities, a proportional allocation method based on container storage time is adopted according to the service characteristics of the facilities.

[0009] Furthermore, the workload-mileage allocation method includes: Based on the starting and destination locations in the horizontal transport operation instructions for the target container, and combined with the spatial data of the terminal, the optimal transport route is dynamically calculated using a path planning algorithm as the theoretical travel distance. Obtain the total mileage and total energy consumption of the corresponding level transport vehicle on the day of operation; The energy consumption for this transport is allocated from the total energy consumption of the day based on the ratio of theoretical mileage to total mileage.

[0010] Furthermore, based on the energy consumption data allocated to the target container and the corresponding carbon emission factors, the carbon footprint components generated in the loading and unloading process, horizontal transportation process, and auxiliary production process are calculated respectively, including: The carbon footprint generated during loading and unloading includes the carbon footprint generated by electric loading and unloading equipment and fuel-powered loading and unloading equipment. A direct measurement method is used, and the calculation formula is as follows: ; in: C handling The carbon footprint generated during loading and unloading, expressed in kgCO2; E device_ei Let be the electricity consumption of the i-th electric loading and unloading equipment during the operating time interval, in kWh; E device_dj The fuel consumption of the j-th fuel loading and unloading equipment on that day is expressed in kg. N box_j The number of times the j-th fuel loading / unloading equipment serves the target container on the same day; N total_j The total number of operations performed by the j-th fuel loading / unloading equipment on that day; EF e The carbon emission factor for electricity is kgCO2 / kWh; EF d The carbon emission factor for diesel fuel is expressed as kgCO2 / kg. The electric loading and unloading equipment includes quay cranes, rail-mounted gantry cranes, and converted oil-fired tire cranes; the fuel-fired loading and unloading equipment includes fuel-fired tire cranes, reach stackers, and forklifts. The formula for calculating the carbon footprint component generated during horizontal transportation is as follows: ; In the formula: C transport The carbon footprint component generated during horizontal transportation, in kgCO2; E vehicle_total The electricity or fuel consumption of the uth horizontal transport vehicle on that day, in kWh or kg; D task Let be the theoretical travel distance of the uth horizontal transport vehicle, in km; D total The total mileage of the uth horizontal transport vehicle on that day is in km; EF energy For electricity carbon emission factor, kgCO2 / kWh or diesel carbon emission factor, kgCO2 / kg; The horizontal transport vehicles include internal container trucks, automated guided vehicles, and automated rail transport vehicles; The carbon footprint components of the auxiliary production process include the carbon footprint components of the office scheduling system and the lighting system. The carbon footprint component of the office scheduling system is calculated based on the annual electricity consumption of the office building and scheduling building, the total annual container handling volume at the terminal, and the electricity carbon emission factor. The carbon footprint component of the lighting system is calculated based on the total electricity consumption for lighting during the storage of the target containers, the storage time of the target containers during the lighting period, the total storage time of all containers during the lighting period, and the electricity carbon emission factor.

[0011] Furthermore, the carbon footprint component of the office dispatching system is calculated by dividing the annual electricity consumption of the office building and dispatching building by the total annual container handling volume at the terminal and then multiplying by the electricity carbon emission factor. The calculation method for the carbon footprint of the lighting system is as follows: ; Among them, C lighting The carbon footprint of the lighting system is expressed in kgCO2. E lighting_total The total electricity consumption for lighting during the storage of the target container is expressed in kWh. t storage The storage time of the target container during the lighting period, in hours; T total_storage The total storage time of all containers during the lighting period, in hours; EF e The carbon emission factor for electricity is kgCO2 / kWh.

[0012] Furthermore, S5 also includes visualization analytics: Based on the calculated total carbon footprint of the entire operation process and the carbon footprint components of each stage, a visual report is generated that includes carbon emission structure proportion analysis, comparison analysis with benchmark values ​​of similar containers, and early warning information on abnormal carbon emission stages.

[0013] Furthermore, the method for calculating the total carbon footprint of the entire operation process of the target container by summing the carbon footprint components of each stage includes: ; ; in, C total The total carbon footprint of the target container, expressed in kgCO2; C handling 、C transport 、C auxiliary These are the carbon footprint components generated during loading and unloading, horizontal transportation, and auxiliary production. C TEU Carbon footprint is expressed in TEU, with units of kgCO2 / TEU. TEU factor The conversion factor is 1 for a standard container and 2 for a 40ft container.

