Port carbon emission source tracking and intelligent management and control method and system based on big data analysis

Abstract

The invention provides a port carbon emission source tracking and intelligent management and control method and system based on big data analysis, belongs to the technical field of port carbon emission management and control, and aims to solve the problem that a traditional port carbon emission monitoring method cannot perform targeted tracking management and control on cargo ship individuals. The method comprises the following steps: generating a carbon emission limit distribution basis according to multiple items of information registered by a cargo ship in combination with related rules, data and parameters, determining an agreed limit of carbon emission on the ship, collecting carbon emission data on the ship and port operation in parallel by using port multi-region detection equipment, classifying and integrating the data, and transmitting the data to a port scheduling system and a cargo ship terminal. And finally, comparing the carbon emission data on the ship with the agreed quota in real time, triggering a management and control process, and pushing a warning signal and adjusting port operation arrangement when the quota is exceeded. According to the invention, the cargo ship can know the carbon emission condition of the cargo ship, the dispatching center realizes accurate energy-saving and emission-reducing management and control, and the use efficiency of the port is improved.

Description

A Method and System for Port Carbon Emission Source Tracking and Intelligent Management Based on Big Data Analysis Technical Field

[0001] This invention relates to the field of port carbon emission monitoring technology, and more specifically, to a method and system for port carbon emission source tracking and intelligent management based on big data analysis. Background Technology

[0002] Ports, as key facilities providing cargo storage and replenishment services for cargo ships, generate significant emissions of exhaust gases, wastewater, domestic waste, and fuel waste during ship berthing and operations, while also consuming electricity and fuel. Carbon emissions, as a crucial indicator reflecting the environmental pollution caused by cargo ships and port operations, as well as the port's carrying capacity, require monitoring and control; this is an indispensable part of port supervision.

[0003] Currently, while there are existing technologies for monitoring port carbon emissions—for example, these technologies typically determine the carbon emission component of all fuel-powered equipment within the port based on primary and secondary carbon dioxide emissions, the primary equivalent carbon emission component of all electrical equipment within the port based on electricity consumption, and the secondary equivalent carbon emission component of all refrigerated containers within the port based on refrigerant leakage—the total carbon emissions of the port are thus determined. This approach reduces labor intensity and costs to some extent, improves the accuracy and real-time nature of carbon emission monitoring, and enables refined management of energy consumption and carbon emissions through comparative analysis of carbon emissions per unit of production.

[0004] However, existing port carbon emission monitoring methods have significant shortcomings, failing to conduct targeted tracking and management of individual cargo ship carbon emission sources for different situations. This prevents cargo ships from obtaining timely and accurate information about their own carbon emissions, and makes it difficult for dispatch centers to achieve precise energy conservation and emission reduction management based on individual ship differences. Due to the lack of precise management of individual cargo ships, port resources cannot be utilized to the fullest extent, resulting in a significant reduction in port efficiency. This not only hinders the implementation of energy conservation and emission reduction policies by both cargo ships and ports but also impedes the further development of related technologies. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for port carbon emission source tracking and intelligent management based on big data analysis, the method comprising:

[0006] Based on the cargo ship's registered displacement, unloading volume, energy type, storage time, storage method, and waste type, combined with port carbon emission control rules, historical carbon emission data of similar cargo ships, and port environmental adaptation parameters, a basis for allocating cargo ship carbon emission quotas is generated.

[0007] Based on the allocation criteria for carbon emission allowances for cargo ships, attribute correspondence conversion is performed on various registration information to determine the agreed allowance for carbon emissions on board the corresponding cargo ship, and the allocation criteria for carbon emission allowances for cargo ships and the agreed allowance for carbon emissions on board the port carbon emission big data platform are stored simultaneously.

[0008] Based on the detection equipment distributed at the port channel entrance, along the channel, in the wharf area and at key parts of the cargo ship, when the cargo ship is in the section from the port channel entrance to the port channel exit, the corresponding data of carbon emissions on board and the corresponding data of carbon emissions from port operations are collected in parallel.

[0009] Based on the carbon emission data corresponding to shipboard emissions and the carbon emission data corresponding to port operations, the data are classified and integrated by type to generate carbon emission data for shipboard emissions and carbon emission data for port operations. The data are then transmitted bidirectionally to the port scheduling system and the cargo ship terminal via an encrypted link.

[0010] The system compares the ship's carbon emissions data with the agreed-upon carbon emission limits in real time. The comparison results trigger corresponding control procedures. When the agreed-upon carbon emission limits are exceeded, a violation warning signal is generated and pushed to the cargo ship terminal. Simultaneously, port operation arrangements are adjusted based on the ship's carbon emission data.

[0011] Furthermore, embodiments of the present invention also provide a port carbon emission source tracking and intelligent management system based on big data analysis, comprising:

[0012] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned port carbon emission source tracking and intelligent management method based on big data analysis by executing the machine-executable instructions.

[0013] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the aforementioned port carbon emission source tracking and intelligent management method based on big data analysis.

[0014] Based on the above, from the perspective of individual cargo ships, this method comprehensively considers multi-dimensional information such as the ship's registered displacement, unloading volume, energy type, storage time, storage method, and waste type. Combined with port carbon emission control rules, historical carbon emission data of similar cargo ships, and port environmental adaptation parameters, it generates a basis for allocating carbon emission allowances for cargo ships. Based on this, it determines the agreed-upon carbon emission allowance for each ship, ensuring that each ship receives a carbon emission allowance that aligns with its actual situation. Cargo ships can clearly understand the reasonable range of their own carbon emissions based on this allowance, thus effectively managing their own carbon emission status. In terms of port scheduling and control, detection equipment distributed at port channel entrances, along the channel, in the wharf area, and at key parts of cargo ships is used. When a cargo ship is within the port channel entrance to exit section, corresponding carbon emission data from the ship and corresponding carbon emission data from port operations are collected in parallel. After classification and integration, this data is transmitted bidirectionally to the port scheduling system and the cargo ship terminal via an encrypted link, enabling the port scheduling center to obtain real-time carbon emission information from cargo ships and port operations. By comparing shipboard carbon emission data with agreed-upon emission limits in real time, the dispatch center can accurately trigger corresponding control procedures. When a cargo ship's carbon emissions exceed the agreed-upon limits, a violation warning signal is promptly generated and pushed to the cargo ship's terminal. Simultaneously, port operation arrangements are flexibly adjusted based on the ship's carbon emission data. This allows the dispatch center to achieve precise energy conservation and emission reduction control based on the individual carbon emission status of each cargo ship, effectively avoiding resource waste, maximizing the utilization of port resources, and significantly improving port efficiency. Therefore, this invention helps promote the comprehensive implementation of energy conservation and emission reduction by cargo ships and ports. Through precise carbon emission source tracking and intelligent control, it encourages cargo ships and ports to pay more attention to energy conservation and emission reduction during operation, reducing the environmental impact of carbon emissions. Attached Figure Description

[0015] Figure 1 is a schematic diagram of the execution flow of the port carbon emission source tracking and intelligent management method based on big data analysis provided in an embodiment of the present invention.

[0016] Figure 2 is a schematic diagram of exemplary hardware and software components of the port carbon emission source tracking and intelligent management system based on big data analysis provided in an embodiment of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 is a flowchart illustrating a port carbon emission source tracking and intelligent management method based on big data analysis provided in an embodiment of the present invention. The following is a detailed description of the port carbon emission source tracking and intelligent management method based on big data analysis.

[0018] Step S110: Based on the cargo ship's registered displacement, unloading volume, energy type, storage time, storage method, and waste type, combined with port carbon emission control rules, historical carbon emission data of similar cargo ships, and port environmental adaptation parameters, generate the basis for allocating cargo ship carbon emission quotas.

[0019] In this embodiment, we take the entry of a bulk carrier into a coastal port as an example. After the bulk carrier completes the registration procedures before entering the port, the port's carbon emission big data platform first needs to generate a basis for carbon emission quota allocation for it, which aims to determine the upper limit of its carbon emissions during its stay in the port based on the specific circumstances of the cargo ship and the relevant regulations of the port.

[0020] Step S111: Extract the following information from the cargo ship registration information: displacement, unloading volume, energy type, storage time, storage method, and waste type. Classify and categorize these information according to their carbon emission impact attributes to form a cargo ship registration information attribute set.

[0021] The following information is extracted from the bulk carrier's registration information: displacement, unloading volume, energy type, storage time, storage method, and waste type. For example, the registered displacement of the bulk carrier is a specific value; the unloading volume is determined by its cargo manifest as a certain quantity of cargo; the energy type is registered as marine heavy fuel oil; the planned storage time in port is several days; the storage method is open-air storage; and the expected waste types include domestic waste, a small amount of oily wastewater, and cargo packaging waste. After extraction, the information is categorized and grouped according to carbon emission impact attributes. Displacement, unloading volume, energy type, and waste type are grouped into the set of attributes that directly affect carbon emissions, while storage time and storage method are grouped into the set of attributes that indirectly affect carbon emissions, thus forming the cargo ship registration information attribute set.

[0022] Step S112: Retrieve the quota allocation benchmark clauses, corresponding pollutant emission standards, and adaptation requirements for different types of cargo ships from the port carbon emission control rules, and break them down into rule factors that can be matched with the cargo ship registration information attributes.

[0023] Relevant content was retrieved from the port carbon emission control rules database. The quota allocation benchmark clauses in the port carbon emission control rules stipulate the calculation methods for basic quotas for vessels of different tonnages. The pollutant emission standards clarify the emission concentration and total limits for various pollutants. Different types of cargo ships have different carbon emission control details for bulk carriers in terms of operational processes and energy consumption. This content was broken down; for example, the relationship between displacement and basic quotas in the quota allocation benchmark clauses was decomposed into a rule factor, and the emission coefficients corresponding to different energy types were decomposed into another rule factor. This allows each rule factor to be matched with one or more attributes in the cargo ship registration information attribute set.

[0024] Step S113: Based on the set of cargo ship registration information attributes and rule factors, perform one-to-one matching to establish a mapping relationship between each piece of registration information and the corresponding rule factor, forming a cargo ship registration information-control rule correspondence table.

[0025] The system matches each attribute in the cargo ship registration information attribute set with the derived rule factors one by one. For example, it matches the cargo ship's displacement attribute with the rule factors related to displacement in the quota allocation benchmark clause, and the energy type attribute with the emission coefficient rule factors related to that type of energy in the pollutant emission standards. Through this matching process, a corresponding rule factor is found for each piece of registration information, and a clear mapping relationship is established, ultimately forming a cargo ship registration information-control rule correspondence table. This table shows how each piece of cargo ship registration information is associated with specific clauses in the port carbon emission control rules.

[0026] Step S114: Obtain historical carbon emission data of similar cargo ships with the same type and registration information attribute combination as the cargo ship from the port carbon emission big data platform. The historical carbon emission data of similar cargo ships includes the historical onboard carbon emissions of similar cargo ships, the historical carbon emissions corresponding to the operations of similar cargo ships, and the historical quota matching data of similar cargo ships.

[0027] By combining cargo ship type identification (such as bulk carriers) and registration information attributes (such as similar displacement range, same energy type, similar storage methods, etc.), historical carbon emission data of similar cargo ships with similar conditions are screened from the port carbon emission big data platform. The above data covers the historical onboard carbon emissions of similar cargo ships under the same or similar operating conditions in the past, such as the carbon emissions generated by fuel consumption during the same storage time; the carbon emissions corresponding to the historical operations of similar cargo ships, such as the carbon emissions generated when port loading and unloading equipment serves them; and the historical credit matching data of similar cargo ships, that is, the carbon emission credits obtained by these similar cargo ships under the port environment and control rules at that time and the actual implementation status.

[0028] Step S115: Obtain port environment adaptation parameters, which include air diffusion conditions in the port area, distribution of ecologically sensitive areas, and carbon emission carrying capacity corresponding to the current port operating load.

[0029] Port environmental adaptability parameters are obtained through the port environmental monitoring system and operation scheduling system. Air diffusion conditions in the port area are determined based on long-term meteorological data and real-time meteorological observations provided by the local meteorological department, such as the annual average wind speed, wind direction frequency, and atmospheric stability of the port area. These indicators affect the port area's ability to diffuse and dilute carbon emissions. The distribution of ecologically sensitive areas refers to the location and extent of ecologically sensitive areas such as nature reserves, wetlands, and fishery resource protection areas within a certain range around the port. The carbon emission carrying capacity corresponding to the port's current operational load is calculated based on a comprehensive assessment of factors such as the number of vessels currently in port, the operating status of equipment at each wharf, and the expected vessel arrival schedule in the near future, reflecting the total carbon emissions the port can withstand under current conditions.