[0014] This invention also provides a carbon footprint accounting system for export containers at a port container terminal, the system being used to implement any of the aforementioned methods for calculating the carbon footprint of export containers at a port container terminal, the system comprising: The multi-source data acquisition module is used to acquire the entire process operation instruction sequence of the target container, as well as the spatial data of the terminal, the operating data of the terminal equipment, and the time-series energy consumption data. The data association and matching module, connected to the multi-source data acquisition module, is used to match fragmented energy consumption data that are associated with each operation instruction of the target container in both time and space from the time-series energy consumption data, using the equipment identifier and operation time interval in the operation instruction sequence as association conditions. The differentiated energy consumption allocation calculation module, connected to the data association and matching module, is used to allocate the part of the fragmented energy consumption data that belongs to the common consumption to the target container when the same equipment serves multiple containers, including the target container, within the same working time interval. The carbon footprint accounting module, connected to the differentiated allocation calculation module, is used to calculate the carbon footprint components generated in the loading and unloading process, horizontal transportation process, and auxiliary production process based on the energy consumption data allocated to the target container and the corresponding carbon emission factors. The carbon footprint output module, connected to the carbon footprint calculation module, is used to accumulate the carbon footprint components of each stage to obtain the total carbon footprint of the entire operation process of the target container.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: Firstly, this invention obtains the entire process operation instruction sequence by using the target container number as an index, extracts the equipment number and operation start and end timestamps to form a time window composite key, queries and extracts the continuous energy consumption data sequence within the corresponding time window from the time-series energy consumption data, and further introduces spatial coordinate data for trajectory comparison to achieve spatial correlation verification. This solves the problems in the prior art where it is impossible to distinguish whether the energy consumption data truly serves the target container based solely on the equipment number and time interval, and the unreliable matching results caused by missing or abnormal original data. It improves the correlation accuracy and data quality between energy consumption data and individual container operations in both time and space dimensions, and reduces the calculation errors caused by data noise and spatial mismatch.

[0016] Secondly, this invention designs differentiated energy consumption allocation rules for different equipment types. For electric fixed loading and unloading equipment, it adopts a direct metering method; for fuel-powered mobile loading and unloading equipment, it adopts a work frequency allocation method based on the ratio of the number of operations to the target container to the total number of operations of the equipment on that day; for horizontal transport vehicles, it adopts a work volume-mileage allocation method based on the ratio of the theoretical mileage of a single transport task of the target container to the total mileage of the vehicle on that day; and for auxiliary production facilities, it adopts a proportional allocation method based on the container storage time according to their service characteristics. This solves the problems of the existing technology where the energy consumption allocation method is singular when the same equipment serves multiple containers at the same time, does not consider the operating characteristics and service differences of different equipment, and has unfair allocation results. It improves the scientificity and accuracy of energy consumption allocation and reduces the deviation in single-container carbon footprint accounting caused by unreasonable allocation.

[0017] Third, this invention unifies the accounting boundary across the entire operation process from gate entry to loading completion, integrating multi-source data from the production operating system, energy management system, vehicle-mounted terminal, and geographic information system. This enables the separate accounting and cumulative summarization of carbon footprints in each stage of loading and unloading, horizontal transportation, and auxiliary production. The carbon footprint is converted into a unit TEU carbon footprint based on container type. Simultaneously, a visualized report is generated, including carbon emission structure ratio analysis, comparison analysis with benchmark values ​​of similar containers, and early warning information on abnormal carbon emission stages. This solves the problems of coarse accounting granularity, incomplete boundaries, and inability to trace emission sources in existing technologies, improving the precision of carbon footprint accounting and decision support capabilities. It provides reliable data support for refined carbon management, carbon cost accounting, green service pricing, and carbon emission reduction decisions at the terminal. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for calculating the carbon footprint of a single container exported from a port container terminal, provided in an embodiment of the present invention; Figure 2 A flowchart of the workload-mileage allocation method for calculating the carbon footprint of a single container exported from a port container terminal, provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of a port container terminal export single container carbon footprint accounting system provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0023] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Example 1 like Figure 1 As shown, this invention provides a method for calculating the carbon footprint of a single container exported from a port container terminal, including: S1, obtain the full-process operation instruction sequence of the target container, and obtain the spatial data of the terminal, the operation data of the terminal equipment, and the time-series energy consumption data; S2, using the equipment identifier and operation time interval in the operation instruction sequence as association conditions, matches the fragmented energy consumption data that is associated with each operation instruction of the target container in both time and space from the time-series energy consumption data; Based on the container number of the target container, the equipment number, operation start timestamp, and operation end timestamp corresponding to each operation instruction are extracted from the sequence of operation instructions throughout the entire process, forming a composite key of time window consisting of the equipment number, operation start timestamp, and operation end timestamp; Based on the time window composite key, query and extract time-series energy consumption data to obtain the continuous energy consumption data sequence of the corresponding device within the time window; The obtained energy consumption data sequence is subjected to integrity verification and outlier removal.

[0025] In one possible implementation, this embodiment takes a 20ft export container at a specialized container terminal in a port as an example. The entire sequence of operational instructions for the container is retrieved from the Port Production Operating System (TOS), indexed by the container number. The query reveals five operational instructions for the container: gate entry, yard unloading, yard pickup, horizontal transport, and loading. Each instruction includes the start time, end time, starting location, and destination location. Simultaneously, time-series energy consumption data for the relevant equipment on the corresponding date is obtained from the Energy Management System (EMS), with a sampling frequency of 1 minute per instance. Data items include timestamps, equipment numbers, and instantaneous power (kW) or instantaneous fuel consumption (L / h). Spatial data from the terminal's electronic map is obtained from a Geographic Information System (GIS) for subsequent distance calculations; real-time mileage data from the internal container truck IC12 is obtained from the vehicle terminal.

[0026] Iterate through the five work instructions mentioned above. For each instruction, extract the equipment number, work start timestamp, and work end timestamp, and combine them into a time window composite key, for example: [RTG08, 2025-10-15 08:50:00, 2025-10-15 08:58:00]. Use each composite key as a query condition to retrieve data from the EMS database. The EMS returns the energy consumption data sequence within that time window, for example: [2025-10-15 08:50:00, 2025-10-15 08:58:00, Equipment RTG08, Instantaneous Power 17.2kW, Cumulative Electricity Consumption 2.41kWh]. Calculate the total electricity consumption within this time window as 2.41 kWh using the difference in cumulative electricity consumption between adjacent timestamps, and save this as the fragmented energy consumption data for that work instruction. For fuel-powered equipment, EMS returns an instantaneous fuel consumption sequence, and the total fuel consumption within an 8-minute transport mission is calculated by integrating the time.

[0027] For stationary equipment, the operating location is fixed, and spatial correlation verification passes automatically. For mobile equipment, further spatial correlation matching is performed. Specifically, the starting and destination positions are extracted from the TOS command, and the real-time location trajectory points of the equipment within the time window are obtained from the equipment positioning system. Using a GIS path planning algorithm, the theoretically optimal path is calculated based on the starting and ending points. Then, it is determined whether the actual trajectory points are within the buffer zone of the theoretical path, i.e., the offset is ≤15 meters. After comparison, the maximum offset of all trajectory points from the theoretical path is 6 meters, and the spatial consistency verification passes, confirming that the power consumption within this time window does indeed serve the transportation task of the target container.

[0028] Each captured energy consumption data sequence undergoes automated preprocessing. Specifically, it checks whether the number of returned data records matches the time window length. For example, the time window length for RTG08 is 8 minutes (08:50:00 to 08:58:00), and the expected number of data points is 9. If fewer than 9 data points are actually returned, it is determined that data is missing, triggering an alarm and marking the energy consumption data for that operation instruction as incomplete. Subsequent estimation based on historical averages is then used. Statistical screening is performed on the instantaneous power values ​​in the sequence. For example, assuming that the instantaneous power range for RTG08 in normal stacking operations is 15–25 kW, if a record shows an instantaneous power of 0 kW or exceeding 50 kW, it is determined to be an outlier. In this embodiment, all 9 records are within the normal range and there are no outliers. If an outlier is detected, it is replaced smoothly using the average of adjacent time points, or the outlier is directly removed and filled using linear interpolation.

[0029] This invention uses the target container number as an index to obtain the entire process operation instruction sequence from the production operating system, and simultaneously collects terminal spatial data, equipment operation data, and time-series energy consumption data. It constructs a unified framework for acquiring multi-source heterogeneous data, solving the problem of isolated and difficult-to-integrate operation instructions, equipment status, energy consumption data, and spatial location in existing technologies, thus improving the comprehensiveness of data collection. By using the equipment identifier and operation time interval in the operation instruction sequence as association conditions, specifically by extracting the equipment number and operation start and end timestamps to form a time window composite key, it queries and extracts time-series energy consumption data. Simultaneously, it performs integrity verification and outlier removal on the extracted energy consumption data sequence, and further introduces spatial coordinate data for trajectory comparison to achieve spatial association verification. This solves the problems in existing technologies where relying solely on equipment number and time interval cannot distinguish whether energy consumption data truly serves the target container, and where missing or abnormal raw data leads to unreliable matching results. It improves the accuracy and quality of the association between energy consumption data and individual container operations in both time and space dimensions, and reduces calculation errors caused by data noise and spatial mismatch.

[0030] S3, when the same equipment serves multiple containers, including the target container, within the same operating time interval, a differentiated energy consumption allocation rule is executed according to the equipment type, and the part of the fragmented energy consumption data that belongs to common consumption is allocated to the target container. Differentiated energy consumption allocation rules are applied based on equipment type, including: For stationary loading and unloading equipment of the electric type, a direct metering method is adopted that fully includes the matched fragmented energy consumption data; For mobile loading and unloading equipment handling fuel, the operation frequency allocation method is adopted, which is based on the ratio of the number of operations to the target container to the total number of operations of the equipment on that day. For horizontal transport vehicles, the workload-mileage allocation method is adopted, which is based on the ratio of the theoretical mileage of a single transport task of the target container to the total mileage of the vehicle on that day. For auxiliary production facilities, a proportional allocation method based on container storage time is adopted according to the service characteristics of the facilities.