[0030] Step S116: Based on the historical carbon emission data of similar cargo ships, perform attribute correspondence analysis, extract the historical carbon emission change patterns corresponding to each item in the cargo ship registration information, and generate a carbon emission attribute correspondence model for similar cargo ships.

[0031] Attribute correspondence analysis was performed on the historical carbon emission data of similar cargo ships. For example, analyzing the relationship between the displacement of similar cargo ships and their historical carbon emissions revealed that larger displacement ships tend to have higher carbon emissions under the same operating conditions. Analysis of similar cargo ships using marine heavy fuel oil showed that their carbon emissions per unit of fuel consumption were relatively stable. Through these analyses, historical carbon emission variation patterns corresponding to various aspects of cargo ship registration information (displacement, energy type, etc.) were extracted, such as the carbon emission range corresponding to different displacement intervals and the carbon emission coefficients for different energy types. Based on these variation patterns, a mathematical model was constructed—the similar cargo ship carbon emission attribute correspondence model—which can output corresponding historical carbon emission prediction values ​​based on the input cargo ship registration information attributes.

[0032] Step S117: Input the cargo ship registration information-control rule correspondence table into the carbon emission attribute correspondence model of similar cargo ships, adjust the model output results in combination with port environment adaptation parameters, correct the quota allocation deviation, and generate the adjusted model output results.

[0033] The previously generated correspondence table of cargo ship registration information and control rules is input into the carbon emission attribute correspondence model for similar cargo ships. Based on the registration information and rule factors in the correspondence table, and combined with historical carbon emission variation patterns, the model initially outputs a predicted carbon emission allowance. Then, this predicted value is adjusted by incorporating port environmental adaptation parameters. For example, if the air diffusion conditions in the current port area are poor, or if ecologically sensitive areas are close to the terminal, the carbon emission allowance output by the model needs to be appropriately reduced to minimize the impact on the surrounding environment. Similarly, if the port's current operational load is high, and the carbon emission carrying capacity is close to its upper limit, the model output also needs to be adjusted downwards to correct for allowance allocation biases, thus generating the adjusted model output.

[0034] Step S118: Integrate the adjusted model output results, cargo ship registration information-control rule correspondence table, historical carbon emission data characteristics of similar cargo ships, and port environment adaptation parameters to form a multi-dimensional data-supported basic dataset for quota allocation.

[0035] The adjusted model output, the correspondence table between cargo ship registration information and control rules, features extracted from historical carbon emission data of similar cargo ships (such as average carbon emissions and carbon emission fluctuation range), and port environmental adaptation parameters are integrated. This data is then organized according to a logical structure to ensure interrelation and verification between different parts, forming a basic dataset for quota allocation. This basic dataset contains information on all key factors affecting cargo ship carbon emission quotas.

[0036] Step S119: Based on the basic dataset for quota allocation, perform attribute mapping and logical connection, remove data with connection conflicts, and generate a verified basic dataset for quota allocation.

[0037] The data in the basic dataset for quota allocation undergoes attribute correspondence and logical consistency checks. For example, this involves checking the accuracy of the correspondence between cargo ship registration information attributes and control rule factors, identifying logical contradictions between historical carbon emission data characteristics of similar cargo ships and the adjusted model output, and verifying whether the adjustment of port environment adaptation parameters to quotas is reasonable. If data inconsistencies are found, such as inconsistencies between the rule factor corresponding to a certain registration information item and the patterns reflected in historical data characteristics, the conflicting data is verified and removed. After this verification process, a verified basic dataset for quota allocation is generated, ensuring data consistency and reliability.

[0038] Step S1110: Based on the verified quota allocation dataset, determine the proportion of each data item in the quota allocation and generate a structured basis for the allocation of carbon emission quotas for cargo ships.

[0039] Based on the verified baseline dataset for carbon emission quota allocation, the influence percentage of each data point in the allocation is determined. For example, based on historical data and expert experience, a higher influence percentage is assigned to the energy type in the cargo ship registration information, as different energy types have significantly different carbon emission coefficients; a certain influence percentage is assigned to the distribution of ecologically sensitive areas in the port environmental adaptation parameters to reflect the importance attached to ecological protection. Based on the influence percentages of each data point, combined with the verified data values, a comprehensive calculation and analysis are performed to ultimately generate the basis for the allocation of carbon emission quotas for cargo ships. This basis lists how each factor affects the final carbon emission quota and how the specific quota values ​​are derived.

[0040] Step S120: Based on the carbon emission quota allocation criteria for cargo ships, perform attribute correspondence conversion on various registration information to determine the agreed quota for carbon emissions on board the cargo ship, and simultaneously store the carbon emission quota allocation criteria and the agreed quota for carbon emissions on board the port carbon emission big data platform.

[0041] After generating the basis for allocating carbon emission allowances for cargo ships, attribute conversions are performed on various items in the cargo ship registration information based on this basis. For example, based on the emission coefficient corresponding to the energy type in the allowance allocation basis, combined with the cargo ship's expected energy consumption, the carbon emission allowance corresponding to that energy type is calculated; based on the treatment method and emission factor corresponding to the waste type, the carbon emission allowance during the waste treatment process is calculated. The carbon emission allowances calculated from various registration information are accumulated and comprehensively adjusted to determine the agreed allowance for onboard carbon emissions for the bulk carrier. After determination, the carbon emission allowance allocation basis and the agreed allowance for onboard carbon emissions are synchronously stored in a designated database table of the port carbon emission big data platform through a data interface for subsequent querying, comparison, and management during cargo ship operations.

[0042] Step S130: Based on the detection equipment distributed at the port channel entrance, along the channel, in the wharf area and at key parts of the cargo ship, when the cargo ship is in the section from the port channel entrance to the port channel exit, collect the corresponding data of carbon emissions on board and the corresponding data of carbon emissions from port operations in parallel.

[0043] Upon the bulk carrier's entry into the port channel, this embodiment immediately activates various monitoring devices distributed at the port channel entrance, along the channel, in the wharf area, and at key parts of the cargo ship. These devices will collect carbon emission data from both the ship and port operations during the cargo ship's entire stay in port, from the channel entrance to the channel exit. The ship's carbon emission data primarily comes from monitoring devices at key parts of the cargo ship, while the port operation carbon emission data comes from various monitoring devices within the port area. Through this parallel data collection method, comprehensive and real-time information on the cargo ship's carbon emissions during its stay in port can be obtained.

[0044] Step S131: Capture the cargo ship's entry signal through the positioning and sensing equipment at the port channel entrance, and simultaneously trigger the start of the data collection process by various detection devices distributed in key parts of the cargo ship and the port area.

[0045] Positioning and sensing devices such as GPS receivers and video surveillance cameras are installed at the port channel entrance. When a bulk carrier enters the pre-set monitoring area at the port channel entrance, the GPS receiver receives the GPS signal sent by the carrier, and the video surveillance camera simultaneously captures the image information of the carrier. In this embodiment, by combining this information, it is determined that the carrier has entered the port channel entrance, and a carrier entry signal is generated. This carrier entry signal is transmitted to the port detection equipment control center via a wired network. The control center immediately sends start commands to various detection devices distributed at key parts of the carrier (such as chimney outlets, wastewater discharge outlets, waste storage areas, energy supply systems, etc.) and in the port area (such as along the channel and dock areas), triggering these detection devices to start the data collection process.

[0046] Step S132: Activate the exhaust gas detection equipment at the exhaust ship's chimney outlet, capture the carbon dioxide and carbon monoxide content data corresponding to carbon emissions in the exhaust gas at fixed collection intervals, simultaneously measure the exhaust gas volume flow rate, and record the corresponding information of the cargo ship's operating status at the time of collection, such as the ship's sailing speed, engine operating power, and fuel supply rate.

[0047] Exhaust gas detection equipment is installed at the exhaust outlet of the cargo ship's funnel. This equipment is activated upon receiving a start command. The built-in gas sensors detect the levels of carbon dioxide and carbon monoxide in the exhaust gas at fixed sampling intervals (e.g., every 10 seconds), obtaining data on gas composition. Simultaneously, the equipment's volumetric flow meter measures the exhaust gas volumetric flow rate in real time. During each data collection of gas composition and volumetric flow rate data, the equipment obtains information corresponding to the ship's operating status at the time of collection, such as its speed, engine power, and fuel supply rate, through data interfaces with the ship's navigation and engine control systems. This information is then timestamped to align with the gas composition and exhaust gas volumetric flow rate data to ensure data synchronization.

[0048] Step S133: Bind the gas composition data with the corresponding cargo ship operating status information at the time of collection to generate exhaust gas emission-operating status corresponding data unit, and label the collection equipment number and detection point information.

[0049] Each collected gas composition data (carbon dioxide and carbon monoxide content) is linked to the corresponding cargo ship operating status information (ship speed, engine power, fuel supply rate, etc.) at the time of collection. Using data association technology, data from different sources but with the same timestamp are combined to form a data unit containing gas composition, exhaust gas volumetric flow rate, and cargo ship operating status—a data unit corresponding to exhaust emissions and operating status. This data unit also includes the unique serial number of the exhaust gas detection equipment that collected the data and its specific detection point information on the cargo ship (e.g., left side of the chimney outlet, distance from the outlet, etc.) to facilitate subsequent data traceability and analysis.

[0050] Step S134: Sort all exhaust gas emission-operation status corresponding data units according to the collection time order to form a time-series exhaust gas emission corresponding dataset, add labels for occasional interruption cases, and combine with subsequent collected data to connect trends.

[0051] All generated exhaust emission-operating status data units are sorted according to the order of their acquisition time. For example, starting with the first data unit acquired after the cargo ship enters the channel entrance, each subsequent data unit is arranged sequentially, forming a time-series exhaust emission dataset organized along a time axis. During data acquisition, if data acquisition is interrupted due to temporary equipment malfunctions or signal interference, this embodiment will supplement the dataset with a marker indicating the interrupted period, the cause of the interruption, and its duration. After the interruption ends, in subsequent data acquisition, this embodiment will combine the data trends before the interruption with the initial data after the interruption, using interpolation or trend analysis to reasonably connect the trends of the interrupted period, ensuring good continuity throughout the entire exhaust emission dataset.

[0052] Step S135: Activate the flow detection equipment at the wastewater discharge outlet on the cargo ship deck to collect wastewater discharge velocity data in real time, calculate the wastewater discharge volume per unit time by combining the discharge pipeline specifications, record the discharge period and discharge status, and generate a corresponding dataset for wastewater discharge.

[0053] A flow detection device is installed at the wastewater discharge outlet on the cargo ship deck. This device activates upon receiving a start command. The device's built-in velocity sensor collects real-time flow velocity data of the discharged wastewater. Simultaneously, this embodiment retrieves the specifications (such as pipe inner diameter and cross-sectional area) of the wastewater discharge pipe from a database. Based on the flow velocity data and pipe cross-sectional area, the wastewater discharge volume per unit time is calculated using the formula (wastewater discharge volume per unit time = flow velocity × pipe cross-sectional area). During the data collection process, the start and end times (i.e., discharge periods) of each wastewater discharge, as well as the discharge status (e.g., continuous discharge, intermittent discharge), are recorded. The data on wastewater discharge volume per unit time, discharge periods, and discharge status are then organized chronologically to generate a corresponding wastewater discharge dataset.

[0054] Step S136: Run the weight detection equipment in the cargo ship's waste storage area to monitor the weight of domestic waste and fuel waste in real time, record the weight changes before and after each disposal, and generate a waste weight change dataset.

[0055] The cargo ship's waste storage area is divided into a domestic waste storage area and a fuel waste storage area, each equipped with weight detection equipment. Upon the cargo ship's entry into the port channel, these weight detection devices are activated and undergo zeroing and accuracy calibration, entering real-time monitoring mode. The equipment collects the total weight data of the corresponding storage area in real time, recording instantaneous weight values ​​at fixed time intervals (e.g., once per minute), generating continuous weight time-series data, and labeling the storage area type (domestic waste or fuel waste) and monitoring time. When waste is disposed of in a storage area causing a weight change, the equipment records the start and end times of the weight change, captures the stable weight values ​​before and after the change, calculates the difference between the two stable weight values ​​as the weight of a single disposal, and labels the disposal time, waste type, and corresponding cargo ship operation (e.g., crew domestic waste disposal, equipment maintenance disposal, etc.), generating a single waste disposal weight data unit. All single waste disposal weight data units are categorized and aggregated according to waste type, and combined with the weight time-series data to generate a waste weight change dataset.