[0031] like Figure 2 As shown, the workload-mileage allocation method includes: Based on the starting and destination locations in the horizontal transport operation instructions for the target container, and combined with the spatial data of the terminal, the optimal transport route is dynamically calculated using a path planning algorithm as the theoretical travel distance. Obtain the total mileage and total energy consumption of the corresponding level transport vehicle on the day of operation; The energy consumption for this transport is allocated from the total energy consumption of the day based on the ratio of theoretical mileage to total mileage.

[0032] In one possible implementation, it is assumed that during the target container's operating period, multiple containers share equipment. For example, fuel-powered mobile loading and unloading equipment is shared by multiple containers and requires cost-sharing. Electric stationary loading and unloading equipment and horizontal transport vehicles are used exclusively in this example and do not require cost-sharing. Auxiliary production facilities need to be cost-sharing according to regulations. Specifically, RTG05 consumes 2.3 kWh of electricity during its operating period, and since this equipment only serves the target container during this time, a direct metering method is used, and the full 2.3 kWh is included in the energy consumption of the target container's loading and unloading process.

[0033] For fuel-powered equipment, a job allocation method based on the ratio of the number of operations performed on the target container to the total number of operations performed by the equipment that day is adopted. Specifically, the total number of operations performed by the reach stacker F10 on that day is obtained from the TOS system. A query shows that F10 completed 45 operations that day. The number of operations performed by F10 on the target container is then counted. The query reveals that F10 only performed one operation on that container that day. The allocation ratio is calculated as the ratio of the number of operations performed on the target container to the total number of operations performed that day, i.e., 1 / 45 ≈ 0.022. The total fuel consumption of F10 that day is then obtained. A query from the EMS system shows that F10's total fuel consumption for that day was 120 kg. The fuel consumption allocated to the target container is calculated as the product of the total fuel consumption for that day and the allocation ratio. The calculated fuel consumption allocated to the target container is 2.667 kg. Multiplying this by the diesel carbon emission factor yields the carbon footprint.

[0034] The mileage for a single container transport task is allocated based on the ratio of the theoretical mileage to the vehicle's total daily mileage. Specifically, in this embodiment, the transport vehicle IC09 serves multiple container transport tasks on the same day. A section of the target container's horizontal transport is performed by the inner truck IC09, starting from yard B02-C05 and ending at the front edge of quay crane QC03. A GIS route planning algorithm is used to calculate the optimal transport path from the starting coordinates to the destination coordinates based on information such as road networks, restricted areas, and one-way streets in the terminal's electronic map. The algorithm employs Dijkstra's shortest path algorithm, considering the shortest distance and traffic efficiency weights. The calculated theoretical optimal path length is 1.35 km. A query from the vehicle terminal system shows that IC09's total mileage on the day of operation was 108 km, and its total fuel consumption was 24.3 kg.

[0035] The allocation ratio is calculated as the ratio of the theoretical mileage to the total mileage traveled on the day, which is 1.35 km / 108 km, or 0.0125. The allocated energy consumption is the product of the total energy consumption on the day and the allocation ratio, which is 24.3 kg × 0.0125 = 0.30375 kg. Multiplying this by the diesel carbon emission factor, the carbon footprint of this horizontal transport is 0.96 kg CO2.

[0036] The lighting system only provides service to containers during storage, and its energy consumption is directly related to the storage time, using a storage time-based allocation method. Specifically, according to the TOS system records, the target container was unloaded and entered the yard at 09:10:00 on June 10, 2026, and was picked up and left the yard at 14:00:00 on June 11, 2026. The calculated storage time is 28 hours and 50 minutes, or 28.833 hours. The total storage time of all containers on site that day is calculated. Assuming the terminal handled 1500 containers that day, the total storage time for each container is 43200 TEUs. The EMS query shows that the total electricity consumption of the yard lighting system that day was 3000 kWh. Calculations show that the allocated energy consumption is: total daily lighting electricity consumption × (target box storage time / total daily storage time) = 3000 kWh × (28.833 / 43200) = 3000 × 0.0006674 ≈ 2.002 kWh. Multiplying this by the electricity carbon emission factor, the carbon footprint of the lighting system is 1.16 kgCO2.

[0037] This invention designs differentiated energy consumption allocation rules for different equipment types, specifically including: a direct metering method for electric stationary loading and unloading equipment; a work frequency allocation method based on the ratio of the number of operations to the target container to the total number of operations per day for fuel-powered mobile loading and unloading equipment; a work volume-mileage allocation method based on the ratio of the theoretical mileage of a single transport task to the total mileage per day for horizontal transport vehicles; and a proportional allocation method based on container storage time for auxiliary production facilities according to their service characteristics. This invention solves the problems of existing technologies where the energy consumption allocation method is singular when the same equipment serves multiple containers simultaneously, fails to consider the operating characteristics and service differences of different equipment, and results in unfair allocation. It improves the scientificity and accuracy of energy consumption allocation and reduces the deviation in single-container carbon footprint accounting caused by unreasonable allocation.

[0038] S4, based on the energy consumption data allocated to the target container and the corresponding carbon emission factors, calculates the carbon footprint components generated in the loading and unloading, horizontal transportation, and auxiliary production processes, including: The carbon footprint generated during loading and unloading includes the carbon footprint generated by electric loading and unloading equipment and fuel-powered loading and unloading equipment. A direct measurement method is used, and the calculation formula is as follows: ; in: C handling The carbon footprint generated during loading and unloading, expressed in kgCO2; E device_ei Let be the electricity consumption of the i-th electric loading and unloading equipment during the operating time interval, in kWh; E device_dj The fuel consumption of the j-th fuel loading and unloading equipment on that day is expressed in kg. N box_j The number of times the j-th fuel loading / unloading equipment serves the target container on the same day; N total_j The total number of operations performed by the j-th fuel loading / unloading equipment on that day; EF e The carbon emission factor for electricity is kgCO2 / kWh; EF d Carbon emission factor for diesel fuel, kgCO2 / kg; The electric loading and unloading equipment includes quay cranes, rail-mounted gantry cranes, and converted oil-fired tire cranes; the fuel-fired loading and unloading equipment includes fuel-fired tire cranes, reach stackers, and forklifts. The formula for calculating the carbon footprint component generated during horizontal transportation is as follows: ; In the formula: C transportThe carbon footprint component generated during horizontal transportation, in kgCO2; E vehicle_total The electricity or fuel consumption of the uth horizontal transport vehicle on that day, in kWh or kg; D task Let be the theoretical travel distance of the uth horizontal transport vehicle, in km; D total The total mileage of the uth horizontal transport vehicle on that day is in km; EF energy For electricity carbon emission factor, kgCO2 / kWh or diesel carbon emission factor, kgCO2 / kg; The horizontal transport vehicles include internal container trucks, automated guided vehicles, and automated rail transport vehicles; The carbon footprint components of the auxiliary production process include the carbon footprint components of the office scheduling system and the lighting system. The carbon footprint component of the office scheduling system is calculated based on the annual electricity consumption of the office building and scheduling building, the total annual container handling volume at the terminal, and the electricity carbon emission factor. The carbon footprint component of the lighting system is calculated based on the total electricity consumption for lighting during the storage of the target containers, the storage time of the target containers during the lighting period, the total storage time of all containers during the lighting period, and the electricity carbon emission factor.

[0039] The carbon footprint component of the office dispatching system is calculated by dividing the annual electricity consumption of the office building and dispatching building by the total annual container handling volume at the terminal and then multiplying by the electricity carbon emission factor. The calculation method for the carbon footprint of the lighting system is as follows: ; Among them, C lighting The carbon footprint of the lighting system is expressed in kgCO2. E lighting_total The total electricity consumption for lighting during the storage of the target container is expressed in kWh. t storage The storage time of the target container during the lighting period, in hours; T total_storage The total storage time of all containers during the lighting period, in hours; EF e The carbon emission factor for electricity is kgCO2 / kWh.

[0040] In one possible implementation, in this embodiment, the electricity carbon emission factor EF e Take 0.58 kgCO2 / kWh, diesel carbon emission factor EF dThe carbon footprint is calculated as 3.16 kgCO2 / kg. The loading and unloading process includes both electric and fuel-powered equipment. These are calculated separately and then summed. Specifically, the carbon footprint of the electric loading and unloading equipment RTG05 is equal to the allocated electricity consumption multiplied by the electricity carbon emission factor, resulting in 1.334 kgCO2. The carbon footprint of the fuel-powered loading and unloading equipment, the front-end crane F10, is equal to the allocated fuel consumption multiplied by the diesel carbon emission factor, resulting in 8.428 kgCO2. Adding these two parts together, the total carbon footprint of the loading and unloading process is 9.762 kgCO2. The horizontal transport process is completed solely by the transport vehicle IC09; therefore, the carbon footprint of the horizontal transport process is 0.96 kgCO2.

[0041] The auxiliary production process includes two parts: the office scheduling system and the lighting system. These are calculated separately and then summed. First, calculate the total electricity consumption of the office and scheduling buildings. Assume that in a certain year, the total electricity consumption of the office building is 2500 kWh, the total electricity consumption of the scheduling building is 1800 kWh, and the total container throughput of the terminal is 1500 TEU. Then, the allocated electricity consumption is (2500 + 1800) / 1500 = 2.8667 kWh / TEU, and the carbon footprint is 2.8667 × 0.58 = 1.6627 kg CO2.

[0042] The carbon footprint component of the lighting system is calculated using the following formula: ; Assuming the total electricity consumption for lighting during the storage period of the target container is 3000 kWh, this value refers to the total electricity consumption allocated to lighting during the storage period for that container, obtained by accumulating over time from the lighting master table. The storage time of the target container during the lighting period is 28.833 hours, and the total storage time of all containers during the lighting period is 43200 container-hours. Therefore, the percentage of storage time is 28.833 / 43200 = 0.0006674, the allocated electricity consumption for lighting is 3000 × 0.0006674 = 2.0022 kWh, and the carbon footprint is 2.0022 × 0.58 = 1.1613 kg CO2.