[0056] Step S1361: After the cargo ship enters the port channel entrance, the weight detection equipment installed in the domestic waste storage area and the fuel waste storage area is activated simultaneously to complete the zeroing and accuracy calibration of the equipment and enter the real-time monitoring state.

[0057] Once the bulk carrier enters the port channel entrance, this embodiment sends a start command to the weight detection equipment installed in the municipal solid waste storage area and the fuel waste storage area. Upon receiving the command, the equipment first performs a zeroing operation, clearing previous weight data records and setting the current weight display to zero. Then, it performs accuracy calibration, adjusting the measurement accuracy using a built-in calibration program or by using standard weights to ensure the measurement error is within acceptable limits. After calibration, the equipment enters real-time monitoring mode and begins continuously collecting weight data from the storage areas.

[0058] Step S1362: The weight detection device collects the total weight data of the corresponding storage area in real time, records the instantaneous weight value at fixed time intervals, generates continuous weight time series data, and marks the storage area type and monitoring time.

[0059] In real-time monitoring mode, the sensors of the weight detection equipment continuously sense the total weight of the storage area and automatically record the instantaneous weight value at fixed time intervals (e.g., every 30 seconds). These instantaneous weight values ​​are arranged in chronological order to form continuous weight time-series data. Each weight time-series data entry clearly indicates the corresponding storage area type (whether it is a municipal solid waste storage area or a fuel waste storage area) and the specific monitoring time, so as to facilitate subsequent analysis and processing of weight changes for different types of waste.

[0060] Step S1363: When a weight change event in the storage area is detected, record the start and end times of the weight change, capture the stable weight values ​​before and after the change, and calculate the difference between the two stable weight values ​​as the single delivery weight.

[0061] The weight detection device has a built-in weight change detection algorithm. When it detects a significant change in the weight value of the storage area within a short period of time (exceeding a preset weight change threshold), it determines that a weight change event has occurred. At this time, the device immediately records the start time (the moment when the weight begins to change significantly) and the end time (the moment when the weight change tends to stabilize). Before the start time, the device captures the stable weight value of the storage area (i.e., the stable weight value before disposal); after the end time, it captures the value after the weight stabilizes again (i.e., the stable weight value after disposal). The difference between the stable weight value after disposal and the stable weight value before disposal is calculated, and this difference is the weight of the waste disposed of in a single disposal.

[0062] Step S1364: Identify the weight of a single waste disposal, marking the disposal time, waste type, and corresponding cargo ship operation stage, and generate a single waste disposal weight data unit.

[0063] The equipment provides detailed information about each calculated single disposal weight. This includes the specific disposal time (accurate to the second), the type of waste (domestic waste or fuel waste) determined based on the storage area type, and the corresponding operational stage of the cargo ship (e.g., domestic waste disposal after crew meals, fuel waste disposal after main engine maintenance, etc.) determined by combining this information with the single disposal weight to generate a single waste disposal weight data unit containing information such as disposal weight, disposal time, waste type, and operational stage.

[0064] Step S1365: Classify and aggregate all single waste disposal weight data units according to waste type to form a household waste disposal weight dataset and a fuel waste disposal weight dataset, maintaining consistency in chronological order.

[0065] This embodiment scans and classifies all generated single-time waste disposal weight data units. Based on the waste type identifier in the data units, disposal data units belonging to household waste are grouped into one category, forming a household waste disposal weight dataset; disposal data units belonging to fuel waste are grouped into another category, forming a fuel waste disposal weight dataset. During the classification and collection process, the data units within each dataset are arranged in chronological order of disposal time, i.e., maintaining a consistent temporal order, to facilitate subsequent analysis of the disposal patterns of different types of waste at different time periods.

[0066] Step S1366: Based on the data sets of domestic waste and fuel waste, extract the total amount and frequency of waste disposal for each time period, and generate corresponding data on waste disposal and operation progress by combining the data sets of cargo ship operation progress.

[0067] Data is extracted and statistically analyzed from datasets of municipal solid waste and fuel waste disposal at regular time intervals (e.g., hourly). For each time period, the total amount of municipal solid waste and fuel waste disposed of (by summing the weights of all individual disposals within that period) and the disposal frequency (the number of disposals within that period) are calculated. Simultaneously, the operational progress information of the bulk carrier (such as the start time of loading / unloading operations, estimated end time, and division of different operational stages) is obtained from the cargo ship operation scheduling system. The total amount of waste disposed of and the disposal frequency for each time period are correlated with the corresponding cargo ship operation progress information to generate data corresponding to waste disposal and operational progress, thereby analyzing the relationship between waste disposal and cargo ship operational activities.

[0068] Step S1367: Integrate the time-series data of waste weight, the data unit of single waste disposal weight, and the data corresponding to waste disposal and operation progress to form an initial waste weight change dataset.

[0069] The previously generated time-series waste weight data (including continuous weight records from the municipal solid waste and fuel waste storage areas), all single waste disposal weight data units (categorized by type), and data corresponding to waste disposal and operational progress are integrated. This data is then linked along a time dimension, allowing users to view the real-time weight of the storage areas at a specific point in time or over a given period, as well as the waste disposal situation and corresponding operational progress during that time. This integration process creates an initial waste weight change dataset, which contains detailed information on waste weight changes over time, along with relevant auxiliary data.

[0070] Step S1368: Combine the initial waste weight change dataset with the waste type information registered on the cargo ship to verify the consistency between waste type and registration information, and generate verification data corresponding to waste weight and registration type.

[0071] Waste type information is retrieved from the cargo ship registration information, which records the types of waste the ship is expected to generate. The waste types (domestic waste and fuel waste) recorded in the initial waste weight change dataset are compared with the waste type information registered with the cargo ship. For example, if the cargo ship's registered waste types do not include fuel waste, but the initial dataset shows records of fuel waste disposal, this indicates an inconsistency. Through this verification process, waste weight and registered type correspondence verification data is generated. This data records the degree of match between the actual disposal of each waste type and the registered information.

[0072] Step S1369: Based on the verification data of the correspondence between waste weight and registered type, supplement the annotation of the carbon emission corresponding attribute of each type of waste, update the initial waste weight change dataset, and generate a complete waste weight change dataset.

[0073] Based on the verification data corresponding to the waste weight and registered type, for waste types that pass verification, the corresponding carbon emission attributes (such as the carbon emissions per unit weight of waste during processing, the degradation characteristics of the waste, etc.) are retrieved from the carbon emission factor database for each type of waste. These carbon emission attributes are then added to the corresponding data units of the initial waste weight change dataset. For example, the carbon emission factor for household waste is labeled in the household waste weight data unit, and the carbon emission factor for fuel waste is labeled in the fuel waste weight data unit. Waste types found to be inconsistent with the registered information during verification are specially marked and recorded. After these updates, the initial waste weight change dataset is improved, generating a complete waste weight change dataset.

[0074] Step S137: Turn on the energy consumption detection equipment of the cargo ship's energy supply system, collect the fuel consumption and electricity consumption on board, record the equipment operation type corresponding to the fuel consumption period and electricity consumption, and generate a dataset corresponding to the energy consumption on board.

[0075] The cargo ship's energy supply system includes a fuel oil supply system and an electrical supply system. Consumption monitoring equipment is installed at key nodes of these systems. This equipment is activated when the monitoring process begins. The fuel oil consumption monitoring equipment collects real-time data on fuel oil consumption using flow meters installed in fuel oil lines or level sensors in fuel tanks. The electrical consumption monitoring equipment collects data on the electrical consumption of various equipment on board using meters installed on the main switchboard and various branch power distribution units. During data collection, the start and end times of fuel oil consumption (fuel oil consumption periods) and the corresponding equipment operating types (e.g., main engine operation, auxiliary engine operation, deck machinery operation, lighting equipment operation, etc.) are recorded simultaneously. The data on fuel oil consumption, electrical consumption, fuel oil consumption periods, and equipment operating types are then organized chronologically to generate a dataset corresponding to the ship's energy consumption.

[0076] Step S138: Activate the energy consumption detection equipment in the dock area, collect the fuel consumption of the loading and unloading equipment serving the cargo ship and the electricity consumption provided by the dock power supply system to the cargo ship's operating area, and generate a corresponding dataset of dock energy consumption.

[0077] Fuel consumption monitoring equipment is installed on loading and unloading equipment (such as gantry cranes and container spreaders) serving cargo ships in the wharf area. Power consumption monitoring equipment is installed on the lines supplying power to the cargo ship's operating area (such as the power interface after the ship berths). When the bulk carrier docks and begins operations, the wharf's energy consumption monitoring equipment is activated. The fuel consumption monitoring equipment collects data on the fuel consumption of the loading and unloading equipment during operations, while the power consumption monitoring equipment collects data on the power consumption provided by the wharf's power supply system to the cargo ship's operating area. This data is then categorized and organized according to operating time and equipment type to generate a corresponding dataset for wharf energy consumption.

[0078] Step S139: Activate the exhaust gas detection equipment along the port channel and in the wharf area to collect data on the carbon emission composition and concentration of exhaust gas from port loading and unloading equipment and transport vehicles corresponding to cargo ship operations, and generate a corresponding dataset of port exhaust gas emissions.

[0079] Multiple fixed exhaust gas monitoring devices are installed along the port's waterways and at the docks. These devices are activated after cargo ships enter the port. The built-in gas sensors on these devices collect real-time data on the composition and concentration of carbon-emission gases (such as carbon dioxide, carbon monoxide, and methane) in the surrounding air. The data collection area covers the operating areas of port loading and unloading equipment (such as gantry cranes and forklifts) and the travel routes of transport vehicles (such as container trucks and trailers). The collected gas composition and concentration data are recorded sequentially according to the location and time of the monitoring devices, generating a port exhaust gas emission dataset that reflects the exhaust gas emissions generated by port operations.

[0080] Step S1310: Integrate the datasets corresponding to exhaust gas emissions, wastewater emissions, waste weight changes, shipboard energy consumption, dock energy consumption, and port exhaust gas emissions to form parallel-collected shipboard carbon emission data and port operation carbon emission data.

[0081] The exhaust gas emission dataset, wastewater discharge dataset, waste weight change dataset, and shipboard energy consumption dataset generated in the above steps are integrated to form shipboard carbon emission data, as these data all originate from the cargo ship's own emissions and consumption. The dock energy consumption dataset and port exhaust gas emission dataset are then integrated to form port operation carbon emission data, which comes from port operations serving cargo ships. During the integration process, it is ensured that all datasets have a consistent time range (the time period from when the cargo ship is in the port channel entrance to exit), and the data is formatted and redundant data is removed, ultimately forming parallel-collected shipboard carbon emission data and port operation carbon emission data.

[0082] Step S140: Based on the carbon emission data corresponding to the shipboard carbon emissions and the carbon emission data corresponding to the port operations, classify and integrate them by type to generate carbon emission data for the shipboard carbon emissions and carbon emission data for the operations carbon emissions, and transmit them bidirectionally to the port scheduling system and the cargo ship terminal through an encrypted link.

[0083] After acquiring carbon emission data corresponding to shipboard emissions and port operations emissions, the data is subdivided and categorized for integration based on data type. Shipboard carbon emission data is further divided into five categories: exhaust gas, wastewater, waste, fuel oil, and electricity, with carbon emissions calculated for each category. Similarly, port operation carbon emission data is subdivided into three categories: loading / unloading, transportation, and warehousing, with carbon emissions calculated for each category as well. This integration generates shipboard carbon emission data and operational carbon emission data, which are then transmitted bidirectionally to the port scheduling system and cargo ship terminals via an encrypted link, ensuring data security and integrity during transmission.

[0084] Step S141: Retrieve the carbon emission data corresponding to shipboard emissions and port operations collected in parallel, and subdivide them according to data type. The shipboard data is divided into five categories: exhaust gas, wastewater, garbage, fuel oil, and electricity. The operation data is divided into three categories: loading and unloading, transportation, and warehousing.

[0085] This embodiment retrieves parallel-collected shipboard carbon emission data and port operation carbon emission data from the data buffer. The shipboard carbon emission data is analyzed and categorized into five types based on its source and nature: exhaust gas data (from the exhaust gas emission dataset), wastewater data (from the wastewater emission dataset), waste weight change data (from the waste weight change dataset), fuel oil data (from the fuel oil consumption data in the shipboard energy consumption dataset), and electricity consumption data (from the electricity consumption data in the shipboard energy consumption dataset). A similar analysis is performed on the port operation carbon emission data, categorized by operational stage: loading and unloading data (from the energy consumption data of loading and unloading equipment in the terminal energy consumption dataset and the exhaust gas emission data of loading and unloading equipment in the port exhaust gas emission dataset), transportation data (from the exhaust gas emission data of transport vehicles in the port exhaust gas emission dataset), and warehousing data (from the energy consumption data of warehousing equipment in the terminal energy consumption dataset, etc.).