[0043] This invention calculates the carbon footprint component of the loading and unloading process by multiplying the energy consumption data of the loading and unloading equipment allocated to the target container by the corresponding electricity or fuel carbon emission factor, and calculates the carbon footprint component of the horizontal transport process by multiplying the energy consumption data of the horizontal transport vehicles by the corresponding electricity or fuel carbon emission factor. Furthermore, it separately calculates the carbon footprint components of the office scheduling system and the lighting system for the auxiliary production process. This solves the problems in the prior art where the energy consumption of auxiliary production is simply averaged and ignores the differences in its service characteristics, and the problem of commingling the carbon footprints of each process and making it impossible to trace the emission source. This improves the precision and transparency of the carbon footprint calculation process and reduces the calculation error caused by improper allocation of auxiliary energy consumption.

[0044] S5, summing up the carbon footprint components of each stage, yields the total carbon footprint of the entire operation process of the target container.

[0045] The total carbon footprint of the entire operation of the target container is calculated by summing the carbon footprint components of each stage, including: ; ; in, C total The total carbon footprint of the target container, expressed in kgCO2; C handling 、C transport 、C auxiliary These are the carbon footprint components generated during loading and unloading, horizontal transportation, and auxiliary production. C TEU Carbon footprint is expressed in TEU, with units of kgCO2 / TEU. TEU factor The conversion factor is 1 for a standard container and 2 for a 40ft container.

[0046] S5 also includes visualization analytics: Based on the calculated total carbon footprint of the entire operation process and the carbon footprint components of each stage, a visual report is generated that includes carbon emission structure proportion analysis, comparison analysis with benchmark values ​​of similar containers, and early warning information on abnormal carbon emission stages.

[0047] In one possible implementation, the carbon footprint components of the target container at each stage are obtained as follows: carbon footprint component C for loading and unloading. handling = 9.762 kgCO2, carbon footprint component of horizontal transportation C transport = 0.96 kgCO2, carbon footprint component of auxiliary production processes C auxiliary = 2.824 kgCO2. The target container is a 20-foot standard container, and according to the standard container conversion factor, the value of a 20-foot container is taken as 1. The total carbon footprint of the entire operation process is calculated using an additive formula, and the calculated total carbon footprint is 13.546 kgCO2.

[0048] The percentage of carbon footprint at each stage was calculated, showing that loading and unloading accounted for 72.07%, horizontal transport for 7.09%, and auxiliary production for 20.84%. The analysis concludes that the container's carbon emissions primarily originate from loading and unloading, accounting for over 72%, followed by auxiliary production, with horizontal transport accounting for a smaller proportion. Key areas for improvement include optimizing the energy efficiency of loading and unloading equipment, such as replacing fuel-powered reach stackers with electric ones, or optimizing operational processes to reduce energy consumption per container.

[0049] The average carbon footprint benchmark for 20-foot exported containers at the same terminal during the same period was retrieved from the database. Assuming the average total carbon footprint of 20-foot exported containers at this terminal on that day was 10.2 kg CO2 / TEU, the target container's total carbon footprint was 13.546 kg CO2 / TEU, exceeding the benchmark by approximately 32.8%. Further analysis was conducted to determine the deviations from the benchmark at each stage. Deviation in loading and unloading: The carbon footprint of the target container loading and unloading was 9.762 kgCO2, which is about 30.2% higher than the benchmark loading and unloading average. The main reason may be that the container was loaded using a fuel-powered front-end crane while the benchmark equipment was mostly electric. Deviation in auxiliary production process: The carbon footprint of the target container auxiliary process is 2.824 kgCO2, which is about 34.5% higher than the baseline auxiliary average. The main reason is that the container was stored for 28.833 hours, which is significantly longer than the average storage time on that day.

[0050] An anomaly warning is triggered when the carbon footprint of any stage exceeds the benchmark value for similar containers by more than 50%. In this embodiment, the loading and unloading stage exceeds the benchmark by 30.2%, which is less than the 50% threshold and therefore does not trigger a warning, but is marked as high. The production support stage exceeds the benchmark by 34.5%, which is also less than the 50% threshold and therefore does not trigger a warning. If a container is stored for 60 hours, the carbon footprint of the lighting system will increase exponentially, triggering an abnormal storage time warning and prompting an investigation into whether the container is being held up in the yard.

[0051] The final visualization report uses a pie chart to show the carbon emission structure proportions, a bar chart to show the comparison of the carbon footprint of each stage of the container with the benchmark value, and a text list to list the stages with high emissions and suggested optimization measures.