[0086] Step S142: Perform conversion processing on each type of data. Combine the preset carbon emission conversion standard, calculate the exhaust gas emission mass by combining the exhaust gas component content and exhaust gas volume flow rate, and convert the wastewater volume, garbage weight, fuel consumption, and electricity consumption into corresponding carbon emission data.

[0087] For exhaust gas data, the exhaust gas emission mass is calculated based on the pre-defined carbon emission conversion standard's formula (exhaust gas emission mass = exhaust gas component content × exhaust gas volumetric flow rate). This is combined with the exhaust gas component content and volumetric flow rate data from the corresponding dataset. Then, based on the carbon emission factors of each carbon emission gas component in the exhaust gas, the corresponding carbon emission data is converted. For wastewater data, the wastewater volume data from the corresponding dataset is converted to corresponding carbon emission data based on the pre-defined carbon emission conversion standard's relationship between wastewater volume and carbon emissions (e.g., carbon emissions generated by pollutant degradation per unit volume of wastewater). For waste data, the waste weight data from the waste weight change dataset is converted to corresponding carbon emission data based on the pre-defined carbon emission conversion standard's carbon emission factors for different types of waste (carbon emissions generated per unit weight of waste). For fuel-related data, the fuel consumption data in the ship's energy consumption dataset is converted to corresponding carbon emission data based on the pre-defined carbon emission conversion standard's relationship between fuel consumption and carbon emissions (e.g., carbon emissions per unit volume or unit mass of fuel combustion). For electricity-related data, the electricity consumption data in the ship's energy consumption dataset is converted to corresponding carbon emission data based on the pre-defined carbon emission conversion standard's relationship between electricity consumption and carbon emissions (e.g., the carbon emission coefficient per unit kilowatt-hour of electricity, which considers factors such as power generation methods). A similar method is used for the loading / unloading, transportation, and warehousing data in port operations carbon emission data, converting them according to their respective carbon emission conversion standards to obtain the corresponding carbon emission data.

[0088] Step S143: The converted carbon emission data of various types on board are summed to generate carbon emission data on board. The proportion of each subdivided carbon emission and the conversion basis are marked to form a structured carbon emission dataset on board.

[0089] The converted carbon emission data for five categories—exhaust gas, wastewater, waste, fuel oil, and electricity—are summed to obtain the total carbon emissions of the bulk carrier while it is in port. Simultaneously, the proportion of each type of carbon emission to the total is calculated (the percentage of each sub-category of carbon emissions), such as the proportion of exhaust gas carbon emissions and fuel oil carbon emissions. The conversion basis for each type of carbon emission is also noted, such as the source of the carbon emission factor used and the standard number referenced in the conversion formula. The total carbon emission data, the sub-categories of carbon emissions, the proportions, and the conversion basis are organized according to a specific structure to form a structured shipboard carbon emission dataset. This structured shipboard carbon emission dataset typically exists in tabular or hierarchical data structure form.

[0090] Step S144: Accumulate the converted carbon emission data of various port operations to generate operation carbon emission data, and label the corresponding operation links, equipment types and conversion basis to form a structured operation carbon emission dataset.

[0091] The converted carbon emission data for port operations—loading / unloading, transportation, and warehousing—are summed to obtain the total carbon emission data for port operations serving the bulk carrier. For each type of operation carbon emission data, the corresponding operational stage (e.g., loading or unloading in the loading / unloading stage), the specific equipment type (e.g., gantry cranes in the loading / unloading stage, container trucks in the transportation stage), and the carbon emission conversion standards and parameters used in the conversion process (e.g., carbon emission factors of equipment, conversion coefficients between energy consumption and carbon emissions, etc.) are labeled. The total operational carbon emission data, the carbon emission data of each subdivided operation, the operational stage, the equipment type, and the conversion basis are organized into a structured operational carbon emission dataset, similar in structure to the structured shipboard carbon emission dataset, facilitating subsequent data processing and analysis.

[0092] Step S145: Perform time-series alignment on the structured shipboard carbon emission dataset and the structured operational carbon emission dataset to match the shipboard carbon emission data and operational carbon emission data for the same time period, thereby generating a time-series carbon emission dataset.

[0093] The timestamp information of the data from the structured shipboard carbon emissions dataset and the structured operational carbon emissions dataset is extracted. Using the port's standard clock time as a reference, the data from both datasets are arranged chronologically. For shipboard carbon emissions data and operational carbon emissions data with the same or similar timestamps, a correspondence is established to ensure that shipboard carbon emissions and port operational carbon emissions within the same time period (e.g., within a specific hour or operational procedure) can be matched. Through this time-series alignment process, the two structured datasets are integrated into a time-series carbon emissions dataset containing a time dimension. Each data record in this time-series carbon emissions dataset contains shipboard carbon emissions data, operational carbon emissions data, and related auxiliary information for the corresponding time period.

[0094] Step S146: Encrypt the time-series carbon emission data set using a dual approach of port binding encryption and data transmission encryption.

[0095] To ensure the security of the time-series carbon emission dataset during transmission and prevent data leakage or tampering, encryption is required. The encryption process employs a dual approach: first, port binding encryption is performed. In this embodiment, a binding relationship is established between the network ports of the data sender and receiver, allowing data transmission only between authorized ports; unauthorized connections are rejected. Second, data transmission encryption is performed using symmetric encryption algorithms (such as AES) or asymmetric encryption algorithms (such as RSA) to encrypt the content of the time-series carbon emission dataset, converting the original data into encrypted ciphertext. During encryption, an encryption key is generated and transmitted to the receiver through a secure key distribution mechanism to ensure the receiver can correctly decrypt the data.

[0096] Step S147: Establish an encrypted communication link, connecting one end to the port scheduling system data receiving module and the other end to the cargo ship terminal information receiving unit to maintain continuous operation of the transmission channel.

[0097] An encrypted communication link is established between the port data center server and the cargo ship terminal equipment using network configuration tools. One end of the link is configured as a data receiving module connecting to the port scheduling system, responsible for listening to a designated encrypted port and receiving encrypted data. The other end is configured as an information receiving unit connecting to the cargo ship terminal, which is also configured with a corresponding encrypted port and decryption program. During link establishment, network parameters (such as IP address, port number, and transmission protocol) and encryption parameters (such as encryption algorithm type and key length) are negotiated. After the link is established, a heartbeat detection mechanism periodically sends detection data packets to monitor the link's connectivity, ensuring continuous and stable operation of the transmission channel. If a link interruption is detected, a reconnection mechanism is immediately triggered.

[0098] Step S148: Transmit the time-series carbon emission data set synchronously to the port scheduling system and cargo ship terminal through an encrypted link, and record the transmission time, data integrity mark and reception status feedback.

[0099] After encryption, this embodiment initiates the data transmission procedure, synchronously transmitting the time-series carbon emission data set to the port scheduling system and cargo ship terminal via the established encrypted communication link. During transmission, the start and end times of data transmission are recorded (i.e., transmission time). To ensure no data loss or corruption during transmission, a data integrity marker (such as a CRC checksum or hash value) is generated for each data block. Upon receiving the data, the port scheduling system and cargo ship terminal verify the data integrity and provide feedback on the reception status to the sender (e.g., successful reception, reception failure, incomplete data, etc.). The sender records this reception status feedback information for subsequent data retransmission or error handling.

[0100] Step S149: After receiving the time-series carbon emission data set, the port scheduling system classifies and stores it according to the cargo ship identifier and time period, and synchronously triggers the data statistics process to generate a real-time carbon emission statistics report.

[0101] After receiving the time-series carbon emission data set, the data receiving module of the port dispatching system first decrypts the data, using a previously negotiated key to restore the encrypted data to the original time-series carbon emission data. Then, based on the ship identification information (such as ship name, IMO number, etc.) and time period information contained in the data, the data is stored in the corresponding database table of the port carbon emission big data platform, achieving classified storage by ship and time period. After storage, this embodiment automatically triggers the data statistics process, calling the preset statistical analysis algorithm to process the received data, calculate the cumulative carbon emissions of the bulk carrier in different time periods, carbon emissions per unit of operation, and the proportion of various carbon emission sources, and display these indicators in the form of reports, generating real-time carbon emission statistics reports for port management personnel to view and make decisions.

[0102] Step S1410: After receiving the time-series carbon emission data set, the cargo ship terminal decodes and displays the ship's carbon emission data, operational carbon emission data and corresponding statistical information in a preset format, and stores the received data synchronously.

[0103] After receiving the time-series carbon emission data set, the information receiving unit of the cargo ship terminal performs a decryption operation similar to that of the port scheduling system, decoding the encrypted data into the raw data. Then, according to the cargo ship terminal's preset display format (such as charts, lists, numerical values, etc.), it displays the ship's carbon emission data, operational carbon emission data, and statistical information extracted from the data (such as real-time emissions, cumulative emissions, and differences from agreed-upon limits) on the terminal's screen, allowing the crew to easily monitor the cargo ship's carbon emission status in real time. Simultaneously, the cargo ship terminal stores the received time-series carbon emission data set on local storage devices (such as hard drives, memory cards, etc.) for later review and data backup.

[0104] Step S150: Compare the ship's carbon emission data with the agreed-upon carbon emission limit in real time, and trigger the corresponding control process based on the comparison results. If the agreed-upon carbon emission limit is exceeded, a violation warning signal is generated and pushed to the cargo ship terminal. Simultaneously, adjust the port operation arrangements based on the ship's carbon emission data.

[0105] After receiving the ship's carbon emission data, the port dispatch system compares it in real time with the agreed-upon carbon emission allowance for that cargo ship, which is stored in the port's carbon emission big data platform. Based on the comparison results (such as whether the agreed-upon allowance is exceeded, and the percentage exceeding it), different control procedures are triggered. When the ship's carbon emission data exceeds the agreed-upon allowance, this embodiment immediately generates a violation warning signal and pushes it to the cargo ship's terminal via an encrypted communication link, reminding the crew to take emission reduction measures. Simultaneously, this embodiment combines real-time ship carbon emission data to analyze potential high-carbon emission links in the current port operation schedule and adjusts the port's operation plan, such as optimizing loading and unloading sequences and adjusting equipment operating parameters, to reduce overall carbon emissions.

[0106] Step S151: Extract shipboard carbon emission data from the classified and integrated carbon emission data, perform cumulative calculations according to the collection period, generate real-time cumulative shipboard carbon emission data, and label the corresponding cumulative period and data source.

[0107] From the categorized, integrated, and time-series processed carbon emission dataset, the data segment belonging to shipboard carbon emissions is selected. Shipboard carbon emissions are cumulatively calculated according to the data collection period (e.g., hourly, per shift). For example, the carbon emissions from exhaust gas, wastewater, waste, fuel oil, and electricity consumption within a specific hour are added together to obtain the cumulative shipboard carbon emissions for that hour. The cumulative results for each time period are arranged chronologically to generate real-time cumulative shipboard carbon emissions data. The data includes annotations for the corresponding cumulative time period (e.g., 10:00-11:00 on May 20, 2024) and the specific source of the data (e.g., which detection equipment and dataset it came from) to ensure data traceability.

[0108] Step S152: Retrieve the agreed-upon carbon emission allowance for the cargo ship from the port carbon emission big data platform, and determine the applicable time period, calculation basis, and corresponding control requirements for the agreed-upon carbon emission allowance.

[0109] The port dispatch system queries the port's carbon emission big data platform database using the ship's identification information (such as IMO number) to retrieve the agreed-upon carbon emission allowance data for that bulk carrier. This agreed-upon allowance data includes the specific value of the allowance, the applicable period (i.e., the time period during which the allowance is valid, usually while the ship is in port), the calculation basis (i.e., the ship registration information, port control rules, historical data, environmental parameters, etc. on which the allowance is generated), and the corresponding control requirements (such as handling measures and warning thresholds for exceeding the allowance).

[0110] Step S153: Based on the real-time cumulative shipboard carbon emissions data and the agreed-upon shipboard carbon emissions quota, compare the data time-by-time, calculate the difference between the two and the corresponding percentage of the difference, and generate comparison result data.

[0111] The cumulative value of shipboard carbon emissions for each time period in the real-time cumulative data is compared with the agreed-upon carbon emission allowance for each time period. For each time period, the difference between the real-time cumulative shipboard carbon emissions and the agreed-upon allowance is calculated (real-time cumulative value - agreed-upon allowance). If the difference is positive, it indicates that the carbon emissions for that time period exceed the allowance; if it is negative or zero, it indicates that they do not exceed the allowance. The percentage of the difference is also calculated (difference ÷ agreed-upon allowance × 100%) to reflect the degree of exceedance or non-exceedance. The comparison results for each time period (including the difference, percentage of the difference, and whether it exceeds the allowance) are organized into structured data to generate the comparison result data.

[0112] Step S154: Determine the carbon emission level based on the comparison results. When the real-time cumulative carbon emission data on board does not exceed the agreed limit for carbon emissions on board, execute the current port operation arrangement, generate normal carbon emission alert data, and send it to the cargo ship terminal.

[0113] This embodiment analyzes the comparative results data to determine the current carbon emission level of the bulk carrier. When the real-time cumulative carbon emissions data for all time periods do not exceed the agreed-upon carbon emission limit for the ship, or when the overall cumulative emissions do not exceed the agreed-upon limit, the carbon emission level is determined to be normal. At this time, the port scheduling system does not change the current port operation schedule and continues to perform loading, unloading, transportation, and warehousing operations according to the original plan. Simultaneously, a normal carbon emission alert is generated, which includes information such as the current cumulative carbon emissions, remaining allowance, and carbon emission trend. This alert is sent to the cargo ship terminal via an encrypted communication link to inform the crew that the current carbon emission situation is normal.

[0114] Step S155: When the real-time cumulative shipboard carbon emissions data exceeds the agreed-upon limit for shipboard carbon emissions, the carbon emission exceedance control process is triggered, extracting the exceedance period, exceedance difference, exceedance percentage, and corresponding carbon emission data to generate exceedance-related data.

[0115] If the comparison results show that the real-time cumulative shipboard carbon emissions exceed the agreed-upon limit (whether it's an exceedance in a single time period or an overall cumulative exceedance), this embodiment immediately triggers the carbon emission exceedance control process. In this process, this embodiment extracts detailed information related to the exceedance from the comparison results and the time-series carbon emission data set: the exceedance period (which period(s) exceeded the limit), the exceedance difference (the specific amount exceeded in each exceedance period), the exceedance percentage (the percentage exceeded in each exceedance period), and the corresponding carbon emission data for the exceedance period (such as detailed data on exhaust emissions, fuel consumption, etc.). This information is then integrated to generate exceedance-related data.

[0116] Step S156: Generate a violation warning signal based on the data related to exceeding the standard. The violation warning signal includes a description of the fact of exceeding the standard, the type of carbon emission corresponding to the exceeding standard, and the direction of emission reduction adjustment to be taken. The signal is structured according to a preset format.

[0117] A violation warning signal is generated based on the relevant data regarding exceeding the limits. The description of the exceeding facts clearly states the time period during which the bulk carrier's carbon emissions exceeded the agreed-upon limit, along with the specific amount and percentage exceeding the limit. The section on the type of carbon emissions exceeding the limit indicates which type or groups of carbon emissions caused the exceedance (e.g., excessive exhaust emissions, excessive fuel consumption, etc.). The section on the direction of emission reduction adjustments proposes some suggested emission reduction measures based on the type of exceedance and port operations (e.g., reducing main engine power, reducing the use of unnecessary electrical equipment, optimizing waste disposal methods, etc.). The above content is structured according to a preset format (e.g., XML, JSON, etc.) to ensure that the signal content is clear, concise, and easy to parse.

[0118] Step S157: Push the violation warning signal to the cargo ship terminal through the priority communication link, trigger the cargo ship terminal's audio and visual prompt function, and simultaneously highlight the core content of the violation warning signal on the terminal interface.

[0119] This embodiment can push the generated violation warning signal through a dedicated priority communication link. Upon receiving the violation warning signal, the cargo ship terminal immediately triggers its built-in audio-visual prompts, such as emitting an alarm sound at a specific frequency, while the indicator light on the terminal screen flashes red. Furthermore, the core content of the violation warning signal is prominently displayed on the cargo ship terminal's display interface (e.g., in red font, pop-up window, bold text), such as "Carbon emissions exceeded the limit during XX period, exceeding the allowance by XX tons; immediate emission reduction measures are recommended," ensuring that the crew can promptly notice and view the detailed information.

[0120] Step S158: Retrieve the current port operation plan for the cargo ship, including berth arrangements, unloading sequence, storage area allocation, equipment scheduling, and time nodes for each stage.

[0121] The port dispatching system retrieves the current port operation plan for the bulk carrier from the port operation plan database. This port operation plan includes detailed information: berth arrangement (which berth at which terminal the cargo ship will berth at), unloading sequence (the order and batches of cargo unloading), storage area allocation (which yard and area the unloaded cargo will be stored in), equipment scheduling (the specific loading and unloading equipment, transport vehicles, etc. serving the cargo ship), and time nodes for each stage (such as the estimated start time of loading and unloading, the estimated completion time of unloading, and the estimated departure time).

[0122] Step S159: Adjust the port operation plan based on the data related to exceeding the standard, suspend non-urgent operations, re-plan the loading and unloading sequence and operation time, optimize the operating parameters of the operating equipment to reduce the corresponding carbon emissions, and generate the adjusted port operation plan.

[0123] Based on the data indicating the periods and types of carbon emissions exceeding emission standards, this analysis examines which operational steps in the current port operation plan may have contributed to the excessive carbon emissions. For example, if the period of excessive emissions coincides with the full-load operation of a large loading / unloading machine, and fuel consumption of this machine is the primary source of carbon emissions, adjustments to the machine's operational schedule should be considered. First, non-urgent tasks in the current operation plan (such as auxiliary cargo handling and equipment maintenance) should be suspended to reduce unnecessary energy consumption. Then, the loading / unloading sequence should be replanned, shifting high-carbon-emission operations to periods with lower overall port carbon emission loads; the operation time should be rearranged, appropriately extending the duration of high-carbon-emission operations to reduce carbon emission intensity per unit time. Simultaneously, the operating parameters of the equipment should be optimized, such as reducing the operating speed of loading / unloading equipment, adjusting transport vehicle routes to reduce empty mileage, and rationally allocating storage areas to shorten cargo transport distances, thereby reducing carbon emissions at the corresponding stages. Based on these combined adjustments, a revised port operation plan is generated.

[0124] Step S1591: Extract the core information from the data related to exceeding the standard, determine the type of carbon emissions exceeding the standard, the time period of exceeding the standard and the corresponding port operation links, and delineate the scope of operations that need to be adjusted.

[0125] Core information was extracted from the data related to the exceedances. For example, by analyzing the carbon emission data corresponding to the periods of exceedance, it was determined that the main type of carbon emission exceedance was excessive fuel consumption. Further analysis of the port operation plan revealed that the port operation in progress during the period of exceedance was the unloading of bulk cargo from the bulk carrier by a gantry crane. Therefore, the scope of operations requiring adjustment was defined as the unloading operation of the bulk carrier, as well as the related equipment and transportation services supporting this unloading operation.

[0126] Step S1592: Retrieve all tasks in the current port operation plan of the cargo ship, classify them according to their urgency and carbon emission correspondence, and distinguish between urgent and non-urgent operation tasks.

[0127] All tasks in the current port operation plan for the bulk carrier are retrieved from the port operation plan database, including tasks such as main cargo unloading, cargo sampling and inspection, deck cleaning, spare parts replenishment, and fuel refueling. The urgency of each task is determined based on its impact on the vessel's departure time; for example, main cargo unloading directly affects departure time and is considered an urgent task, while deck cleaning and spare parts replenishment are relatively less urgent and are considered non-urgent tasks. Simultaneously, the carbon emission correlation is determined based on the carbon emissions generated during the execution of each task; for example, fuel refueling has higher carbon emissions and a higher carbon emission correlation, while cargo sampling and inspection has lower carbon emissions and a lower carbon emission correlation. Tasks are then categorized according to urgency and carbon emission correlation, clearly distinguishing between urgent and non-urgent tasks.

[0128] Step S1593: Send a non-urgent task suspension instruction to the terminal operation system, determine the name of the suspended task, the corresponding operation stage and the suspension duration, and simultaneously mark the suspension reason and the corresponding information for exceeding the limit.

[0129] Based on the classification results, the port dispatch system sends a suspension instruction for non-emergency operations to the terminal operations system. The instruction clearly lists the names of the tasks to be suspended (e.g., deck cleaning, spare parts replenishment), the corresponding operational stages (e.g., auxiliary operations during ship berthing), and the expected suspension duration (e.g., suspension until the main cargo unloading operation is adjusted). Simultaneously, the instruction also indicates the reason for the suspension (e.g., the bulk carrier's carbon emissions exceed standards, requiring priority for emission reduction measures) and the corresponding information about the emissions (e.g., the period and type of emissions exceeding standards), so that terminal operations system operators are aware of the background and necessity of the suspension.

[0130] Step S1594: Based on the current berth vacancy status, equipment load, and other cargo ship operation progress, and in conjunction with the type of excessive carbon emissions, replan the loading and unloading sequence and operation period of the cargo ship, and adjust carbon emission intensive operations to periods with lower equipment load.

[0131] This embodiment queries the current berth availability at the port to determine if other berths can be temporarily repurposed; it queries the load of operating equipment to understand the current workload of each loading and unloading device; and it queries the operational progress of other cargo ships to assess the overall operational pressure on the port. Given that the excessive carbon emissions correspond to excessive fuel consumption, which primarily originates from the unloading operations of gantry cranes, the originally planned continuous unloading of bulk cargo is divided into several time slots. The carbon-intensive, full-load unloading phases are rescheduled to periods with relatively lower gantry crane loads (such as breaks between other cargo ship operations or after equipment maintenance) to avoid concentrated carbon emissions caused by multiple high-energy-consuming devices operating at full capacity simultaneously.

[0132] Step S1595: Optimize the equipment operating parameters of the corresponding operation, adjust the operating speed of loading and unloading equipment, the travel path of transport vehicles, and the energy consumption parameters of storage equipment, reduce the carbon emissions corresponding to the operation, and generate an equipment parameter optimization plan.

[0133] For identified operational processes requiring adjustment (gantry crane unloading, transport vehicle transportation, etc.), optimize their equipment operating parameters. For gantry cranes, appropriately reduce their lifting speed and trolley travel speed to reduce fuel consumption per unit time while ensuring operational efficiency. For transport vehicles, replan driving routes based on the location of the cargo storage area to avoid congested sections and reduce idling time and empty mileage. For related equipment in the storage area (such as stackers and reclaimers), adjust their energy consumption parameters (such as motor operating frequency and hydraulic system pressure) to ensure they operate at low energy consumption. Compile the above parameter adjustments into an equipment parameter optimization plan, specifying the exact adjustment values ​​and implementation time for each piece of equipment.

[0134] Step S1596: Reset the time nodes of each operation, extend the time interval of carbon emission intensive operation, avoid carbon emission concentration, and generate an adjusted operation sequence table.

[0135] Based on the redesigned loading and unloading sequence, operation periods, and optimized equipment parameters, the time nodes for each operational phase of the bulk carrier were reset. For carbon-intensive operations (such as the period when the gantry crane is unloading at full load), the time intervals were appropriately extended. For example, the unloading of a batch of cargo originally planned to be completed within 1 hour was extended to 1.5 hours, thus reducing the intensity of operations per unit time and avoiding concentrated carbon emissions. The start time, estimated completion time, and duration of each operational phase were arranged in chronological order to generate an adjusted operation sequence table, which served as the basis for the new operation execution.

[0136] Step S1597: Integrate the adjusted loading and unloading sequence, the adjusted work sequence table, and the equipment parameter optimization scheme to form an updated draft work plan.

[0137] The revised loading and unloading sequence, the adjusted work schedule, and the optimized equipment parameter plan are integrated. This ensures the loading and unloading sequence aligns with the time arrangement in the work schedule, and that parameter adjustments in the optimized equipment parameter plan can be implemented within the corresponding time periods in the work schedule. During integration, conflicts and omissions are checked between the various components, such as the smoothness of workflow transitions and whether equipment parameter adjustments match the work periods. The resulting integrated draft of an updated work plan includes all adjustments.

[0138] Step S1598: Push the updated draft work plan to the terminal operation system, warehouse management system, and transportation scheduling system to obtain feedback from each system.

[0139] In this embodiment, the updated draft work plan is pushed to the terminal operations system, warehouse management system, and transportation scheduling system via an internal data interface. The terminal operations system provides feedback on the feasibility of adjusting the loading and unloading sequence and equipment parameters; the warehouse management system provides feedback on adjusting the allocation of warehouse areas and cargo storage time; and the transportation scheduling system provides feedback on adjusting the travel routes of transport vehicles and transportation periods. Each system returns its feedback to the port scheduling system within a specified time (e.g., within 30 minutes).