[0052] This invention obtains the total carbon footprint of the target container's entire operation process by accumulating the carbon footprint components of loading and unloading, horizontal transportation, and auxiliary production. It then calculates the carbon footprint per TEU based on container type conversion factors, achieving standardized output of the carbon footprint per container. Simultaneously, based on the calculated total carbon footprint and the carbon footprint components of each stage, it generates a visualized report containing carbon emission structure proportion analysis, comparison analysis with benchmark values ​​for similar containers, and early warning information for abnormal carbon emission stages. This solves the problems of existing technologies that only output single values, lack stage decomposition and comparative references, and cannot identify abnormal emission sources. It improves the interpretability and decision support capabilities of carbon footprint accounting results and reduces the difficulty for managers to identify high-energy-consuming stages.

[0053] Example 2 like Figure 3 As shown, the present invention also provides a carbon footprint accounting system for export containers at a port container terminal. The system is used to implement the carbon footprint accounting method for export containers at a port container terminal as described in any of Embodiment 1. The system includes: The multi-source data acquisition module is used to acquire the entire process operation instruction sequence of the target container, as well as the spatial data of the terminal, the operating data of the terminal equipment, and the time-series energy consumption data. The data association and matching module, connected to the multi-source data acquisition module, is used to match fragmented energy consumption data that are associated with each operation instruction of the target container in both time and space from the time-series energy consumption data, using the equipment identifier and operation time interval in the operation instruction sequence as association conditions. The differentiated energy consumption allocation calculation module, connected to the data association and matching module, is used to allocate the part of the fragmented energy consumption data that belongs to the common consumption to the target container when the same equipment serves multiple containers, including the target container, within the same working time interval. The carbon footprint accounting module, connected to the differentiated allocation calculation module, is used to calculate the carbon footprint components generated in the loading and unloading process, horizontal transportation process, and auxiliary production process based on the energy consumption data allocated to the target container and the corresponding carbon emission factors. The carbon footprint output module, connected to the carbon footprint calculation module, is used to accumulate the carbon footprint components of each stage to obtain the total carbon footprint of the entire operation process of the target container.

[0054] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for calculating the carbon footprint of a single export container at a port container terminal, characterized in that, include: S1, obtain the full-process operation instruction sequence of the target container, and obtain the spatial data of the terminal, the operation data of the terminal equipment, and the time-series energy consumption data; S2, using the equipment identifier and operation time interval in the operation instruction sequence as association conditions, matches the fragmented energy consumption data that is associated with each operation instruction of the target container in both time and space from the time-series energy consumption data; S3, when the same equipment serves multiple containers, including the target container, within the same operating time interval, a differentiated energy consumption allocation rule is executed according to the equipment type, and the part of the fragmented energy consumption data that belongs to common consumption is allocated to the target container. S4, based on the energy consumption data allocated to the target container and the corresponding carbon emission factors, calculates the carbon footprint components generated in the loading and unloading process, horizontal transportation process and auxiliary production process respectively; S5, summing up the carbon footprint components of each stage, yields the total carbon footprint of the entire operation process of the target container.

2. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 1, characterized in that, Based on the container number of the target container, the equipment number, operation start timestamp, and operation end timestamp corresponding to each operation instruction are extracted from the sequence of operation instructions throughout the entire process, forming a composite key of time window consisting of the equipment number, operation start timestamp, and operation end timestamp; Based on the time window composite key, query and extract time-series energy consumption data to obtain the continuous energy consumption data sequence of the corresponding device within the time window; The obtained energy consumption data sequence is subjected to integrity verification and outlier removal.

3. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 1, characterized in that, The implementation of differentiated energy consumption allocation rules based on equipment type includes: For stationary loading and unloading equipment of the electric type, a direct metering method is adopted that fully includes the matched fragmented energy consumption data; For mobile loading and unloading equipment handling fuel, the operation frequency allocation method is adopted, which is based on the ratio of the number of operations to the target container to the total number of operations of the equipment on that day. For horizontal transport vehicles, the workload-mileage allocation method is adopted, which is based on the ratio of the theoretical mileage of a single transport task of the target container to the total mileage of the vehicle on that day. For auxiliary production facilities, a proportional allocation method based on container storage time is adopted according to the service characteristics of the facilities.

4. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 3, characterized in that, The workload-mileage allocation method includes: Based on the starting and destination locations in the horizontal transport operation instructions for the target container, and combined with the spatial data of the terminal, the optimal transport route is dynamically calculated using a path planning algorithm as the theoretical travel distance. Obtain the total mileage and total energy consumption of the corresponding level transport vehicle on the day of operation; The energy consumption for this transport is allocated from the total energy consumption of the day based on the ratio of theoretical mileage to total mileage.

5. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 1, characterized in that, The carbon footprint components generated during loading and unloading, horizontal transportation, and auxiliary production are calculated based on energy consumption data allocated to the target container and the corresponding carbon emission factors, including: The carbon footprint generated during loading and unloading includes the carbon footprint generated by electric loading and unloading equipment and fuel-powered loading and unloading equipment. A direct measurement method is used, and the calculation formula is as follows: ； in: C handling The carbon footprint generated during loading and unloading, expressed in kgCO2; E device_ei Let be the electricity consumption of the i-th electric loading and unloading equipment during the operating time interval, in kWh; E device_dj The fuel consumption of the j-th fuel loading and unloading equipment on that day is expressed in kg. N box_j The number of times the j-th fuel loading / unloading equipment serves the target container on the same day; N total_j The total number of operations performed by the j-th fuel loading / unloading equipment on that day; EF e The carbon emission factor for electricity is kgCO2 / kWh; EF d Carbon emission factor for diesel fuel, kgCO2 / kg; The electric loading and unloading equipment includes quay cranes, rail-mounted gantry cranes, and converted oil-fired tire cranes; the fuel-fired loading and unloading equipment includes fuel-fired tire cranes, reach stackers, and forklifts. The formula for calculating the carbon footprint component generated during horizontal transportation is as follows: ； In the formula: C transport The carbon footprint component generated during horizontal transportation, in kgCO2; E vehicle_total The electricity or fuel consumption of the uth horizontal transport vehicle on that day, in kWh or kg; D task Let be the theoretical travel distance of the uth horizontal transport vehicle, in km; D total The total mileage of the uth horizontal transport vehicle on that day is in km; EF energy For electricity carbon emission factor, kgCO2 / kWh or diesel carbon emission factor, kgCO2 / kg; The horizontal transport vehicles include internal container trucks, automated guided vehicles, and automated rail transport vehicles; The carbon footprint components of the auxiliary production process include the carbon footprint components of the office scheduling system and the lighting system. The carbon footprint component of the office scheduling system is calculated based on the annual electricity consumption of the office building and scheduling building, the total annual container handling volume at the terminal, and the electricity carbon emission factor. The carbon footprint component of the lighting system is calculated based on the total electricity consumption for lighting during the storage of the target containers, the storage time of the target containers during the lighting period, the total storage time of all containers during the lighting period, and the electricity carbon emission factor.

6. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 5, characterized in that, The carbon footprint component of the office dispatch system is calculated by dividing the annual electricity consumption of the office building and dispatch building by the total annual container handling volume at the terminal and then multiplying by the electricity carbon emission factor. The calculation method for the carbon footprint of the lighting system is as follows: ； Among them, C lighting The carbon footprint of the lighting system is expressed in kgCO2. E lighting_total The total electricity consumption for lighting during the storage of the target container is expressed in kWh. t storage The storage time of the target container during the lighting period, in hours; T total_storage The total storage time of all containers during the lighting period, in hours; EF e The carbon emission factor for electricity is kgCO2 / kWh.

7. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 1, characterized in that, S5 also includes visualization analytics: Based on the calculated total carbon footprint of the entire operation process and the carbon footprint components of each stage, a visual report is generated that includes carbon emission structure proportion analysis, comparison analysis with benchmark values ​​of similar containers, and early warning information on abnormal carbon emission stages.

8. The method for calculating the carbon footprint of a single container exported from a port container terminal according to claim 1, characterized in that, The method for calculating the total carbon footprint of the entire operation of the target container by summing the carbon footprint components of each stage includes: ； ； in, C total The total carbon footprint of the target container, expressed in kgCO2; C handling 、C transport 、C auxiliary These are the carbon footprint components generated during loading and unloading, horizontal transportation, and auxiliary production. C TEU Carbon footprint is expressed in TEU, with units of kgCO2 / TEU. TEU factor The conversion factor is 1 for a standard container and 2 for a 40ft container.

9. A carbon footprint accounting system for a single container exported from a port container terminal, characterized in that, The system is used to implement the carbon footprint accounting method for export containers at port container terminals as described in any one of claims 1 to 8, and the system includes: The multi-source data acquisition module is used to acquire the entire process operation instruction sequence of the target container, as well as the spatial data of the terminal, the operating data of the terminal equipment, and the time-series energy consumption data. The data association and matching module, connected to the multi-source data acquisition module, is used to match fragmented energy consumption data that are associated with each operation instruction of the target container in both time and space from the time-series energy consumption data, using the equipment identifier and operation time interval in the operation instruction sequence as association conditions. The differentiated energy consumption allocation calculation module, connected to the data association and matching module, is used to allocate the part of the fragmented energy consumption data that belongs to the common consumption to the target container when the same equipment serves multiple containers, including the target container, within the same working time interval. The carbon footprint accounting module, connected to the differentiated allocation calculation module, is used to calculate the carbon footprint components generated in the loading and unloading process, horizontal transportation process, and auxiliary production process based on the energy consumption data allocated to the target container and the corresponding carbon emission factors. The carbon footprint output module, connected to the carbon footprint calculation module, is used to accumulate the carbon footprint components of each stage to obtain the total carbon footprint of the entire operation process of the target container.

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