[0140] Step S1599: Fine-tune the revised draft of the operation plan based on feedback, generate the revised port operation plan, and push the revised port operation plan to the terminal operation system, warehouse management system and cargo ship terminal. Simultaneously update the port operation scheduling ledger and record the control measures for exceeding standards and the content of operation adjustments.

[0141] The port scheduling system summarizes and analyzes feedback from various systems. For reasonable feedback (such as the terminal operations system's report that a certain piece of equipment cannot operate stably under adjusted parameters), the updated draft operation plan is fine-tuned, such as appropriately modifying the equipment's operating parameters or adjusting the corresponding operation time slots. After fine-tuning, the final adjusted port operation plan is generated. This plan is then pushed back to the terminal operations system, warehouse management system, and cargo ship terminals to ensure that all implementing parties receive the latest operation arrangements. Simultaneously, the port operation scheduling log is updated, detailing the control measures taken for this carbon emission exceedance event (such as suspending non-urgent tasks, adjusting operation sequences, etc.) and the content of the operation adjustments (such as specific time node changes, equipment parameter changes, etc.) for subsequent inquiries and audits.

[0142] Step S1510: Push the adjusted port operation plan to the terminal operation system, warehouse management system and cargo ship terminal, and update the port operation scheduling ledger simultaneously, recording the control measures for exceeding standards and the content of operation adjustments.

[0143] The port dispatching system pushes the finalized, adjusted port operation plan to the terminal operations system, warehouse management system, and cargo ship terminal via data interfaces. The terminal operations system allocates loading and unloading equipment and personnel according to the new plan; the warehouse management system adjusts the storage areas and stacking methods for goods; and the cargo ship terminal displays the new plan to the crew, informing them of the changes in the operational arrangements. Simultaneously, the port dispatching system's operation scheduling ledger module automatically updates, recording key information from the adjusted port operation plan (such as time nodes for each stage and equipment allocation) in the ledger. It also specifically records the control measures taken due to excessive carbon emissions and the specific details of the operational adjustments, including comparisons before and after the adjustments, ensuring the entire process is traceable.

[0144] Step S210: A step to verify the accuracy of the data on changes in the weight of materials on board based on changes in draft.

[0145] In addition to the core steps mentioned above, the method also includes a step of verifying the accuracy of shipboard material weight change data based on changes in draft. This step aims to indirectly verify the accuracy of shipboard material (such as cargo, fuel oil, fresh water, garbage, etc.) weight change data by measuring changes in the ship's draft, preventing data distortion caused by equipment malfunction or improper human operation, thereby improving the reliability of carbon emission data.

[0146] For example, step S211: activate the visual recognition equipment installed at the entrance and exit of the port channel, and simultaneously start the draft depth detection equipment carried by the cargo ship to build a dual detection mechanism.

[0147] As the bulk carrier approaches the port channel entrance, the port control system sends activation commands to the visual recognition devices installed at the channel entrance and exit. These devices activate and preheat, adjusting parameters such as camera angle and focus to ensure a clear image of the carrier's waterline. Simultaneously, the Vessel Traffic Management System (VTS) sends a command to the bulk carrier to activate its onboard draft detection equipment (such as a pressure sensor-type draft gauge installed on the hull). This combination of port visual recognition and onboard equipment detection creates a dual detection mechanism, improving the accuracy and redundancy of draft data.

[0148] Step S212: When the cargo ship enters the port channel entrance, the initial draft depth data is collected simultaneously by visual recognition equipment and draft depth detection equipment, and the collection time and cargo ship position information are recorded.

[0149] When the bulk carrier enters the pre-designated detection area at the port channel entrance, the visual recognition equipment at the channel entrance continuously photographs the ship's hull, identifies the waterline position using image recognition algorithms, and calculates the initial draft depth data based on pre-defined hull parameters. Simultaneously, the ship's onboard draft depth detection equipment also collects and records the draft depth data in real time. This embodiment synchronizes the initial draft depth data collected by the two sets of equipment, records the precise time of collection and the ship's position information in the channel (obtained via GPS positioning), and stores the above data in association.

[0150] Step S213: During the cargo ship's docking operations, collect draft depth data synchronously at fixed time intervals, and combine it with the corresponding unloading volume data, wastewater discharge volume, garbage disposal volume and fuel consumption volume for the corresponding time period.

[0151] During the loading and unloading operations of the bulk carrier at the dock, visual recognition equipment at the port channel entrance and exit (or temporary visual recognition equipment added near the dock) and the ship's own draft depth detection equipment synchronously collect draft depth data at fixed time intervals (e.g., once per hour). After each collection, this embodiment retrieves the corresponding unloading volume data (weight of unloaded cargo), wastewater discharge data, garbage disposal data, and fuel consumption data from the port operation database and the ship's carbon emission corresponding dataset for the corresponding time period. This data is then correlated with the draft depth data collected during that time period to form a comprehensive dataset containing draft depth and changes in the weight of various materials.

[0152] Step S214: When the cargo ship leaves the port channel exit, collect the final draft data synchronously, combine it with the initial draft data to calculate the total draft change, and generate a time series dataset of draft change.

[0153] When the bulk carrier completes all operations and is about to depart from the port channel exit, the visual recognition equipment at the channel exit and the ship's own draft depth detection equipment synchronously collect the final draft depth data again, recording the collection time and the ship's position information. This embodiment compares the final draft depth data with the initial draft depth data collected upon entering the port to calculate the total draft change (initial draft depth - final draft depth; if the final draft depth is less than the initial draft, the change is positive, indicating a reduction in ship weight). Simultaneously, the draft depth data collected at fixed time intervals during the operations are arranged chronologically to generate a draft change time-series dataset, which reflects the change in the ship's draft over time while in port.

[0154] Step S215: Based on the cargo ship's registered displacement, unloading volume, and collected data on wastewater discharge, garbage disposal, and fuel consumption, calculate the theoretical draft change and determine the theoretical range of change.

[0155] According to the principles of ship statics, there is a certain relationship between the change in draft of a cargo ship and the change in the weight of the cargo on board. This embodiment obtains the ship's displacement data (such as full load displacement and lightship displacement) from its registration information, and combines this with actual unloading data during its port stay (total unloading weight), collected data on wastewater discharge (converted to weight), garbage disposal (total weight), and fuel consumption (total weight), etc., to calculate the theoretical change in draft using ship stability calculation software or simplified empirical formulas. Considering potential errors in the calculation process (such as differences in cargo density, the effect of ship trim, etc.), a reasonable theoretical range for the change is set (e.g., theoretical change ±5%).

[0156] Step S216: Compare the time series dataset of draft depth changes with the theoretical range of draft depth changes, analyze the difference in changes for each time period, and generate the draft depth change comparison results.

[0157] This embodiment compares the actual draft change (relative to the initial draft) at each acquisition time in the draft change time-series dataset with the theoretical draft change range time-by-time. The difference between the actual and theoretical change for each time period is calculated, and it is determined whether this difference is within the theoretical range. For example, if the theoretical draft change for a certain time period is X, and the theoretical range is X ± ΔX, and the actual change for that time period is Y, the difference between Y and X is calculated as D = YX. If |D| ≤ ΔX, then the change for that time period is within a reasonable range; otherwise, it exceeds the theoretical range. The comparison results for all time periods (whether within the range, the magnitude of the difference, etc.) are then compiled into a draft change comparison result.

[0158] Step S217: When the actual change difference exceeds the theoretical change range, combine the unloading volume data, wastewater discharge volume, garbage disposal volume and fuel consumption data of the corresponding time period to analyze the cause of the data anomaly and investigate the acquisition deviation of the detection equipment.

[0159] If the actual draft depth change difference exceeds the theoretical range in the comparison results, this embodiment initiates anomaly analysis. Detailed data such as unloading volume, wastewater discharge, garbage disposal, and fuel consumption for the corresponding abnormal period are retrieved to check for abrupt changes or unreasonable values. For example, if the actual draft depth change is significantly less than the theoretical change for a certain period, it may be due to an underestimation of the unloading volume or incomplete recording of garbage disposal data for that period. Simultaneously, the operation logs of the relevant detection equipment for that period are checked for equipment malfunctions, calibration anomalies, data transmission interruptions, etc., to determine if the data anomaly is caused by a bias in the detection equipment's data acquisition.

[0160] Step S218: Generate an accuracy verification report for weight-related emission data based on the investigation results, indicate the credibility of the weight-related emission data on board, and propose directions for re-collection or correction for abnormal data segments.

[0161] Based on the anomaly analysis and equipment inspection results, an accuracy verification report for weight-related emissions data is generated. The report details the actual, theoretical, and difference values ​​of draft depth changes for each time period, indicating whether they are within a reasonable range. It also identifies which data segments of the ship's weight-related emissions data (such as unloading volume, waste disposal volume, and fuel consumption) are highly reliable and which segments exhibit anomalies. For anomaly-prone data segments, recommendations for re-collection (e.g., requiring the cargo ship to remeasure fuel consumption for a specific time period) or data correction directions (e.g., adjusting the original data by inferring a reasonable unloading volume range based on draft depth changes) are proposed based on the investigation results.

[0162] Step S219: When it is verified that there is an abnormal deviation in the ship's weight-related emission data, a data abnormality warning signal is generated and pushed to the port scheduling system and the cargo ship terminal to trigger the data review process.

[0163] If the verification report confirms significant anomalies in the ship's weight-related emissions data (such as exceeding a preset error threshold) that cannot be resolved through simple correction, a data anomaly warning signal is generated. This warning signal includes the time range of the abnormal data segment, the type of emissions involved, the degree of deviation, and a summary of the verification report. The signal is pushed to the port dispatch system and the cargo ship terminal via an encrypted communication link. Upon receiving the warning signal, the port dispatch system automatically triggers a data review process, notifying relevant management personnel for manual intervention. Upon receiving the signal, the cargo ship terminal reminds the crew to cooperate in data review and cause investigation.

[0164] Step S2110: Store the draft depth change verification data, accuracy verification report and anomaly handling results in the port carbon emission big data platform and establish a correspondence with the emission data related to the ship's weight.

[0165] After completing the draft depth change verification, data accuracy analysis, and anomaly handling, this embodiment organizes and archives the draft depth change verification data (visual recognition data, shipboard detection data), accuracy verification report, and anomaly handling results (such as whether data was re-collected, corrected data, and records of manual intervention) generated throughout the process. This data is then stored in the corresponding database of the port carbon emission big data platform via a data interface. During storage, a clear correspondence is established between the above data and the shipboard weight-related emission data of the bulk carrier using the ship's identifier and timestamp, forming a complete audit trail to facilitate subsequent traceability and quality assessment of carbon emission data.

[0166] Step S310: The step of triggering a global control signal based on the port's total carbon emissions.

[0167] Furthermore, the method includes a step of triggering a global control signal based on the port's total carbon emissions. This step starts from the overall port level, by monitoring the port's total carbon emissions and comparing them with preset carrying capacity thresholds. When certain conditions are met, a global control signal is triggered to balance the port's total carbon emissions and ensure the port's sustainable operation and environmental safety.

[0168] For example, in step S311: the port scheduling system summarizes the carbon emission data of all cargo ships and the carbon emission data of port operations in real time, calculates them cumulatively by time period, and generates time-series data of the total carbon emissions of the port.

[0169] The port scheduling system obtains real-time carbon emission data (from time-series carbon emission data sets of each cargo ship) and port operation carbon emission data (from carbon emission data of terminals, storage yards, transportation, and other operational processes) from the port carbon emission big data platform via a data integration interface. In this embodiment, the above data is aggregated at fixed time intervals (e.g., every 15 minutes, every hour), adding the carbon emissions from all cargo ships to the carbon emissions from port operations to obtain the total port carbon emissions for that period. The total carbon emissions for different periods are then arranged chronologically to generate time-series data of the total port carbon emissions, reflecting the trend of port carbon emissions over time.

[0170] Step S312: Retrieve the port's carbon emission carrying capacity threshold, which is set based on environmental adaptation parameters, ecological protection requirements, and port operation capacity, and determine the upper limit and warning threshold of the port's carbon emission carrying capacity threshold.

[0171] The port's carbon emission carrying capacity threshold is retrieved from the port environmental management database. This threshold is set by comprehensively considering port environmental suitability parameters (such as air diffusion conditions and the environmental capacity of surrounding ecologically sensitive areas), local government-set ecological protection requirements (such as total carbon emission control targets), and the port's own operational capabilities (such as the carbon emission level corresponding to the maximum cargo handling capacity). The port's carbon emission carrying capacity threshold typically includes two important indicators: an upper limit and a warning threshold. The upper limit is the maximum carbon emission allowed by the port within a certain period (such as one day or one month); the warning threshold is usually set as a percentage of the upper limit (such as 80%) to provide early warning that the port's carbon emissions are approaching the carrying capacity limit.

[0172] Step S313: Compare the time series data of the total carbon emissions of the port with the warning threshold. When the cumulative emissions reach the warning threshold, execute the port carbon emission warning process and generate an entry prohibition warning signal. The entry prohibition warning signal includes the current total carbon emissions of the port, the warning threshold, the remaining carrying capacity and suggested adjustment measures, and pushes it to the terminal of the cargo ship that has been registered and is waiting to enter the port outside the port channel entrance.

[0173] This embodiment dynamically compares the real-time generated port total carbon emission time-series data with the warning threshold. When the cumulative carbon emissions of the port are detected to have reached the warning threshold, the port carbon emission warning process is immediately executed. In this process, this embodiment calculates the difference between the current total carbon emissions of the port and the warning threshold to determine the remaining carbon emission carrying capacity (upper limit - current cumulative value). Based on the current port operations and the registration information of cargo ships waiting to enter the port, suggested adjustment measures are generated (such as suggesting that subsequent cargo ships delay their entry into the port, or prioritizing the entry of low-emission cargo ships, etc.). The information such as the current total carbon emissions of the port, the warning threshold, the remaining carrying capacity, and the suggested adjustment measures are integrated to generate an entry prohibition warning signal, which is pushed to the terminals of all cargo ships that have registered at the port and are waiting to enter the channel entrance through the Vessel Traffic Management System (VTS) or maritime VHF communication.

[0174] Step S314: Simultaneously send early warning prompts to the port's internal operation system, optimize existing operation arrangements, adjust the execution sequence of carbon emission-intensive operations, and slow down the rate of emission growth.

[0175] While issuing warning signals for prohibited entry, the port dispatch system also sends warning messages to various operational systems within the port (such as the terminal operations system, warehouse management system, and transportation dispatch system). The messages state that the port's carbon emissions have reached the warning level and require each system to optimize its existing operational schedules. For example, the terminal operations system can adjust the execution timing of carbon-intensive operations (such as the full-load loading and unloading of large equipment) to be carried out at night or during other periods with relatively lower carbon emissions; the transportation dispatch system can optimize the scheduling of transport vehicles to reduce empty runs and congestion, thereby reducing carbon emissions during transportation; and the warehouse management system can rationally plan cargo storage to reduce the number of cargo transfers. Through these measures, the overall growth rate of the port's carbon emissions can be slowed down.

[0176] Step S315: Continuously monitor the time-series data changes of the port's total carbon emissions. When the cumulative emissions reach the upper limit of the port's carbon emission carrying capacity threshold, trigger the port carbon emission control upper limit process.

[0177] After implementing the early warning process, this embodiment continues to monitor the changing trend of the port's overall carbon emissions over time. If the port's overall carbon emissions continue to rise after the early warning measures are taken, and the cumulative emissions reach the upper limit of the port's carbon emission carrying capacity threshold, this embodiment immediately triggers the port carbon emission control upper limit process, aiming to quickly control the port's carbon emissions from continuing to increase.

[0178] Step S316: Generate an entry prohibition signal to inform cargo ships waiting to enter the port that they are temporarily prohibited from entering the port channel, indicate the expected time range for lifting the entry prohibition, and push the signal to the terminals of all registered cargo ships waiting to enter the port.

[0179] In the port carbon emission control ceiling process, this embodiment first generates an entry prohibition signal. This signal clearly informs all cargo ships registered at the port and waiting to enter the channel that they are temporarily prohibited from entering the port channel because the port's current carbon emissions have reached its carrying capacity limit. Simultaneously, based on the port's current operational progress, the projected carbon emission decline trend, and subsequent operational plans, this embodiment estimates the timeframe within which the port's carbon emissions may fall below the warning threshold and marks this projected lifting timeframe in the entry prohibition signal. Using the same data transmission method as when sending the entry prohibition warning signal, the entry prohibition signal is pushed to the terminals of all registered cargo ships waiting to enter the port.

[0180] Step S317: Suspend non-essential carbon-intensive operations within the port, prioritize emergency operations, reschedule the operation sequence and departure time of all cargo ships in port, and promote a reduction in carbon emissions.

[0181] To rapidly reduce overall port carbon emissions, this implementation plan sends instructions to all port operation systems, requiring the suspension of all non-essential carbon-intensive operations (such as non-urgent cargo handling and unloaded commissioning of large equipment). Emergency operations ensuring basic port operations and vessel safety (such as emergency ship repairs and hazardous materials unloading) are prioritized for execution to ensure operational safety. Simultaneously, the port scheduling system re-plans the operational sequence of all cargo ships in port, prioritizing those with lower carbon emissions or nearing completion of their operations to expedite their departure; the departure times of cargo ships in port are adjusted, with lower-emission ships departing earlier to free up port resources and reduce continuous carbon emissions from vessels in port, thereby gradually reducing overall port carbon emissions.

[0182] Step S318: Update the time series data of the port's total carbon emissions in real time. When the emissions drop below the warning threshold, generate a warning cancellation signal and push it to the terminal of the cargo ship waiting to enter the port and the port operation system.

[0183] Even after implementing the upper limit of control measures, this embodiment still monitors the changes in the time-series data of the port's total carbon emissions in real time. When the port's cumulative carbon emissions drop below the warning threshold, it indicates that the port's carbon emission pressure has been alleviated and it is ready to receive new cargo ships. At this time, an entry ban warning cancellation signal is generated. This signal includes the current total carbon emissions of the port, the fact that they have dropped below the warning threshold, and a notification to resume normal entry order. This entry ban warning cancellation signal is pushed to the terminals of all cargo ships waiting to enter the port, informing them that they can prepare to enter the port; at the same time, it is pushed to the port operation system, notifying the internal operation system that normal operation arrangements can be gradually resumed.

[0184] Step S319: When emissions drop to a safe level, generate a no-entry signal release signal, restore normal port operations and cargo ship entry order, record the overall control process and effect data, and update the port carbon emission big data platform simultaneously.

[0185] With adjustments to port operations and the departure of some cargo ships, overall port carbon emissions have continued to decline. When emissions drop to a lower, safer level (e.g., below 70% of the warning threshold), this embodiment generates an entry ban lifting signal. This signal officially announces the lifting of previous entry bans, fully restoring normal port operations and cargo ship arrival order. This embodiment records in detail all measures taken during this overall control process (e.g., entry ban warnings, entry bans, operational adjustments, etc.), the timelines for each stage, and the final carbon emission control effect data (e.g., the rate of emission reduction, the time to restore normal order, etc.). These records are synchronously updated to the port carbon emission big data platform as a reference for future optimization of port carbon emission control strategies.

[0186] Based on the same inventive concept, please refer to Figure 2, which shows a schematic block diagram of the structure of a port carbon emission source tracking and intelligent management system 100 based on big data analysis, provided in an embodiment of this application, for executing the above-described port carbon emission source tracking and intelligent management method based on big data analysis. The port carbon emission source tracking and intelligent management system 100 based on big data analysis may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0187] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the port carbon emission source tracking and intelligent management system 100 based on big data analytics, and are configured separately. However, it should be understood that the machine-readable storage medium 120 may also be independent of the port carbon emission source tracking and intelligent management system 100 based on big data analytics, and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may also be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0188] The processor 130 is the control center of the port carbon emission source tracking and intelligent management system 100 based on big data analysis. It connects various parts of the system via various interfaces and lines, and executes software programs and / or modules stored in the machine-readable storage medium 120, as well as accessing data stored in the machine-readable storage medium 120. This allows for the execution of various functions and data processing within the system, thereby providing overall monitoring of the system. Optionally, the processor 130 may include one or more processing cores; for example, it may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to realize the port carbon emission source tracking and intelligent management method based on big data analysis provided in the aforementioned method embodiments.

[0189] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for tracking and intelligently managing port carbon emission sources based on big data analysis, characterized in that, The method includes: generating a basis for allocating carbon emission allowances for cargo ships based on their registered displacement, unloading volume, energy type, storage time, storage method, and waste type, combined with port carbon emission control rules, historical carbon emission data of similar cargo ships, and port environmental adaptation parameters; performing attribute-based conversion on various registration information based on the carbon emission allowance allocation basis to determine the agreed-upon allowance for the ship's carbon emissions, and simultaneously storing the carbon emission allowance allocation basis and the agreed-upon allowance for the ship's carbon emissions to the port carbon emission big data platform; and using detection equipment distributed at port channel entrances, along the channel, in the wharf area, and at key parts of the cargo ship, monitoring the carbon emission allowances while the cargo ship is in port. Within the section from the entrance of the waterway to the exit of the port waterway, carbon emission data corresponding to shipboard emissions and carbon emission data corresponding to port operations are collected in parallel. Based on the carbon emission data corresponding to shipboard emissions and carbon emission data corresponding to port operations, the data are classified and integrated by type to generate carbon emission data for shipboard emissions and carbon emission data for operations. This data is then transmitted bidirectionally to the port scheduling system and the cargo ship terminal via an encrypted link. The carbon emission data for shipboard emissions is compared in real time with the agreed-upon limit for carbon emissions on board. Based on the comparison results, corresponding control procedures are triggered. If the agreed-upon limit for carbon emissions on board is exceeded, a violation warning signal is generated and pushed to the cargo ship terminal. Simultaneously, port operation arrangements are adjusted based on the carbon emission data for shipboard emissions.

2. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 1, characterized in that, The process involves generating a basis for allocating carbon emission allowances for cargo ships based on their registered displacement, unloading volume, energy type, storage time, storage method, and waste type, combined with port carbon emission control rules, historical carbon emission data of similar cargo ships, and port environmental adaptation parameters. This includes: extracting various data points from the cargo ship registration information, such as displacement, unloading volume, energy type, storage time, storage method, and waste type, and classifying and grouping them according to their carbon emission impact attributes to form a set of cargo ship registration information attributes; and retrieving the allowance allocation benchmark clauses, corresponding pollutant emission standards, and other relevant information from the port carbon emission control rules. The compatibility requirements for similar cargo ships are broken down into rule factors that can be matched with the attributes of the cargo ship registration information. Based on the set of cargo ship registration information attributes and the rule factors, a one-to-one matching is performed to establish a mapping relationship between each piece of registration information and its corresponding rule factor, forming a cargo ship registration information-control rule correspondence table. Historical carbon emission data of similar cargo ships with the same type and combination of registration information attributes are obtained from the port carbon emission big data platform. This historical carbon emission data includes the historical onboard carbon emissions of similar cargo ships, the historical carbon emissions corresponding to the operations of similar cargo ships, and the historical carbon emissions of similar cargo ships. Historical quota adaptation data; obtaining port environment adaptation parameters, including air diffusion conditions in the port area, distribution of ecologically sensitive areas, and carbon emission carrying capacity corresponding to the current port operating load; performing attribute correspondence analysis based on historical carbon emission data of similar cargo ships, extracting historical carbon emission change patterns corresponding to various contents of cargo ship registration information, and generating a carbon emission attribute correspondence model for similar cargo ships; inputting the cargo ship registration information-control rule correspondence table into the carbon emission attribute correspondence model for similar cargo ships, and adjusting the model output results in conjunction with port environment adaptation parameters to correct quota allocation deviations, generating adjusted model output results; integrating the adjusted model output results, the cargo ship registration information-control rule correspondence table, historical carbon emission data characteristics of similar cargo ships, and port environment adaptation parameters to form a multi-dimensional data-supported quota allocation basic dataset; performing attribute correspondence and logical connection based on the quota allocation basic dataset, eliminating data with connection conflicts, and generating a verified quota allocation basic dataset; based on the verified quota allocation basic dataset, determining the influence ratio of each data in quota allocation, and generating a structured basis for cargo ship carbon emission quota allocation.

3. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 1, characterized in that, Based on detection equipment distributed at port channel entrances, along the channel, in the wharf area, and at key parts of cargo ships, the system collects carbon emission data corresponding to both shipboard emissions and port operation emissions in parallel when the cargo ship is within the port channel entrance to port channel exit area. This includes: capturing the cargo ship's entry signal through positioning and sensing equipment at the port channel entrance, simultaneously triggering the start of the data collection process by various detection devices distributed at key parts of the cargo ship and in the port area; activating exhaust gas detection equipment at the cargo ship's chimney outlet to capture the carbon dioxide and carbon monoxide content data corresponding to carbon emissions in the exhaust gas at fixed collection intervals, simultaneously measuring the exhaust gas volumetric flow rate, and recording the data. This system collects cargo ship operating status information corresponding to the ship's sailing speed, engine operating power, and fuel supply rate at specific times. It then binds gas composition data with the corresponding cargo ship operating status information at the time of collection, generating exhaust emission-operating status data units, and labeling them with the data acquisition device number and detection point information. All exhaust emission-operating status data units are sorted according to the collection time sequence to form a time-series exhaust emission dataset. Additional labels are added for occasional interruptions, and trend connections are established with subsequent data collection. Finally, the system activates the flow detection equipment at the cargo ship's deck wastewater discharge outlet to collect wastewater discharge velocity data in real time, combining this data with the discharge pipeline... The system calculates wastewater discharge per unit time based on specifications and parameters, records discharge periods and status, and generates a corresponding wastewater discharge dataset. It operates weight detection equipment in the cargo ship's waste storage area to monitor the weight of domestic waste and fuel waste in real time, recording weight changes before and after each disposal, and generating a waste weight change dataset. It activates the energy consumption detection equipment in the cargo ship's energy supply system to collect data on fuel consumption and electricity consumption, simultaneously recording the time periods of fuel consumption and the corresponding equipment operation types, generating a corresponding energy consumption dataset. Finally, it activates energy consumption detection equipment in the dock area to collect data on fuel consumption from the dock's loading and unloading equipment serving the cargo ship. The energy consumption of the cargo ship and the power supply provided by the dock power supply system to the operating area are used to generate a corresponding dataset of dock energy consumption. The exhaust gas detection equipment along the port channel and in the dock area is activated to collect data on the composition and concentration of carbon emissions in the exhaust gas emitted by the cargo ship's operations from port loading and unloading equipment and transport vehicles, generating a corresponding dataset of port exhaust emissions. The corresponding datasets of exhaust emissions, wastewater discharge, garbage weight change, shipboard energy consumption, dock energy consumption, and port exhaust emissions are integrated to form parallel data on shipboard carbon emissions and carbon emissions from port operations.

4. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 3, characterized in that, The activated exhaust gas detection equipment at the cargo ship's chimney outlet captures data on the carbon dioxide and carbon monoxide content in the exhaust gas at fixed intervals, simultaneously measures the exhaust gas volumetric flow rate, and records the cargo ship's operating status information at the time of collection, including: after the cargo ship enters the port channel entrance area, the exhaust gas detection equipment installed at the fixed detection point at the chimney outlet is automatically activated, completes equipment self-check and sensor sensitivity calibration, and enters a stable collection state; a fixed collection interval is set, and the multi-channel gas sensor and flow sensor in the exhaust gas detection equipment are activated at the fixed collection interval to simultaneously collect data on the carbon dioxide and carbon monoxide content in the exhaust gas and the exhaust gas volumetric flow rate; the precise time of each collection, the cargo ship's speed, engine power, and fuel supply rate are simultaneously recorded to ensure a one-to-one correspondence between the gas composition data and the operating status information; and the gas composition data collected each time is analyzed. Instantaneous value recording and short-term mean calculation generate instantaneous and mean data of gas composition within a single collection cycle. These data are then bound to corresponding cargo ship operating status information to generate exhaust emission-operating status data units, labeled with the collection equipment number and detection point information. The exhaust emission-operating status data units are sorted according to collection time sequence to form a time-series exhaust emission dataset. This time-series dataset undergoes continuous review, with additional markers added for data collection interruptions, and trends are established by integrating with subsequently collected exhaust emission-operating status data units. The time-series exhaust emission dataset is then stored in conjunction with cargo ship identification and port channel entry time to form raw exhaust emission data with time-series markers and cargo ship identification. Based on the raw exhaust emission data, the gas composition concentration change trend within each collection cycle is extracted, and the causes of change are analyzed in conjunction with corresponding operating status information, then added to the exhaust emission dataset.

5. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 1, characterized in that, The process involves real-time comparison of shipboard carbon emission data with the agreed-upon carbon emission allowance. Based on the comparison results, corresponding control procedures are triggered. If the shipboard carbon emission allowance is exceeded, a violation warning signal is generated and sent to the cargo ship terminal. Simultaneously, port operation arrangements are adjusted based on the shipboard carbon emission data. This includes: extracting shipboard carbon emission data from the categorized and integrated carbon emission data, accumulating it by collection period to generate real-time cumulative shipboard carbon emission data, and labeling the corresponding accumulation period and data source; retrieving the agreed-upon carbon emission allowance for the cargo ship from the port carbon emission big data platform, determining the applicable period, calculation basis, and corresponding control requirements for the agreed-upon allowance; comparing the real-time cumulative shipboard carbon emission data with the agreed-upon allowance for each time period, calculating the difference and its corresponding percentage, and generating comparison result data; determining the carbon emission level based on the comparison result data; when the real-time cumulative shipboard carbon emission data does not exceed the agreed-upon allowance, executing the current port operation arrangements, generating a normal carbon emission warning signal and sending it to the cargo ship terminal; when the real-time cumulative shipboard carbon emission data... When the agreed carbon emission limit on board is exceeded, the carbon emission exceedance control process is triggered. This process extracts the time period exceeding the limit, the difference in exceedance value, the percentage of exceedance, and the corresponding carbon emission data to generate exceedance-related data. Based on this data, a violation warning signal is generated. This warning signal includes a description of the exceedance, the type of carbon emission exceeding the limit, and the required emission reduction adjustments, all structured according to a preset format. The violation warning signal is pushed to the cargo ship terminal via a priority communication link, triggering the terminal's audio-visual alert function and simultaneously highlighting the core content of the violation warning signal on the terminal interface. The current port operation plan for the cargo ship is retrieved, including berth arrangements, unloading sequence, storage area allocation, equipment scheduling, and time nodes for each stage. Based on the exceedance-related data, the port operation plan is adjusted, suspending non-urgent operations, replanning the loading and unloading sequence and operation periods, and optimizing equipment operating parameters to reduce corresponding carbon emissions, generating an adjusted port operation plan. This adjusted port operation plan is then pushed to the terminal operation system, storage management system, and cargo ship terminal, simultaneously updating the port operation scheduling ledger and recording the exceedance control measures and operational adjustments.

6. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 3, characterized in that, The weight detection equipment in the cargo ship's waste storage area monitors the weight of domestic waste and fuel waste in real time, recording the weight changes before and after each disposal and generating a waste weight change dataset. This includes: upon the cargo ship entering the port channel entrance, the weight detection equipment installed in the domestic waste and fuel waste storage areas is simultaneously activated, completing zeroing and accuracy calibration, and entering real-time monitoring mode; the weight detection equipment collects the total weight data of the corresponding storage areas in real time, records instantaneous weight values ​​at fixed time intervals, generating continuous weight time-series data, and labeling the storage area type and monitoring time; when a weight change event is detected in the storage area, the start and end times of the weight change are recorded, the stable weight values ​​before and after the change are captured, and the difference between the two stable weight values ​​is calculated as the single disposal weight; the single disposal weight is identified, labeling the disposal time, waste type, and corresponding cargo ship operation stage, generating a single waste disposal weight data unit; and the data is then processed by waste type. The waste type classification system categorizes and aggregates all single waste disposal weight data units to form a household waste disposal weight dataset and a fuel waste disposal weight dataset, maintaining chronological consistency. Based on the household waste and fuel waste disposal weight datasets, the total waste disposal volume and frequency for each time period are extracted, and waste disposal and operation progress are correlated to generate data. The waste weight chronological data, single waste disposal weight data units, and waste disposal and operation progress correlation data are integrated to form an initial waste weight change dataset. The consistency between waste type and registration information is verified by combining the initial waste weight change dataset with the waste type information registered on the cargo ship, generating waste weight and registered type correspondence verification data. Based on the waste weight and registered type correspondence verification data, the carbon emission attributes corresponding to each type of waste are supplemented and labeled, the initial waste weight change dataset is updated, and a complete waste weight change dataset is generated.

7. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 1, characterized in that, The process involves classifying and integrating shipboard carbon emission data and port operation carbon emission data by type to generate shipboard carbon emission data and operation carbon emission data. This data is then transmitted bidirectionally to the port scheduling system and cargo ship terminal via an encrypted link. This includes: retrieving parallel-collected shipboard and port operation carbon emission data; subdividing the data by type (shipboard data is categorized into five types: exhaust gas, wastewater, garbage, fuel oil, and electricity); and operation data is categorized into three types: loading / unloading, transportation, and warehousing); performing conversion processing on each type of data; calculating exhaust gas emission mass by combining exhaust gas component content and volumetric flow rate with a preset carbon emission conversion standard; and converting wastewater volume, garbage weight, fuel oil consumption, and electricity consumption into corresponding carbon emission data; summing the converted shipboard carbon emission data to generate shipboard carbon emission data, marking the proportion and conversion basis of each subdivided carbon emission, forming a structured shipboard carbon emission dataset; and summing the converted port operation carbon emission data to generate operation carbon emission data, marking the corresponding operation stage and equipment type. The system generates a structured carbon emission dataset for operations, based on the conversion criteria. It then performs time-series alignment between the structured shipboard carbon emission dataset and the structured operational carbon emission dataset, ensuring that shipboard carbon emission data and operational carbon emission data for the same time period correspond and match, generating a time-series carbon emission dataset. This time-series carbon emission dataset is encrypted using both port-binding encryption and data transmission encryption. An encrypted communication link is established, connecting the port dispatch system's data receiving module on one end and the cargo ship terminal's information receiving unit on the other, maintaining continuous operation of the transmission channel. The time-series carbon emission dataset is synchronously transmitted to both the port dispatch system and the cargo ship terminal via the encrypted link, recording the transmission time, data integrity markers, and reception status feedback. Upon receiving the time-series carbon emission dataset, the port dispatch system categorizes and stores it according to cargo ship identification and time period, synchronously triggering the data statistics process to generate real-time carbon emission statistics reports. Upon receiving the time-series carbon emission dataset, the cargo ship terminal decodes and displays the shipboard carbon emission data, operational carbon emission data, and corresponding statistical information in a preset format, synchronously storing the received data.

8. The port carbon emission source tracking and intelligent management method based on big data analysis according to claim 5, characterized in that, The process of adjusting port operation plans based on data related to emissions exceeding standards, suspending non-urgent operations, rescheduling loading and unloading sequences and operation periods, and optimizing operating parameters of equipment to reduce corresponding carbon emissions, generates an adjusted port operation plan. This includes: extracting core information from the data related to emissions exceeding standards, determining the type of carbon emissions exceeding standards, the time period exceeding standards, and the corresponding port operation stages, and defining the scope of operations requiring adjustment; retrieving all tasks from the current port operation plan for the cargo ship, classifying them according to their urgency and carbon emission correlation, and distinguishing between urgent and non-urgent operations; sending a suspension instruction for non-urgent operations to the terminal operation system, specifying the name of the suspended task, the corresponding operation stage, and the suspension duration, and simultaneously marking the suspension reason and emissions-related information; and rescheduling the loading and unloading sequence of the cargo ship based on the current berth availability, equipment load, and the operation progress of other cargo ships, combined with the type of carbon emissions exceeding standards. The process involves: adjusting carbon-intensive operations to periods of lower equipment load; optimizing equipment operating parameters for corresponding operations, including adjusting loading and unloading equipment operating rates, transport vehicle routes, and warehousing equipment energy consumption parameters to reduce carbon emissions and generate equipment parameter optimization schemes; resetting the time nodes for each operation, extending the time intervals for carbon-intensive operations to avoid concentrated carbon emissions, and generating an adjusted operation sequence table; integrating the adjusted loading and unloading sequence, the adjusted operation sequence table, and the equipment parameter optimization scheme to form an updated draft operation plan; pushing the updated draft operation plan to the terminal loading and unloading control system, warehousing management system, and transportation scheduling system to obtain feedback from each system; fine-tuning the updated draft operation plan based on feedback to generate an adjusted port operation plan; and synchronizing the adjusted port operation plan to the relevant execution systems to ensure that each stage is executed according to the adjusted port operation plan.

9. A port carbon emission source tracking and intelligent management system based on big data analysis, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the port carbon emission source tracking and intelligent management method based on big data analysis as described in any one of claims 1 to 8 by executing the machine-executable instructions.

10. A computer program product, characterized in that, The computer program product includes machine-executable instructions stored in a computer-readable storage medium. The processor of the computer device reads the machine-executable instructions from the computer-readable storage medium and executes the machine-executable instructions, causing the computer device to perform the port carbon emission source tracking and intelligent management method based on big data analysis as described in any one of claims 1 to 8.

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